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Abstract
Age Prediction and Performance Comparison by Adaptive Network based Fuzzy Inference System using Subtractive Clustering
Highlights
THE architecture and learning procedure underlying Adaptive Network based Fuzzy Inference System (ANFIS) is presented, which is a fuzzy inference system implemented in the framework of adaptive networks
THE architecture and learning procedure underlying ANFIS is presented, which is a fuzzy inference system implemented in the framework of adaptive networks
The objective of this research is to reduce the Root Mean Square Error (RMSE) with fewer numbers of rules in order to achieve high speed and less time consumed in both learning and application phases
Summary
THE architecture and learning procedure underlying ANFIS is presented, which is a fuzzy inference system implemented in the framework of adaptive networks. With the increase in the complexity of the process being modeled, the difficulty in developing dependable fuzzy rules and membership functions increases. This has led to the development of another approach which is mostly known as ANFIS approach. A hybrid system named ANFIS has been proposed by Jang (1993) It has the benefits of both fuzzy logic [Junhong Nie & Derek Linkens, 1998] and neural networks [James A. Firstly the training and testing data of abalone [archive.ics.uci.edu/ml/datasets.html] and monk’s problem dataset [archive.ics.uci.edu/ml/datasets.html] are divided. They are loaded into the ANFIS editor. It is developed in the MATLAB® V7.9.0.529 (R2009b) [MATLAB] environment
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