October 2008

Special Report: Process Control and Information Systems

Predicting important parameters using artificial neural networks

Consider them for complex nonlinear processes

Ramakumar, K. R., Gujarat Refinery, Indian Oil Corporation Limited

Artificial neural networks (ANNs) are a powerful tool for modeling nonlinear processes. In a system, various parameters are encountered that do not obey a linear relationship vis-à-vis other parameters. ANNs are one of the answers for such complex systems. In general, ANNs are utilized to establish a relationship between a set of inputs and a set of outputs. Familiarizing the basics. Based on the inputs (x1, x2, . . . , xn) and the weights assigned to each input stream (w1, w2, . . . , wn), the neuron processes this input- (X1 w1 + x2 w2 + . . . + xn wn) and gives an output based on the assigned activation function (also called transfer function, Fig. 1). This output in turn could be a

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