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Article
Neuro-Fuzzy Approach for Development of New Neuron Model
Int. J. of Soft Computing - A Fusion of Foundations, Methodologies and Applications (2003)
  • Manmohan, Dayalbagh Educational Institute
  • D. K. Chaturvedi, Dayalbagh Educational Institute
  • P. S. Satsangi, Dayalbagh Educational Institute
  • P. K. Kalra
Abstract
The training time of ANN depends on size of ANN (i.e. number of hidden layers and number of neurons in each layer), size of training data, their normalization range and type of mapping of training patterns (like X–Y, X–DY, DX–Y and DX–DY), error functions and learning algorithms. The efforts have been done in past to reduce training time of ANN by selection of an optimal network and modification in learning algorithms. In this paper, an attempt has been made to develop a new neuron model using neuro-fuzzy approach to overcome the problems of ANN incorporating the features of fuzzy systems at a neuron level. Fuzzifying the neuron structure, which incorporates the features of simple neuron as well as high order neuron, has used this synergetic approach.
Keywords
  • ANN,
  • Neuro-fuzzy approach,
  • Backpropagation,
  • Benchmark problems
Publication Date
October, 2003
Citation Information
Manmohan None, D. K. Chaturvedi, P. S. Satsangi and P. K. Kalra. "Neuro-Fuzzy Approach for Development of New Neuron Model" Int. J. of Soft Computing - A Fusion of Foundations, Methodologies and Applications Vol. 8 Iss. 1 (2003)
Available at: http://works.bepress.com/dk_chaturvedi/13/