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A two-layer algorithm based on PSO for solving unit commitment problem

SOFT COMPUTING 2020年第12期24卷 9161-9178页

作者： Zhai, Yu Liao, Xiaofeng Mu, Nankun Le, Junqing Southwest Univ Chongqing Key Lab Nonlinear Circuits & Intelligen Coll Elect & Informat Engn Chongqing 400715 Peoples R China Chongqing Univ Coll Comp Sci Minist Educ Key Lab Dependable Serv Comp Cyber Phys Soc Chongqing 400044 Peoples R China

It is well known that electric generators consume huge amounts of energy every year. Nowadays, research for the unit commitment problem (UCP) has become a very important task in a power plant. However, the existing optimal methods for solving UCP are very easy to fall into local optimum, resulting in poor performance. Moreover, as no separate layering of economic load distribution, the existing algorithms are very inefficient. Toward this end, a new algorithm named improved simulated annealing particle swarm optimization (ISAPSO) is proposed in this paper. The proposed algorithm consists of a two-layer structure which is designed to simplify the complex problem of UCP. Specifically, in the upper layer, the algorithm based on elitist strategy PSO and SA is much easier to jump out of the local optimum when solving UCP and thus gets a better solution. In the lower layer, convex optimization approach is used to improve the search efficiency of ISAPSO. Furthermore, several methods are also designed to solve the problem-related constraints, which can save a lot of computing resources. Finally, the experimental results show that the cost performance of ISAPSO is better than that of the existing algorithms.

关键词： Unit commitment problem Economic load distribution Particle swarm optimization Simulated annealing algorithm

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Offset or Onset Frame: A Multi-Stream Convolutional Neural Network with CapsuleNet Module for Micro-expression Recognition 5

Offset or Onset Frame: A Multi-Stream Convolutional Neural N...

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5th International Conference on intelligent informatics and Biomedical Sciences (ICIIBMS)

作者： Liu, Nian Liu, Xinyu Zhang, Zhihao Xu, Xueming Chen, Tong Southwest Univ Chongqing Key Lab Nonlinear Circuit & Intelligent Chool Elect & Informat Engn Chongqing 400715 Peoples R China

ISBN: (纸本)9781728180229

Micro-expression is a spontaneous facial expression, which may reveal people's real emotions. The micro-expression recognition has recently attracted much attention in psychology and computer vision community. In this paper, we designed a multi-stream Convolutional Neural Network (CNN) combined with the Capsule Network(CapsNet) module,named CNNCapsNet, to improve the performance of micro-expression recognition. Firstly, both vertical and horizontal optical flow are computed from the onset to the apex, and from the apex to the offset frame respectively, which is the first time that the offset frame information has been taken into account in the field of micro-expression recognition. Secondly, these four optical flow images and the grayscale image of apex frame are input into the five-stream CNN model to extract features. Finally, CapsNet completes micro-expression recognition by learning the features extracted by CNN. The method proposed in this paper are evaluated using the Leave-One-Subject-Out (LOSO) cross-validation protocol on CASME II. The results show that the offset information, which is often neglected, is more important than onset information for the recognition task. Our CNNCapsNet framework can achieve the accuracy of 64.63% for the five-class micro-expression classification.

关键词： micro-expression recognition CNN CapsNet deep learning optical flow

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Fixed-Time Stable Neurodynamic Flow to Sparse Signal Recovery via Nonconvex L1-β2-Norm

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NEURAL COMPUTATION 2022年第8期34卷 1727-1755页

作者： Zhao, You Liao, Xiaofeng He, Xing Chongqing Univ Coll Comp Sci Key Lab Dependable Serv Comp Cyber Phys Soc Minist Educ Chongqing 400044 Peoples R China Southwest Univ Chongqing Key Lab Nonlinear Circuits & Intelligen Coll Elect & Informat Engn Chongqing 400715 Peoples R China

This letter develops a novel fixed-time stable neurodynamic flow (FTSNF) implemented in a dynamical system for solving the nonconvex, nonsmooth model L1- beta 2, beta is an element of [0, 1] to recover a sparse signal. FTSNF is composed of many neuron-like elements running in parallel. It is very efficient and has provable fixed-time convergence. First, a closed-form solution of the proximal operator to model L-1-(beta 2), beta is an element of [0, 1] is presented based on the classic soft thresholding of the L-1-norm. Next, the proposed FTSNF is proven to have a fixed-time convergence property without additional assumptions on the convexity and strong monotonicity of the objective functions. In addition, we show that FTSNF can be transformed into other proximal neurodynamic flows that have exponential and finite-time convergence properties. The simulation results of sparse signal recovery verify the effectiveness and superiority of the proposed FTSNF.

关键词： Dynamical systems

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Extended Random Fourier Features Recursive Non-quadratic Algorithm 2

Extended Random Fourier Features Recursive Non-quadratic Alg...

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IEEE 2nd International Conference on Electronic information and Communication Technology (ICEICT)

作者： Xiong, Kui Qian, Guobing Long, Zhengji Wang, Shiyuan Southwest Univ Coll Elect & Informat Engn Chongqing Peoples R China Chongqing Key Lab Nonlinear Circuits & Intelligen Chongqing Peoples R China

ISBN: (纸本)9781538692981

The extended recursive non-quadratic (EX-RNQ) algorithm can improve the filtering performance of the extended recursive least-squares (EX-RLS) algorithm in the state-space model. To improve the filtering performance of EX-RNQ, a novel extended random Fourier features recursive non-quadratic (EX-RFFRNQ) algorithm is proposed by transforming the input data into a high-dimensional feature space with the random Fourier features (RFF) method under the non-quadratic error criterion. In comparison with the kernel adaptive filters (KAFs), the proposed EX-RFFRNQ algorithm can achieve the performance improvement in a fixed-dimensional network structure and thus require no sparsification. Monte Carlo simulations on channel tracking and short-term chaotic time-series prediction validate the superiority of the proposed EX-RFFRNQ algorithm.

关键词： State-space model non-quadratic error criterion random Fourier features complexity nonlinear problems

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Global Dissipativity of Inertial Neural Networks with Proportional Delay via New Generalized Halanay Inequalities

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NEURAL PROCESSING LETTERS 2018年第3期48卷 1543-1561页

作者： Li, Hongfei Li, Chuandong Zhang, Wei Xu, Jing Southwest Univ Chongqing Key Lab Nonlinear Circuits & Intelligen Coll Elect & Informat Engn Chongqing 400715 Peoples R China Xinjiang Agr Univ Coll Comp & Informa Engn Urumqi 830052 Xinjiang Peoples R China

This article is devoted to the global dissipativity of inertial neural networks with proportional delay. A novel generalized Halanay inequality which involves proportional delay is established. By constructing a new generalized Halanay inequality, several new explicit delay-independent conditions are derived in terms of linear matrix inequalities to ensure the global dissipativity of the considered system. Moreover, a new differential delay inequality which involves unbounded time-varying delay is considered. Due to the proportional delay is one type of unbounded time-varying delays, new analysis techniques can effectively avoid the difficulties caused by proportional delay by applying a new differential delay inequality. Especially, several novel delay-dependent sufficient conditions are obtained to guarantee the global dissipativity of the considered system. Finally, two simulations examples are provided to illustrate the validity of the proposed theoretical analysis.

关键词： Inertial neural networks Global dissipativity Proportional delay Generalized Halanay inequalities Linear matrix inequalities

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The Effect of Long-term Apnea on the Autonomic Nervous System 11

The Effect of Long-term Apnea on the Autonomic Nervous Syste...

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11th International Conference on intelligent Human-Machine Systems and Cybernetics (IHMSC)

作者： Kong, Fanmeng Wen, Wanhui Liu, Guangyuan Yang, Xuehong Xiong, Ronglong Southwest Univ Sch Elect & Informat Engn Chongqing Peoples R China Chongqing Key Lab Nonlinear Circuits & Intelligen Chongqing Peoples R China

ISBN: (纸本)9781728118598

The complexity of physiological system can be well represented by the complexity of time series. In this paper, complexity analysis was performed on the inter-beat-interval(IBI) of apnea syndrome and normal sleep population. Hurst exponent, sample entropy and fractal dimension were used as three indicators of complexity measurement. The results show that optimal combination of statistical and nonlinear features with the Support Vector Machine (SVM) can achieve the apnea recognition accuracy of up to 75.51%. In the complexity measurement, t-test shows that the average of Hurst exponent, sample entropy or fractal dimension of the normal sleep group was significantly larger than that of the apnea group. The results show that long-term apnea syndrome will cause certain damage to the autonomic nerve system.

关键词： sleep apnea inter-beat-interval feature selection complexity analysis SUM

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Robust multi-view non-negative matrix factorization with adaptive graph and diversity constraints

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informatION SCIENCES 2023年第1期634卷 587-607页

作者： Li, Chenglu Che, Hangjun Leung, Man-Fai Liu, Cheng Yan, Zheng Southwest Univ Coll Elect & Informat Engn Chongqing Peoples R China Chongqing Key Lab Nonlinear Circuits & Intelligent Chongqing Peoples R China Anglia Ruskin Univ Fac Sci & Engn Sch Comp & Informat Sci Cambridge England Shantou Univ Dept Comp Sci Shantou Peoples R China Univ Technol Sydney Sydney Australia

Multi-view clustering (MVC) has received extensive attention due to its efficient processing of high-dimensional data. Most of the existing multi-view clustering methods are based on non-negative matrix factorization (NMF), which can achieve dimensionality reduction and interpretable representation. However, there are following issues in the existing researches: (1) The existing methods based on NMF using Frobenius norm are sensitive to noises and outliers. (2) Many methods only use the information shared by multi-view data, while ignoring the diverse information between views. (3) The data graph constructed by the conventional K Nearest Neighbors (KNN) method may misclassify neighbors and degrade the clustering performance. To address the above problems, we propose a novel robust multi-view clustering method. Specifically, l(2,1)-norm is introduced to measure the factorization error to improve the robustness of NMF. Additionally, a diversity constraint is utilized to learn the diverse relationship of multi-view data, and an adaptive graph method via information entropy is designed to overcome the shortcomings of misclassifying neighbors. Finally, an iterative updating algorithm is developed to solve the optimization model, which can make the objective function monotonically non-increasing. The effectiveness of the proposed method is substantiated by comparing with eleven state-of-the-art methods on five real-world and four synthetic multi-view datasets for clustering tasks.

关键词： Robust non-negative matrix factorization Multi-view clustering Diversity constraint Adaptive graph constraint

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Integral-based event-triggered fault estimation and impulsive fault-tolerant control for networked control systems applied to underwater vehicles

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NEUROCOMPUTING 2021年 442卷 36-47页

作者： Li, Hongfei Pan, Jie Zhang, Xiaoyu Yu, Junzhi Peking Univ Coll Engn Dept Adv Mfg & Robot BIC ESATState Key Lab Turbulence & Complex Syst Beijing 100871 Peoples R China Southwest Univ Coll Elect & Informat Engn Chongqing Key Lab Nonlinear Circuits & Intelligen Chongqing 400715 Peoples R China

This article considers the fault estimation and impulsive fault-tolerant control for networked control sys-tems (NCSs) with via integral-based event-triggered mechanism (IETM). According to a novel IETM, a fault and state estimation observer is established as a distributed delay model. To enhance the reliability and safety of actuator fault model, an impulsive fault tolerant controller is designed to compensate the impact of actuator faults and to estimate faults online. Moreover, based on augmented Lyapunov-Kroasovskii functional (LKF), sufficient conditions to determine the global asymptotical stability for the estimation error system are presented. Furthermore, this fault estimation method based on integral -based event-triggered scheme (IETS) is applied to the underwater vehicle heading control system. The designed fault-tolerant control approach solves the problem of the actuator failure of the nonlinear underwater vehicle heading control system. Finally, numerical examples are further provided to verify the effectiveness and advantages of the proposed approach. ? 2021 Elsevier B.V. All rights reserved.

关键词： Networked control systems (NCSs) Impulsive fault-tolerant control (IFTC) Actuator fault Integral-based event-triggered mechanism (IETM) Underwater vehicle

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A continuous-time neurodynamic approach in matrix form for rank minimization

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NEURAL NETWORKS 2024年 172卷 106128页

作者： Zhang, Meng He, Xing Southwest Univ Sch Elect & Informat Engn Chongqing Key Lab Nonlinear Circuits & Intelligent Chongqing 400715 Peoples R China

This article proposes a continuous-time neurodynamic approach for solving the rank minimization under affine constraints. As opposed to the traditional neurodynamic approach, the proposed neurodynamic approach extends the form of the variables from the vector form to the matrix form. First, a continuous-time neurodynamic approach with variables in matrix form is developed by combining the optimal rank r projection and the gradient. Then, the optimality of the proposed neurodynamic approach is rigorously analyzed by demonstrating that the objective function satisfies the functional property which is called as (2r, 4r)-restricted strong convexity and smoothness ((2r, 4r)-RSCS). Furthermore, the convergence and stability analysis of the proposed neurodynamic approach is rigorously conducted by establishing appropriate Lyapunov functions and considering the relevant restricted isometry property (RIP) condition associated with the affine transformation. Finally, through experiments involving low-rank matrix recovery under affine transformations and the completion of low-rank real image, the effectiveness of this approach has been demonstrated, along with its superiority compared to the vector-based approach.

关键词： Continuous-time Matrix differential equations Neurodynamic approach Rank minimization Low-rank matrix recovery

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S-asymptotically w-periodic mild solutions for noninstantaneous impulsive integro-differential equations with state-dependent delay

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MATHEMATICAL METHODS IN THE APPLIED SCIENCES 2023年第9期46卷 11229-11245页

作者： Wang, Yinuo Li, Chuandong Deng, Hao Wu, Hongjuan Southwest Univ Coll Elect & Informat Engn Chongqing Key Lab Nonlinear Circuits & Intelligent Chongqing 400715 Peoples R China

A kind of nonlinear noninstantaneous impulsive equation with state-dependent delay is studied here. By utilizing suitable fixed point theorem and the theory of semigroup in Banach space, the uniqueness and existence results of S-asymptotically w-periodic mild solutions are obtained, respectively. In the end, two examples are presented to demonstrate the validity of the obtained results.

关键词： fixed point theorem integro-differential equations noninstantaneous impulse S-asymptoticallyw-periodic mild solution state-dependent delay

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