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Titlebook: Intelligent Engineering Systems and Computational Cybernetics; J. A. Tenreiro Machado,Béla Pátkai,Imre J. Rudas Book 2009 Springer Science

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书目名称Intelligent Engineering Systems and Computational Cybernetics
编辑J. A. Tenreiro Machado,Béla Pátkai,Imre J. Rudas
视频videohttp://file.papertrans.cn/470/469658/469658.mp4
概述State of the art in the subject
图书封面Titlebook: Intelligent Engineering Systems and Computational Cybernetics;  J. A. Tenreiro Machado,Béla Pátkai,Imre J. Rudas Book 2009 Springer Science
描述.Engineering practice often has to deal with complex systems of multiple variable and multiple parameter models almost always with strong non-linear coupling. The conventional analytical techniques-based approaches for describing and predicting the behaviour of such systems in many cases are doomed to failure from the outset, even in the phase of the construction of a more or less appropriate mathematical model. These approaches normally are too categorical in the sense that in the name of “modelling accuracy” they try to describe all the structural details of the real physical system to be modelled. This can significantly increase the intricacy of the model and may result in a enormous computational burden without achieving considerable improvement of the solution. The best paradigm exemplifying this situation may be the classic perturbation theory: the less significant the achievable correction, the more work has to be invested to obtain it. ..A further important component of machine intelligence is a kind of “structural uniformity” giving room and possibility to model arbitrary particular details a priori not specified and unknown. This idea is similar to the ready-to-wear indus
出版日期Book 2009
关键词Computational Intelligence; Evolution; Norm; Performance; Web Services; communication; complex systems; com
版次1
doihttps://doi.org/10.1007/978-1-4020-8678-6
isbn_softcover978-90-481-7949-7
isbn_ebook978-1-4020-8678-6
copyrightSpringer Science+Business Media B.V. 2009
The information of publication is updating

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978-90-481-7949-7Springer Science+Business Media B.V. 2009
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Computational Intelligence Approach to Condition Monitoring: Incremental Learning and Its Applicatiow data set as it becomes available without compromising the performance of the previously learned data. Two incremental learning algorithm are implemented, the first method uses Fuzzy ARTMAP (FAM) algorithm and the second uses Learn++ algorithm. Experimental results show that both methods can accommodate both new data and new classes.
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Book 2009oupling. The conventional analytical techniques-based approaches for describing and predicting the behaviour of such systems in many cases are doomed to failure from the outset, even in the phase of the construction of a more or less appropriate mathematical model. These approaches normally are too
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