지식 기반 패턴 인식용 무작위 생성 히든 노드를 갖춘 소프트맥스 방사형 기반 함수 신경망 설계

In this research, we proposed a new classification method which is an expanded version of the generic radial basis function neural networks. In the proposed classification method, the expanded version of RBFNNs consists of two main parts. And the first part is the soft-max function to help us handle the output distribution as a multinomial distribution. Secondly, radial basis functions, which can be considered as hidden nodes in the conventional neural networks, are defined not by using a clustering algorithm but in a random manner. When the soft-max function is applied to the networks, iterative learning algorithms such as nonlinear least square estimation and error back-propagation algorithm should be used to value the coefficient of the networks. In order to validate the proposed classification method, several machine learning data sets are used.