Evolutionary polymorphic neural network in chemical process modeling

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@Article{Gao:2001:CCE,

• author = "Li Gao and Norman W. Loney",
• title = "Evolutionary polymorphic neural network in chemical process modeling",
• journal = "Computers \& Chemical Engineering",
• year = "2001",
• volume = "25",
• pages = "1403--1410",
• number = "11-12",
• keywords = "genetic algorithms, genetic programming, Evolutionary polymorphic neural network (EPNN), Neural network, Process modeling",
• owner = "wlangdon",
• URL = "http://www.sciencedirect.com/science/article/B6TFT-449TFB0-2/2/b9c50f18933d4b739a9d8a2843b45548",
• ISSN = "0098-1354",
• DOI = "doi:10.1016/S0098-1354(01)00708-6",
• abstract = "Evolutionary polymorphic neural network (EPNN) is a novel approach to modelling dynamic process systems. This approach has its basis in artificial neural networks and evolutionary computing. As demonstrated in the studied dynamic CSTR system, EPNN produces less error than a traditional recurrent neural network with a less number of neurons. Furthermore, EPNN performs networked symbolic regressions for input-output data, while it performs multiple step ahead prediction through adaptable feedback structures formed during evolution. In addition, the extracted symbolic formulae from EPNN can be used for further theoretical analysis and process optimisation.",

}

Genetic Programming entries for Li Gao Norman W Loney

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