Computational Intelligence for Modelling and Prediction

by Saman K. Halgamuge (Editor), Lipo Wang (Editor)

Overview

The application of Computational Intelligence in emerging research areas such as Granular Computing, Mechatronics, and Bioinformatics shows its usefulness often emphasized by Prof Lotfi Zadeh, the inventor of fuzzy logic and many others. This book contains recent advances in Computational Intelligence methods for modeling, optimization and prediction and covers a large number of applications. The book presents new Computational Intelligence theory and methods for modeling and prediction. The range of the various applications is captured with 5 chapters in image processing, 2 chapters in audio processing, 3 chapters in commerce and finance, 2 chapters in communication networks and 6 chapters containing other applications.

Product Details

ISBN-13:	9783540260714
Publisher:	Springer Berlin Heidelberg
Publication date:	10/06/2005
Series:	Studies in Computational Intelligence , #2
Edition description:	2005
Pages:	414
Product dimensions:	6.10(w) x 9.25(h) x (d)

Table of Contents

From the contents: Symmetrization of Fuzzy Operators: Notes On Data Aggregation.- An Integrity Estimation Using Fuzzy Logic.- Connectionist Fuzzy Relational Systems.- Semantic Indexing and Fuzzy Relevance Model in Information Retrieval.- Backward Reasoning on Rule-Based Systems Modeled by Fuzzy Petri Nets Through Backward Tree.- On The Generalization of Fuzzy Rough Approximation Based on Asymmetric Relation.- A new approach for the fuzzy shortest path problem.- Distances Between Intuitionistic Fuzzy Sets and their Applications in Reasoning.- Efficient Reasoning With Fuzzy Words.- Intuitionistic Fuzzy Relational Images.- Thematic Fuzzy Prediction of Weed Dispersal Using Spatial Dataset.- Optimization of Image Compression Method Based on Fuzzy Relational Equations by Overlap Level of Fuzzy Sets.- Multi-layer Image Transmission with Inverse Pyramidal Decomposition.- Multiple Feature Relevance Feedback in Content-Based Image Retrieval using Probabilistic Inference Networks.