Two-degree-of-freedom fuzzy model using associative memories and its applications

Abstract

We propose a model for a physical plant system. This model simulates the steps of an expert's training, and that simulates an expert's knowledge, about steady state operation of a plant, about the plant's dynamic transitions knowledge, and the training steps. To represent this model, we use an associative memory system called fuzzy associative memory organizing units system (FAMOUS) to structure two types of knowledge: 1. (1) static fuzzy knowledge (SFK), i.e., about operations corresponding to each operational condition, and 2. (2) dynamic fuzzy knowledge (DFK), i.e., about dynamic state-transition patterns generation under all conditions. We call this model using two types of fuzzy knowledge a two-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 experts. The two-degree-of-freedom fuzzy model, however, can represent the featured phenomena by using a combination of compact SFK and DFK similar to the knowledge acquired through experience by human beings. Fuzzy knowledge from experts is initially put into FAMOUS, and then refined according to the expert's ideal operations and the plant's states by using a learning algorithm. After learning the fuzzy knowledge, the uncertain knowledge is more desirable for representing the featured phenomena than before learning. The two-degree-of-freedom fuzzy model uses associative memories to achieve operation and prediction close to those of human beings. In addition, application examples are reported: the flight control of a small four-propeller flying vehicle (similar to a helicopter) and the smooth running of a pump station of a sewage treatment plant. We also give an outline of the fuzzy-type associative memory and the two-degree-of-freedom fuzzy model, the extraction and refinement of knowledge for stabilizing a physical plant, and for a series of dynamic state-transition patterns together with the characteristic fluctuations.