Article ID: | iaor20043295 |
Country: | Netherlands |
Volume: | 36 |
Issue: | 3 |
Start Page Number: | 283 |
End Page Number: | 296 |
Publication Date: | Jan 2004 |
Journal: | Decision Support Systems |
Authors: | Dorsey Robert E., Sexton Randall S., Sikander Naheel A. |
Keywords: | genetic algorithms |
A major limitation to current artificial neural network research is the inability to adequately identify unnecessary weights in the solution. If a method were found that would allow unnecessary weights to be identified, decision-makers would gain crucial information about the problem at hand as well as benefit by having a network that was more effective and efficient. The Neural Network Simultaneous Optimization Algorithm (NNSOA) is proposed for supervised training in multilayer feedforward neural networks. We demonstrate with Monte Carlo studies that the NNSOA can be used to obtain both a global solution and simultaneously identify a parsimonious network structure.