Classification and Approximation with Rule-Based Networks

This thesis describes the architecture of learning systems which can explain their decisions through a rule-based knowledge representation. Two problems in learning are addressed: pattern classification and function approximation. In Part I, a pattern classifier for discrete-valued problems is presented. The system utilizes an information-theoretic algorithm for constructing informative rules from example data. These rules are then used to construct a computational network to perform parallel inference and posterior probability estimation. The network can be extended incrementally; that is, new data can be incorporated without repeating the training on previous data. It is shown that this technique performs comparably with other techniques including the backpropagation network while having unique advantages in incremental learning capability, training efficiency, and knowledge representation. Examples are shown of rule-based classification and explanation. In Part II, we present a method for the learning of fuzzy logic membership functions and rules to predict a numerical function from examples of the function and its independent variables. This method uses a three-step approach to building a complete function approximation system: first, learning the membership functions and creating a cell-based rule representation; second, simplifying the cell-based rules using an information-theoretic approach for induction of rules from discrete-valued data; and finally, constructing a computational network to compute the function value given its independent variables. Applications of the system to adaptive control are suggested, including a method for learning a complete control system for an unknown plant. Experimental validation of the suggested methods using a ball-and-beam system is shown.