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Quantized minimum error entropy with fiducial points for robust regression

NEURAL networkS 2023年 168卷 405-418页

作者： Zheng, Yunfei Wang, Shiyuan Chen, Badong Southwest Univ Coll Elect & Informat Engn Chongqing 400715 Peoples R China Xi An Jiao Tong Univ Inst Artificial Intelligence & Robot Xian 710049 Peoples R China

Minimum error entropy with fiducial points (MEEF) has received a lot of attention, due to its outstanding performance to curb the negative influence caused by non-Gaussian noises in the fields of machine learning and signal processing. However, the estimate of the information potential of MEEF involves a double summation operator based on all available error samples, which can result in large computational burden in many practical scenarios. In this paper, an efficient quantization method is therefore adopted to represent the primary set of error samples with a smaller subset, generating a quantized MEEF (QMEEF). Some basic properties of QMEEF are presented and proved from theoretical perspectives. In addition, we have applied this new criterion to train a class of linear-in-parameters models, including the commonly used linear regression model, random vector functional link network, and broad learning system as special cases. Experimental results on various datasets are reported to demonstrate the desirable performance of the proposed methods to perform regression tasks with contaminated data.

关键词： Minimum error entropy with fiducial points Quantized method Robust regression random vector functional link network Broad learning system

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Design of solar air conditioning system integrated with photovoltaic panels and thermoelectric coolers: Experimental analysis and machine learning modeling by random vector functional link coupled with white whale optimization

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THERMAL SCIENCE AND ENGINEERING PROGRESS 2023年 44卷

作者： Zayed, Mohamed E. Aboelmaaref, Moustafa M. Chazy, Mohamed Tanta Univ Fac Engn Dept Mech Power Engn Tanta 31521 Egypt Sohag Univ Fac Technol & Educ Mech Engn Dept Sohag 82524 Egypt

This research introduces a microclimate solar cooling system to enhance human thermal comfort and reduce electrical grid energy-based consumption. A novel solar photovoltaic thermoelectric air conditioner (SPVTEAC) for local air conditioning of a 1.0 m3 compartment was experimentally examined under several interior cooling loads. In this system, PV modules generate electric power, which is directly utilized to power the SPVTEAC and lead acid batteries for the self-service night operation of the hybrid system. The cooling performance of the SPVTEAC is analyzed in terms of the input PV current, cooling capacity, coefficient of the performance (COP), and mean air-cooled room temperature. Moreover, an optimized artificial intelligent modeling is also developed to model and validate the performance of SPVTEAC. The optimized modeling consists of an advanced random vector functional link network (RVFLN) coupled with a white whale optimizer (WWO) to propose fine-tuned fast modeling and explore the optimal parameters of the RVFLN. The experimentations were carried out for the SPVTEAC at hot climatic conditions of Sohag, Egypt, and their results were compared with the predicted RVFLNWWO and stand-alone RVFLN findings. Furthermore, the robustness of the two models is evaluated using six statistical measures. The experimental outcomes indicated that the SPVTEAC could maintain a daily average air compartment temperature of 27.50 degrees C with COP of 1.14 when the interior thermal load, average air intake temperature, and air volume flowrate were 195 W, 36.50 degrees C, and 14.40 m3/h, respectively. Also, the statistical transactions revealed a unique fit between the experimental results and the predicted findings obtained by the proposed artificial intelligent model. It is asserted that the determination coefficient (R2) of the predicted PV electric current for the RVFLN-WWO and stand-alone RVFLN was 1.00 and 0.9151, respectively.

关键词： Solar photovoltaic air conditioner Thermoelectric cooling Performance analysis Artificial intelligence modeling random vector functional link network White whale optimizer

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Visual significance model based temporal signature for video shot boundary detection

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MULTIMEDIA TOOLS AND APPLICATIONS 2023年第15期82卷 23037-23054页

作者： Sasithradevi, A. Roomi, S. Mohamed Mansoor Nirmala, P. Vellore Inst Technol Ctr Adv Data Sci Chennai India Thiagarajar Coll Engn Dept Elect & Commun Engn Madurai India Vellore Inst Technol Sch Elect Engn Chennai India

Video shot boundary detection (VSBD) is the fundamental step for video processing algorithms. The goal of any VSBD algorithm is to detect the transitions (abrupt or subtle) in the given video precisely. In this paper, a visual significance model that is suitable for describing transitions in video is introduced. The proposed visual significance model is composed of parameters like color, texture, edge, motion and focus computed over the frames in the video. Once the visually significant region is identified in each frame of the video, the temporal signature is generated through the dissimilarity measure of the visual significance model. The temporal signature is further examined using standard random vector functional link (RVFL) networks for categorizing the transitions as Abrupt Transitions (AT), Subtle Transitions (ST) and No Transitions (NT). To validate the performance of the proposed visual significance model based VSBD Framework, it is evaluated on benchmarks like VIDEOSEG2004 and TRECVID2001 to detect and categorize the transitions. Comparison of F1-Score measure with prominent early methods reveals that the proposed framework is a promising model for detecting the transitions in videos even in the presence of varying illumination conditions, fast camera and object motion.

关键词： Video transitions Visual significance random vector functional link network F1-score

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Data-driven Adaptive Control of CRAC in Data Center Based on Online Incremental RVFL 12

Data-driven Adaptive Control of CRAC in Data Center Based on...

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IEEE 12th Data Driven Control and Learning Systems Conference (DDCLS)

作者： Huang, Jiangyang Zhangl, Zhengxuan Yang, Xu Tu, Rang Univ Sci & Technol Beijing Sch Automat & Elect Engn Beijing 100083 Peoples R China Univ Sci & Technol Beijing Sch Civil & Resources Engn Beijing 100083 Peoples R China

ISBN: (纸本)9798350321050

An adaptive control method based on online identification of controller parameters is proposed for the cooling process of Computer Room Air Conditioning(CRAC) in data centers with complex nonlinear and dynamic characteristics. Firstly, the nonlinear system is divided into low-order linear part and high-order unmodeled dynamic term. The random vector functional link network is used to identify the controller parameters. The unmodeled dynamic compensator is introduced into the control strategy to offset the influence of uncertain dynamic term on the closed-loop control system of CRAC-Cabinet system. Then, in order to solve the problem that the identification ability of the offline model is reduced due to the changes of the location layout of the cabinet, the number and placement of electronic devices and other factors in the data center, the online addition of hidden layer nodes is added, and the output weight of the original hidden layer is recalculated and updated when new nodes are added. The simulation results show that the control and tracking ability of the nonlinear adaptive controller with unmodeled dynamic compensation is better than that of the adaptive controller based on linear model, and the experimental results show that the incremental learning network model has faster response speed and higher tracking accuracy than the original model.

关键词： Adaptive control CRAC Nonlinear random vector functional link network incremental learning

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Weighted Kernel Ridge Regression based randomized network for Alzheimer's Disease Diagnosis using Susceptibility Weighted Images

Weighted Kernel Ridge Regression based Randomized Network fo...

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International Joint Conference on Neural networks (IJCNN)

作者： Tanveer, M. Verma, Shradha Sharma, Rahul Goel, Tripti Suganthan, P. N. Indian Inst Technol Indore Dept Math Indore 453552 India Natl Inst Technol Silchar Biomed Imaging Lab Silchar 788010 Assam India Qatar Univ KINDI Ctr Comp Res Coll Engn Doha 2713 Qatar

ISBN: (纸本)9781665488679

Alzheimer's disease (AD) is a neurological disorder that primarily affects the elderly and is characterized by cognitive decline and memory loss. Recent research has shown that susceptibility-weighted imaging (SWI) images are useful for diagnosing AD because they reveal abnormally high iron deposition in certain brain regions of people with the disease. Machine learning (ML) algorithms, particularly deep learning (DL) networks, are making incredible strides in AD diagnosis using imaging data to assist physicians in making decisions. The random-vector functional link network (RVFL) is an example of a single-hidden-layer feedforward network that uses a closedform solution-based approach to offer a variety of feature mapping functions and kernels. In the proposed paper, SWI image features are extracted with a DL network, ResNet 50, and afterward classified with a kernel ridge regression-based RVFL network. To manage data with an unbalanced class distribution, we present a weighted kernel ridge regression-based RVFL network that is capable of generalizing to balanced data. We used SWI images from the publicly accessible OASIS dataset to evaluate the proposed methods for AD diagnosis. Experiment results show that the proposed model outperforms the state-ofthe-art models.

关键词： Alzheimer's Disease Deep Learning Kernel Ridge Regression random vector functional link network Susceptibility Weighted Images

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Selective ensemble deep bidirectional RVFLN for landslide displacement prediction

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NATURAL HAZARDS 2022年第1期112卷 725-745页

作者： Yu, Xiaoyang Lian, Cheng Su, Yixin Xu, Bingrong Wang, Xiaoping Yao, Wei Tang, Huiming Wuhan Univ Technol Sch Automat Wuhan 430074 Peoples R China Huazhong Univ Sci & Technol Sch Automat Wuhan 430074 Peoples R China South Cent Univ Nationalities Sch Comp Sci Wuhan 430074 Peoples R China China Univ Geosci Fac Engn Wuhan 430074 Peoples R China

Landslide displacement prediction is a challenging and important subject in landslide research. To improve the prediction accuracy of and reduce disasters caused by landslides, we propose a selective ensemble deep bidirectional random vector functional link network (sedb-RVFLN) for landslide displacement prediction in which each independent hidden layer is linked to a different output layer. In this paper, to reduce the number of hidden nodes without affecting the efficiency of network training, an incremental learning method is utilized to make some hidden nodes not randomly chosen. Moreover, we apply selected partial hidden layers instead of all hidden layers to construct a selective ensemble. The ensemble method adopted by sedb-RVFLN does not require training multiple independent networks, and the entire sedb-RVFLN only needs to be trained once. Finally, we conduct extensive experiments on real landslide datasets from the Huangdeng Hydropower Station in China to demonstrate the effectiveness of our model.

关键词： Landslide displacement prediction random vector functional link network Incremental learning Selective ensemble

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A Data Model-Based Diagnosis Method for Voltage Sensor Intermittent Faults of Motor Drive System 2

A Data Model-Based Diagnosis Method for Voltage Sensor Inter...

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2nd IEEE International Power Electronics and Application Symposium, PEAS 2023

作者： Xiong, Wei Gou, Bin Wu, Qian School of Electrical Engineering Southwest Jiaotong University Cheng du China

ISBN: (纸本)9798350313765

This paper proposed a data model-based diagnosis method for voltage sensor intermittent faults(IFs) of motor drive system. The nonlinear mapping relationship between historical data and induction motor stator currents is quickly mined by combining nonlinear autoregression with exogenous inputs (NARX) and random vector functional link (RVFL) to obtain a currents estimator. Subsequently, a sliding time window is designed to construct the currents residuals to detect the appearance and disappearance times of IFs. © 2023 IEEE.

关键词： data model inverter nonlinear regression random vector functional link network sensor intermittent faults(IFs)

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Study on applicable coverage extension of theory-based generalization errors bounds to the variants of RVFL network and ELM

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NEUROCOMPUTING 2024年 596卷

作者： Kim, Meejoung Heo, Jun Korea Univ Res Inst Informat & Commun Technol Seoul South Korea Korea Univ Sch Elect Engn Seoul South Korea

This study investigates the coverage extendibility of generalized prediction error estimation for the random vector functional link (RVFL) network and generalized extreme learning machine (GELM). GELM is an ELM applying the limiting behavior of Moore-Penrose generalized inverse (M-P GI) to the output matrix of the last hidden layer of ELM. The RVFL network, the ELM, and their variants are currently employed in various areas due to their fast learning and are expected to be adopted in more applications. Additionally, obtaining prediction error bounds quickly for these networks could greatly increase their utilities. Therefore, exploring the error bounds of the variants is crucial. Recently, the upper and lower bounds of generalized prediction error and their associated tail probabilities for the RVFL network and GELM were analyzed. This study aims to extend the coverage of the existing error bounds to variants of RVFL and ELM. To this end, we investigate each solution of the objective functions for the existing RVFL and ELM variants, compare it with the solution obtained by using the limiting behavior of M-P GI, and then discuss the applicability of the probability inequalities for the prediction error to the variant. Specifically, we conceptualize a restricted solution vector and prove that the restricted solution of a variant of RVFL is identical to that of GELM, with an appropriately selected regularization parameter. The mechanism used in the proof can be applied to other variants, which have different objective functions but have similar forms of solution. Experiments are performed with the CAIDA (Center for Applied Internet Data Analysis) dataset. Numerical results verify the analysis, showing that the generalization prediction error bounds and their associated tail probabilities for ELM (RVFL) variants in applicability coverage are almost identical to those of the GELM (RVFL). In addition, performance-based network similarity is introduced and computed with th

关键词： random vector functional link network Extreme learning machine Variants of networks Limiting behavior of Moore -Penrose generalized inverse Coverage extension of theoretical bound

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A new representation learning based maximum power operation towards improved energy management integration with DG controllers for photovoltaic generators using online deep exponentially expanded RVFLN algorithm

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APPLIED SOFT COMPUTING 2024年 166卷

作者： Satpathy, Anshuman Dhar, Snehamoy Dash, P. K. Bisoi, Ranjeeta Nayak, Niranjan Siksha O Anusandhan Deemed Be Univ Elect Engn ITER Bhubaneswar India Siksha O Anusandhan Deemed be Univ Dept Elect & Elect Engn ITER Bhubaneswar India Siksha O Anusandhan Deemed be Univ Multidisciplinary Res Cell Bhubaneswar India

To incorporate Local Energy Management System (LEMS) based Tertiary Controller (TC) operation with multiple Photovoltaic based Distributed Generations (PV-DGs) level Primary Controller/ PC: Independent DG Controllers (IDGC) more adequately, a new representation learning (Symmetrical Input-Output weight Encoded Data Compression/ SIODC, with Exponential Expansion based random vector functional link network towards Softmax layer's Control Reference Estimation/ ExRVFLN-SoCRE) based MPPT controller is proposed in this paper in terms of Secondary Controllers (SCs). A new Hybrid LEMS (HyLEMS) is presented towards the proposed Deep Neural network based SCs (i.e. DNN-SCs) in a distributed (SCdist), as well as centralized (SCcent) manner, to ease the computational, and communication requirements. To investigate that multiple PV-DGs based duty cycle (kth instant Control References/CRs estimation) controllers are considered here with auxiliary Battery Energy Storage Systems (BESS), and AC utility, integrated to Common DC feeder in terms of DC-DC converter dynamics. To avoid Control Reference Estimation Error (CREE) due to initial randomization, optimization subroutines are incorporated for SIODC by generalized semi-supervised learning, and for ExRVFLN-SoCRE by Lagrange multiplier weighted, rms based cost function. The proposed control performance is verified in MATLAB Simulink (R) based average modeling, and validated through dSPACE DS1104 based RTI with multi-PV (emulators) test-bench as well.

关键词： Energy management system Photovoltaic MPPT control random vector functional link network DC Microgrid

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Opti-EN-net: Optimized ensemble deep neural network for the classification of Retinal Detachment through fundus images

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BIOMEDICAL SIGNAL PROCESSING AND CONTROL 2024年 91卷

作者： Yadav, Sonal Murugan, R. Goel, Tripti Natl Inst Technol Silchar Dept Elect & Commun Engn Biomed Imaging Lab BIOMIL Silchar 788010 Assam India

Background and problem statement: Retinal Detachment is a severe ocular situation that may lead to serious visual impairment without prior warning. The early identification of retinal detachment can enhance the probability of successful reattachment and improve vision outcomes. Manual diagnosing of retinal detachment is laborious, tedious, and very hard for enormous clinical applications. We proposed an innovative computer-assisted screening tool to identify retinal detachment from retinographic color fundus images. Methods: This work utilized five stages. Firstly, the data augmentation approaches are considered to increase the assorted variety of samples. Secondly, preprocessing techniques are used for better image analysis. Then, Resnet50 architecture is trained to extract relevant characteristics from the preprocessed fundus images. Afterward, a genetic algorithm selects the optimal features from the extracted features. Subsequently, the structure of Resnet50 undergoes a modification by removing the layers after the fully connected layer (FCL) with two randomized neural networks to classify retinal detachment, namely an extreme learning machine and a random vector functional-link network. In addition, the weights and biases of the earlier Resnet50 layers are frozen during the training these two classification algorithms. Finally, the outcomes of these two classification algorithms are ensembled using a majority voting mechanism to improve overall classification performance. Results: The performance scores of this work reveal that the Resnet50-GA-RVFL and Resnet50-GA-ELM models achieved accuracies of 98.70% and 97.78%, respectively. In contrast, our proposed Opti-EN-net model exhibited an increased accuracy of 99.44%, outperforming several recent state-of-the-art methodologies. Conclusion: The outcomes reveal that our proposed Opti-EN-net framework has promising capabilities in accurately identifying retinal detachment in its early stages, thereby reducing the wor

关键词： Retinal detachment Wavelet packet decomposition Convolution neural network Genetic algorithm random vector functional link network Extreme learning machine

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