Neuro-Fuzzy Approach for Development of New Neuron ModelInt. J. of Soft Computing - A Fusion of Foundations, Methodologies and Applications (2003)
AbstractThe 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.
- Neuro-fuzzy approach,
- Benchmark problems
Publication DateOctober, 2003
Citation InformationManmohan 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/