COMPARISON OF THE EFFECTS DIMENSIONALTY METHODS IN THE TRAINING OF NEURO-FUZZY (ANFIS) CLASSIFICATIONS

Abstract

Adaptive Network Based Fuzzy Inference Systems (ANFIS) is a hybrid artificial intelligence method that uses artificial neural network models with parallel computing and learning features and fuzzy logic extraction. The creation of models with more input parameter counts with ANFIS is not very convenient for applications. Dimension reduction methods are shown as a solution to this problem. Dimensional Reduction is the method used to represent the data in a lower dimensional space. Reduction of the number of input parameters by using Auto-Encoder and Principle Component Analysis and reduction of the number of input parameters and formation of the optimal solution of probing with ANFIS model constitute the framework of this work