Cooperative schemes for conventional and neural expert systems

Summary form only given. Cooperative schemes between neural net models and fuzzy expert systems were investigated. Performance of a specific neural net model with back-propagation models is discussed. Information is initially received by the expert system (ES) in a form that is useful for certain tasks. Information is represented in more than one form, reflecting interpretations of uncertainties and ambiguities in data instances. The multiple representations emerge from different scoring methods applied at input time to designated uncertainties, and on subsequent analysis, through use of linguistic relative preferences choices. The ES responds to new data as it arises, so that its adjustments do not required massive relearning. The information contained in the fuzzy system is then made available to a modified form of interactive activation and competition model (MIAC). MIAC is implemented as a collection of objects in an object-oriented system