Stability analysis of fuzzy control systems subject to uncertain grades of membership

Author name used in this publication: F. H. F. LeungCentre for Multimedia Signal Processing, Department of Electronic and Information Engineering2005-2006 > Academic research: refereed > Publication in refereed journalVersion of RecordPublishe

Fuzzy combination of fuzzy and switching state-feedback controllers for nonlinear systems subject to parameter uncertainties

Centre for Multimedia Signal Processing, Department of Electronic and Information Engineering2004-2005 > Academic research: refereed > Publication in refereed journalVersion of RecordPublishe

Design and training for combinational neural-logic systems

Centre for Multimedia Signal Processing, Department of Electronic and Information Engineering2006-2007 > Academic research: refereed > Publication in refereed journalVersion of RecordPublishe

Digit and command interpretation for electronic book using neural network and genetic algorithm

Centre for Multimedia Signal Processing, Department of Electronic and Information Engineering2004-2005 > Academic research: refereed > Publication in refereed journalVersion of RecordPublishe

Design and stabilization of sampled-data neural-network-based control systems

Author name used in this publication: F. H. F. Leung"Centre for Multimedia Signal Processing, Department of Electronic and Information Engineering"Refereed conference paper2005-2006 > Academic research: refereed > Refereed conference paperVersion of RecordPublishe

LMI relaxed stability conditions for fuzzy-model-based control systems

Author name used in this publication: F. H. F. LeungCentre for Multimedia Signal Processing, Department of Electronic and Information EngineeringRefereed conference paper2006-2007 > Academic research: refereed > Refereed conference paperVersion of RecordPublishe

Playing Tic-Tac-Toe Using Genetic Neural Network with Double Transfer functions

Computational intelligence is a powerful tool for game development. In this paper, an algorithm of playing the game Tic-Tac-Toe with computational intelligence is developed. This algorithm is learned by a Neural Network with Double Transfer functions (NNDTF), which is trained by genetic algorithm (GA). In the NNDTF, the neuron has two transfer functions and exhibits a node-to-node relationship in the hidden layer that enhances the learning ability of the network. A Tic-Tac-Toe game is used to show that the NNDTF provide a better performance than the traditional neural network does

Sampled-data fuzzy controller for time-delay nonlinear systems : fuzzy-model-based LMI approach

Centre for Multimedia Signal Processing, Department of Electronic and Information Engineering2006-2007 > Academic research: refereed > Publication in refereed journalVersion of RecordPublishe

LMI-based stability and performance conditions for continuous-time nonlinear systems in Takagi-Sugeno's form

Centre for Multimedia Signal Processing, Department of Electronic and Information Engineering2007-2008 > Academic research: refereed > Publication in refereed journalVersion of RecordPublishe

An improved genetic algorithm based fuzzy-tuned neural network

This paper presents a fuzzy-tuned neural network, which is trained by an improved genetic algorithm (GA). The fuzzy-tuned neural network consists of a neural-fuzzy network and a modified neural network. In the modified neural network, a neuron model with two activation functions is used so that the degree of freedom of the network function can be increased. The neural-fuzzy network governs some of the parameters of the neuron model. It will be shown that the performance of the proposed fuzzy-tuned neural network is better than that of the traditional neural network with a similar number of parameters. An improved GA is proposed to train the parameters of the proposed network. Sets of improved genetic operations are presented. The performance of the improved GA will be shown to be better than that of the traditional GA. Some application examples are given to illustrate the merits of the proposed neural network and the improved GA. © World Scientific Publishing Company