How many training sets are sufficient for neural network with 81 neurons in the input layer?

.... and 1 output, approximately or on average?
LamiaaaAsked:
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GwynforWebCommented:
My experience of neural networks using back propagation is that you need a huge number training inputs. I have found them disappointing which might be a function of me not using them that much, and as such not that skilled at designing the intermediate layers which can effect the ease with which it can be trained. In short I have found them disappointing.

81 inputs is a lot of inputs and the ability to train the network is related to there being an underlying structure to the input for the network to detect, the more pronounced this structure the easier the network can be trained. Some times pre-processing the input to expose more of the structure before hand can improve training and performance.

A neural network is merely a way of designing a function that gives required output for a given input. There are other ways of designing these functions other than neural networks.
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LamiaaaAuthor Commented:
Thanks, if the input is in the form of matrices, what other "functions" can be used?
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GwynforWebCommented:
Unfortunately there is no easy deterministic way of training intelligence into a response function for a given input. The other technique that is commonly used is Fuzzy Logic but without knowing the details of your problem I have no idea if it will work for you.

What ever technique you use will require skill on your part in its use. If you where to post some details of your problem I might be able to make better suggestions. It might well be that Neural Networks are the way to go and you have a design or training problem,

 I am assuming you are using back propagation for training if you have a forward -feeding network or do you have a feedback network?
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