 A comparative study on the performance of fuzzy logic, Bayesian logic and neural network towards decision-makingDharmpal Singh, Jagannibas Paul Choudhury and Mallika De International Journal of Data Analysis Techniques and Strategies, 2012, vol. 4, issue 2, 205-216 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. (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:ids:injdan:v:4:y:2012:i:2:p:205-216 Access Statistics for this article More articles in International Journal of Data Analysis Techniques and Strategies from Inderscience Enterprises Ltd |
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