Название: Soft Computing with NeuroFuzzy systems
Автор: Jovan Pehcevski
Издательство: Arcler Press
Год: 2021
Страниц: 337
Язык: английский
Формат: pdf (true)
Размер: 17.9 MB

This book covers different topics from soft computing and neuro-fuzzy systems, including intelligent neuro-fuzzy models, adaptive neuro-fuzzy systems, neuro-fuzzy inference systems, and neuro-fuzzy control. Section 1 focuses on intelligent neuro-fuzzy models, describing fuzzy-neuro model for intelligent credit risk management; method to improve airborne pollution forecasting by using ant colony optimization; TSK-type recurrent neuro-fuzzy systems for fault prognosis; and neuro-fuzzy model for QoS based selection of web service. Section 2 focuses on adaptive neuro-fuzzy systems, describing adaptive neuro-fuzzy logic system for heavy metal sorption in aquatic environments; automatic heart disease diagnosis system based on artificial neural network (ANN); reliability estimation of services oriented systems using adaptive neuro fuzzy inference system; and prediction of soil fractions (sand, silt and clay) in surface layer based on natural radionuclides concentration.

Section 3 focuses on neuro-fuzzy inference systems, describing adaptive neuro-fuzzy inference system for prediction of effective thermal conductivity of polymer-matrix composites; application of adaptive neuro-fuzzy inference system in supply chain management evaluation; application of the adaptive neuro-fuzzy inference system for optimal design of reinforced concrete beams; comparison between neural network and adaptive neuro-fuzzy inference system for forecasting chaotic traffic volumes; and development of an alternative method for the sovereign credit rating system based on adaptive neuro-fuzzy inference system. Section 4 focuses on neuro-fuzzy control, describing implementation of adaptive neuro fuzzy inference system in speed control of induction motor drives; neuro-fuzzy based interline power flow controller for real time power flow control in multiline power system; controlling speed of dc motor with fuzzy controller in comparison with ANFIS controller; a neuro-fuzzy controller for collaborative applications in robotics using LabVIEW; and adaptive fuzzy sliding mode control scheme for robotic systems.