Applications Of Computational Intelligence Volume 2
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Applications of Computational Intelligence, Volume 2
Computational Intelligence (CI) is the theory, design, application, and development of biologically and linguistically motivated computational paradigms. CI mainly includes three parts: neural networks, fuzzy systems, and evolutionary computation. CI has been widely used to solve complex problems in various domains, such as image processing, point cloud processing, and classification. Due to the great advantages of CI in dealing with practical application problems, more and more researchers have paid attention to the theoretical research and application of CI in recent years. This reprint reports the latest research on applications of computational intelligence. Many novel and interesting methods are introduced, which provide guiding significance for the further development of computational intelligence.
Foundations of Computational Intelligence Volume 2
Author: Aboul-Ella Hassanien
language: en
Publisher: Springer Science & Business Media
Release Date: 2009-06-15
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).
Practical Applications of Computational Intelligence Techniques
Author: Lakhmi Jain
language: en
Publisher: Springer Science & Business Media
Release Date: 2012-12-06
Computational intelligence paradigms have attracted the growing interest of researchers, scientists, engineers and application engineers in a number of everyday applications. These applications are not limited to any particular field and include engineering, business, banking and consumer electronics. Computational intelligence paradigms include artificial intelligence, artificial neural networks, fuzzy systems and evolutionary computing. Artificial neural networks can mimic the biological information processing mechanism in a very limited sense. Evolutionary computing algorithms are used for optimisation applications, and fuzzy logic provides a basis for representing uncertain and imprecise knowledge. Practical Applications of Computational Intelligence Techniques contains twelve chapters providing actual application of these techniques in the real world. Such examples include, but are not limited to, intelligent household appliances, aerial spray models, industrial applications and medical diagnostics and practice. This book will be useful to researchers, practicing engineers/scientists and students, who are interested in developing practical applications in a computational intelligence environment.