Neural network-based approximate reasoning: principles and implementation

Abstract

Instead of seeking a structure mapping from a fuzzy reasoning system to a neural network, this paper is intended to find a functional mapping from a fuzzy logic-based algorithm to the network-based approach. By viewing the given rule-base as defining a global linguistic association constrained by fuzzy sets, approximate reasoning is implemented here by a Backpropagation Neural Network (BNN) with the aid of fuzzy set theory. By paying particular attention to the generalization capability of the BNN, the underlying principles have been examined in detail using two examples: a small demonstration at the linguistic level, and a more realistic problem of multivariable fuzzy control of blood pressure. The simulation results not only indicate the feasibility of the BNN-based approach, but also reveal some deeper similarities which exist in the two methods, which may have some important implications for future studies into fuzzy control. In addition, this work may be considered as another application example of the BNN in the case of continuous outputs and on a relatively larger scale (in the second example the BNN has 26 inputs and 13 outputs, with a total of 2013 weights and thresholds).