A Genetic Algorithm Learning Based CMAC with Gaussian Basis Function and Its Application in Function Learning

• Main Authors: Jing-Wen Shiau, 蕭景文
• Other Authors: Hung-Ching Lu
• Format: Others
• Language: en_US
• Published: 2004
• Online Access: http://ndltd.ncl.edu.tw/handle/54869360266144201231

碩士 === 大同大學 === 電機工程學系(所) === 93 === Cerebellar Model Arithmetic Controller (CMAC) is one of neural networks and its advantage is fast learning property, good generalization capability, and ease of implementation by hardware. It is, however, difficult to decide various parameters of CMAC in advance. Genetic Algorithm (GA) is one of Evolutionary Algorithms (EAs), and is efficient in local search. Employing genetic algorithms on the design and training of CMAC allows the CMAC parameters to be easily optimized. CMAC can be viewed as a radial basis function (RBF) network. The conventional CMAC uses a local constant basis function (also called rectangle function) to model the hypercube structure. A disadvantage is that its output is always constant within each quantized state and the derivative information of input and output variables cannot be preserved. If the local constant basis functions are replaced by non-constant differentiable basis functions, the derivative information will be able to be stored into the structure as well. Therefore, we use Gaussian basis function (GBF) to improve the accuracy of GA-CMAC. In the experimental results, the GA-CMAC with GBF is performed to demonstrate the improvement of accuracy in modeling.