"Muna Adhikari" <email@example.com> wrote in message <firstname.lastname@example.org>... > Dear Sir or Madam; > > I am a graduate student in Physics from NEPAL. I am going to study the effects of soil moisture content and its prediction using 15 minute remotely sensed data. I am going to design the prediction based on neural network, as it is said that it is good on forecasting. However, I need to test and validate the instantaneous data from the sensors. I need to do some experimental work. I need to know how can I used neural network model in my case? > > I found in some literature that people use multi layer backpropagation neural network for the static design. And then, they performed sliding window algorithm or accumulated training algorithm to make the system online? Could you provide me some idea and if some sample of matlab how I could use sliding window algorithm or accumulated training and generalization of the neural network. > > I have sample of remotely sensed data of 15 minute duration of 6 months and total sample data is 17,280. My input data are: soil temperature, air temperature, precipitation, soil adjusted vegetation index and land surface temperature. My output data is: soil moisture content. > > Also, the effect of soil moisture content is polynomial equation based on the emphirical physical modeling.
I think the word "effect"is being misused. Do you mean you have a polynomial equation the estimates the current soil moisture and now you want a neural net that will predict future values?
The latter is a timeseries problem suitable for narxnet.
See the narxnet documentation and examples and try the SISO simpleseries_dataset. Also look at NEWSGROUP and ANSWERS posts by searching on narxnet. Next try the MIMO pollution_dataset with 8 inputs and 3 outputs. Or you can just consider one of the outputs instead of considering all three.