Research And Application Of Pattern Classification Algorithm Based On Parallel Particle Swarm Optimization Algorithm

Pattern classification is a kind of technology used in a lot of project fields including automatic control monitor, image recognition, troubled diagnose, supplies compound, medical diagnosis, etc. The classical categorized method of pattern classification is mainly to count the analytical method based on pluralism. In recent years, artificial neural network technology becomes effective tool that pattern classified gradually too. These two kinds of methods have their own strong points. Pluralism counts the analytical method calculates normally, there are clear probability meanings, but need abundant samples, and should comply with certain distribution. The artificial neural network technology is strong in ability to express and suitable for extensive range, but the network is difficult in design, it is time-consuming to train, some extreme value are deficient.The neural network using in pattern classification problems mostly adopt multi-layer feed-forward neural network and use the back-propagation algorithm (BP algorithm). But BP algorithm depends on the choice of initial weight value excessively, restrain the speed slowly and apt to fall into the local optimization. The above mentioned defects of BP algorithm make the output of the neural network of its training have inconsistency and unpredictability, causing the dependability of patter classification is reduced. The parallel search tactics and global optimization of genetic algorithm make it become the general neural network train algorithm increasingly. Proving through the experiment, compared with BP algorithm, the neural network training by GA algorithm can improve the classifying correct rate and accelerate the convergence of training. But the complicated heredity operations such as selection, duplication, crossing, and mutation make neural network training time present with exponent increase with the scale and complexity of the problems. In defect of effective some local search mechanism, the algorithm disappear slowly phenomenon of convergence while closing to the optimization value. Particle swarm optimization is a kind of optimization algorithm of intellectual theory based on population, this algorithm search the optimization value using the cooperation and competition of the particles. PSO has kept the global search tactics, adopted the model of velocity-displacement, and has avoided complicated hereditary operations.With the constant development of scientific calculation and the complicated search...