@inproceedings{2c34235c00da43b0bf24ac33b4fabff2,

title = "Discrete modeling of uncertain continuous systems having an interval structure using genetic algorithms",

abstract = "In this paper, an evolutionary approach is proposed to obtain the discrete-time transfer function for uncertain continuous-time systems having interval uncertainties. Based on a worst-case analysis, the problem to derive the discrete-time model is first formulated as multiple mono-objective optimization problems for coefficients in the discrete model, and subsequently minimized and maximized via a proposed genetic algorithm to obtain the lower and upper bounds of the coefficient functions. The problem of non-linearly coupled coefficients with exponential nature occurred in the exact discrete-time transfer function is therefore circumvented while preserving the interval structure in the resulting discrete model by using this approach. Because of the time-consuming process that genetic algorithms generally exhibit, particularly the problem nature which requires undertaking a large number of evolution processes, parallel computation for the proposed evolutionary approach in a MATLAB-based working environment is therefore proposed to accelerate the derivation process.",

keywords = "Discrete modeling, Discretization, Genetic algorithms, Interval plant, Parallel computation, Sampled-data systems, Uncertain systems",

author = "Hsu, {Chen Chien} and Chang, {Shih Chi} and Kuo, {Hsin Yen}",

year = "2005",

language = "English",

isbn = "0889864810",

series = "Proceedings of the IASTED International Conference on Computational Intelligence",

pages = "310--315",

booktitle = "Proceedings of the IASTED International Conference on Computational Intelligence",

note = "IASTED International Conference on Computational Intelligence ; Conference date: 04-07-2005 Through 06-07-2005",

}