Li-Biao Zhang

Solving multi objective optimization problems using particle swarm optimization

An algorithm for solving multiobjective optimization problems is presented based on PSO through the improvement of the selection manner for global and individual extremum. The search for the Pareto optimal set of multiobjective optimization problems is performed. Numerical simulations show the effectiveness of the proposed algorithm.

Automatic fuzzy rule extraction based on particle swarm optimization

The extraction of fuzzy rules is always a difficult problem to fuzzy system. We have proposed a pruning algorithm to optimize fuzzy neural network based on particle swarm optimization algorithm. It can evolve both the fuzzy neural networks topology and weighting parameters. In a real problem, it can automatically obtain the near-optimal structure of fuzzy neural network according to the requirements. The experiment has proved that the method is applicable and efficient.

Automatic generating fuzzy rules with a particle swarm optimization

We have proposed a pruning algorithm to obtain the desirable fuzzy rules based on particle swarm optimization. Compared the standard particle swarm optimization, in the proposed algorithm we adopted a binary value vector and a real values vector to represent a solution, and used the different equation to update the different parameters. Numerical simulations show the effectiveness of the proposed algorithm.

The methods of improving variable illumination for face recognition

The illumination fluctuation is one of the important factors that influence the precision of face recognition. When environmental illumination is changed, the implementation method which is often used in the laboratory may not be effective any longer. Several traditional methods for compensating and improving variable illumination are discussed in this paper. Then a novel algorithm based on wavelet analysis is presented to find the invariance of illumination. Several empirical tests are given to demonstrate the effectiveness of our method. The method can be applied to a real system. And it can also improve the system robustness and adaptability.

Face recognition using adaptive resonance theory

Human face detection and recognition are challenged questions in pattern recognition field. After the facial features such as eyes, nose and mouth are detected in an image which contains a face, the rectangle area surrounding facial features is obtained. The pixels number of the rectangle area is large and the intensity values of these pixels are often treated as a feature vector. It is very important to drop the dimension of the vector for an effective recognition. Three means for dimensional reduction in the feature extraction field are often used, including average values of weighted intensity, wavelet transform and principle component analysis. The compact face feature vector is the eigenvector to be recognized. A face recognition method using ART2 is proposed in the paper. Experiment results show that it is preferable in recognition as well as it could increase or decrease samples rapidly.

Fuzzy Neural Network Optimization by a Multi-Objective Particle Swarm Optimization Algorithm

A cathode-ray tube has a phosphor screen which is made by using a mixture of a long persistent phosphor and a short persistent phosphor. A light emission from the short persistent phosphor has a spike figure, so that a light pen can have sufficient sensitivity therefor, and a light emission from the long persistent phosphor is continuous within the one cycle period of the frame frequency, so that flicker is not noticeable.

Particle Swarm Optimization of T-S Fuzzy Model

This paper introduces a new algorithm for Takagi -Sugeno (T-S) fuzzy modeling based on particle swarm optimization. Compared the standard particle swarm optimization, in the proposed algorithm a mixed code is adopted to represent a solution. Binary codes represent the structure of T-S fuzzy model, and real values represent corresponding parameters. Numerical simulations show the effectiveness of the proposed algorithm.

Optimizing a Fuzzy Neural Network with a Hierarchical Genetic Algorithm

We have proposed an algorithm to optimize fuzzy neural network based on hierarchical genetic algorithm. It can evolve both the fuzzy neural networks topology and weighting parameters. In a real problem, it can automatically obtain the near-optimal structure of fuzzy neural network according to the requirements. Numerical simulations show the effectiveness of the proposed algorithm.

Fuzzy rule extraction by two-objective particle swarm optimization and application for taste identification of tea

The extraction of fuzzy rules is always a difficult problem to fuzzy system, in this problem performance and complexity are two conflicting criteria. We have proposed a two-objective algorithm based on particle swarm optimization algorithm and the weighted fuzzy neural network. It can evolve both the fuzzy neural networks topology and weighting parameters and obtained the near-optimal structure of fuzzy neural network for taste identification of tea. Numerical simulations show the effectiveness of the proposed algorithm.

A method of improving performance of fuzzy neural network based on differential evolution

Differential evolution is a powerful evolutionary inspired search technique for global optimization. We have proposed a new algorithm based on differential Evolution to solve the fuzzy neural network design problem, it can identify an optimal and efficient fuzzy neural network structure for a given problem. Numerical simulations show the effectiveness of the proposed algorithm.