A Genetic Adapted Neural Network Analysis of Performance of the Nutrient Removal Plant at Rotorua
Yoon‐Seok Timothy Hong, Rao Bhamidimairi, Tim Charleson · 1998
Due to the nature of strong nonlinear mapping, neural networks provide advantages as a modeling and identification tool over a structure-based model. However, the determination of the architecture of the artificial neural networks (ANNs) and the selection of key input variables is not easy. A genetic adapted neural network (GANN), which is a combination of time-delay neural network and genetic algorithms, was developed and applied to the Bardcnpho type process. In a GANN, a three-step modelling procedure was performed: (1) selection of significant input variables to maximise the predictive accuracy for each specific output: (2) finding a suitable network topology for the ANN-based process estimator, (3) sensitivity analysis. The results demonstrate that the modelling technique presented using a GANN provides a valuable t(X)l for predicting the outputs with high levels of accuracy and identifying key operating variables. This work will permit the development of a reliable control strategy thus reducing the burden of the process engineer.