Foundations of Computational Intelligence Volume 2: Approximate Reasoning

ISBN-13: 9783642015328 / Angielski / Twarda / 2009 / 312 str.

Aboul-Ella Hassanien; Ajith Abraham; Francisco Herrera

Opis

Foundations of Computational Intelligence Volume 2: Approximation Reasoning: Theoretical Foundations and Applications Human reasoning usually is very approximate and involves various types of - certainties. Approximate reasoning is the computational modelling of any part of the process used by humans to reason about natural phenomena or to solve real world problems. The scope of this book includes fuzzy sets, Dempster-Shafer theory, multi-valued logic, probability, random sets, and rough set, near set and hybrid intelligent systems. Besides research articles and expository papers on t- ory and algorithms of approximation reasoning, papers on numerical experiments and real world applications were also encouraged. This Volume comprises of 12 chapters including an overview chapter providing an up-to-date and state-of-the research on the applications of Computational Intelligence techniques for - proximation reasoning. The Volume is divided into 2 parts: Part-I: Approximate Reasoning - Theoretical Foundations Part-II: Approximate Reasoning - Success Stories and Real World Applications Part I on Approximate Reasoning - Theoretical Foundations contains four ch- ters that describe several approaches of fuzzy and Para consistent annotated logic approximation reasoning. In Chapter 1, "Fuzzy Sets, Near Sets, and Rough Sets for Your Computational Intelligence Toolbox" by Peters considers how a user might utilize fuzzy sets, near sets, and rough sets, taken separately or taken together in hybridizations as part of a computational intelligence toolbox. In multi-criteria decision making, it is necessary to aggregate (combine) utility values corresponding to several criteria (parameters).

Spis treści

Approximate Reasoning - Theoretical Foundations and Applications.- Fuzzy Sets, Near Sets, and Rough Sets for Your Computational Intelligence Toolbox.- Fuzzy without Fuzzy: Why Fuzzy-Related Aggregation Techniques Are Often Better Even in Situations without True Fuzziness.- Intermediate Degrees Are Needed for the World to Be Cognizable: Towards a New Justification for Fuzzy Logic Ideas.- Paraconsistent Annotated Logic Program Before-after EVALPSN and Its Application.- Approximate Reasoning - Success Stories and Real World Applications.- A Fuzzy Set Approach to Software Reliability Modeling.- Computational Methods for Investment Portfolio: The Use of Fuzzy Measures and Constraint Programming for Risk Management.- A Bayesian Solution to the Modifiable Areal Unit Problem.- Fuzzy Logic Control in Communication Networks.- Adaptation in Classification Systems.- Music Instrument Estimation in Polyphonic Sound Based on Short-Term Spectrum Match.- Ultrasound Biomicroscopy Glaucoma Images Analysis Based on Rough Set and Pulse Coupled Neural Network.- An Overview of Fuzzy C-Means Based Image Clustering Algorithms.

O autorze

Dr. Aboul-Ella Hassanien is a Professor in the Faculty of Computers and Information at Cairo University, Egypt, and Visiting Professor at the College of Business Administration, Kuwait University.
Dr. Aboul-Ella Hassanien is a Professor in the Faculty of Computers and Information at Cairo University, Egypt, and Visiting Professor at the College of Business Administration, Kuwait University. Dr. Ajith Abraham is Director of the Machine Intelligence Research (MIR) Labs, a global network of research laboratories with headquarters near Seattle, WA, USA. He is an author/co-author of more than 750 scientific publications. He is founding Chair of the International Conference of Computational Aspects of Social Networks (CASoN), Chair of IEEE Systems Man and Cybernetics Society Technical Committee on Soft Computing (since 2008), and a Distinguished Lecturer of the IEEE Computer Society representing Europe (since 2011).

Blurb

Human reasoning usually is very approximate and involves various types of uncertainties. Approximate reasoning is the computational modelling of any part of the process used by humans to reason about natural phenomena or to solve real world problems. The scope of this book includes fuzzy sets, Dempster-Shafer theory, multi-valued logic, probability, random sets, and rough set, near set and hybrid intelligent systems. Besides research articles and expository papers on theory and algorithms of approximation reasoning, papers on numerical experiments and real world applications were also encouraged. This Volume comprises of 12 chapters including an overview chapter providing an up-to-date and state-of-the research on the applications of Computational Intelligence techniques for approximation reasoning. The Volume is divided into 2 parts: Part-I: Approximate Reasoning – Theoretical Foundations and Part-II: Approximate Reasoning – Success Stories and Real World Applications.