Heuristic pattern correction scheme using adaptively trained generalized regression neural networks
Tetsuya Hoya
Jonathon Chambers
2134/5790
https://repository.lboro.ac.uk/articles/journal_contribution/Heuristic_pattern_correction_scheme_using_adaptively_trained_generalized_regression_neural_networks/9562001
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
2010-01-14 14:31:44
Generalized regression neural networks (GRNNs)
Incremental learning
Pattern classification
Pattern correction
Mechanical Engineering not elsewhere classified