Neurofuzzy Anticipatory Systems: A New Approach to Intelligent Control
Anticipatory systems are systems whose change of state is based on information about present as well as future states. Planning and acting on the basis of anticipations of the future is an omnipresent feature of human control strategies, deeply permeating our daily experience and considered as the hallmark of natural intelligence. Yet, as the eminent mathematical biologist Robert Rosen has pointed out in his book Anticipatory Systems (1985), such control strategies are curiously absent from existing formal approaches to automatic control and decision-making processes. Recent developments in biology, ethology and cognitive sciences, however, as well as advancements in the technology of computer-based predictive models, compel us to reconsider the role of anticipation in intelligent systems and to the extent possible incorporate it in our formal approaches to control. Significant improvements in neural predictive computing when combined with the flexibility of fuzzy systems, supports the development of neurofuzzy anticipatory control architectures that integrate planning and control sequencing functions with feedback control algorithms. A review of the role of anticipation in intelligent systems and a new approach for neurofuzzy anticipatory control using radial basis neural predictive models and fuzzy if/then rules is presented.