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 pa...
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.
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 t...
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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 simultan...
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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 ...
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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 ...
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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 dist...
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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 enzymat...
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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 pr...
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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.
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 w...
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.
作者:
Yang, YiWang, ZeSong, YuJia, ZiyuWang, BoyuJung, Tzyy-PingWan, FengMacau 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 re...
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