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Soft Computing with NeuroFuzzy systems

Jovan Pehcevski

334 pages
Arcler Education Inc
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.
Author Bio
Jovan obtained his PhD in Computer Science from RMIT University in Melbourne, Australia in 2007. His research interests include big data, business intelligence and predictive analytics, data and information science, information retrieval, XML, web services and service-oriented architectures, and relational and NoSQL database systems. He has published over 30 journal and conference papers and he also serves as a journal and conference reviewer. He is currently working as a Dean and Associate Professor at European University in Skopje, Macedonia.