Publication Type : Conference Paper
Publisher : Fuzzy Systems
Source : Fuzzy Systems, 2003. FUZZ '03. The 12th IEEE International Conference on (2003)
Keywords : Application software, asymmetric Gaussian membership functions, asymmetric subsethood product system, benchmark problems, cardinality expression, Computer science, function approximation, Fuzzy control, fuzzy if-then rules, fuzzy neural inference system, fuzzy neural nets, Fuzzy neural networks, fuzzy set theory, Fuzzy sets, Fuzzy systems, gradient descent learning framework, gradient methods, hepatitis diagnosis, inference mechanisms, iris, iris data classification, Liver Diseases, medical expert systems, minimal number of rules, Narazaki-Ralescu function approximation, Pattern classification, Physics, product aggregation operator, signal fuzzy sets, volume defuzzification, weight fuzzy sets
Campus : Coimbatore
School : School of Engineering
Department : Computer Science
Year : 2003
Abstract : This paper presents some applications of an asymmetric subsethood product fuzzy neural inference system (ASuP-FuNIS). The ASuPFuNIS model extends SuPFuNIS by permitting signal and weight fuzzy sets to be modeled by asymmetric Gaussian membership functions. The asymmetric subsethood product network admits both numeric as well as linguistic inputs. Numeric inputs are fuzzified prior to their application to the network; linguistic inputs are presented without modification. The network architecture directly embeds fuzzy if-then rules, and connections represent antecedent and consequent fuzzy sets. The model uses mutual subsethood based activation spread and a product aggregation operator that works in conjunction with volume defuzzification in a gradient descent learning framework. The model is economical in terms of the number of rules required to solve difficult problems and is robust against random variations in data sets. Simulation results on three benchmark problems-the Hepatitis diagnosis, Iris data classification and the Narazaki-Ralescu function approximation problem-show that the subsethood based model performs excellently with minimal number of rules.
Cite this Research Publication : Dr. Shunmuga Velayutham C. and Kumar, S., “Some applications of an asymmetric subsethood product fuzzy neural inference system”, in Fuzzy Systems, 2003. FUZZ '03. The 12th IEEE International Conference on, 2003.