Common Optimization of Adaptive Preprocessing Units and a Neural Network during the Learning Period. Application in EEG Pattern Recognition

In this study, a proposition of simultaneous training of the neural network (multilayer perceptron) and adaptive preprocessing unit is presented. This cooperation enables the network to affect the preprocessing and as a consequence to vary the locations of pattern vectors in a feature space. Thus, during the learning process the network tries to find a good separation of classes of patterns, which results in convergence of the whole learning process. The strategy was developed in order to make efficient EEG monitoring in neonates possible. A comparison of the method presented herein with the known learning strategies for neural networks shows the need for using it as an alternative learning process. The convergence of the whole system is also discussed. Copyright 1997 Elsevier Science Ltd.

[1]  F. Holsboer,et al.  Effects of pulsatile cortisol infusion on sleep‐EEG and nocturnal growth hormone release in healthy men , 1994, Journal of sleep research.

[2]  Teuvo Kohonen,et al.  Self-Organization and Associative Memory , 1988 .

[3]  Alexander H. G. Rinnooy Kan,et al.  A stochastic method for global optimization , 1982, Math. Program..

[4]  J. S. Barlow,et al.  Computer characterization of tracé alternant and REM sleep patterns in the neonatal EEG by adaptive segmentation--an exploratory study. , 1985, Electroencephalography and clinical neurophysiology.

[5]  Qing Yang,et al.  Pattern recognition by a distributed neural network: An industrial application , 1991, Neural Networks.

[6]  Michael Eiselt,et al.  Predictive value of pattern selective spectral analysis of neonatal EEG , 1991 .

[7]  J. Kiefer,et al.  Stochastic Estimation of the Maximum of a Regression Function , 1952 .

[8]  A Värri,et al.  Automatic identification of significant graphoelements in multichannel EEG recordings by adaptive segmentation and fuzzy clustering. , 1991, International journal of bio-medical computing.

[9]  King-Sun Fu,et al.  Syntactic Pattern Recognition And Applications , 1968 .

[10]  Bärbel Schack,et al.  Adaptive quantile estimation and its application in analysis of biological signals , 1993 .

[11]  James C. Bezdek,et al.  Generalized clustering networks and Kohonen's self-organizing scheme , 1993, IEEE Trans. Neural Networks.

[12]  Tim Niblett,et al.  Constructing Decision Trees in Noisy Domains , 1987, EWSL.

[13]  Donald F. Specht,et al.  Probabilistic neural networks and the polynomial Adaline as complementary techniques for classification , 1990, IEEE Trans. Neural Networks.

[14]  Yoshio Mogami,et al.  A hybrid algorithm for finding the global minimum of error function of neural networks and its applications , 1994, Neural Networks.

[15]  Allen I. Selverston,et al.  A consideration of invertebrate central pattern generators as computational data bases , 1988, Neural Networks.

[16]  M. A. Styblinski,et al.  Experiments in nonconvex optimization: Stochastic approximation with function smoothing and simulated annealing , 1990, Neural Networks.

[17]  Christian Lebiere,et al.  The Cascade-Correlation Learning Architecture , 1989, NIPS.

[18]  Yoshio Hirose,et al.  Backpropagation algorithm which varies the number of hidden units , 1989, International 1989 Joint Conference on Neural Networks.

[19]  George Cybenko,et al.  Approximation by superpositions of a sigmoidal function , 1992, Math. Control. Signals Syst..

[20]  Roger J.-B. Wets,et al.  Minimization by Random Search Techniques , 1981, Math. Oper. Res..

[21]  Donald F. Specht,et al.  Probabilistic neural networks , 1990, Neural Networks.