A New CMAC Neural Network Model with Adaptive Quantization Input Layer
作者单位：Department of Control EngineeringP.O.Box 329Harbin Institute of Technology
会议名称：《1996 3rd International Conference on Signal Processing(ICSP’96)》
会议日期：1996年
学科分类：12[管理学] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 081104[工学-模式识别与智能系统] 08[工学] 0835[工学-软件工程] 0811[工学-控制科学与工程] 0812[工学-计算机科学与技术（可授工学、理学学位）]
关 键 词：Neural networks Adaptive systems Quantization Signal processing algorithms Function approximation Control engineering Laboratories Algorithm design and analysis Arithmetic Robots
摘 要：正In this paper,we first discuss the structure, principle and learning algorithm of CMAC neural network model.A new adaptive quantization method based on competitive learning is then proposed to quantimize the inputs of CMAC according to the degree of variations of the approximated *** analysis and simulation results show that with the input layer using this algorithm CMAC can approximate more accurately and efficiently than the original model using equal-size quantization method.