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19 August 1998 Regression modeling method of the artificial neural networks
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The variations of real procedure are generally nonlinear. It is a common method to deal with nonlinear problem by linear regression model. The parameter estimation obtained by this method is unbiased. The precision of curve fitting is low. This paper puts forward a method that uses artificial neural network technique to make nonlinear regression analysis. Artificial neural networks is make up of a great number of nonlinear processing units united each other, and also is a super scale nonlinear self-adaptive information processing system, so it can deal with nonlinear regression problems properly. Based on the BP model of multi-layer feed forward neural networks, we can obtain a group of deviation values with relation to right values to make the error between network output and expectancy output minimal. At last we make practical calculation on the problem of dynamic analysis of ground water level and the results are satisfactory.
© (1998) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Ping Li and Xiaofeng Mu "Regression modeling method of the artificial neural networks", Proc. SPIE 3561, Electronic Imaging and Multimedia Systems II, (19 August 1998);

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