Neuro-Fuzzy Approaches to Anticipatory Control

Anticipatory systems are systems where change of state is based on information pertaining to present as well as future states. Cellular organisms, industrial processes, global markets, provide many examples of behavior where global output is the result of anticipated not only current state. In the global economy, for example, the anticipation of an oil shortage or of a significant default of foreign loans can have profound effects upon the course of the economy, whether or not the anticipated events come to pass [3]. Participants in the economy build up models of the rest of the economy and use them to make predictions. The models are more prescriptive (prescribing what should be done in a given situation) than descriptive (describing the options of a given situation) and involve strategies appropriately formulated in terms of lookahead, or anticipation of market conditions. In an industrial process, the prescriptions are typically Standard Operating Procedures (SOPs), dictating actions to be taken under specific conditions. The accumulated experience of various decision-makers at all levels of the process provides increasingly refined SOPs and progressively more sophisticated interactions amongst them and computer tools designed to assist them. As another example, consider a car driven on a busy highway. The driver and the car taken together are a simple, everyday example of an anticipatory system. An automobile driver makes decisions on the basis of predicting what may be happening in the future, not simply reacting to what happens at the present. Driving requires one to be aware of future system inputs by observing the curvature and grade of the road ahead, road conditions and the behavior of other drivers. Perceptual information received at the present, may be thought of as input to internal predictive models. Such a system, however, is very difficult to model using conventional approaches. In part the difficulty relates to the fact that conventional predictive models are unduly constrained by excessive precision. Generally, in situations like the driver-car system, it is important for a decision-maker (the driver) to use a parsimonious description of the overall situation, that is, a model at the appropriate level of precision. Predictions about the future are not very precise and of course they may be wrong. Yet, their efficacy does not rest on precision as much as on the more general issue of accuracy and their successful utilization. High levels of precision may not only be unnecessary for problems utilizing predicted values, they may very well be counterproductive. An over-precise driver may actually be a dangerous driver.