Multi-layer feed-forward neural network approach to case-based reasoning
多层前馈神经网络在基于案例推理的应用

When the case-based reasoning system learns new cases, the size of the case base will increase, and lead to the problem of more retrieving time and low efficiency in case retrieval. Multi-layer feed-forward neural network is a constructive neural network, which can be easily constructed and understood with features of the training examples and has lower complexity of time or space. The covering algorithm of the multi-layer feed-forward neural network has high reorganization of the test set besides recognizing the whole training data, and has been used to classify many difficult problems effectively and rapidly. The ease base is partitioned to several sub-case bases by a multi-layer forward neural networks as well as covering algorithm, and a new problem can be retrieved only in a special sub-case base. These can be used to deal with the problem of CBR system performance especially in large scale of case base, which can guarantee the two all, competence and efficiency.