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| Content Provider | ACM Digital Library |
|---|---|
| Author | Adams, Kevin |
| Abstract | Sensitivity analysis for integer optimization models is a very time-consuming process not conducive to applications requiring near real-time performance. The premise of this research is that the sensitivity of the optimization function in mixed integer non-linear systems creates a pattern. We demonstrate that a supervised neural network can be used to identify, classify and learn these patterns. The learned patterns can be represented in the various weights used in the neural network configuration. Using historical optimal information represented in the weights, the neural network can map the input constraints of our system to the optimal solution through functionally approximation with a high degree of accuracy. This functional approximation can be calculated in a small fixed determinate amount of time. In order to maintain good performance in a dynamic environment, the system uses feedback from an off-line component to identify and classify the constraint pattern changes. The proposed approach is demonstrated through simulation and case study to perform extremely well. |
| ISBN | 0780395190 |
| Language | English |
| Publisher | Association for Computing Machinery (ACM) |
| Publisher Date | 2005-12-04 |
| Access Restriction | Subscribed |
| Content Type | Text |
| Resource Type | Article |
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