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There are 6659 results for: content related to: Theory of interacting neural networks

  1. Making Decisions

    Minds and Machines: Connectionism and Psychological Modeling

    Michael R. W. Dawson, Pages: 170–202, 2008

    Published Online : 14 JAN 2008, DOI: 10.1002/9780470752999.ch10

  2. Perceptron Learning

    Knowledge Discovery with Support Vector Machines

    Lutz Hamel, Pages: 61–72, 2009

    Published Online : 26 OCT 2009, DOI: 10.1002/9780470503065.ch5

  3. You have free access to this content
    Connectionist Models and Linguistic Theory: Investigations of Stress Systems in Language

    Cognitive Science

    Volume 18, Issue 1, January 1994, Pages: 1–50, Prahlad Gupta and David S. Touretzky

    Article first published online : 11 FEB 2010, DOI: 10.1207/s15516709cog1801_1


    Computational Intelligence

    Volume 9, Issue 2, May 1993, Pages: 155–170, MOSTEFA GOLEA and MARIO MARCHAND

    Article first published online : 2 APR 2007, DOI: 10.1111/j.1467-8640.1993.tb00305.x

  5. Further Higher Moments in Portfolio Selection and A Priori Detection of Bankruptcy, Under Multi-layer Perceptron Neural Networks, Hybrid Neuro-genetic MLPs, and the Voted Perceptron

    International Journal of Finance & Economics

    Volume 20, Issue 4, October 2015, Pages: 341–361, Nikolaos Loukeris and Iordanis Eleftheriadis

    Article first published online : 1 SEP 2015, DOI: 10.1002/ijfe.1521

  6. Artificial neural networks for pattern recognition

    Concepts in Magnetic Resonance

    Volume 8, Issue 5, 1996, Pages: 303–324, Simon A. Corne

    Article first published online : 7 DEC 1998, DOI: 10.1002/(SICI)1099-0534(1996)8:5<303::AID-CMR1>3.0.CO;2-2

  7. Perceptron Learning in Engineering Design

    Computer-Aided Civil and Infrastructure Engineering

    Volume 4, Issue 4, December 1989, Pages: 247–256, H. ADELI and C. YEH

    Article first published online : 6 NOV 2008, DOI: 10.1111/j.1467-8667.1989.tb00026.x

  8. Comparison of two optimization methods to derive energy parameters for protein folding: Perceptron and Z score

    Proteins: Structure, Function, and Bioinformatics

    Volume 41, Issue 2, 1 November 2000, Pages: 192–201, Michele Vendruscolo, Leonid A. Mirny, Eugene I. Shakhnovich and Eytan Domany

    Article first published online : 17 AUG 2000, DOI: 10.1002/1097-0134(20001101)41:2<192::AID-PROT40>3.0.CO;2-3

  9. Some Practical Considerations of Predictability and Learning Algorithms for Various Signals

    Recurrent Neural Networks for Prediction: Learning Algorithms, Architectures and Stability

    Danilo P. Mandic, Jonathon A. Chambers, Pages: 171–198, 2002

    Published Online : 21 JUN 2002, DOI: 10.1002/047084535X.ch11

  10. A supervised learning method using duality in the artificial neuron model

    Systems and Computers in Japan

    Volume 36, Issue 9, August 2005, Pages: 34–42, Keiichi Yamada, Susumu Kuroyanagi and Akira Iwata

    Article first published online : 10 JUN 2005, DOI: 10.1002/scj.20206

  11. Statistical learning theory: a tutorial

    Wiley Interdisciplinary Reviews: Computational Statistics

    Volume 3, Issue 6, November/December 2011, Pages: 543–556, Sanjeev R. Kulkarni and Gilbert Harman

    Article first published online : 10 JUN 2011, DOI: 10.1002/wics.179

  12. Adaptive neural networks and their applications

    International Journal of Intelligent Systems

    Volume 8, Issue 4, 1993, Pages: 453–507, Bernard Widrow and Michael A. Lehr

    Article first published online : 13 MAR 2007, DOI: 10.1002/int.4550080403

  13. Artificial Neural Networks for Forecasting of Fuzzy Time Series

    Computer-Aided Civil and Infrastructure Engineering

    Volume 25, Issue 5, July 2010, Pages: 363–374, U. Reuter and B. Möller

    Article first published online : 10 FEB 2010, DOI: 10.1111/j.1467-8667.2009.00646.x

  14. Analysis of ensemble learning using simple perceptrons based on on-line learning theory

    Systems and Computers in Japan

    Volume 36, Issue 12, 15 November 2005, Pages: 63–74, Seiji Miyoshi, Kazuyuki Hara and Masato Okada

    Article first published online : 14 SEP 2005, DOI: 10.1002/scj.20336

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    How to train a neural network: An introduction to the new computational paradigm


    Volume 1, Issue 6, July/August 1996, Pages: 13–28, Jeffrey Johnson and Philip Picton

    Article first published online : 16 MAY 2013, DOI: 10.1002/cplx.6130010606

  16. Approximation Structures

    Adaptive Approximation Based Control: Unifying Neural, Fuzzy and Traditional Adaptive Approximation Approaches

    Jay A. Farrell, Marios M. Polycarpou, Pages: 71–114, 2006

    Published Online : 19 APR 2006, DOI: 10.1002/0471781819.ch3

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    Appendix G: Thirty Years of Adaptive Neural Networks: Perceptron, Madaline, and Backpropagation

    Adaptive Inverse Control: A Signal Processing Approach, Reissue Edition

    Bernard Widrow, Eugene Walach, Pages: 409–474, 2007

    Published Online : 16 JUL 2007, DOI: 10.1002/9780470231616.app7

  18. You have free access to this content
    Classifying adults' and children's faces by sex: computational investigations of subcategorical feature encoding

    Cognitive Science

    Volume 25, Issue 5, September 2001, Pages: 819–838, Yi D. Cheng, Alice J. O'Toole and Hervé Abdi

    Article first published online : 11 FEB 2010, DOI: 10.1207/s15516709cog2505_8

  19. Geometric learning algorithm for elementary perceptron and its convergence conditions

    Electronics and Communications in Japan (Part III: Fundamental Electronic Science)

    Volume 82, Issue 9, September 1999, Pages: 29–38, Seiji Miyoshi, Kazushi Ikeda and Kenji Nakayama

    Article first published online : 14 APR 1999, DOI: 10.1002/(SICI)1520-6440(199909)82:9<29::AID-ECJC4>3.0.CO;2-P

  20. You have free access to this content
    Feature Discovery by Competitive Learning

    Cognitive Science

    Volume 9, Issue 1, January 1985, Pages: 75–112, David E. Rumelhart and David Zipser

    Article first published online : 11 FEB 2010, DOI: 10.1207/s15516709cog0901_5