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Study of Evolutionary Connectionism from the perspective of Fuzzy Neural Network and Neuro-Fuzzy Inference model

Published on November 2011 by Rajesh S. Prasad
2nd National Conference on Information and Communication Technology
Foundation of Computer Science USA
NCICT - Number 8
November 2011
Authors: Rajesh S. Prasad
82a3f5c7-fe25-40c5-9d47-3cc79f1e499f

Rajesh S. Prasad . Study of Evolutionary Connectionism from the perspective of Fuzzy Neural Network and Neuro-Fuzzy Inference model. 2nd National Conference on Information and Communication Technology. NCICT, 8 (November 2011), 19-23.

@article{
author = { Rajesh S. Prasad },
title = { Study of Evolutionary Connectionism from the perspective of Fuzzy Neural Network and Neuro-Fuzzy Inference model },
journal = { 2nd National Conference on Information and Communication Technology },
issue_date = { November 2011 },
volume = { NCICT },
number = { 8 },
month = { November },
year = { 2011 },
issn = 0975-8887,
pages = { 19-23 },
numpages = 5,
url = { /proceedings/ncict/number8/4240-ncict061/ },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Proceeding Article
%1 2nd National Conference on Information and Communication Technology
%A Rajesh S. Prasad
%T Study of Evolutionary Connectionism from the perspective of Fuzzy Neural Network and Neuro-Fuzzy Inference model
%J 2nd National Conference on Information and Communication Technology
%@ 0975-8887
%V NCICT
%N 8
%P 19-23
%D 2011
%I International Journal of Computer Applications
Abstract

Connectionism is a movement in cognitive science which hopes to explain human intellectual abilities using neural networks (also known as ‘neural nets’). Evolutionary Connectionist System is an adaptive, incremental learning and knowledge representation system that evolves its structure and functionality, where in the core of the system is a connectionist architecture that consists of neurons (information processing units) and connections between them. Neural networks are simplified models of the brain composed of large numbers of units (the analogs of neurons) together with weights that measure the strength of connections between the units. These weights model the effects of the synapses that link one neuron to another. Experiments on models of this kind have demonstrated an ability to understand information, adapt knowledge and evolve intelligence.

References
  1. Amari, S. and Kasabov, N., “Brain-like Computing and Intelligent Information System, Springer Verlag, Singapore.
  2. Arbib M, “The Handbook of Brain Theory and Neural Networks”, MIT Press, Cambridge, MA, (1995, 2002).
  3. Nikola Kasabov, “Evolving Connectionist Systems”, Springer, Second Edition, 978-1-84628-345-1.
  4. Elman, Jeffrey L., “Distributed representations, Simple recurrent networks, and grammatical Structure”, Machine Learning, 7, pp. 195-225.
  5. Kasabov N. AND Kozma R., “ Neuro-Fuzzy Techniques for Intelligent Information System”, Physical Verlag (Springer Verlag), Heidelberg, 1999.
  6. Rajesh Prasad, U.V. Kulkarni, “A Novel Evolutionary Connectionist Text Summarizer”, International Conference on Anticounterfeiting, Security and Identification, ASID 2009, HongKong, by IEEE HongKong section.
  7. J. L. Elman, “Distributed representations, simple recurrent networks, and grammatical structure,” Machine Learning, vol. 7, pp. 195–225, 1991.
  8. Rajesh Prasad, U.V. Kulkarni, “Connectionist Approach to Generic Text Summarization”, WASET, International Conference on Neural Networks, Oslo, Norway, July 2008.
Index Terms

Computer Science
Information Sciences

Keywords

Evolving Connectionism Connectionist systems Evolving systems Fuzzy Neural Networks (FNN) Neuro-Fuzzy system