A Comparative Analysis Of Fuzzy, Neuro-Fuzzy And Fuzzy-Ga Based Approaches For Software Reusability Evaluation

Classification is one of the major problems solved by the artificial neural networks. The problem deals with the mapping of the input data into classes. Voice recognition, pattern matching, face recognition, character recognition etc. are these types of problems. In the past few years we have seen a great increase in the methods to solve these problems. In this paper we have proposed a new method for solving these problems. The method is inspired from the neuro-fuzzy logic approach to problem solving. Here we have proposed the various changes that may be made in various layers for the system to better handle classificatory problems. We first cluster the training data so that the rest of the algorithm works efficiently with limited data to handle. The clustering is based on class identification of various inputs given as training data. A sort of fuzzy logic approach serves as a means to classify the unknown input to a class based on the rules formed from inputs given in training data. Rules are in form of representative of every cluster and their matching class. The centre and power of the representative are the parameters that are optimized using a training algorithm similar to the backpropagation algorithm of the artificial neural networks. This algorithm is optimized by Genetic Algorithms. We tested the algorithm on the famous classificatory problem of picture learning. We got a good efficiency which proves the effectiveness of the algorithm.

show abstract

A Comparative Analysis Of Fuzzy, Neuro-Fuzzy And Fuzzy-Ga Based Approaches For Software Reusability Evaluation

Cited by 6 publications

References 7 publications

Fuzzy Neuro Systems for Machine Learning for Large Data Sets

Fuzzy Neuro Systems for Machine Learning for Large Data Sets

Machine Learning-Based Software Quality Prediction Models: State of the Art

A novel approach to classificatory problem using neuro-fuzzy architecture

Contact Info

Product

Resources

About