Dynamic System Identification using Recurrent Radial Basis Function Network

This paper presents a local neural network structure called spatiotemporally local network, by combining the radial basis function network (RBFN) and the local recurrent networks. Three local structures are proposed and the algorithms are compared for nonlinear dynamic system identification. System dynamics can be fully modeled with the fast learning of the proposed neural network structure.

[1]  Barak A. Pearlmutter.  Learning State Space Trajectories in Recurrent Neural Networks , 1989, Neural Computation.

[2]  Jooyoung Park,et al.  Universal Approximation Using Radial-Basis-Function Networks , 1991, Neural Computation.

[3]  John C. Platt.  A Resource-Allocating Network for Function Interpolation , 1991, Neural Computation.

[4]  Ronald J. Williams,et al.  A Learning Algorithm for Continually Running Fully Recurrent Neural Networks , 1989, Neural Computation.

[5]  F. Girosi,et al.  Networks for approximation and learning , 1990, Proc. IEEE.

[6]  Pineda,et al.  Generalization of back-propagation to recurrent neural networks. , 1987, Physical review letters.

[7]  Kumpati S. Narendra,et al.  Identification and control of dynamical systems using neural networks , 1990, IEEE Trans. Neural Networks.

[8]  Shlomo Geva,et al.  A constructive method for multivariate function approximation by multilayer perceptrons , 1992, IEEE Trans. Neural Networks.

[9]  R. R. Leighton,et al.  The autoregressive backpropagation algorithm , 1991, IJCNN-91-Seattle International Joint Conference on Neural Networks.

[10]  Ah Chung Tsoi,et al.  FIR and IIR Synapses, a New Neural Network Architecture for Time Series Modeling , 1991, Neural Computation.

[11]  L.G. Kraft,et al.  A comparison between CMAC neural network control and two traditional adaptive control systems , 1990, IEEE Control Systems Magazine.

[12]  Stephen H. Lane,et al.  Higher-Order CMAC Neural Networks - Theory and Practice , 1991, 1991 American Control Conference.

[13]  Fernando J. Pineda,et al.  Dynamics and architecture for neural computation , 1988, J. Complex..

[14]  E. Hartman,et al.  Semi-local units for prediction , 1991, IJCNN-91-Seattle International Joint Conference on Neural Networks.

[15]  James S. Albus,et al.  New Approach to Manipulator Control: The Cerebellar Model Articulation Controller (CMAC)1 , 1975 .

[16]  John Moody,et al.  Fast Learning in Networks of Locally-Tuned Processing Units , 1989, Neural Computation.

[17]  P J Webros.  BACKPROPAGATION THROUGH TIME: WHAT IT DOES AND HOW TO DO IT , 1990 .

[18]  Y. Wong.  How Gaussian radial basis functions work , 1991, IJCNN-91-Seattle International Joint Conference on Neural Networks.

[19]  Sheng Chen,et al.  Practical identification of NARMAX models using radial basis functions , 1990 .

[20]  Kwang Y. Lee,et al.  System Identification and Control using Diagonal Recurrent Neural Networks , 1992, 1992 American Control Conference.

[21]  D. Broomhead,et al.  Radial Basis Functions, Multi-Variable Functional Interpolation and Adaptive Networks , 1988 .