A novel approach to classificatory problem using neuro-fuzzy architecture
Rahul Kala, Anupam Shukla, Ritu Tiwari · International Journal of Systems Control and Communications · 2011
In this paper, we propose a new method for solving these problems inspired from the neuro-fuzzy logic approach for classificatory problems. We first cluster the training data based on class identification of inputs. A sort of fuzzy approach serves as a means to classify the unknown inputs. Rules are in the form of representative of every cluster and their matching class. The centre and power of the representative are the parameters that are optimised using a training algorithm and further by Genetic Algorithms. We tested the algorithm on the famous classificatory problem of picture learning.