自适应性神经网络预测模型及其在农机动力需求预测中的应用

Self Configuring ANN Model and Application in Demand Forecasting of Agricultural Machine Power

建立了一个自适应性神经网络模型，它在Ｂ－Ｐ网络模型基础上，对网络的自身结构及学习规则进行了动态优化。网络能自组织和自学习自己的结构，即在学习过程中，网络可根据具体问题自动调整本身的结构，从而使结构达到最优。学习速度具有动态调节功能，根据每次学习时得到的误差不同，网络不断调整学习速率，从而在不引起系统振荡的情况下加速了收敛过程。在此基础上，对我国农机总动力需求进行了预测，预测结果和实际结果有很好的一致性。

This paper presents a prediction model of self configuring Artificial Neural Network(ANN) based on back propagation learning (B P) algorithm which is well known in the artificial neural network literature. So far, most studies on learning characteristics of B P models have been concentrating on adjusting the connection weights of neural cells in which the rules of learning and connection mode remain unchanged. Considering many factors, such as structure and self learning configuring intensively affect the properties of B P models, this paper established a new self configuring algorithm which can adjust the structure automatically by means of increasing or decreasing the number of hidden nodes. The model can also modify the learning rate of the network automatically in accordance with the value of output error which controls the learning rate. When the output error becomes large, the learning rate is also enlarged, then the enlarged learning rate leads to the small error output, so the convergence speed is improved and the system oscillation is avoided. Furthermore the time series were used as the model couples of input and output of artificial neural network, and the program about constructing prediction model was introduced. In order to simplify the algorithm and speed learning rate, the input data and sample data were processed by a special method. The results showed that the prediction values based on the model are in good agreement with the original values by referring the maximal relative error.