Complex Stochastic Systems Modeling and Control via Iterative Machine Learning

Complex stochastic systems require the control of their stochastic distributions. This keynote paper addresses both modelling and control of such systems and consists of the following aspects: 1) neural network based modelling of the stochastic distribution systems; 2) control framework for the stochastic profile control of the systems; 3) iterative learning of the space variables so as to achieve a batch-by-batch improvement of the closed loop performance. The above includes our originated research on complex stochastic systems in terms of probability density function (pdfs) control, where neural networks such as RBF was used to approximate the output pdfs and the system dynamics. This was followed by the iterative machine learning for the RBF basis functions on a batch-by-batch basis so as to improve the closed loop performance both in the time and in the space. Applications to particle size distribution control and 3D paper Web distribution control was discussed in the presentation