Performance Improvement of Radial Basis Function Neural Networks Using Adaptive Feature Extraction

적응적 특징추출을 이용한 Radial Basis Function 신경망의 성능개선

  • 조용현 (대구효성가톨릭대학교 공과대학 컴퓨터정보통신공학부)
  • Published : 2000.06.01


This paper proposes a new RBF neural network that determines the number and the center of hidden neurons based on the adaptive feature extraction for the input data. The principal component analysis is applied for extracting adaptively the features by reducing the dimension of the given input data. It can simultaneously achieve a superior property of both the principal component analysis by mapping input data into set of statistically independent features and the RBF neural networks. The proposed neural networks has been applied to classify the 200 breast cancer databases by 2-class. The simulation results shows that the proposed neural networks has better performances of the learning time and the classification for test data, in comparison with those using the k-means clustering algorithm. And it is affected less than the k-means clustering algorithm by the initial weight setting and the scope of the smoothing factor.