Advances in Computational Intelligence and Learning: Methods and Applications presents new developments and applications in the area of Computational Intelligence, which essentially describes methods and approaches that mimic biologically intelligent behavior in order to solve problems that have been difficult to solve by classical mathematics. Generally Fuzzy Technology, Artificial Neural Nets and Evolutionary Computing are considered to be such approaches. The Editors have assembled new contributions in the areas of fuzzy sets, neural sets and machine learning, as well as combinations of them (so called hybrid methods) in the first part of the book. The second part of the book is dedicated to applications in the areas that are considered to be most relevant to Computational Intelligence.
Preface. Methodologies. Accuracy and Transparency of Fuzzy Systems; R. Babuska. Should Tendency Assessment Precede Rule Extraction by Clustering? (No!); J.C. Bezdek, et al. A Review of Wavelet Networks, Wavenets, Fuzzy Wavenets and their Applications; M. Thuillard. Investigating Neural Network Efficiency and Structure by Weight Investigation; M. Lefley, T. Kinsella. An Evaluation of Confidence Bound Estimation Methods for Neural Networks; L. Yang, et al. Compensation of Periodic Disturbances in Continuous Processing Plants by Means of a Neural Controller; M. Rau, D. Schroder. Predictive Control with Restricted Genetic Optimisation; S. Garrido, et al. Adaptive Parameterization of Evolutionary Algorithms and Chaotic Populations; M. Annunziato, S. Pizzuti. Neuro-Fuzzy Systems for Rule-Based Modelling of Dynamic Processes; M.B. Gorzalczany, A. Gluszek. Hybrid Intelligent Architectures using a Neurofuzzy Approach; L.P. Maguire, et al. Unifying Learning with Evolution Through Baldwinian Evolution and Lamarckism; C. Giraud-Carrier. Using An Evolutionary Strategy to Select Input Features for a Neural Network Classifier; J. Strackeljan, A. Schubert. Advances in Machine Learning; M.W. van Someren. Symbolic and Neural Learning of Named-Entity Recognition and Classification Systems in Two Languages; G. Petasis, et al. Fuzzy Model-Based Reinforcement Learning; M. Appl, W. Brauer. A Cellular Space for Feature Extraction and Classification; C. Kuhn, J. Wernstedt. Applications. A Fuzzy Approach to Taming the Bullwhip Effect; C. Carlsson, R. Fuller. Forecast of Short Term Trends in Stock Exchange using Fuzzy Rules and Neural Networks on Multiresolution Processed Signals; A. Tsakonas, et al. Customer Relationship Management: A Combined Approachby Customer Segmentation and Database Marketing; M. Nelke. A New Vendor Evaluation Product for SAP R/3(R) Systems; U. Grimmer, et al. About Robustness of Fuzzy Logic PD and PID Controller under Changes of Reasoning Methods; B.S. Butkiewicz. Control of MIMO Dead Time Processes Using Fuzzy Relational Models; B.A. Gormandy, et al. Fuzzy Sliding Mode Controllers Synthesis through Genetic Optimization; M. Dotoli, et al. Fuzzy RED: Congestion Control for TCP/IP Diff-Serv; L. Rossides, et al. The Use of Reinforcement Learning Algorithms in Traffic Control of High Speed Networks; A. Atlasis, A. Vasilakos. Fuzzy Reasoning in WCDMA Radio Resource Functions; T. Frantti, P. Mahonen. Odour Classification based on Computational Intelligence Techniques; G. Tselentis, et al. Fuzzy Rule Based Systems for Diagnosis of Stone Construction Cracks of Buildings; S. Shtovba, et al. Automated Design of Multi-Drilling Gear Machines; G. Klene, et al. Optimal Design of Alloy Steels Using Genetic Algorithms; M. Mahfouf. Intelligent Systems in Biomedicine; M.F. Abbod, et al. Diagnosis of Aphasia Using Neural and Fuzzy Techniques; J. Jantzen, et al. Gene Expression Data Mining for Functional Genomics using Fuzzy Technology; R. Guthke, et al. Symbolic, Neural and Neuro-fuzzy Approaches to Pattern Recognition in Cardiotocograms; O. Fontenla-Romero, et al. Perspectives of Computational Intelligence; G. Tselentis, M.W. van Someren. Index.