Neural network programming in bioprocess variable estimation and state prediction.
A neural network program with efficient learning ability for bioprocess variable estimation and state prediction was developed. A 3 layer, feed-forward neural network architecture was used, and the program was written in Quick C ver 2.5 for an IBM compatible computer with a 80486/33 MHz processor. A back propagation training algorithm was used based on learning by pattern and momentum in a combination as used to adjust the connection of weights of the neurons in adjacent layers. The delta rule was applied in a gradient descent search technique to minimize a cost function equal to the mean square difference between the target and the network output. A non-linear, sigmoidal logistic transfer function was used in squashing the weighted sum of the inputs of each neuron to a limited range output. A good neural network prediction model was obtained by training with a sequence of past time course data of a typical bioprocess. The well trained neural network estimated accurately and rapidly the state variables with or without noise even under varying process dynamics.
Linko
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Department of Chemical Engineering, Helsinki University of Technology, Espoo, Finland.
Zhu
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YH
D000465:Algorithms; D001709:Biotechnology / Q000379:methods*; D003198:Computer Simulation; D007700:Kinetics; D016571:Neural Networks, Computer