异因同果关联神经网络的研究

提出了一种异因同果关联神经网络模型,可以从不同角度分别建立不同的模型,并由其得到互不相同的模型预测值。异因同果关联神经网络模型将不同角度建立的模型有机结合起来,进而能够将多个神经网络模型进行综合考虑,得到一个综合的统一的模型预测结果。研究了新型模型的机理,结合实例进行仿真并与传统的神经网络模型的预测仿真结果比较,结果表明新型模型具有更高的预测精度。

Research on the Neural Network of Different-Causations-Same-Result-Combination

An innovative neural networks model is put forward. When solving practical problems,different neural network models with different predictions on the same problem can be set up from different affecting factors. This Different -Causations -Same -Result -Combination neural network models composes of many different neural nework models ,integrates these models and jointly use their predicted data by ensembly learning to gain an integrative prediction. The mechanism is explored and compared with the conventional BP models in the domain of freight traffic predicting problem. The results indicate :the predicting precision of this new model exceeds to the predicting precision of BP models.