Chun Wan Yeung

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A new hybrid particle swarm optimization (PSO) that incorporates a wavelet-theory-based mutation operation is proposed. It applies the wavelet theory to enhance the PSO in exploring the solution space more effectively for a better solution. A suite of benchmark test functions and three industrial applications (solving the load flow problems, modeling the(More)
An improved hybrid particle swarm optimization (PSO) that incorporates a wavelet-based mutation operation is proposed. It applies wavelet theory to enhance PSO in exploring solution spaces more effectively for better solutions. A suite of benchmark test functions and an application example on tuning an associative-memory neural network are employed to(More)
To improve cancer diagnosis and drug development, the classification of tumor types based on genomic information is important. As DNA microarray studies produce a large amount of data, expression data are highly redundant and noisy, and most genes are believed to be uninformative with respect to the studied classes. Only a fraction of genes may present(More)
—An improved hybrid particle swarm optimization (PSO) that incorporates a wavelet-based multi-mutation operation is proposed. It applies wavelet theory to enhance PSO in exploring solution spaces more effectively for better solutions. A suite of benchmark test functions are employed to evaluate the performance of the proposed method. It is shown empirically(More)
This paper presents the stability analysis of fuzzy-model-based control systems. A fuzzy controller with fuzzy feedback gains is proposed to control a nonlinear system represented by the T-S fuzzy model. The fuzzy feedback gains effectively enhance the nonlinearity of the fuzzy controller to strengthen the stabilization ability. To facilitate the stability(More)
This paper presents the control of nonlinear systems with a neural network. In the proposed neural network, the neuron has two activation functions and exhibits a node-to-node relationship in the hidden layer. By using a genetic algorithm with arithmetic crossover and non-uniform mutation, the parameters of the proposed neural network can be tuned.(More)
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