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Titlebook: Computational Intelligence Techniques for Bioprocess Modelling, Supervision and Control; Maria Carmo Nicoletti,Lakhmi C. Jain Book 2009 Sp

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书目名称Computational Intelligence Techniques for Bioprocess Modelling, Supervision and Control
编辑Maria Carmo Nicoletti,Lakhmi C. Jain
视频video
概述Latest research on the computational intelligence techniques for bioprocess, modelling, and control
丛书名称Studies in Computational Intelligence
图书封面Titlebook: Computational Intelligence Techniques for Bioprocess Modelling, Supervision and Control;  Maria Carmo Nicoletti,Lakhmi C. Jain Book 2009 Sp
描述Computational Intelligence (CI) and Bioprocess are well-established research areas which have much to offer each other. Under the perspective of the CI area, Biop- cess can be considered a vast application area with a growing number of complex and challenging tasks to be dealt with, whose solutions can contribute to boosting the development of new intelligent techniques as well as to help the refinement and s- cialization of many of the already existing techniques. Under the perspective of the Bioprocess area, CI can be considered a useful repertoire of theories, methods and techniques that can contribute and offer interesting alternative approaches for solving many of its problems, particularly those hard to solve using conventional techniques. Although throughout the past years CI and Bioprocess areas have accumulated substantial specific knowledge and progress has been quick and with a high degree of success, we believe there is still a long way to go in order to use the potentialities of the available CI techniques and knowledge at their full extent, as tools for supporting problem solving in bioprocesses. One of the reasons is the fact that both areas have progressed steadily
出版日期Book 2009
关键词Monitor; algorithm; algorithms; artificial neural network; computational intelligence; control; genetic al
版次1
doihttps://doi.org/10.1007/978-3-642-01888-6
isbn_softcover978-3-642-42486-1
isbn_ebook978-3-642-01888-6Series ISSN 1860-949X Series E-ISSN 1860-9503
issn_series 1860-949X
copyrightSpringer-Verlag Berlin Heidelberg 2009
The information of publication is updating

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978-3-642-42486-1Springer-Verlag Berlin Heidelberg 2009
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Guolin Ma,Shufan Wen,Yun Huang,Yubin Zhoue first part of the chapter a theoretical framework for estimation of general process kinetic rates based on Artificial Neural Network (ANN) models is introduced. Two scenarios are considered: i) Partly known (measured) process states and completely known kinetic parameters; ii) Partly known process
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