A Review of Online Learning in Supervised Neural Networks

Online Learning Algorithm

Therefore the online learning algorithm must guarantee that these two coupled systems (neural network and manipulator) work in a converging fashion.

From: Neural Systems for Robotics , 1997

Dictionary learning for medical image denoising, reconstruction, and segmentation

T. Tong , ... D. Rueckert , in Machine Learning and Medical Imaging, 2016

6.ii.4 Online Dictionary Learning

A stochastic online learning algorithm was proposed in Mairal et al. (2009) in order to learn dictionaries for a large set of training signals. A relaxed version of the objective part for DL using the l _i-norm is formulated every bit

(six.8) $\begin{array}{l}\hfill \begin{array}{cc}〈\widehat{\mathbf{D}},\widehat{\mathbf{\Gamma }}〉& =\underset{\mathbf{D},\mathbf{\Gamma }}{argmin}{‖\mathbf{Y}-\mathbf{D}\mathbf{\Gamma }‖}_{\mathrm{two}}^2+\lambda {‖\mathbf{\Gamma }‖}_{\mathrm{ane}}\\ & =\underset{\mathbf{D},\mathbf{\Gamma }}{argmin}\frac{1}{N}\sum _{i=1}^N\left({‖{\mathbf{y}}_i-\mathbf{D}{\mathit{\gamma }}_i‖}_2^2+\lambda {‖{\mathit{\gamma }}_i‖}_{\mathrm{i}}\right).\end{array}\end{array}$

This is not jointly convex over D and Γ . In gild to find the optimized solution, a stochastic gradient descent approach was utilized in Mairal et al. (2009) to update D sequentially. Instead of using the total training gear up at each iteration every bit in the Grand-SVD algorithm, the online DL algorithm updates the dictionary atoms by accessing one training indicate at a time. Assuming that the set of training signals are contained and identically distributed (i.i.d.), ane signal is drawn for updating D at each iteration as in the stochastic gradient descent. The online optimization process is summarized in Algorithm half-dozen.2. It follows classic DL algorithms and alternates the thin coding step with the DL step. However, at the electric current iteration, the new dictionary D _t uses the previous dictionary D _t−ane as a warm restart, which is different from other DL algorithms. The new dictionary D _t is updated by minimizing the post-obit function (Mairal et al., 2009):

Algorithm half dozen.2

Online Dictionary Learning Algorithm

(6.9) $\begin{array}{l}\hfill {\mathbf{D}}_t=\underset{\mathbf{D}}{argmin}\frac{\mathrm{ane}}{t}\sum _{i=1}^t\left({‖{\mathbf{y}}_i-\mathbf{D}{\mathit{\gamma }}_i‖}_2^2+\lambda {‖{\mathit{\gamma }}_i‖}_1\right).\end{array}$

The coding coefficients ${\widehat{\mathit{\gamma }}}_i$ computed during the previous iterations aggregate past information. The information from past coefficients ${\widehat{\mathit{\gamma }}}_{\mathrm{ane}},{\widehat{\mathit{\gamma }}}_{\mathrm{two}},\dots ,{\widehat{\mathit{\gamma }}}_t$ is carried forward in matrices:

(6.10) $\begin{array}{l}\hfill {\mathbf{A}}_t\leftarrow {\mathbf{A}}_{t-\mathrm{one}}+{\widehat{\mathit{\gamma }}}_t{\widehat{\mathit{\gamma }}}_t^T\text{and}{\mathbf{B}}_T\leftarrow {\mathbf{B}}_{t-1}+{\mathbf{y}}_t{\widehat{\mathit{\gamma }}}_t^T.\end{array}$

This enables updating dictionaries based on by information without accessing the past training samples once more. The new dictionary D _t can then exist optimized past using these matrices and the previous dictionary D _t−1 every bit initialization. This optimization strategy leads to faster convergence performance and amend dictionaries than classical batch algorithms, scaling up gracefully to big datasets even with millions of training samples (Mairal et al., 2009).

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Stable Manipulator Trajectory Command Using Neural Networks

Yichuang Jin , ... Alan Winfield , in Neural Systems for Robotics, 1997

v.9 Conclusion

This chapter presents how to use neural networks for trajectory command of robotic manipulators. Offline learning algorithms, neurocontrol structures, and online learning algorithms are all addressed. The human relationship between offline learning and online learning is stressed. The offline learning algorithm guarantees that the neural network volition finally accurately approximate the modified manipulator dynamics within the grooming data sets. The command structures and online learning algorithms guarantee that the closed-loop system will be asymptotically stable and the tracking errors will asymptotically approach zip.

At that place is a difficulty in applying the theoretical results, i.e., it is difficult to ascertain the neural network approximation accuracy, which is used to design the neural controller. This difficulty is overcome in simulation by trial and error. Nevertheless, simulations also prove that the results are not sensitive to approximation accuracy, although this is non theoretically proved.

The suggested neural network controller can piece of work together with conventional adaptive controllers. Since the number of unknown manipulator parameters in conventional adaptive control is much smaller than the number of adjustable weights in a neural network, the learning speed of adaptive control is much quicker than the learning speed of neural networks. If the manipulator structured model (with unknown parameters) is available a priori and the implementation of an adaptive controller is not hard, then it is desirable that the neural network and the adaptive controller work together in order to maintain quick response, high precision, and robustness. In this example, the adaptive controller is used to cope with structured uncertainties and the neural network is used to cope with unstructured uncertainties.

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Photographic camera-Based Techniques

A. Enis Çetin , ... Steven Verstockt , in Methods and Techniques for Fire Detection, 2016

2.7 Wildfire Detection from Moving Aeriform Platforms

Cameras installed on unmanned aeriform vehicles (UAVs) or aircrafts tin can exist used increment coverage area for early on detection of wildfires. Since the platform is always in motion and the environment changes constantly, online learning algorithms can be used to reduce imitation alarms for this detection method.

Traditional wildfire detection systems first observe wearisome-moving regions with stationary cameras and and so utilize rule- or learning-based algorithms to make up one's mind existence of smoke. Since aerial platforms are in motion, it is difficult to extract moving smoke regions. Therefore in this section, a wildfire detection system is described that does not use motion information. Get-go the whole epitome is segmented using a segmentation algorithm that tin distinguish fume regions equally a separate segment. And then features are extracted from the segments that satisfy certain criteria. The features are classified using online learning algorithms.

A graph-based prototype segmentation method is used to segment smoke regions. This method can maintain particular in low-laissez passer regions and discard detail in high-pass regions. This helps the algorithm to segment smoke regions which have low-frequency characteristics compared to the forest terrain. In Fig. 2.8, segmentation consequence of a real wildfire is displayed that is recorded from a helicopter.

Figure ii.8. Division results for helicopter images.

Color assay of candidate segments is performed to find smoke-colored regions. Wildfire smoke normally has greyness-to-white color in early stages. Thresholds in YUV color space are used to check color content of segments.

Features are extracted from the segments that satisfy colour condition. The features represent the color, texture, and shape characteristics of regions. RGB histograms are used to course color features. Dual-tree complex wavelet transform (DT-CWT) is used to course texture features [72]. Zernike moments are used to extract shape features [73].

Online binary classification algorithms learn the weights of each feature element in real fourth dimension with user supervision. Passive aggressive (PA) method is a maximum-margin algorithm that maximizes the margin defined by $y_t\left(w_t\cdot x_t\right)$ , where y _t is the label, westward _t is the weight vector, x _t is the characteristic vector [74]. The procedure used to update the weight vector depends on the specific application.

Fig. ii.9 shows sample wildfire detection results. The bounding boxes of segments are marked with rectangles and the segments classified as smoke are marked blood-red.

Figure ii.9. Wildfire smoke detection results.

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Computational Analysis of Affect, Personality, and Engagement in Homo–Robot Interactions⁎

Oya Celiktutan , ... Hatice Gunes , in Computer Vision for Assistive Healthcare, 2018

x.three.1.2 Experimental Results

In this piece of work, nosotros focused on a total of seven AUs, namely, inner forehead raiser (AU1), outer brow raiser (AU2), forehead lowerer (AU4), cheek raiser (AU6), lip corner puller (AU12), lips parted (AU25), and jaw drop (AU26). For these AUs, we evaluated the performance of the proposed AU detection pipeline using the MMI Facial Expression dataset [74], one of the virtually widely used benchmark datasets in the field.

Experimental Setup. For each AU, we trained an SVM classifier using the one-vs-all approach, namely, positive samples were the images where the AU was displayed, and the negative samples were all the other images where the AU was not displayed, including neutral samples. Nosotros used a linear c-SVM [75] and fixed the c parameter to $c={10}^{-3}$ .

We used the MMI Facial Expression [74] database, which contains a total of 329 video sequences with annotations provided for the temporal segments of onset, noon, and offset. In guild to increase the number of training samples, we selected multiple frames from the apex segment. Subjects ofttimes displayed eye movements or small head movements; therefore, the frames extracted from the noon segment were not identical. Similarly, in order to create negative samples, for δ-appearance and δ-shape representations, we randomly picked pairs of frames with neutral expressions. This resulted in a total of 6349 grooming samples; however, some AUs (eastward.g. AU1, AU12) have a relatively small number of samples. Nosotros handled the data imbalance issue by limiting the number of negative samples. More explicitly, for each AU, nosotros formed 20% of the grooming samples from the positive samples, forty% from the negative samples with neutral faces, and forty% from the negative samples with nonneutral faces.

Results. We evaluated AU detection performance using five-fold discipline-contained cantankerous validation. Table ten.1 presents AU detection results with respect to the four individual features, and their combination via the consensus fusion approach in terms of (a) the alternative forced pick (2AFC) metric [76], (b) the TPR, and (c) the FPR. The 2AFC metric can be divers as the area A underneath the receiver-operator characteristic (ROC) bend, and an upper bound for the uncertainty of the A statistic for $n_p$ positive and ${\mathrm{northward}}_n$ negative samples, $s=\sqrt{A(1-A)/\min \{{\mathrm{due\; north}}_p,n_{\mathrm{northward}}\}}$ . Looking at the AFC scores (Tabular array 10.1(a)), the all-time performing private feature is the δ-advent feature, and the consensus fusion achieves a higher AFC score than the δ-appearance feature for four AUs (AU1, AU6, AU12, AU26) out of seven AUs. The principal advantage of the consensus fusion is the low FPR, as given in Table 10.1(b) (the corresponding TPRs are provided in Table 10.1(c)). We also used the best performing trained models in the existent-fourth dimension demonstration.

Table 10.1. AU detection operation in terms of (a) the culling forced choice (2AFC) score, (b) the false positive rate (FPR), and (c) the true positive rate (TPR). Bold text indicates the best (i.eastward. highest) score

	AU1	AU2	AU4	AU6	AU12	AU25	AU26
(a) 2AFC
Shape	0.74	0.53	0.67	0.61	0.79	0.73	0.53
Appearance	0.74	0.73	0.65	0.78	0.82	0.78	0.67
δ-shape	0.78	0.67	0.71	0.74	0.78	0.82	0.64
δ-advent	0.90	0.92	0.87	0.82	0.92	0.89	0.78
Fusion	0.91	0.89	0.78	0.87	0.93	0.86	0.79
(b) FPR
Shape	0.41	0.87	0.49	0.77	0.40	0.44	0.77
Appearance	0.45	0.46	0.l	0.35	0.31	0.32	0.58
δ-shape	0.41	0.62	0.46	0.42	0.45	0.thirty	0.51
δ-appearance	0.15	0.12	0.21	0.28	0.12	0.17	0.35
Fusion	0.02	0.03	0.04	0.12	0.06	0.02	0.11
(c) TPR
Shape	0.89	0.93	0.82	1.00	0.98	0.xc	0.83
Advent	0.92	0.92	0.80	0.90	0.94	0.88	0.93
δ-shape	0.98	0.96	0.87	0.90	ane.00	0.93	0.79
δ-appearance	0.96	0.96	0.95	0.91	0.96	0.95	0.91
Fusion	0.84	0.81	0.61	0.86	0.92	0.73	0.68

Real-Fourth dimension Demonstration. Nosotros performed the real-time implementation using C++. For the initial face detection in each session, nosotros used the Viola–Jones face up detector [77] and so tracked the face using the SDM method [71]. Nosotros redetected the face when tracking failed. The real-time implementation was integrated onto the Nao robot every bit shown in Fig. 10.1. The computational power of the Nao robot did not allow us to run the AU detection algorithm in real-time. For this reason, we used a pair of external cameras plugged into a laptop (Intel Core i6, sixteen GB RAM), and ran the AU detection algorithm on the laptop. Every bit shown in Fig. ten.1, these cameras were fastened to Nao's head using custom 3D printed glasses. AU detection from the robot'due south point of view is shown in Fig. 10.2. Vertical and horizontal bars indicate the head pose, and the color green is associated with frontal or most frontal head poses that yield more reliable AU detection. The detected AUs are highlighted in blueish on the left-hand side of each frame; for instance, AU1 and AU2 are detected in Fig. 10.2A.

Effigy ten.1. The robotic platform used during real-fourth dimension public demonstrations.

Figure 10.2. AU detection results under different illumination conditions, i.eastward. (A–B) vs (C–D). Vertical and horizontal bars indicate the head rotation; the color green is associated with frontal/virtually frontal head poses. The detected AUs in each face prototype are highlighted in blue: (A, C) AU1 and AU2; (B, D) AU4. (For estimation of the colors in this figure, the reader is referred to the spider web version of this affiliate.)

We demonstrated the real-fourth dimension AU detection method through confront-to-face interactions with the Nao robot in a series of public engagement events. For this purpose, we designed an interactive game where Nao asked participants to assist him ameliorate his emotional intelligence by displaying facial expressions of emotion, such equally happiness, sadness, etc. The participant could choose to display any AU such as pulling lip corners up (grinning), pulling eyebrows upwards (surprise), dropping the oral cavity/chin (surprise), lowering the eyebrows (frown), etc. To collect the neutral confront that was needed for the δ-appearance and δ-shape representations, we asked the participant at the starting time of the session to stand up still and look at the camera. Since the neutral face was collected just for the frontal face, we did not take into business relationship AUs detected in the non-frontal faces.

As illustrated in Fig. 10.2, Nao attempted to recognize each AU displayed by the participant, and inferred the expressed emotion based on the rule based approach, and then asked the participant for feedback in the course of whether the recognized emotion was correct or not. All the same, an online learning algorithm was not considered, similarly to [31]. Sample images from the Cambridge Scientific discipline Festival that took place in Cambridge, United Kingdom, on March 13, 2017, ^one are given in Fig. 10.three. The images illustrate the moment that one of the participants from the public displayed different facial expressions of emotions.

Effigy 10.three. Photos from the public demonstration at the Cambridge Science Festival (Image copyright: Academy of Cambridge).

Here, we presented a real-life demonstration of the proposed bear on analysis approach in a scientific discipline communication scenario. Still, this approach can be utilized in a health scenario, where, similarly to [32], the robot would provide assistance to children with Autism Spectrum Disorders for improving their facial emotion expression/recognition adequacy.

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Intention Inference for Human-Robot Collaboration in Assistive Robotics

H.C. Ravichandar , A. Dani , in Human Modelling for Bio-Inspired Robotics, 2017

five Conclusion

In this chapter, we presented a new methodology to infer homo intentions denoted by the target locations of reaching motions using an NN-based approximate E-M algorithm with online model learning. NNs were used to model the highly nonlinear human arm motion dynamics. An identifier-based online learning algorithm was developed to iteratively larn new motion dynamics as new measurements become available. A set of half dozen experiments was carried out to validate the proposed algorithm. It was shown from Experiment i that the proposed algorithm, with the help of online learning, could successfully infer intentions of new human subjects with considerably different initial atmospheric condition, motion profiles, and goal locations. Experiment 2 was used to compare the proposed algorithm with a simple Euclidean distance-based approach and to show that intention could be inferred with some objects randomly placed close to each other. Experiment 3 was used to validate the proposed algorithm on Cornell's CAD-120 dataset. Experiments 4–half-dozen showed that the proposed algorithm could be used in assistive robotics applications to infer human intentions and perform respective tasks.

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Multiagent Systems: Learning, Strategic Behavior, Cooperation, and Network Germination

Cem Tekin , ... Mihaela van der Schaar , in Cooperative and Graph Betoken Processing, 2018

Abstract

Many applications ranging from crowdsourcing to recommender systems involve informationally decentralized agents repeatedly interacting with each other in order to reach their goals. These networked agents base their decisions on incomplete information, which they gather through interactions with their neighbors or through cooperation, which is often plush. This chapter presents a discussion on decentralized learning algorithms that enable the agents to accomplish their goals through repeated interaction. Starting time, we hash out cooperative online learning algorithms that assist the agents to discover beneficial connections with each other and exploit these connections to maximize the reward. For this case, we explain the relation between the learning speed, network topology, and cooperation cost. So, we focus on how informationally decentralized agents grade cooperation networks through learning. We explicate how learning features prominently in many real-world interactions, and profoundly affects the evolution of social networks. Links that otherwise would not have formed may now appear, and a much greater variety of network configurations tin be reached. We show that the affect of learning on efficiency and social welfare could exist both positive or negative. We also demonstrate the employ of the aforementioned methods in popularity prediction, recommender systems, skillful choice, and multimedia content aggregation.

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Control of Motion and Compliance

Katja Mombaur , ... Auke Ijspeert , in Bioinspired Legged Locomotion, 2017

 4.eight.4 Discussion

As discussed in this chapter, CPGs present several interesting properties every bit locomotion controllers for robots. They can exhibit stable limit cycle behavior. They are well-suited to be coupled to and entrained past a mechanical system. They allow for shine modulation of locomotion (i.e., their limit cycle properties typically act as filters of abrupt input signal changes). They tin can exist implemented in a distributed fashion (e.g., coupled oscillators on different microcontrollers). And they offer a good substrate for learning algorithms (online or offline).

These interesting properties are probably also the reasons why CPGs evolved in biological systems and why they are found in then many animals both vertebrate and invertebrate. CPGs complement feedback loops based on reflexes by adding a feedforward component to the whole locomotor circuitry. As discussed earlier this is useful for generating locomotor patterns that can and so be shaped by sensory feedback, for handling racket in the sensory signals (Kuo, 2002) and for simplifying the control of speed (Dzeladini et al., 2014). Note that unlike what is sometimes believed, CPGs do not demand to produce stereotyped behavior; and many biological and robotic CPGs can exist extensively modulated to produce rich motor behavior (eastward.g., gait transitions between multiple gaits).

In robotics, CPG controllers are well-suited for fast locomotion, robust locomotion on unstructured terrains, compliant robots, modular/reconfigurable robots, and robots for which an authentic dynamical model does not be. They also have a corking potential for the control of prostheses and exoskeletons (Ronsse et al., 2011).

They are non so well-suited for accurate anxiety placement, accurate full-body command, and rich motor skills (eastward.grand., rapid transitions and superimposition of different motor behaviors). As well if an accurate dynamic model of the robot and its environment exist, alternative model-based control approaches such every bit optimal command might exist amend alternatives.

Both in terms of biology and robotics, much research remains to exist done to improve decode the operation of CPGs and the blueprint of better CPG-based controllers for robots. For example, designing a CPG-based controller for a particular robot remains a scrap of an fine art, and more than generic blueprint methods would be useful. Too it is not yet articulate how to generate the rich motor skills exhibited by animals, with combinations of discrete and rhythmic movements, rapid transients, and superimposition of different motor behaviors. I promising approach for this is the concept of a modular control compages made of motor primitives, i.e., building blocks of motor behavior that tin can exist combined in several means (Thoroughman and Shadmehr, 2000; Wink and Hochner, 2005). CPGs could be viewed as one particular blazon of motor primitive for generating periodic behavior, which can be combined with other primitives for richer motor behavior.

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Independent Component Analysis

Jen-Tzung Chien , in Source Separation and Motorcar Learning, 2019

4.5.5 System Evaluation

The experimental setup for online Gaussian process ICA (OLGP-ICA) is consistent with that for nonstationary Bayesian ICA (NB-ICA), which has been mentioned in Section iv.4.4. Several other ICA procedures, including VB-ICA (Lawrence and Bishop, 2000), BICA-HMM (Choudrey and Roberts, 2003), switching ICA (Hirayama et al., 2007), GP-ICA (Park and Choi, 2008), online VB-ICA (Honkela and Valpola, 2003), NS-ICA (Everson and Roberts, 2000) and SMC-ICA (Ahmed et al., 2000, Costagli and Kuruoğlu, 2007), were included for comparison. The variational Bayesian ICA (VB-ICA) (Lawrence and Bishop, 2000) conducted the batch learning without compensating the nonstationary mixing condition. The Bayesian ICA with hidden Markov sources (BICA-HMM) (Choudrey and Roberts, 2003) introduced an HMM to characterize dynamic sources just with batch preparation fashion. The switching ICA (S-ICA) (Hirayama et al., 2007) treated the state of affairs of a sudden presence and absence of sources but still in batch grooming fashion. The status of moving sensors or sources was disregarded in Choudrey and Roberts (2003), Hirayama et al. (2007). The GP-ICA (Park and Choi, 2008) represented the temporal correlation of source signals rather than that of mixing coefficients. Batch training was performed. The online VB-ICA (OVB-ICA) (Honkela and Valpola, 2003) ran sequential and variational learning for nonstationary source signals while the mixing coefficients were kept unchanged. The nonstationary ICA (NS-ICA) (Everson and Roberts, 2000) implemented a sequential learning algorithm to compensate nonstationary mixing condition for mixing coefficients rather than that for source signals. The SMC-ICA (Ahmed et al., 2000, Costagli and Kuruoğlu, 2007 ) constructed a particle filter as a sequential Bayesian arroyo to source separation based on sequential importance sampling where temporally-correlated mixing status was overlooked. This section presents the sequential and variational learning based on NB-ICA and OLGP-ICA where online Bayesian learning and temporally-correlated modeling are both performed to learn parameters of source signals and the mixing matrix. In the implementation, the minibatch size in online learning algorithms using NS-ICA, SMC-ICA, NB-ICA and OLGP-ICA was consistently specified equally 0.25 s. The prediction order in temporally-correlated modeling based on GP-ICA and OLGP-ICA was fixed to be $p=\mathrm{six}$ . Using variational Bayesian learning, the source signals and mixing coefficients were estimated and obtained from the variational parameters, sufficient statistics, or modes of variational distributions.

Fig. 4.22 shows the square error between true and the estimated mixing coefficients in ${\mathbf{A}}_t$ and ${\mathbf{A}}_t^{(n)}$ at different minibatches of the experimental data every bit displayed in Fig. iv.17. Different ICA methods are investigated and shown by different colors. The mensurate of square error was calculated from four estimated coefficients in the $2\times \mathrm{two}$ matrix ${\mathbf{A}}_t^{(n)}$ and Fifty samples in ${\mathbf{Ten}}^{(n)}={\{{\mathbf{10}}_t^{(n)}\}}_{t=1}^L$ at each minibatch n. OLGP-ICA attains a lower foursquare error curve than NB-ICA for virtually time frames. These ii ICA methods are more accurate than NS-ICA and SMC-ICA at various minibatches. The comeback of SMC-ICA relative to NS-ICA is desirable. Nevertheless, SMC-ICA does not piece of work as well as NB-ICA and OLGP-ICA in this prepare of experiments. In addition to the gear up of mixed signals adopted in Section 4.iv.4, the other five sets of mixed signals (5 seconds on average) were collected to conduct a statistically meaningful evaluation. The same scenarios, addressed in Section 4.4.4, were applied simply simulated with different speakers and music sources, which were sampled from the same dataset. As well, dissimilar mixing matrices with varying frequencies $f_{\mathrm{ane}}$ and $f_2$ were adopted. System evaluation was performed by measuring the absolute errors of predictability and the SIRs using different ICAs. The evaluation measure is shown over a full of vi sets of experimental data.

Effigy four.22. Comparison of square errors of the estimated mixing coefficients by using NS-ICA (black), SMC-ICA (pink in web version or lite gray in print version), NB-ICA (blue in web version or nighttime greyness in print version) and OLGP-ICA (red in spider web version or mid grayness in print version).

Speech and music source signals are temporally correlated in nature. Mixing such highly correlated source signals results in a very complex signal which is more complicated than private source signals. The trouble of blind source separation turns out to decompose and identify the minimally complex source signals. Following (Stone, 2001), the performance of source separation is assessed in terms of the complexity of demixed signals. This metric is ancillary with the ICA objective functions based on independence or not-Gaussianity. This complexity is measured by the temporal predictability of a signal. The higher the predictability of a bespeak sample from its previous samples, the lower the measured signal complexity. The temporal predictability of a source or demixed bespeak ${\mathbf{s}}_{\mathrm{grand}}=\{s_{m,t}\}$ is calculated by (Stone, 2001)

(4.147) $F({\mathbf{s}}_j)=\log \frac{{\sum }_t{({\mathrm{southward}}_{tj}-{\overline{s}}_j)}^{\mathrm{two}}}{{\sum }_t{({\mathrm{southward}}_{tj}-{\tilde{s}}_{tj})}^2}$

where

(four.148) ${\overline{s}}_j=\overline{\eta }{\overline{s}}_j+(1-\overline{\eta }){\mathrm{southward}}_{tj}$

with $\overline{\eta }=0.99$ and

(4.149) ${\tilde{s}}_{tj}=\tilde{\eta }{\widetilde{\mathrm{due\; south}}}_{tj}+(\mathrm{ane}-\tilde{\eta })s_{tj}$

with $\tilde{\eta }=0.5$ . In Eq. (4.147), the numerator is interpreted as an overall variance of a demixed betoken while the denominator measures the extent to which a source or demixed sample $s_{tj}$ is predicted by a curt-term moving average ${\tilde{s}}_{tj}$ of previous samples in ${\mathbf{due\; south}}_j$ . High predictability happens in case of a loftier value of the overall indicate variance in the numerator and a low value of prediction error of a shine signal in the denominator. Fig. 4.23 compares the performance of different ICA methods past evaluating the absolute error of temporal predictabilities $|F({\mathbf{s}}_j)-F({\widehat{\mathbf{southward}}}_j)|$ betwixt true and demixed source signals $\{{\mathbf{s}}_j,{\hat{\mathbf{s}}}_j\}$ which is calculated and averaged over six exam examples. Two channel signals, equally given in Fig. iv.17, are examined. The lower the error an ICA method achieves, the meliorate the performance this method obtains regarding predictability or complexity in demixed signals, which is as accurate as that in true source signals. In this comparing, OLGP-ICA attains the lowest error in temporal predictability. In general, the ICAs with online learning (OVB-ICA, NS-ICA, SMC-ICA, NB-ICA and OLGP-ICA) outperform those with batch learning (VB-ICA, BICA-HMM, S-ICA and GP-ICA). Among different online learning methods, OLGP-ICA has the lowest predictability error because the nonstationary mixing condition is deliberately tackled by characterizing temporal information in source signals equally well as mixing coefficients at each individual minibatch.

Figure iv.23. Comparison of absolute errors of temporal predictabilities between truthful and demixed source signals where different ICA methods are evaluated.

On the other hand, different ICA methods are evaluated in terms of signal-to-interference ratios (SIRs) in decibels which are calculated over all signal samples of true ${\mathbf{s}}_j=\{s_{tj}\}$ and estimated source signals ${\widehat{\mathbf{southward}}}_j=\{{\widehat{\mathrm{due\; south}}}_{tj}\}$ at diverse minibatches. SIRs are calculated according to Eq. (2.10). Fig. four.24 compares the SIRs of two channel signals by using different ICAs which are averaged over six test examples. Among these ICA methods, the lowest SIRs and the highest SIRs are obtained by VB-ICA and OLGP-ICA, respectively. The operation of VB-ICA is poor because VB-ICA does not handle the nonstationary condition for source separation. The reason why S-ICA obtains higher SIRs than BICA-HMM is considering switching ICA properly estimates the source signals with sudden presence and absenteeism via switching indicators. GP-ICA conducts temporally-correlated modeling and accordingly performs improve than Southward-ICA. Again, the ICAs with online learning are amend than those with batch learning in terms of SIRs. Basically, GP-ICA and OLGP-ICA suffer from high computation cost.

Figure 4.24. Comparing of signal-to-interference ratios of demixed signals where different ICA methods are evaluated.

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Modeling, diagnostics, optimization, and command of internal combustion engines via modern machine learning techniques: A review and future directions

Masoud Aliramezani , ... Mahdi Shahbakhti , in Progress in Energy and Combustion Science, 2022

iii.2.two Online calibration

ML can facilitate adaptive ICE calibration by providing efficient online learning algorithms. These algorithms can be used for adaptive optimization of the engine emissions and performance [50,331]. This could assistance to significantly reduce the time and effort required for engine calibration. Online ML-based learning techniques tin can as well provide a robust and powerful tool for realtime optimization to accommodate to system variations, unexpected ecology changes, and deviations in ICE inputs such as fuel quality variation as detailed below.

As indicated in Section 2.one.two, ELM is an excellent choice for online learning and this has made it a promising approach for online scale of ICEs. For example, an engine calibration algorithm is developed in [332] using online extreme learning machine. This approach suggests a procedure that uses sequential Design of Experiment (DoE) algorithm for given ICE operating points. The output signals of unlike engine measurement systems are collected for given actuator commend signals so the results were used through an online ELM technique to learn the relation between the sensors and the actuators. Another fast calibration method was proposed to calibrate injection timing and idle throttle valve position based on sparse Bayesian farthermost learning car (SBELM) and meta-heuristic optimization for an SI engine fueled with ethanol and gasoline[333]. ELM has increasingly been used to predict the performance and emissions of different types of internal combustion engines [334–337].

A stochastic gradient- based Extreme Learning Car (SG-ELM) based online calibration algorithm is given in[56]. The SG-ELM algorithm stabilizes the online learning process which guarantees that the estimated model parameters stay bounded between specific limits during the learning process. In[51], a fast scale method is developed using thin Bayesian ELM (SBELM) and meta heuristic optimization is used to optimize operation of a dual-injection engine. These automobile learning and meta heuristic optimization results showed promising and effective functioning in calibrating a dual-injection engine. Another instance of ML-based Water ice online learning is an online ELM-based optimization and modeling technique proposed in[13,338] which was used for engine calibration and showed a significant improvement in calibration efficiency compared to conventional model-based scale methods. The proposed algorithm optimises the local model parameters to provide the optimal DoE and then determines the optimal parameters for each engine operating point as schematically shown in Fig. 13.

Fig. 13. ELM-based workflow of the point-past-point online engine calibration. Reprinted from[13] with permission of Elsevier.

Equally mentioned higher up, the online calibration strategies help address parameter variations and disturbances on engine emissions and functioning. Some examples of these include i) Fuel quality variations from ane gas station to another or from 1 season to another (due east.grand., winter vs summer), ii) ambient temperature, peculiarly at farthermost weather for which the ICE is not calibrated, iii) ambience air humidity variations, iv) big atmospheric pressure variations, particularly for vehicle operation at high altitude, v) vehicle aging including exhaust aftertreatment systems and vehicle to vehicle variation.

In our prior study[33], a connected vehicle distributed meta-regression (CV-DMR) training arroyo was adult to mitigate the effect of fuel quality variation past self-adapting the engine calibration. The engine was calibrated with a specific fuel (type 1). Then, the objective was to let the ICE combustion phasing (CA50) model adapt to another fuel (blazon two) past applying the CV-DMR algorithm on engine/vehicle performance. In add-on to adapting for fuel variation, the transient engine data for changing operating weather condition was utilized to evaluate the performance of the meta-learning method[33] for transient ICE operation amid 206 experimental operating conditions.

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A review of global maximum power bespeak tracking techniques of photovoltaic system nether partial shading weather

Faiza Belhachat , Cherif Larbes , in Renewable and Sustainable Energy Reviews, 2018

3.2.ix A hybrid MPPT based on directly adaptive neural control and voltage traverse (DANC-VT)

The authors [66] combined an adaptive neural network control with the feedback load voltage traverse. Showtime, the feedback load voltage traversal method is used to apace reach the reference voltage, so the DANC online learning algorithm is used to stabilize the meridian value. The simulation results show that the proposed method can runway the global maximum power point (GMPP) of the PV array before and subsequently under PSC. Compared to other traditional algorithms, the algorithm is unproblematic and has better tracking accuracy, rapidity and stability. The entire photovoltaic system tracking procedure shown in Fig. 37.

Fig. 37. Simulation results of DANC with voltage scan method [66].

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Source: https://www.sciencedirect.com/topics/engineering/online-learning-algorithm

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