Neural network programming in bioprocess variable estimation and state prediction

• Published in: Journal of biotechnology Vol. 21; no. 3; p. 253
• Main Authors: Linko, P, Zhu, Y H
• Format: Journal Article
• Language: English
• Published: Netherlands 01-12-1991
• Subjects: Algorithms; Biotechnology - methods; Computer Simulation; Kinetics; Neural Networks (Computer)
• ISSN: 0168-1656
• DOI: 10.1016/0168-1656(91)90046-X

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.