Fuzzy adaptive recurrent counterpropagation neural networks: A neural network architecture for qualitative modeling and real-time simulation of dynamic processes.

Abstract

In this dissertation, a new artificial neural network (ANN) architecture called fuzzy adaptive recurrent counterpropagation neural network (FARCNN) is presented. FARCNNs can be directly synthesized from a set of training data, making system behavioral learning extremely fast. FARCNNs can be applied directly and effectively to model both static and dynamic system behavior based on observed input/output behavioral patterns alone without need of knowing anything about the internal structure of the system under study. The FARCNN architecture is derived from the methodology of fuzzy inductive reasoning and a basic form of counterpropagation neural networks (CNNs) for efficient implementation of finite state machines. Analog signals are converted to fuzzy signals by use of a new type of fuzzy A/D converter, thereby keeping the size of the Kohonen layer of the CNN manageably small. Fuzzy inferencing is accomplished by an application-independent feedforward network trained by means of backpropagation. Global feedback is used to represent full system dynamics. The FARCNN architecture combines the advantages of the quantitative approach (neural network) with that of the qualitative approach (fuzzy logic) as an efficient autonomous system modeling methodology. It also makes the simulation of mixed quantitative and qualitative models more feasible. In simulation experiments, we shall show that FARCNNs can be applied directly and easily to different types of systems, including static continuous nonlinear systems, discrete sequential systems, and as part of large dynamic continuous nonlinear control systems, embedding the FARCNN into much larger industry-sized quantitative models, even permitting a feedback structure to be placed around the FARCNN.