Evolving Systems

author: Plamen Angelov, Department of Communication Systems, Lancaster University
published: Oct. 5, 2007,   recorded: September 2007,   views: 9719


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One of the important research challenges today is to develop new theoretical methods, algorithms, and implementations of systems with a higher level of flexibility and autonomy, we can say with higher level of intelligence. These systems have to be able to evolve their structure and knowledge on the environment and ultimately – evolve their intelligence. To address the problems of modelling, control, prediction, classification and data processing in a dynamically changing and evolving environment, a system must be able to fully adapt its structure and adjust its parameters, rather than use a pre-trained and a fixed structure. That is, the system must be able to evolve, to self-develop, to self-organize, to self-evaluate and to self-improve. The talk will concentrate on the problems and results the author encountered during last several years of research in this emerging area as well as on the approach to on-line identification of a particular type of fuzzy models – so called Takagi-Sugeno fuzzy models including some applications, in particular to mobile robots, mobile communications, process modelling and control, on-line evolving classification intelligent (inferential) sensors.

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Reviews and comments:

Comment1 Diego Stéfano, May 26, 2011 at 2:45 a.m.:

Nice tutorial. But where is the second part?

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