THE NEURO-FUZZY DIAGNOSTIC MODEL SYNTHESIS WITH HASHED TRANSFORMATION IN THE SEQUENCE AND PARALLEL MODE

Abstract

Context. The urgent task of improving the speed of neuro-fuzzy model construction by the precedents has been solved. Objective is a creation of a neuro-fuzzy network synthesis method with high speed of computations and allowing to realize the synthesis of neuro-fuzzy networks in parallel mode. Method. The method of neuro-fuzzy model constructing by precedents, which reduces the dimension of the input data by hashing transformation to the one-dimensional axis saving local cluster topology in a feature space, estimates the significance of the features and instances on the basis of selected clusters, and also forms a partition of the original feature space in an automatic mode, synthesizes structure and adjusts parameters of the neuro-fuzzy model automatically, excluding from the training process of the neuro-fuzzy model the uninformative data, thus simplifying the structure of the obtained model, allows to perform most computationally costly operations in parallel mode, that allows to automate the process of neuro-fuzzy model synthesis by precedents, as well as to increase the speed of neuro-fuzzy model construction both in sequential and in parallel implementation of computations. Results. The software implementing proposed method have been developed and used in computational experiments investigating the properties of the method. The experiments confirmed the efficiency of the proposed method and software. Conclusions. The experiments also allow to recommend them for use in practice to solve the problems of diagnosis and automatic classification by the features.