Life System Modeling and Intelligent Computing: International Conference on Life System Modeling and Simulation, LSMS 2010, and International Conference on Intelligent Computing for Sustainable Energy and Environment, ICSEE 2010, Wuxi, China, September 17-20, 2010, Proceedings
Minrui Fei, Li Jia, George W. Irwin
Springer Science & Business Media, 2010 - Computers - 518 pages
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 sustainable environment, the two most challenging issues currently facing humanity. The two LSMS 2010 and ICSEE 2010 conferences served as an important platform for synergizing these two research streams.
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analysis Berlin Heidelberg 2010 calculated Chen China cluster complex Computer constraints convergence DBDE deﬁned diﬀerent Differential Evolution distribution network dynamic eﬀects eﬃciency electric equation error estimation feature points fictitious play ﬂow follows forecasting function fuzzy random genetic algorithm global global optimization grid Heidelberg Hopf bifurcation IEEE IEEE Transactions improved input iteration LNCS load LSMS/ICSEE MBDE metaheuristic method mode motor power consumption neural networks node nonlinear obtained operation optimization problem output paper parameters Particle Swarm Optimization performance pheromone PID controller pixels players PMSM population position proposed PSO algorithm RBF network Research resource samples scheduling Shanghai Shanghai University shown signal simulation solution solve speed Springer-Verlag Berlin Heidelberg stability stochastic strategy Sugeno measure support vector machines Table Technology tion torque UCAV update variables velocity voltage Wang wind farm