Parallel recursive prediction error algorithm for training layered neural networks

REFERENCES

• CHEN , S. , and S. A. BILLINGS , 1989 , Representation of non-linear systems the NARMAX model . International Journal of Control , 49 , 1013 – 1032 .
   Web of Science ®Google Scholar
• CHEN , S. , BILLINGS , S. A. , and GRANT , P. M. , 1990 , Non-linear system identification using neural networks . International Journal of Control , 51 , 1191 – 1214 .
   Web of Science ®Google Scholar
• CHEN , S. , GIBSON , G. J. , COWAN , C F. N. , and GRANT , P. M. , 1989 , Recursive prediction error algorithm for training multilayer perceptrons . Proceedings of the I.E.E.E. Colloquium on Adaptive Algorithms for Signal Estimation and Control , Edinburgh , Scotland .
   Google Scholar
• GIBSON , G. J. , Siu , S. , and COWAN , C. F. N. , 1989 , Application of multilayer perceptrons as adaptive channel equalisers . Proceedings of the I.E.E.E. International Conference on Acoustics, Speech, and Signal Processing , Glasgow , Scotland , pp. 1183 – 1186 .
   Google Scholar
• GOODWIN , G. C. , and PAYNE , R. L. , 1977 , Dynamic System Identification Experiment Design and Data Analysis ( New York Academic Press ).
   Google Scholar
• KOLLIAS , S. , and ANASTASSIOU , D. , 1989 , An adaptive least squares algorithm for the efficient training of artificial neural networks . I.E.E.E. Transactions on Circuits and Systems , 36 , pp. 1092 – 1101 .
   Google Scholar
• LEONTARITIS , I. J. , and BILLINGS , S. A. , 1985 , Input-output parametric models for non-linear systems. Part I deterministic non-linear systems; Part II stochastic non-linear systems . International Journal of Control , 41 , 303 – 344 .
   Web of Science ®Google Scholar
• LIPPMANN , R. P. , 1987 , An introduction to computing with neural nets . I.E.E.E. ASSP Magazine , 4 .
   Google Scholar
• LJUNG , L. , and SÖDERSTRÖM , T. , 1983 , Theory and Practice of Recursive Identification ( Cambridge , Mass MIT Press ).
   Google Scholar
• RUMELHART , D. E. , HINTON , G. E. , and WILLIAMS , R. J. , 1986 , Learning internal representations by error propagation . Parallel Distributed Processing Explorations in the Micro-structure of Cognition , edited by D. E. Rumelhart and J. L. McClelland ( Cambridge , Mass MIT Press ), pp. 318 – 362 .
   Google Scholar
• SALGADO , M. E. , GOODWIN , G. C , and MIDDLETON , R. H. , 1988 , Modified least squares algorithm incorporating exponential resetting and forgetting . International Journal of Control , 47 , 477 – 491 .
   Web of Science ®Google Scholar
• SINGHAL , S. , and Wu , L. , 1989 , Training feed-forward networks with the extended Kalman algorithm . Proceedings of the I.E.E.E. International Conference on Acoustics. Speech, and Signal Processing, Glasgow, Scotland , pp. 1187 – 1190 .
   Google Scholar