May-01-2020, 11:25 PM
Dear all,
My master thesis is about the prediction of gas emissions from a coal-fired thermal plant using neural network.
With the data from the coal-fired thermal plant and using Python I made the prediction for the emission of Nox, SO2 and CO. However, the problem encountered was to get a neural network with an error within an acceptable standard (low MSE)
I would like your help on the method of selecting the input variables in order to obtain a network with a small prediction error.
Researching I found something about Garson's method that uses the weight of connections. Does anyone know how I should proceed so that my network reports the weight of each connection? What lines of programming in Python should I include to generate this data?
Any other suggest to classifier the input variables?
Thank you.
My master thesis is about the prediction of gas emissions from a coal-fired thermal plant using neural network.
With the data from the coal-fired thermal plant and using Python I made the prediction for the emission of Nox, SO2 and CO. However, the problem encountered was to get a neural network with an error within an acceptable standard (low MSE)
I would like your help on the method of selecting the input variables in order to obtain a network with a small prediction error.
Researching I found something about Garson's method that uses the weight of connections. Does anyone know how I should proceed so that my network reports the weight of each connection? What lines of programming in Python should I include to generate this data?
Any other suggest to classifier the input variables?
Thank you.
