Soft Computing: Developments, Methods and Applications


Alan Casey (Editor)

Series: Computer Science, Technology and Applications
BISAC: COM062000

This book discusses developments, methods and applications of soft computing. Chapter One depicts the increasing efficiency of the soft computing algorithms and fuzzy information processing models by developing a library of universal analytic models for fuzzy arithmetic operations with asymmetrical triangular fuzzy numbers (TrFNs). Chapter Two examines the determination of stability number of layered slope using Adaptive Neuro-Fuzzy Inference System (ANFIS), Gaussian Process Regression (GPR), Relevance Vector Machine (RVM), Extreme Learning Machine (ELM). Chapter Three discusses the intensive developing of Soft Computing systems especially Wavelet-Neuro-Fuzzy Systems (WNFS) in Dynamic Data Mining tasks, when the data are fed sequentially to the processing in on-line mode. (Imprint: Nova)

Table of Contents

Table of Contents


Chapter 1. Reduced Library of the Soft Computing Analytic Models for Arithmetic Operations with Asymmetrical Fuzzy Numbers
Yuriy P. Kondratenko and Nina Y. Kondratenko (Department of Electrical Engineering and Computer Science, Washkewicz College of Engineering, Cleveland State University, Cleveland, OH, USA, and others)

Chapter 2. Determination of Stability Number of Layered Slope Using ANFIS, GPR, RVM and ELM
J. Jagan, G. Meghana and Pijush Samui (Research Scholar, VIT University, Vellore, India, and others)

Chapter 3. Multilayer Wavelet-Neuro-Fuzzy Systems in Dynamic Data Mining Tasks
Yevgeniy Bodyanskiy, Olena Vynokurova, Iryna Pliss, Pavlo Mulesa (Kharkiv National University of Radio Electronics, Kharkiv, Ukraine, and others)


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