Interclass member mapping without retraining the network

Abstract

This paper introduces a recurrent network for mapping of interclass members without performing a learning process. This allows a member of class A to be mapped to a member of class B. Given sample members of each class, a backpropagation network is trained to form the corresponding class boundaries. Upon completion of the training process, the weights obtained are used in a recurrent network which performs the interelass member mapping without any further training. This mapping is achieved as the recurrent network evolves in time. The initial state of the network is mapped to its equilibrium state. The interclass member mapping network (IMMN) has many applications in self-correcting systems. In this paper, the IMMN is developed to represent two classes, namely class B (for instance, a class for representing members with desirable and correct features) and class A (members with incorrect features). The self-correcting aspect of the network is demonstrated by mapping a member from class A to a member of class B. Two examples are given. One represents a case of linearly separable classes. The other explores an application example, where two categories are used, namely, poorly and well-designed manufacturing parts. Given a poorly designed part, the network will suggest corrections resulting in a well-designed part. This example has nonlinear decision regions and shows the generalization capability of the network.