**Previous message:**Per Anebert: "Expert System today"**Next in thread:**Giorgio Corani: "Re: coupling neural networks and fuzzy logic for forecasting purposes"**Reply:**Giorgio Corani: "Re: coupling neural networks and fuzzy logic for forecasting purposes"**Reply:**Tomas Jura: "Re: coupling neural networks and fuzzy logic for forecasting purposes"**Reply:**Greg Heath: "Re: coupling neural networks and fuzzy logic for forecasting purposes"**Reply:**Russell Hersberger: "Re: coupling neural networks and fuzzy logic for forecasting purposes"**Reply:**Jeff Dean: "Re: coupling neural networks and fuzzy logic for forecasting purposes"**Messages sorted by:**[ date ] [ thread ] [ subject ] [ author ]

If we think about a forecast issued by a neural network, we can argue

that it doesn't know about the state of the dynamic system: it get the

values of input to the system and returns the predicted output of the

system. However, there are applications where the state of the system

has a key role. I'm dealing with flood forecasting: the inputs are past

measurements of rainfall and water level (this is the autoregressive

part of the input). In this case, the increase in water level caused by

an identical rainfall event depends heavily on the state of the basin.

Roughly speaking: an identical rainfall event causes a small water

level increase if the soil is dry, and a very larger increase is the

soil is wet and does not infiltrate water.

The problem i would to address is that my knowledge about the state of

the system is quite fuzzy (i can say for example the basin is more or

less wet).

My idea is to configure different neural predictors (i.e. for dry,

medium and wet soil conditions), and then in real time operations, to

switch in a fuzzy way from each to other.

However, it implies several problems.

For example every data of the training set belongs, with a different

rate of membership, to the dry, to the medium and to the wet situation.

How is then possible to calibrate the model?

Maybe it can be convenient to include all the data with membership

different from 0, and then, when evaluating the objective function, to

weight the related errors on the base of their membership.

I apologize for this long posting.

I welcome suggestion of papers which deal with such problems.

best regards, Giorgio Corani

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**Next message:**S. F. Thomas: "Re: Stupid question"**Previous message:**Per Anebert: "Expert System today"**Next in thread:**Giorgio Corani: "Re: coupling neural networks and fuzzy logic for forecasting purposes"**Reply:**Giorgio Corani: "Re: coupling neural networks and fuzzy logic for forecasting purposes"**Reply:**Tomas Jura: "Re: coupling neural networks and fuzzy logic for forecasting purposes"**Reply:**Greg Heath: "Re: coupling neural networks and fuzzy logic for forecasting purposes"**Reply:**Russell Hersberger: "Re: coupling neural networks and fuzzy logic for forecasting purposes"**Reply:**Jeff Dean: "Re: coupling neural networks and fuzzy logic for forecasting purposes"**Messages sorted by:**[ date ] [ thread ] [ subject ] [ author ]

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