By Alan De Mause
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Personnaz, L. and Drefys, G. (1993). Neural Networks and Nonlinear Adaptive Filtering: Unifying Concepts and New Algorithms. Neural Computation, 5:165-199.  Ngia, L. (1997). An overview of the fundamental problems and solutions to echo cancellation. Technical Report CTH-TE-62, Chalmers University of Technology, Goteborg.  Poggio, T. and Girosi, F. (1990). Regularization Algorithms for Learning That Are Equivalent to Multilayer Networks. Science, 247:978-982.  Pucar, P. and Sjoberg, J.
4 A plot of the network initial state adjustment learned by carrying out the procedure outlined in the text. , predictions are improved for novel portions of the series) suggests a more homogeneous approach, in which we attempt to train directly a subnetwork to perform the initial state adjustment. The idea is to convert available information, here a single value of the time series, into network initial states. We term the subnetwork that performs such an operation the initiator and term the subnetwork that is responsible for state evolution the evolver.
Modelling of Talker Echo in Telephony Circuits. Master's thesis, Chalmers University of Technology, Goteborg.  MacKay, D. (1991). Bayesian Methods for Adaptive Models. PhD thesis, Caltech, Pasadena CA.  MacKay, D. (1992). Bayesian Interpolation. Neural Computation, 4(3}: 415-447.  Moody, J. (1992). The Effective Number of Parameters: An Analysis of Generalization and Regularization in Nonlinear Learning Systems. , Hanson, S. and Lippmann, R. ), Advances in Neural Information Processing Systems 4, Morgan Kaufmann Publishers, San Mateo, CA.