Solving nonlinear programming problems through simulations of neural networks on a personal computer

Author(s):  
T.A. Shirey ◽  
L.J. Tung ◽  
B.W. Kwan ◽  
S. Foo ◽  
L. Anderson
1991 ◽  
Vol 02 (04) ◽  
pp. 331-339 ◽  
Author(s):  
Jiahan Chen ◽  
Michael A. Shanblatt ◽  
Chia-Yiu Maa

A method for improving the performance of artificial neural networks for linear and nonlinear programming is presented. By analyzing the behavior of the conventional penalty function, the reason for the inherent degenerating accuracy is discovered. Based on this, a new combination penalty function is proposed which can ensure that the equilibrium point is acceptably close to the optimal point. A known neural network model has been modified by using the new penalty function and the corresponding circuit scheme is given. Simulation results show that the relative error for linear and nonlinear programming is substantially reduced by the new method.


1995 ◽  
Vol 73 (9) ◽  
pp. 1412-1426 ◽  
Author(s):  
D. Cabrol-Bass ◽  
C. Cachet ◽  
C. Cleva ◽  
A. Eghbaldar ◽  
T.P. Forrest

In the last few years, intensive research by several groups has shown that neural networks can be used to analyse spectral data for structural elucidation, and that their performance approaches that of an expert in the field. The construction of such networks, their training and evaluation, requires large structural and spectral databases and significant computational resources and time. However, once the network has been completed it can be used very effectively for practical applications on an ordinary desktop computer. In this article we describe the methodology for creating such a network for infrared and mass spectra, and present a program for use on a personal computer, either connected to a spectrometer or independently. The program accepts data in ASCII format, both for the network description and for the spectral information. This approach permits the use of neural networks in an analytical laboratory with limited computational resources. Keywords: neural networks, infrared spectroscopy, mass spectroscopy, structure determination.


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