Neural network modeling stack

Optimal Control of Batch Processes Using Particle Swam Optimisation with Stacked Neural Network Models... 

The study demonstrates that particle swam optimisation is a powerful optimisation technique, especially when the objective function has several local rninirna. Conventional optimisation techniques could be trapped in local minima but PSO could in general find the global rninimrun. Stacked neural networks can not only given better prediction performance but also provide model prediction confidence bounds. In order to improve the reliability of neural network model based optimisation, an additional term is introduced in the optimisation objective to penalize wide model prediction confidence bormd. The proposed technique is successfully demonstrated on a simulated fed-batch reactor. 

Y. Tian, J. Zhang, and J. Morris, Modeling and optimal control of a batch polymerization reactor using a hybrid stacked recurrent neural network model, Ind. Eng. Chem. Res., 40, 4525-4535, 2001. 

F. Leon, C.C. Piuleac, and S. Curteanu, Stacked neural network modeling applied to the synthesis of polyacrylamide-based multicomponent hydrogels, Macromol. React. Eng, 4 (9-10), 537-636, Sept. 2010. 

An optimal control strategy for batch processes using particle swam optimisation (PSO) and stacked neural networks is presented in this paper. Stacked neural networks are used to improve model generalisation capability, as well as provide model prediction confidence bounds. In order to improve the reliability of the calculated optimal control policy, an additional term is introduced in the optimisation objective function to penalise wide model prediction confidence bounds. PSO can cope with multiple local minima and could generally find the global minimum. Application to a simulated fed-batch process demonstrates that the proposed technique is very effective. 

Modelling the Fed-Batch Process Using Stacked Neural Networks... 

Sridhar, D.V., Seagrave, R.C. and Bartlett, E.B. (1996). Process modeling using stacked neural networks, AIChE J., 42, 2529-2539. 

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