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In many pattern classification problems, an intelligent neural system is required which can learn the newly encountered but misclassified patterns incrementally, while keeping a good classification performance over the past patterns stored in the network. In the paper, an heuristic pattern correction scheme is proposed using adaptively trained generalized regression neural networks (GRNNs). The scheme is based upon both network growing and dual-stage shrinking mechanisms. In the network growing phase, a subset of the misclassified patterns in each incoming data set is iteratively added into the network until all the patterns in the incoming data set are classified correctly. Then, the redundancy in the growing phase is removed in the dual-stage network shrinking. Both long- and short-term memory models are considered in the network shrinking, which are motivated from biological study of the brain. The learning capability of the proposed scheme is investigated through extensive simulation studies.

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To date, probabilistic neural networks (PNNs) have been widely used in various pattern classification tasks due to their robustness. In this paper, it is shown that by exploiting the flexible network configuration property, the PNN classifiers also exhibit the capability in accommodating new classes. This is verified by extensive simulation studies on using four different domain data sets for pattern classification tasks.