Control of a pH neutralization process using a modified Elman neural net

A modified Elman neural network is utilized to construct control systems for industrial processes. The basic structure of a modified Elman network is introduced. A specific learning algorithm is developed which optimizes not only the feedforward but also the self-feedback connections of such partially recurrent neural networks. The identification system proposed is arranged in a parallel pattern. The control system is devised in a feedforward plus feedback format based on the inverse model identified of the process. Numerical results for the control of a pH neutralization process are also presented.<
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