A back-propagation neural network is only practical in

certain situations. Following are some guidelines on when

you should use another approach:

Can you write down a flow chart or a formula that

accurately describes the problem? If so, then stick with a

traditional programming method.

Is there a simple piece of hardware or software that

already does what you want? If so, then the development

time for a NN might not be worth it.

Do you want the functionality to "evolve" in a direction

that is not pre-defined? If so, then consider using a

Genetic Algorithm (that's another topic!).

Do you have an easy way to generate a significant number of

input/output examples of the desired behavior? If not, then

you won't be able to train your NN to do anything.

Is the problem is very "discrete"? Can the correct answer

can be found in a look-up table of reasonable size? A look-

up table is much simpler and more accurate.

Are precise numeric output values required? NN's are not

good at giving precise numeric answers.

Conversely, here are some situations where a BP NN might be

a good idea:

A large amount of input/output data is available, but

you're not sure how to relate it to the output.

The problem appears to have overwhelming complexity, but

there is clearly a solution.

It is easy to create a number of examples of the correct

behavior.

The solution to the problem may change over time, within

the bounds of the given input and output parameters (i.e.,

today 2+2=4, but in the future we may find that 2+2=3.8).

Outputs can be "fuzzy", or non-numeric.