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Nonlinear time series modelling and prediction using Gaussian RBF networks with enhanced cllustering and RLS learning

Authors
Publisher
IEE
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Disciplines
  • Biology
  • Computer Science
  • Engineering

Abstract

Nonlinear time series modelling and prediction using Gaussian RBF networks with enhanced clustering - Electronics Letters 0 IEE 1995 Electronics Letters Online No: 19950077 M. Arai and J.G.P Binner (Department of Materials Engineering and Materialr Design, University of Nottingham, Universify Park, Nottingham NG7 ZRD, United Kingdom) T.E. Cross (Department of’ Electrical and Electronic Engineering, University of Nottingham, University Purk, Nottingham NG7 2RD. United Kingdom) II Ocrober 1994 Refe- I ATHEY,T.W., STUCHLY, M.A., and STUCHLV, S.S.: ‘Measurement Of radio frequency permittivity of biological tissues with an open- ended coaxial line : Part I ’ , IEEE Trans., 1982, h.TIT-30, (I), pp. 2 MOSIG, J.R., BESSON.JC.E, GEX-FABRY. M., and GARDI0L.F.E.: 82-86 ‘Reflection of an open-ended coaxial line and application to nondestructive measuremet of materials’, IEEE Trans., 1981, Ihl- 30, (1). PP. 46-51 3 GRANT, J.P., CLARKE, R.N., SYMM, G.T., and SPYROU, N.M.: ‘A critical study of the open-ended coaxial line sensor technique for FR and microwave complex permittivity measurements’, J. Phys. E: Sri. Instrum., 1989,22, pp. 757-770 4 ARAI, M., BINNER. J.G.P., CMR. G.E., and CROSS, T.E.: ‘ High temperature dielectric property measurements of engineering ceramics’. Microwaves: Theory and Appliitions in Materials Processing, Am. Ceram. Soc., 1993, Vol. 36, pp. 483-492 Nonlinear time series modelling and prediction using Gaussian RBF networks with enhanced clustering and RLS learning S. Chen Indexing terms: Neural networks. Time series An improved clustering and recursive least squares (RLS) learning algorithm for Gaussian radial basis function (RBF) networks is described for modelling and predicting nonlinear time series. S i g d i i t performance gain can be achieved with a much smaller network compared with the usual clustering and RLS method. Introduction: A powerful learning method for RBF networks is clustering and least squar

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