PSO-Based Evolutionary Learning : System Design and Applications

• Main Authors: Ching-Yi Chen, 陳慶逸
• Other Authors: Fun Ye
• Format: Others
• Language: en_US
• Published: 2006
• Online Access: http://ndltd.ncl.edu.tw/handle/85292847520962344092

博士 === 淡江大學 === 電機工程學系博士班 === 94 === The new paradigm of Swarm Intelligence, called Particle Swarm Optimization (PSO), is one of the well-known evolutionary computation techniques, which can be considered as an efficient tool to find near optimal solution in a searching space. Especially, PSO is a useful method when the problems to be solved are high-dimensional, nonlinear or some specific information is unavailable. PSO combines the social-only model and the cognition-only model to select the adjustable parameters to approach optimal solution, its main advantage is its rapid convergence and small computational requirements, which make it a good candidate for solving optimization problems. In this dissertation, the efficient, robust, and flexible PSO algorithms are proposed to generate some artificial intelligence system in solving some applications, such as cluster analysis, image processing, and neural network training. The first task of this dissertation introduces two types of PSO clustering applications. The first one is given in advance the optimal number of clusters by manual manipulation, and then the PSO is applied to achieve the optimal clustering results. The other one is to use PSO algorithm that includes the cluster validity measure to automatically determine the true number of the cluster centers, and then to extract real cluster centers and to make a good classification. The second task of this dissertation is to develop an evolutional fuzzy particle swarm optimization (FPSO) learning algorithm to automatically extract the near-optimum codebook of vector quantization (VQ) for carrying on image compression. Based on the adaptive learning scheme of the PSO and the flexible membership function of the fuzzy inference system, the dissertation also demonstrates the advance of the FPSOVQ-based image compression system. The last issue of this dissertation is focuses on the topic of radial basis function networks (RBFNs) learning. An innovative hybrid recursive particle swarm optimization (HRPSO) learning algorithm with normalized fuzzy c-mean (NFCM) clustering is proposed to generate radial basis function networks (RBFNs) modeling system with small numbers of descriptive radial basis functions (RBFs) for fast approximating two complex and nonlinear functions.