Application of Chaotic Particle Swarm Optimization in Wavelet Neural Network

Abstract

Currently, the method of optimizing the wavelet neural network with particle swarm plays a certain role in improving the convergence speed and accuracy; however, it is not a good solution for problems of turning into local extrema and poor global search ability. To solve these problems, this paper, based on the particle swarm optimization, puts forward an improved method, which is introducing the chaos mechanism into the algorithm of chaotic particle swarm optimization. Through a series of comparative simulation experiments, it proves that applying this algorithm to optimize the wavelet neural network can successfully solve the problems of turning into local extrema, and improve the convergence speed of the network, in the meantime, reduce the output error and improve the search ability of the algorithm. In general, it helps a lot to improve the overall performance of the wavelet neural network. Keywords: chaotic particle swarm optimization, convergence speed, wavelet neural network IntroductionThe optimism theory and method have existed since ancient time, among which, the relatively representative one is the golden section method. Optimism mainly solves the problem of finding the best solution from many solutions. We can defined optimism as: under certain restrictions, to make the problem reach a best measurement, or to find out a set of parameters, and make certain indicators reach the maximum or minimum. As an important branch of science, the optimism method is gaining more and more attention, and plays important roles in many fields, such as engineering technology, electrical engineering, image processing etc. However, in real life application, since the complexity and nonlinearity of many problems, the target functions of these problems are often discrete and of multi-point value, furthermore, the modeling the problem itself is also very difficult. When applying traditional optimizations like Newton method, dynamic programming, branch and bound method, etc. to solve these complex optimism problems, one usually need to traverse the entire search space, which will waste a lot of time, and can not meet the actual requirement in the aspects of the convergence of the problems and the optimization calculation speed. Therefore, in the current field of optimism, the key job is to seek the efficient optimization.Particle swarm optimization gain the attention of many international scholars in related fields rapidly since its advent. First, Kennedy J and Eberhart R. C. put forward the binary particle swarm optimization in 1997. Then, in 1998, in order to improve the convergence of the algorithm, Shi Y and Eberhart R C introduced the inertia weight parameter into the speed item of the PSO and proposed to dynamically adjust the inertia weight to balance the convergence speed during the process of evolution. This algorithm is called the standard PSO. Then, they put forward the linear decreasing inertia weight LDW-PSO, however, if it deviated from the overall optimum solution in the initial state, then the linear...

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