MATLAB: Prediction ability of a neural network

finite-state machineneural networkspredictionreliabilitysimulationtraining

Hi all,
recently I am doing some simulation and prediction work using neural networks. During the days, I have some theoretical questions regarding to NN. I appreciate it so much if anyone can help me solve them:
1. Is there any relationship between a neural network and a finite-state machine?
From my point of view, I always feel that the training of a neural network is just the process to teach the network to know its input-output relations. Sometimes, if you give a input u_i within the input range [u_min, u_max] but never occurs in the trained data, the reliability of the output is really poor.
2. How to make sure that the prediction of a neural network is reliable?
3. Somebody told me that, if a problem can be solved by traditional methods, never use neural networks. Therefore, I am wondering that, besides the high computation complexity, does neural networks have any other drawback or limitation?
Thank you very much for the help!

Best Answer

1. Probably (I've forgotten the definition of a finite state machine, but the NN is a universal approximator)
2. The net is not a mind reader. It learns via samples from representative I/O examples. As far as it is concerned, [ umin, umax ] is determined by training data. Remember, the default neural net approximation is a linear superposition of bounded tanh functions.
3. The three largest problems with neural networks:
   a. They cannot read minds.
   b. They cannot program themselves.
   c. They must rely on humans.
HTH
Thank you for formally accepting my answer
Greg
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