Research on Image Vectorization Based on Fuzzy Neural Network and Expert System

Abstract

Because the traditional linear vectorization methods have some shortcomings including processing data slowly, being sensitive to noises and being easy to be distorted. Fuzzy rules and its inference mechanism are the assurance of achieving feature fusion. However, the self-learning function of FNN could train its weights; it is difficult to optimize fuzzy rules. Besides, the common FNN training algorithms have low constringency speed and are liable to run into the local optimization.PSO algorithm has high convergence speed and it is simpler on the operation and is more potential on optimizing FNN. Thus, PSO algorithm could be adapted to train FNN weights, and prune the redundancy links, optimize fuzzy rules base. In the paper we present an improving immune genetic algorithm based on chaos theory. The over-spread character and randomness of chaos can be used to initialize population and improve the searching speed, and the initial value sensitivity of chaos can be used to enlarge the searching space. To avoid the local optimization, the algorithm renews population and enhances the diversity of population by using density calculation of immune theory and adjusting new chaos sequence.