A method of building a set of experimental prediction model that requires fewer experimental frequency, shorter prediction time and higher prediction accuracy by using the advantages of combining the experimental data of Taguchi method and neural network learning is disclosed. The error between the experimentally measured result of photolithography and the simulated result of the theoretical model of near field photolithography is set as an objective function of an inverse method for back calculating fiber probe aperture size, which is adopted in the following Taguchi experiment. The analytical result of Taguchi neural network model of the present invention proves that the Taguchi neural network model can provide more accurate prediction result than the conventional Taguchi network model, and at the same time, improve the demerit of requiring massive training examples of the conventional neural network.

 
Web www.patentalert.com

< Training convolutional neural networks on graphics processing units

< Methods and apparatus for target discrimination using observation vector weighting

> Method of and system for evaluating tactile sensations of car seat covers using statistical recursive and artificial neural network models

> Long-term memory neural network modeling memory-chaining functions of the brain wherein a pointer holds information about mutually related neurons and neurons are classified hierarchically by degree of activation

~ 00618