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SSRN 2024年

作者： Fu, Pengju Xie, Yiyuan Su, Ye Ye, Yichen Chai, Junxiong Jiang, Xiao Yang, Hongyu College of Electronic and Information Engineering Southwest University Chongqing China Key Laboratory of Nonlinear Circuits and Intelligent Information Processing Chongqing China Key Laboratory of Networks and Cloud Computing Security of Universities Chongqing China

Machine learning plays a significant role in various fields. As a fundamental part of machine learning, matrix operations are one of the most important computational parts of neural networks. However, the advancement of Moore’s law is hindered by limitations in the number and size of transistors on electronic chips. Therefore, the implementation of optical neural network (ONN) using photonic devices to accomplish matrix-vector multiplication (MVM) has attracted significant attention. Compared with traditional electrical on neural networks, ONN exhibits faster computational speed, higher parallel input capability and greater energy efficiency. Nevertheless, crosstalk and signal loss are inevitable characteristics in optical signal transmission, which can degrade the performance of ONN. In this paper, we present analysis models and simulations to assess the effects of crosstalk and loss on optical computing systems. We have observed that the accuracy of ONN degrades when crosstalk and loss are present. In severe cases, ONN is unable to complete the classification task. To mitigate the interference of crosstalk, we have designed a novel double-layer structure to reduce the number of optical devices required for signal transmission and optimize the impact of crosstalk on ONN performance. For the identical classification task, the ONN based on a double-layer structure exhibits better interference immunity than the generic ONN, with a 10% performance improvement. In particular, performance is improved by 50% when only crosstalk is considered. © 2024, The Authors. All rights reserved.

关键词： Crosstalk

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Reliability Analysis of Optical Neural Networks with Non-Ideal Signal Transmission

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SSRN 2024年

作者： Xie, Yiyuan Fu, Pengju Su, Ye Ye, Yichen Chai, Junxiong Jiang, Xiao Yang, Hongyu College of Electronic and Information Engineering Southwest University Chongqing China Key Laboratory of Nonlinear Circuits and Intelligent Information Processing Chongqing China Key Laboratory of Networks and Cloud Computing Security of Universities Chongqing China

关键词： Crosstalk

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Event-Triggered Optimal Tracking Control for Strict-Feedback nonlinear Systems With Non-Affine nonlinear Faults

arXiv

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arXiv 2024年

作者： Wang, Ling Wang, Xin Wang, Ziming Chongqing Key Laboratory of Nonlinear Circuits and Intelligent Information Processing College of Electronic and Information Engineering Southwest University Chongqing400044 China Guangzhou Nansha511458 China

This article studies the control ideas of the optimal backstepping technique, proposing an event-triggered optimal tracking control scheme for a class of strict-feedback nonlinear systems with non-affine and nonlinear faults. A simplified identifier-critic-actor framework is employed in the reinforcement learning algorithm to achieve optimal control. The identifier estimates the unknown dynamic functions, the critic evaluates the system performance, and the actor implements control actions, enabling modeling and control of anonymous systems for achieving optimal control performance. In this paper, a simplified reinforcement learning algorithm is designed by deriving update rules from the negative gradient of a simple positive function related to the Hamilton-Jacobi-Bellman equation, and it also releases the stringent persistent excitation condition. Then, a fault-tolerant control method is developed by applying filtered signals for controller design. Additionally, to address communication resource reduction, an event-triggered mechanism is employed for designing the actual controller. Finally, the proposed scheme’s feasibility is validated through theoretical analysis and simulation. Copyright © 2024, The Authors. All rights reserved.

关键词： Backstepping

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Recurrent Neural Network for Nonconvex Economic Emission Dispatch

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Journal of Modern Power Systems and Clean Energy 2021年第1期9卷 46-55页

作者： Jiayu Wang Xing He Junjian Huang Guo Chen Chongqing Key Laboratory of Nonlinear Circuits and Intelligent Information Processing College of Electronic and Information EngineeringSouthwest UniversityChongqing 400715China Key Laboratory of Machine Perception and Children's Intelligence Development Chongqing University of EducationChongqing 400067China School of Electrical Engineering and Computer Science University of NewcastleNewcastleNSW 2308Australia

In this paper, an economic emission dispatch(EED) model is developed to reduce fuel cost and environmental pollution emissions. Considering the development of new energy sources in recent years, the EED problem involves thermal units with the valve point effect and WTs. Meanwhile, it complies with demand constraint and generator capacity constraints. A recurrent neural network(RNN) is proposed to search for local optimal solution of the introduced nonconvex EED problem. The optimality and convergence of the proposed dynamic model are given. The RNN algorithm is verified on a power generation system for the optimization of scheduling and minimization of total cost. Moreover, a particle swarm optimization(PSO) algorithm is compared with RNN under the same problematic frame. Numerical simulation results demonstrate that the optimal scheduling given by RNN is more precise and has lower total cost than PSO. In addition, the dynamic variation of power load demand is considered and the power distribution of eight generators during 12 time periods is depicted.

关键词： Recurrent neural network(RNN) nonconvex economic emission dispatch optimization problem

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Mean Square Exponential Stabilization Analysis of Stochastic Neural Networks with Saturated Impulsive Input

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SSRN 2023年

作者： Deng, Hao Li, Chuandong Chang, Fei Wang, Yinuo Chongqing Key Laboratory of Nonlinear Circuits and Intelligent Information Processing College of Electronic and Information Engineering Southwest University Chongqing400715 China

The exponential stabilization of stochastic neural networks in mean square sense with saturated impulsive input is investigated in this paper. Firstly, the saturated term is handled by polyhedral representation method. When the impulsive sequence is determined by average impulsive interval, impulsive density and mode-dependent impulsive density, the sufficient conditions for stability are proposed, respectively. Then, the ellipsoid or the polyhedron are used to estimate the attractive domain, respectively. By transforming the estimation of the attractive domain into a convex optimization problem, a relatively optimum domain of attraction is obtained. Finally, a three-dimensional continuous time Hopfield neural network example is provided to illustrate the effectiveness and rationality of our proposed theoretical results. © 2023, The Authors. All rights reserved.

关键词： Hopfield neural networks

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Stability of Inertial Delayed Neural Networks Via Event-Triggered Impulsive Control

Stability of Inertial Delayed Neural Networks Via Event-Trig...

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2021 International Conference on Neuromorphic Computing, ICNC 2021

作者： Hong, Nan Zhang, Wei Zhang, Tianguang Southwest University Chongqing Key Laboratory of Nonlinear Circuits and Intelligent Information Processing Department of Electronics and Information Engineering Chongqing China

ISBN: (纸本)9781665412872

This paper studies the stability of inertial delayed neural networks via event-triggered impulsive control (ETIC), where impulsive instants are determined by an effective Lyapunov-based impulsive event triggered mechanism (IETM). Based on the IETM, the Zeno behavior is excluded and ensures the globally exponential stability and h-stability of the neural networks by constructing Lyapunov function and some different restrictive conditions. In addition, the delay term in inertial neural networks is well dealt with the two lemmas given in this paper. It is shown that inertial delayed neural networks can be stabilized through this event-triggered impulsive control. Finally, two numerical examples and their simulations are carried out to verify the effectiveness of this method. © 2021 IEEE.

关键词： Time delay

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Distributed Fixed-Time Algorithms for Time-Varying Constrained Optimization Problems

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2025年

作者： He, Xing Li, Yue Zhang, Meng Huang, Tingwen Southwest University Chongqing Key Laboratory of Nonlinear Circuits and Intelligent Information Processing College of Electronic and Information Engineering Chongqing400715 China Shenzhen University of Advanced Technology Faculty of Computer Science and Control Engineering Shenzhen518055 China

In this paper, the distributed form of the zeroing neural network for solving time-varying optimal problems is put forward. Compared with traditional centralized algorithms, distributed algorithms possess better privacy and scalability. This paper initially proposes a centralized time-varying optimisation algorithm with fixed-time convergence and certain robustness, which is based on the integration-enhanced zeroing neural network. Subsequently, the algorithm is enhanced, and two distributed algorithms are designed separately. Both of these two algorithms have a fixed convergence time and certain robustness. Additionally, this paper utilizes the penalty function approach to handle time-varying optimization problems with inequality constraints, thereby making the algorithm more widely applicable. The effectiveness of the algorithm is verified through several numerical examples, and the applicability of the algorithm is demonstrated by solving the package-level state-of-charge balancing problem. © 2020 IEEE.

关键词： Optimization algorithms

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The nyström kernel adagrad least mean square algorithm using K-means sampling 4

The nyström kernel adagrad least mean square algorithm usin...

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4th IEEE International Conference on Electronic Information and Communication Technology, ICEICT 2021

作者： Huang, Xuewei Wang, Daili Qi, Letian Wang, Shiyuan College of Electronic and Information Engineering Southwest University Chongqing Key Laboratory of Nonlinear Circuits and Intelligent Information Processing Chongqing400715 China

ISBN: (纸本)9781665432030

The kernel least mean square (KLMS) using the stochastic gradient descent (SGD) method has been proposed successfully in a Gaussian noise. However, KLMS suffers from performance degradation in terms of convergence rate and filtering accuracy. To this end, an adaptive gradient (Adagrad) optimization algorithm is used to find the solution to the least mean square of error, and the Nyström method is applied into KLMS to reduce its computational and spatial complexity, generating an online Nyström kernel Adagrad least mean square (NKALMS) algorithm. To further improve the filtering accuracy of NKALMS, another online Nyström kernel Adagrad least mean square algorithm using k-means sampling (NKALMS-K) is developed in this paper. Monte Carlo simulations on time series prediction demonstrate that the NKALMS-K algorithm outperforms KLMS and its extensions in the presence of Gaussian noise from the aspects of accuracy and computational efficiency. ©2021 IEEE

关键词： Computational efficiency

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Robust Deep Matrix Factorization with Low-rank and Hypergraph Learning for Multi-view Data processing

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IEEE Transactions on Consumer Electronics 2025年

作者： Pan, Baicheng Che, Hangjun Li, Chenglu Li, Hongfei Southwest University College of Electronic and Information Engineering Chongqing400715 China Hong Kong Baptist University Department of Mathematics Kowloon Tong Hong Kong Southwest University Chongqing Key Laboratory of Nonlinear Circuits and Intelligent Information Processing Chongqing400715 China

As the advancement of sensing technology and cyber-physical equipment, the complexity and dimensionality of the data from consumer internet of things have increased dramatically. processing the complex and multi-view data to analyze the consumption characteristics becomes a significant issue. Deep matrix factorization (DMF) is a widely spread method with the ability to learn intrinsic components in data based on its multiple-layers structures. However, the multiple-layers structures may magnify noises in data and the standard DMF cannot reflect the manifold and low-rank structures on the coefficient matrix well. In the paper, a robust deep matrix factorization with low-rank and hypergraph learning (RDMFLRH) model is proposed for multi-view data processing. Firstly, an arc tangent loss function with less sensitivity to noises and outlines is introduced to enhance robustness. Additionally, low-rank and hypergraph learning are adopted to extract the relationships between clusters and maintain the binary manifold structures from the original space. Then, an optimization algorithm based on the multiplicative update rule is developed to solve the proposed model with convergence proven theoretically and experimentally. Finally, abundant experiments on real-world and noisy datasets validate the superiority of the proposed method, improving around 1% to 10% performance compared with the comparison state-of-the-art methods. © 1975-2011 IEEE.

关键词： Matrix factorization

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Data-driven current sharing and voltage regulation for DC microgrid with unknown voltage loss

Data-driven current sharing and voltage regulation for DC mi...

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2021 International Conference on Security, Pattern Analysis, and Cybernetics, SPAC 2021

作者： Shi, Rui Wang, Aiqing Dong, Tao Chongqing Key Laboratory of Nonlinear Circuits and Intelligent Information Processing College of Electronics and Information Engineering Southwest University Chongqing400715 China

ISBN: (纸本)9781665443227

Current sharing and voltage regulation are two basic issues in DC microgrid. Most of the existing studies about current sharing and voltage regulation are based on the ideal case that there is no voltage loss on power line. However, in practical DC microgrid, many unknown factors may lead to voltage loss on power line, which make the present algorithms are unavailable in practical application. To solve the problem, in this paper, a novel model of current sharing and voltage regulation is proposed by using an unknown nonlinear function to describe the voltage loss on power line. Then, a novel control scheme is proposed based on pre-compensation technology, which can circumvent the unknown voltage loss. The control scheme is consisted of three parts: critic network, compensator and controller. The iterative performance is computed by critic network. The compensator is used to constitute the compensator parameter according to the iterative performance. The controller is used to generate control input by using compensator parameter. Finally, an example is given to demonstrate the effectiveness. © 2021 IEEE.

关键词： Controllers

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