基于Dempster-shafer理论的多个神经网络分类器融合算法

Information Fusion Algorithm Based Evidence Combination

用遗传算法优化神经网络分类器的连接权系数 ,避免采用BP算法存在易于陷入局部极值 ,使每个神经网络分类器的分类接近于理想状态 ,由于每个分类器的特征输入不同 ,不能被一个分类器识别的模式 ,却可能被另一个分类器识别 ,为了提高模式识别的精度 ,可将一个模式识别问题由多个分类器来完成 ,将每一个分类器的输出结果作为一条证据 ,确定各分类器的基本概率指派函数 ,再用证据组合理论融合证据信息 。

This paper applies the evidence combination theory to fuse the information of multi-neural network classifier. In order to make each classifier approach the ideal state, the heredity algorithm is applied to train it. The different capacity of each classifier is caused by different classified feature. Input feature can't be identified by one classifier and may be identified by another, Model identification can be performed by multi-classifier, output result can be thought of evidence, further more,the BPA of each classifier is determined, then the procession of the model identification must be improved.