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Author : Nikola K. Kasabov
Release : 1999
Genre : Automatic speech recognition
Kind : eBook
Book Rating : /5 ( reviews)
Book Synopsis Evolving Connectionist Systems for On-line, Knowledge-based Learning by : Nikola K. Kasabov
Download or read book Evolving Connectionist Systems for On-line, Knowledge-based Learning written by Nikola K. Kasabov. This book was released on 1999. Available in PDF, EPUB and Kindle. Book excerpt:
Author : Nikola Kasabov
Release : 2013-03-14
Genre : Computers
Kind : eBook
Book Rating : 40X/5 ( reviews)
Book Synopsis Evolving Connectionist Systems by : Nikola Kasabov
Download or read book Evolving Connectionist Systems written by Nikola Kasabov. This book was released on 2013-03-14. Available in PDF, EPUB and Kindle. Book excerpt: Many methods and models have been proposed for solving difficult problems such as prediction, planning and knowledge discovery in application areas such as bioinformatics, speech and image analysis. Most, however, are designed to deal with static processes which will not change over time. Some processes - such as speech, biological information and brain signals - are not static, however, and in these cases different models need to be used which can trace, and adapt to, the changes in the processes in an incremental, on-line mode, and often in real time. This book presents generic computational models and techniques that can be used for the development of evolving, adaptive modelling systems. The models and techniques used are connectionist-based (as the evolving brain is a highly suitable paradigm) and, where possible, existing connectionist models have been used and extended. The first part of the book covers methods and techniques, and the second focuses on applications in bioinformatics, brain study, speech, image, and multimodal systems. It also includes an extensive bibliography and an extended glossary. Evolving Connectionist Systems is aimed at anyone who is interested in developing adaptive models and systems to solve challenging real world problems in computing science or engineering. It will also be of interest to researchers and students in life sciences who are interested in finding out how information science and intelligent information processing methods can be applied to their domains.
Author : Nikola K. Kasabov
Release : 2007-08-23
Genre : Computers
Kind : eBook
Book Rating : 477/5 ( reviews)
Book Synopsis Evolving Connectionist Systems by : Nikola K. Kasabov
Download or read book Evolving Connectionist Systems written by Nikola K. Kasabov. This book was released on 2007-08-23. Available in PDF, EPUB and Kindle. Book excerpt: This second edition of the must-read work in the field presents generic computational models and techniques that can be used for the development of evolving, adaptive modeling systems, as well as new trends including computational neuro-genetic modeling and quantum information processing related to evolving systems. New applications, such as autonomous robots, adaptive artificial life systems and adaptive decision support systems are also covered.
Author : Nikola Kasabov
Release : 2009-10-12
Genre : Computers
Kind : eBook
Book Rating : 894/5 ( reviews)
Book Synopsis Evolving Connectionist Systems by : Nikola Kasabov
Download or read book Evolving Connectionist Systems written by Nikola Kasabov. This book was released on 2009-10-12. Available in PDF, EPUB and Kindle. Book excerpt: This second edition of the must-read work in the field presents generic computational models and techniques that can be used for the development of evolving, adaptive modeling systems, as well as new trends including computational neuro-genetic modeling and quantum information processing related to evolving systems. New applications, such as autonomous robots, adaptive artificial life systems and adaptive decision support systems are also covered.
Author : Plamen Angelov
Release : 2010-03-25
Genre : Computers
Kind : eBook
Book Rating : 955/5 ( reviews)
Book Synopsis Evolving Intelligent Systems by : Plamen Angelov
Download or read book Evolving Intelligent Systems written by Plamen Angelov. This book was released on 2010-03-25. Available in PDF, EPUB and Kindle. Book excerpt: From theory to techniques, the first all-in-one resource for EIS There is a clear demand in advanced process industries, defense, and Internet and communication (VoIP) applications for intelligent yet adaptive/evolving systems. Evolving Intelligent Systems is the first self- contained volume that covers this newly established concept in its entirety, from a systematic methodology to case studies to industrial applications. Featuring chapters written by leading world experts, it addresses the progress, trends, and major achievements in this emerging research field, with a strong emphasis on the balance between novel theoretical results and solutions and practical real-life applications. Explains the following fundamental approaches for developing evolving intelligent systems (EIS): the Hierarchical Prioritized Structure the Participatory Learning Paradigm the Evolving Takagi-Sugeno fuzzy systems (eTS+) the evolving clustering algorithm that stems from the well-known Gustafson-Kessel offline clustering algorithm Emphasizes the importance and increased interest in online processing of data streams Outlines the general strategy of using the fuzzy dynamic clustering as a foundation for evolvable information granulation Presents a methodology for developing robust and interpretable evolving fuzzy rule-based systems Introduces an integrated approach to incremental (real-time) feature extraction and classification Proposes a study on the stability of evolving neuro-fuzzy recurrent networks Details methodologies for evolving clustering and classification Reveals different applications of EIS to address real problems in areas of: evolving inferential sensors in chemical and petrochemical industry learning and recognition in robotics Features downloadable software resources Evolving Intelligent Systems is the one-stop reference guide for both theoretical and practical issues for computer scientists, engineers, researchers, applied mathematicians, machine learning and data mining experts, graduate students, and professionals.
