Active Learning for Nonlinear System Identification and Control

Abstract The identification and control of nonlinear systems continues to remain a challenging issue, Neural network models and controllers have often been effective in addressing the situation, However current neural network learning methods have been found to be limited in their generalisation abilities. Recent research has shown active learning methods to be effective in increasing the modelling reliability of a neural network system. An active learning agent has the ability to query its environment in order to make a selection of its training data. One approach to the implementation of active leaning is to use 'querying-by-committee'. This results in considerably reduced data collection and at the same time does not compromise the accuracy of identification. A nonlinear plant with both clean and noisy data is successfully modelled by such a technique and a feedforward neural network controller based upon such a model is demonstrated to perform effectively.