Neurofuzzy approaches to anticipation: a new paradigm for intelligent systems

Anticipatory systems are systems whose change of state is based on predictions about the future of the system and/or its environment. Planning and acting on the basis of anticipations of the future is an omnipresent feature of human control strategies, deeply permeating our daily experience; the human attribute of foresight may be considered as the hallmark of natural intelligence. Yet, as the eminent mathematical biologist Robert Rosen has pointed out, 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 predictions about the future 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. In this paper the role of anticipation in intelligent systems is reviewed and a new approach is presented for anticipatory control algorithms which use the predictive capabilities of neural models in conjunction with the descriptive power of fuzzy if/then rules.