2-DEGREE-OF-FREEDOM FUZZY MODEL USING ASSOCIATIVE MEMORIES AND ITS APPLICATIONS

We propose a model for a physical plant system. This model simulates t he steps of an expert's training, and that simulates an expert's knowl edge, about steady state operation of a plant, about the plant's dynam ic transitions knowledge, and the training steps. To represent this mo del, we use an associative memory system called fuzzy associative memo ry organizing units system (FAMOUS) to structure two types of knowledg e: (1) static fuzzy knowledge (SFK), i.e., about operations correspond ing to each operational condition, and (2) dynamic fuzzy knowledge (DF K), i.e., about dynamic state-transition patterns generation under all conditions. We call this model using two types of fuzzy knowledge a t wo-degree-of-freedom fuzzy model. It is difficult to use if-then rules to represent the featured phenomenon (i.e., a series of dynamic state -transition patterns together with their characteristic fluctuations) because the rule representation is too complex to be acquired from exp erts. The two-degree-of-freedom fuzzy model, however, can represent th e featured phenomena by using a combination of compact SFK and DFK sim ilar to the knowledge acquired through experience by human beings. Fuz zy knowledge from experts is initially put into FAMOUS, and then refin ed according to the expert's ideal operations and the plant's states b y using a learning algorithm. After learning the fuzzy knowledge, the uncertain knowledge is more desirable for representing the featured ph enomena than before learning. The two-degree-of-freedom fuzzy model us es associative memories to achieve operation and prediction close to t hose of human beings. In addition, application examples are reported: the flight control of a small four-propeller flying vehicle (similar t o a helicopter) and the smooth running of a pump station of a sewage t reatment plant. We also give an outline of the fuzzy-type associative memory and the two-degree-of-freedom fuzzy model, the extraction and r efinement of knowledge for stabilizing a physical plant, and for a ser ies of dynamic state-transition patterns together with the characteris tic fluctuations.