An Intelligent Neural Networks System for Adaptive Learning and Prediction of a Bioreactor Benchmark Process

Abstract The adaptive learning and prediction of a highly nonlinear and time-varying bioreactor benchmark process is studied using NeurOn-Line, a graphical tool kit for developing and deploying neural networks in the G2 real time intelligent environment, and a new modified Broyden, Fletcher, Goldfarb, and Shanno (BFGS) quasi-Newton algorithm. The modified BFGS algorithm for the adaptive learning of back propagation (BP) neural networks is developed and embedded into NeurOn-Line by introducing a new search method of learning rate to the full memory BFGS algorithm. Simulation results show that the adaptive learning and prediction neural network system can quickly track the time-varying and nonlinear behavior of the bioreactor.