论文信息 - Dynamic Chemical Process Modelling Using a Multiple Basis Function Genetic Programming Algorithm

Dynamic Chemical Process Modelling Using a Multiple Basis Function Genetic Programming Algorithm

In this contribution we outline how a Multiple Basis Function Genetic Programming (MBF-GP) algorithm can be used to evolve input-output models of dynamic chemical processes. Two case studies are used to compare the performance of the algorithm with that of a standard GP algorithm. It was found that the MBF-GP algorithm was able to develop dynamic process models of a significantly greater accuracy than the standard GP algorithm

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