Algorithmic Design to Mitigate Risks by Neuro- Fuzzy Techniques

Abstract

The heart of research aims to develop an algorithmic paradigm for risk mitigation in software projects by using Neuro-Fuzzy techniques. We have identified the requirements for developing the prototype of required tool which will help in determining the risk level of the project. Various approaches of Artificial Intelligence have been discussed in detail for risk assessment and mitigation in past. The algorithm that has been designed can be implemented using MATLAB or Java Frame works. The system utilizes Fuzzy logic to build the fuzzy inference system which focuses on the production of the membership function which is used for the Fuzzification and Defuzzification process. The training of system is done by using back propagation and Bayesian regulation approach of the neural networks. Neuro-Fuzzy technique has been applied to analyze the risk for dealing with uncertainty and incomplete specifications. The performance can have better results for neuro fuzzy model if we use Bayesian regulation approach as compare to the Back propagation because of low prediction capabilities in Back propagation.