A comparative study on the performance of fuzzy logic, Bayesian logic and neural network towards decision-making Online publication date: Sat, 06-Sep-2014
by Dharmpal Singh; Jagannibas Paul Choudhury; Mallika De
International Journal of Data Analysis Techniques and Strategies (IJDATS), Vol. 4, No. 2, 2012
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.
Existing subscribers:
Go to Inderscience Online Journals to access the Full Text of this article.
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Data Analysis Techniques and Strategies (IJDATS):
Login with your Inderscience username and password:
Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.
If you still need assistance, please email subs@inderscience.com
Our Blog 
Follow us on Twitter 
Visit us on Facebook