Incorporating fuzzy concepts along with dynamic tunneling for fast and robust training of multilayer perceptrons - RoyChowdhury P.; Shukla K.K. (My University, 2025)
This paper proposes a new computational technique for training of fuzzified multilayer perceptrons (MLP), which is based on the fuzzification of input and output along with recently proposed dynamic tunneling technique in MLP. The fuzzified MLP generates fuzzy output from fuzzy input whose connection weights are real numbers. This type of modeling allows realistic representation of natural/real life data which is akin to human reasoning process. The proposed technique is applied to problems like; approximate realization of non-linear mapping of fuzzy numbers, classification of fuzzy patterns, and approximate realization of fuzzy if then rules. The simulation results are provided for eight test examples to demonstrate the learning capabilities of the proposed method. The generalization property of MLP in learning using the above technique is also discussed. Lastly, the proposed technique is applied to some well-known benchmark problems to assess the classification ability. © 2002 Elsevier Science B.V. All rights reserved.