Intelligent modelling, estimation and fusion

Summary form only given. A precursor to control is modelling. For nonlinear, uncertain, time-varying, unknown dynamical processes, neurofuzzy algorithms have many useful properties including convergence in learning, real-time adaptability, and transparency. Unfortunately, they suffer from the curse of dimensionality, and recent research on parsimonious modelling schemas such as LOLIMOT, ASMOD, MASMOD, MENN, have shown how this problem can be effectively overcome. Equally, this method of data-based modelling coupled with local operating point models enables classical linear control and estimation algorithms to be applied directly to these processes. In this seminar the basic theory of intelligent modelling via neurofuzzy algorithms will be developed, and local models, local controllers, intelligent estimators and applications in modelling, control and estimation (tracking) for advanced transportation will be used to illustrate the basic principles