Development of Information Support Aiming at Investigation into Trainability of Hybrid Adaptive Neuro-Fuzzy Regulators
نویسندگان
چکیده
Submitted: Jun 23, 2013; Accepted: Jul 20, 2013; Published: Jul 25, 2013 Abstract: Peculiar features of development of hybrid adaptive systems using neuro-fuzzy network structures are discussed. Quality and amount of information about an object is insufficient. Classical, adaptive, robust, fuzzy, neural methods of regulator designing have been compared. Problem of parameter adjustment of neuro-fuzzy networks has been considered within application of genetic algorithms. Information (software) support has been developed with the aim of investigation into intelligent control systems. Software significantly simplifies development and investigation of proprietary architectures of neuro-fuzzy networks, genetic algorithms. The results of training of neuro-fuzzy networks are presented in order to solve problems of classification, emulation of non-linear functions.
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