Cascade-correlation algorithm and its application in monthly streamflow forecasting

The size and structure of neutral networks must be predefined if the standard backpropagation neural network architecture is used for trainin. On the contrary,the initial network of cascade correlation(CC)is composed of the input layer and output layer while the hidden unit is inserted into the network one by one.The principle of CC is presented in this paper,and the monthly streamflow in the Lasa river is forecasted by using the CC and the BP models.The result shows that the CC model only needs to run five times while the BP model needs 70000 times to reach the same precision.The efficiency of the CC model is much higher than that of the BP model Another conclusion is that the network size of a CC model is smaller than that of the BP mode.