MATLAB: Forecasting using neural network

neural network

hello all, in my application i have to forecast water demand based of climatic variables(temperature and humidity) using neural nwetwork. now i have few question in my mind 1)IS IT NECESSARY THAT THE INPUTS SHOULD HAVE A RELATION WITH OUTPUT AS IN MY CASE FOR SOME VAIRABLES I AM GETTING GOOD REGRESSION BUT FOR SOME ITS NOT GOOD. 2)WHICH TRAINING FUNCTION IS GOOD TRAINLM OR TRAINDM

Best Answer

1. In order for a net to perform well on nondesign data, the input and output variables must be well correlated. However, the significant correlations need not be linear. Nevertheless, I always check the static linear correlations via corrcoef before designing regression and classification nets. Similarly, I always check the auto and cross correlation functions when designing time-series nets.
2. The MATLAB documentation recommends the Levenberg-Marquardt, Conjugate-Gradient and Resilient Backpropagation algorithms. LM tends to be faster for small data sets and RPROP is better for huge data sets.
For important work it may be best to heed the words of Confuscious: "Try all. Choose best!"
Hope this helps.
Greg
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