Function simulation,which is called virtual reality too,is popularly applied to solve uncertain problems.Good performance of hidden layers and perfect capability of function simulation make artificial neural networks one of the best choices to simulate functions with form unknown.Inputs and outputs were used to train the structure of the ar- tificial neural network to make the outputs of network vary with the given inputs and keep consistent with the original data within tolerance.However,we couldn't get expected re- sults by using samples of a simple two-variable-model for the cause of dimensional differ- ence.The way of artificial neural networks to fit functions,which uses 'multi-dimensional surface' of high dimension to fit 'multi-dimensional line' of low dimension,was proved;the conclusion that good effects of fitting don't mean good function modeling when a dimen- sional difference exists was provided,and a suggestion of 'surface collecting' in practical engineering application was proposed when collecting useful data.
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