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Hierarchical genetic algorithm for near optimal feedforward neural network design.

International journal of neural systems 2002年第1期12卷 31-43页

作者： Yen, Gary Lu, Haiming Intelligent Systems and Control Laboratory School of Electrical and Computer Engineering Stillwater 74078 OK United States

In this paper, we propose a genetic algorithm based design procedure for a multi layer feed forward neural network. A hierarchical genetic algorithm is used to evolve both the neural networks topology and weighting parameters. Compared with traditional genetic algorithm based designs for neural networks, the hierarchical approach addresses several deficiencies, including a feasibility check highlighted in literature. A multi objective cost function is used herein to optimize the performance and topology of the evolved neural network simultaneously. In the prediction of Mackey Glass chaotic time series, the networks designed by the proposed approach prove to be competitive, or even superior, to traditional learning algorithms for the multi layer Perceptron networks and radial basis function networks. Based upon the chosen cost function, a linear weight combination decision making approach has been applied to derive an approximated Pareto optimal solution set. Therefore, designing a set of neural networks can be considered as solving a two objective optimization problem.

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Asynchronous Tag-based Physical-Layer Authentication in Wireless Communications

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IEEE Transactions on Wireless Communications 2025年

作者： Tan, Haijun Xu, Yuntao Du, Jiewei Xie, Ning Li, Hongbin Peng Cheng Laboratory Control and Network Foundation Laboratory Frontier Research Center Shenzhen518107 China Shenzhen University State Key Laboratory of Radio Frequency Heterogeneous Integration The Guangdong Key Laboratory of Intelligent Information Processing College of Electronics and Information Engineering Shenzhen518060 China Beijing Jiaotong University School of Electronic and Information Engineering Beijing100044 China National Innovation Center of High Speed Train Qingdao266111 China Stevens Institute of Technology Department of Electrical and Computer Engineering HobokenNJ07030 United States

Tag-based physical-layer authentication (PLA) has gained significant research interest due to its high security and low complexity compared to traditional upper-layer authentication mechanisms. However, conventional tag-based PLA schemes often require strict synchronization between the tag and the source message before transmission, which may limit their practicality and effectiveness. To address this limitation, we propose an asynchronous tag-based PLA scheme, where the tag and the source message are transmitted asynchronously with an intentional time delay introduced at the transmitter. The proposed scheme enhances both compatibility and security without compromising robustness. We provide a theoretical analysis of the proposed scheme, and derive closed-form expressions to characterize its performance. Furthermore, theoretical comparisons with existing methods yield insightful conclusions regarding the advantages of the proposed approach. The scheme is implemented and extensively evaluated through simulations, where the theoretical results exhibit strong agreement with the simulation outcomes, thereby validating its effectiveness. © 2025 IEEE.

关键词： asynchronous closed form intentional timing delay Physical-layer authentication tag-based

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An LMI-based finite-time tracker design using nonlinear sliding surfaces

An LMI-based finite-time tracker design using nonlinear slid...

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20th Iranian Conference on electrical engineering, ICEE 2012

作者： Mobayen, Saleh Johari Majd, Vahid Sojoodi, Mahdi Intelligent Control Systems Laboratory School of Electrical and Computer Engineering Tarbiat Modares University Tehran Iran

ISBN: (纸本)9781467311489

This paper offers a new nonlinear sliding surface to achieve robustness and high performance for uncertain MIMO linear systems. The proposed method improves the transient performance and steady state accuracy simultaneously in a finite time. Exponential boundedness and ultimately boundedness conditions in finite time are provided in the form of linear matrix inequalities (LMIs). The control law is designed to guarantee the existence of the sliding mode around the nonlinear surface, and the damping ratio of the closed-loop system is increased as the output approaches the set-point. A DC motor position tracking problem is considered as a case study. Simulation results are presented to show the effectiveness of the proposed method as a promising way for controlling similar nonlinear systems. © 2012 IEEE.

关键词： Sliding mode control

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Privacy Preservation for Cloud-Edge-Collaborative Energy Management System Using Post-Quantum Homomorphic Encryption

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IEEE Transactions on Smart Grid 2025年

作者： Jiang, Cheng Li, Xue Du, Dajun Qian, Hong Wen, Mi Wu, Lei Findeisen, Rolf Shanghai University of Electric Power Shanghai Key Laboratory of Power Station Automation Technology College of Automation Engineering Shanghai200090 China Shanghai University Shanghai Key Laboratory of Power Station Automation Technology School of Mechatronical Engineering and Automation Shanghai200072 China Shanghai University of Electric Power College of Computer Science and Technology Shanghai201306 China Advanced Cryptography and System Security Key Laboratory of Sichuan Province Chengdu610225 China Stevens Institute of Technology Department of Electrical and Computer Engineering HobokenNJ07030 United States TU Darmstadt Control and Cyber-Physical Systems Laboratory Darmstadt64227 Germany

Cloud-based energy management systems (EMS) in smart grids face privacy challenges, as existing methods based on traditional homomorphic encryption support limited operations and are vulnerable to quantum attacks. We propose a privacy preservation method for smart grid energy management system, leveraging edge-assisted computing and ring learning with errors (RLWE) homomorphic encryption. Our approach enables both addition and multiplication operations on encrypted smart grid data while defending against quantum threats. We demonstrate the methods security under the RLWE hardness assumption and validate its effectiveness through an experimental platform simulating smart grid environments. This work addresses computational limitations in current privacy-preserving techniques, enhancing data security for evolving smart grid technologies © 2010-2012 IEEE.

关键词： Power management (telecommunication)

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A new non-fragile H∞ proportional-integral filtered-error adaptive observer for a class of non-linear systems and its application to synchronous generators

A new non-fragile H∞ proportional-integral filtered-error a...

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作者： Pourgholi, M. Majd, V.J. Intelligent Control Systems Laboratory School of Electrical and Computer Engineering Tarbiat Modares University Tehran Iran

In this paper, a parameter and state estimation problem in the presence of observer gain perturbations and input disturbance is discussed for the Lipschitz systems that are linear in unknown parameters and non-linear in states. A non-linear non-fragile proportional-integral filtered-error adaptive observer is designed, and its stability conditions based on the Lyapunov technique are derived. The optimal gain among a solution set that satisfies the observer stability conditions is derived systematically using the linear matrix inequality approach. Numerical examples, as well as a synchronous generator case study, are provided in which the non-linear terms depend on the unmeasured states. The proposed design is more robust against both disturbance and gain perturbations than those presented in recent research. Moreover, it is feasible for much larger values of the Lipschitz constants than is the case for conventional methods. © Authors 2011.

关键词： Synchronous generators

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Tracker design using composite nonlinear feedback with application to a DC servomotor

Tracker design using composite nonlinear feedback with appli...

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2012 3rd Power Electronics, Drive systems and Technologies Conference, PEDSTC 2012

作者： Mobayen, Saleh Majd, Vahid Johari Intelligent Control Systems Laboratory School of Electrical and Computer Engineering Tarbiat Modares University Tehran Iran

ISBN: (纸本)9781467301114

In this paper, the theory of a nonlinear control technique, i.e., the composite nonlinear feedback control is considered for robust tracking of a class of linear systems with time varying uncertain parameters and disturbances. This controller can guarantee that the tracking error decreases asymptotically to zero in the presence of time varying uncertain parameters and disturbances. For performance improvement of dynamical system, this proposed robust tracking controller consists of linear and nonlinear feedback parts without any switching element. The linear feedback law is designed to yield a closed loop system with a small damping ratio for the existence of a quick response. On the other hand, the nonlinear feedback law is designed to increase the damping ratio as the system output approaches the output of the reference model. Finally, the simulations of applying the control law on a DC servomotor positioning system are given to illustrate the validity of the results developed in this paper. © 2012 IEEE.

关键词： Closed loop systems

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Electrochemical Detection of Lactate and Glucose with Enzyme-Free and Dual-Sensing Using Nickel-Oxide Nanoparticles

SSRN

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SSRN 2025年

作者： Nasiri, Rohollah Guagliano, Giuseppe Van Gastel, Dirkje Sanei, Reihaneh Madadelahi, Masoud Tanriverdi, Selim Jain, Saumey Fayazbaksh, Farzaneh Lee, Sang Won Zhu, Yangzhi Joensson, Haakan N. Russom, Aman Herland, Anna Division of Nanobiotechnology Department of Protein Science Science for Life Laboratory KTH Royal Institute of Technology Solna Sweden AIMES Center for the Advancement of Integrated Medical and Engineering Sciences Department of Neuroscience Karolinska Institute Solna Sweden Department of Chemistry Materials and Chemical Engineering "G. Natta" Politecnico di Milano Milan Italy Division of Micro and Nanosystems Department of Intelligent Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm Sweden Terasaki Institute for Biomedical Innovation Los AngelesCA90064 United States

Detection and monitoring of lactate and glucose levels in biological fluids and cell cultures are critical for understanding metabolic disorders. While electrochemical biosensors are commonly used, traditional enzymatic sensors face stability, reproducibility, and cost challenges. To address these issues, we developed non-enzymatic sensors using nanostructured electrodes, specifically nickel oxide (NiO)-based, for lactate and glucose detection. The sensors were fabricated by drop-casting a NiO/Nafion/ethanol dispersion onto the working electrode, with performance evaluated through cyclic voltammetry and amperometry. Optimal sensitivity and linearity were achieved at a working voltage of ~0.5 V. To address glucose interference, sensors were modified with Prussian Blue, creating a dual sensor for lactate and glucose. The sensors respectively exhibited linear responses for lactate (0.1 – 5 mM) and glucose (0.1 – 5 mM) with detection limits of 0.60 mM and 0.35 mM, and it showed sensitivities of 0.43 ± 0.05 µA/mM (lactate) and 0.82 ± 0.02 µA/mM (glucose) in phosphate buffer solution. Validation against the lactate and glucose assay kits in a cell culture medium showed good agreement, highlighting the potential of these enzyme-free electrochemical sensors for metabolic analysis. Finally, the sensor was integrated with a microfluidic device, demonstrating its potential as a flow-through, enzyme-free metabolic sensor for future organ-on-chip applications. © 2025, The Authors. All rights reserved.

关键词： Cyclic voltammetry

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Nonlinear control and synchronization of a class of nonlinear coupled dynamical systems

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控制理论与应用（英文版） 2013年第4期11卷 623-628页

作者： Wenyan TANG Zhihua QU Xiaoping FAN Hui LONG School of Information Science and Engineering Central South University Hunan Engineering Laboratory for Advanced Control and Intelligent Automation Department of Electrical Engineering and Computer Science University of Central Florida

In this paper, a control problem for a class of nonlinear coupled dynamical systems is proposed and a continuous nonlinear feedback control law is designed using direct Lyapunov method to solve the proposed control problem. Moreover, synchronization problem for a special case of this class nonlinear coupled dynamical systems is concerned. Numerical examples show the effectiveness and advantage of the designed continuous nonlinear control law and derived synchronization result.

关键词： Nonlinear control Synchronization Lyapunov method

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Constructing a fuzzy rule-based system using the ILFN network and Genetic Algorithm.

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International journal of neural systems 2001年第5期11卷 427-443页

作者： Yen, G.G. Meesad, P. Intelligent Systems and Control Laboratory School of Electrical and Computer Engineering Oklahoma State University Stillwater 74078 OK United States

In this paper, a method for automatic construction of a fuzzy rule-based system from numerical data using the Incremental Learning Fuzzy Neural (ILFN) network and the Genetic Algorithm is presented. The ILFN network was developed for pattern classification applications. The ILFN network, which employed fuzzy sets and neural network theory, equips with a fast, one-pass, on-line, and incremental learning algorithm. After trained, the ILFN network stored numerical knowledge in hidden units, which can then be directly interpreted into if then rule bases. However, the rules extracted from the ILFN network are not in an optimized fuzzy linguistic form. In this paper, a knowledge base for fuzzy expert system is extracted from the hidden units of the ILFN classifier. A genetic algorithm is then invoked, in an iterative manner, to reduce number of rules and select only discriminate features from input patterns needed to provide a fuzzy rule-based system. Three computer simulations using a simulated 2-D 3-class data, the well-known Fisher's Iris data set, and the Wisconsin breast cancer data set were performed. The fuzzy rule-based system derived from the proposed method achieved 100% and 97.33% correct classification on the 75 patterns for training set and 75 patterns for test set, respectively. For the Wisconsin breast cancer data set, using 400 patterns for training and 299 patterns for testing, the derived fuzzy rule-based system achieved 99.5% and 98.33% correct classification on the training set and the test set, respectively.

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Exploiting the Intrinsic Neighborhood Semantic Structure for Domain Adaptation in EEG-based Emotion Recognition

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IEEE Transactions on Affective Computing 2025年

作者： Yang, Yi Wang, Ze Song, Yu Jia, Ziyu Wang, Boyu Jung, Tzyy-Ping Wan, Feng Macau University of Science and Technology Macao Centre for Mathematical Sciences Respiratory Disease AI Laboratory on Epidemic Intelligence and Medical Big Data Instrument Applications Faculty of Innovation Engineering 999078 China Tianjin University of Technology School of Electrical Engineering and Automation Tianjin Key Laboratory of New Energy Power Conversion Transmission and Intelligent Control Tianjin300384 China Chinese Academy of Sciences Beijing Key Laboratory of Brainnetome and Brain-Computer Interface and Brainnetome Center Institute of Automation Beijing100045 China Western University Department of Computer Science Brain Mind Institute LondonONN6A 3K7 Canada University of California at San Diego Swartz Center for Computational Neuroscience Institute for Neural Computation La Jolla CA92093 United States University of Macau Department of Electrical and Computer Engineering Faculty of Science and Technology China University of Macau Centre for Cognitive and Brain Sciences Centre for Artificial Intelligence and Robotics Institute of Collaborative Innovation 999078 China

Due to the inherent non-stationarity and individual differences present in electroencephalogram (EEG) signals, developing a generalizable model that performs well on new subjects is challenging in EEG-based emotion recognition. Most existing domain adaptation (DA) methods typically mitigate these discrepancies by aligning the marginal distributions of domain feature representations. However, when there is a significant difference in the class-conditional distribution between domain features and labels, the domain-invariant features learned by aligning marginal distributions may have limited discriminative ability for unlabeled target instances or even prove counterproductive. To address this issue, we propose a Neighborhood Semantic Aware Learning-based Dynamic Graph Attention Convolution (NSAL-DGAT) approach that learns target semantic information by considering the inter-domain semantic topological structure, thereby improving classifier adaptation for target instances. Specifically, the proposed NSAL framework is designed to capitalize on the insight that after domain feature alignment, some target samples and their neighboring source samples exhibit similar semantics. By leveraging the neighborhood topological structure, we extract and incorporate semantic target features to train a more transferable classifier. Besides, we implement an entropy weighting mechanism to emphasize representative target semantic information, encouraging target instances to prioritize high-confidence individuals within the source neighborhood. We have conducted extensive experiments on the public SEED dataset and our collected the Hearing-Impaired EEG Dataset (HIED). The experimental results underscore the efficacy of our proposed NSAL-DGAT approach, showcasing state-of-the-art accuracy in subject-dependent as well as subject-independent scenarios. The source code is available at https://***/YYingDL/NSAL-DGAT. © 2010-2012 IEEE.

关键词： Speech analysis

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