Abstract
In this paper a hybrid neural network modeling approach was presented and used to model a fedbatch bioreactor. This hybrid model is comprised of two parts including a partial first principles model, which reflects the a priori knowledge, and a neural network component, which serves as a nonparametric approximator of difficult-to-model process parameters. This form of hybrid neural network is useful for modeling processes where a partial model can be derived from simple physical considerations but which also includes terms that are difficult to model from first principles. The hybrid model, once learned, can be used for process control and optimization. The concept of combining proposed multi layer perceptron with first principle knowledge is a powerful one, and goes well bioreactor problem.
This study was supported by a grant of Youngsan University(in 2012).
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Kim, BJ. (2012). Improved Multi Layer Perceptron with a Prior Knowledge Applied System Identification. In: Lee, G., Howard, D., Kang, J.J., Ślęzak, D. (eds) Convergence and Hybrid Information Technology. ICHIT 2012. Lecture Notes in Computer Science, vol 7425. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-32645-5_21
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DOI: https://doi.org/10.1007/978-3-642-32645-5_21
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