Nik Kasabov – författare

From theory to techniques, the first all-in-one resource for EISThere 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 Structurethe 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.

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.

Autonomousagentsandmulti-agentsystemsarecomputationalsystemsinwhich several (semi-)autonomous agents interact with each other or work together to performsomesetoftasksorsatisfysomesetofgoals.Thesesystemsmayinvolve computational agents that are homogeneous or heterogeneous,they may involve activities on the part of agents having common or distinct goals, and they may involve participation on the part of humans and intelligent agents. ThisvolumecontainsselectedpapersfromPRIMA2004,the 7thPaci?cRim InternationalWorkshoponMulti-agents,heldinAuckland,NewZealand,during August8-13,2004in conjunctionwith the8thPaci?cRim InternationalConf- ence on Arti?cial Intelligence (PRICAI 2004). PRIMA is a series of workshops on autonomous agents and multi-agents that focusses on the research activities in the Asian and Paci?c Rim countries. PRIMA 2004 was built upon the great successes of its predecessors. Fifty-two papers were submitted to the workshop, each paper was reviewed by three internationally renowned program committee members. After careful review, 24 papers were selected for this volume. We would like to thank all the authors who submitted papers to the workshop.We would also like to thank all the program committee members for their diligent work in reviewing the papers.Wewouldliketothankourinvitedspeakers,SandipSenandToruIshida. Additionally, we thank the editorial sta? of Springer for publishing this volume in the series Lecture Notes in Arti?cial Intelligence. Lastly, we want to thank our sponsors, the Auckland University of Technology's Knowledge Engineering and Discovery Research Institute (KEDRI), and the University of Auckland's Department of Computer Science, for the ?nancial support provided.