Title: A comparative study on the performance of fuzzy logic, Bayesian logic and neural network towards decision-making
Authors: Dharmpal Singh; Jagannibas Paul Choudhury; Mallika De
Addresses: Department of Computer Science and Engineering, JIS College of Engineering, Block 'A' Phase III, Kalyani, Nadia-741235, West Bengal, India. ' Department of Information Technology, Kalyani Government Engineering College, Kalyani, Nadia-741235, West Bengal, India. ' Department of Engineering and Technological, University of Kalyani, Kalyani-741235, Dt. Nadia, West Bengal, India
Abstract: Soft computing models play an important role in the field of recognition, classification, data prediction, etc., and also in various application fields towards decision-making. Soft computing models include fuzzy logic, neural, network, genetic algorithm, particle swarm optimisation, tabu search, harmonie search, clustering, etc. The performance of a particular soft computing model can be ascertained using a particular dataset for the purpose of decision-making. Here, an effort has been made to make a comparison on the performance of fuzzy logic, Bayesian logic and neural network. The model with minimum error has been given preference for selection towards decision-making of information. The same method has been cross-checked based on the residual analysis to verify the earlier proposed observation. The said models have also been cross-checked based on other dataset. Under neural network, perceptron neural network model has been used.
Keywords: fuzzy logic; membership functions; bell shaped function; Bayesian logic; perceptron neural networks; soft computing; decision making.
DOI: 10.1504/IJDATS.2012.046792
International Journal of Data Analysis Techniques and Strategies, 2012 Vol.4 No.2, pp.205 - 216
Published online: 06 Sep 2014 *
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