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Titlebook: Life System Modeling and Intelligent Computing; International Confer Kang Li,Minrui Fei,George W. Irwin Conference proceedings 2010 Springe

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書(shū)目名稱(chēng)Life System Modeling and Intelligent Computing
副標(biāo)題International Confer
編輯Kang Li,Minrui Fei,George W. Irwin
視頻videohttp://file.papertrans.cn/586/585847/585847.mp4
概述Fast track conference proceeding.Unique visibility.State of the art research
叢書(shū)名稱(chēng)Lecture Notes in Computer Science
圖書(shū)封面Titlebook: Life System Modeling and Intelligent Computing; International Confer Kang Li,Minrui Fei,George W. Irwin Conference proceedings 2010 Springe
描述The 2010 International Conference on Life System Modeling and Simulation (LSMS 2010) and the 2010 International Conference on Intelligent Computing for Sustainable Energy and Environment (ICSEE 2010) were formed to bring together researchers and practitioners in the fields of life system modeling/simulation and intelligent computing applied to worldwide sustainable energy and environmental applications. A life system is a broad concept, covering both micro and macro components ra- ing from cells, tissues and organs across to organisms and ecological niches. To c- prehend and predict the complex behavior of even a simple life system can be - tremely difficult using conventional approaches. To meet this challenge, a variety of new theories and methodologies have emerged in recent years on life system modeling and simulation. Along with improved understanding of the behavior of biological systems, novel intelligent computing paradigms and techniques have emerged to h- dle complicated real-world problems and applications. In particular, intelligent c- puting approaches have been valuable in the design and development of systems and facilities for achieving sustainable energy and a sust
出版日期Conference proceedings 2010
關(guān)鍵詞biological simulation; biological systems; biomedical imaging; biomedical signal processing; biomedical
版次1
doihttps://doi.org/10.1007/978-3-642-15597-0
isbn_softcover978-3-642-15596-3
isbn_ebook978-3-642-15597-0Series ISSN 0302-9743 Series E-ISSN 1611-3349
issn_series 0302-9743
copyrightSpringer Berlin Heidelberg 2010
The information of publication is updating

書(shū)目名稱(chēng)Life System Modeling and Intelligent Computing影響因子(影響力)




書(shū)目名稱(chēng)Life System Modeling and Intelligent Computing影響因子(影響力)學(xué)科排名




書(shū)目名稱(chēng)Life System Modeling and Intelligent Computing網(wǎng)絡(luò)公開(kāi)度




書(shū)目名稱(chēng)Life System Modeling and Intelligent Computing網(wǎng)絡(luò)公開(kāi)度學(xué)科排名




書(shū)目名稱(chēng)Life System Modeling and Intelligent Computing被引頻次




書(shū)目名稱(chēng)Life System Modeling and Intelligent Computing被引頻次學(xué)科排名




書(shū)目名稱(chēng)Life System Modeling and Intelligent Computing年度引用




書(shū)目名稱(chēng)Life System Modeling and Intelligent Computing年度引用學(xué)科排名




書(shū)目名稱(chēng)Life System Modeling and Intelligent Computing讀者反饋




書(shū)目名稱(chēng)Life System Modeling and Intelligent Computing讀者反饋學(xué)科排名




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Life System Modeling and Intelligent Computing978-3-642-15597-0Series ISSN 0302-9743 Series E-ISSN 1611-3349
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Lecture Notes in Computer Sciencehttp://image.papertrans.cn/l/image/585847.jpg
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Stochastic Stability and Bifurcation Analysis on Hopfield Neural Networks with Noiseability analysis and Hopf bifurcation analysis. By analyzing the Lyapunov exponent, invariant measure and singular boundary theory , its nonlinear stability is investigated and Hopf bifurcations are demonstrated. The stability and direction of the Hopf bifurcation are determined from the dynamical and phenomenological points of view.
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Fast Forward RBF Network Construction Based on Particle Swarm OptimizationRA with PSO included only needs about two thirds of the computation involved in a PSO assisted OLS algorithm. The effectiveness of the proposed technique is confirmed by a numerical simulation example.
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Data Fusion-Based Extraction Method of Energy Consumption Index for the Ethylene Industry energy consumption status of the ethylene equipments, analyze the actions and opportunities of energy saving, and then suggest the direction of the energy saving and the consumption reduction of ethylene equipments.
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Research on Improved QPSO Algorithm Based on Cooperative Evolution with Two Populationse of the CEQPSO algorithm at a fixed iteration, and the simulation results showed that the proposed algorithm in this paper had a better optimization performance and faster convergence rate than PSO and QPSO algorithms.
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