Title :
Spectrum-based design of sinusoidal RBF neural networks
Author_Institution :
Dept. of Psychol., Univ. of Newcastle, Newcastle upon Tyne, UK
fDate :
6/24/1905 12:00:00 AM
Abstract :
This paper introduces and describes the spectrum-based design of radial basis function (RBF) neural networks. The RBF networks used in this paper work with damped sinusoidal nonlinear activation functions. The concept of the associated data spectrum is introduced, and it is shown how to apply this spectrum to find the number of hidden neurons and their internal parameters for a neural network solution of the related data processing problem. A time series prediction application is presented. The relation of the proposed method to the support vector machine method and the application of the method to select appropriate basis functions for a problem with given data are discussed
Keywords :
forecasting theory; learning automata; network synthesis; radial basis function networks; spectra; time series; associated data spectrum; basis functions selection; damped sinusoidal nonlinear activation functions; data processing; hidden neurons; internal parameters; sinusoidal radial basis function neural networks; spectrum-based design; support vector machine method; time series prediction application; Approximation methods; Artificial neural networks; Data processing; Neural networks; Neurons; Psychology; Radial basis function networks; Support vector machines;
Conference_Titel :
Neural Networks, 2002. IJCNN '02. Proceedings of the 2002 International Joint Conference on
Conference_Location :
Honolulu, HI
Print_ISBN :
0-7803-7278-6
DOI :
10.1109/IJCNN.2002.1007725
