Often a major difficulty in the design of rule-based systems is the process of acquiring the requisite knowledge in the form of If–Then rules. This paper presents a class of fuzzy degraded hyperellipsoidal composite neural networks (FDHECNNs) that are trained to provide appealing solutions to the problem of knowledge acquisition. The values of the network parameters, after sufficient training, are then utilized to generate If–Then rules on the basis of preselected meaningful features. In order to avoid the risk of getting stuck in local minima during the training process, a real-valued genetic algorithm is proposed to train FDHECNNs. The effectiveness of the method is demonstrated on two problems, namely, the “truck backer-upper” problem as well as real-world application of a hypothesis regarding the pathophysiology of diabetes.
