Name
Abstract | ||
|---|---|---|
When the learning algorithm is applied to a MLP structure, different solutions for the weight values can be obtained if the parameters of the applied rule or the initial conditions are changed. Those solutions can present similar performance with respect to learning, but they differ in other aspects, in particular, fault tolerance against weight perturbations. In this paper, a backpropagation algorithm that maximizes fault tolerance is proposed. The algorithm presented explicitly adds a new term to the backpropagation learning rule related to the mean square error degradation in the presence of weight deviations in order to minimize this degradation. The results obtained demonstrate the efficiency of the learning rule proposed here in comparison with other algorithm. |
Year | DOI | Venue |
|---|---|---|
2000 | 10.1023/A:1009698206772 | Neural Processing Letters |
Keywords | Field | DocType |
backpropagation,regularization,multilayer perceptron,fault tolerance,mean square sensitivity | Delta rule,Algorithm,Mean squared error,Fault tolerance,Learning rule,Multilayer perceptron,Artificial intelligence,Backpropagation,Artificial neural network,Perceptron,Machine learning,Mathematics | Journal |
Volume | Issue | ISSN |
12 | 2 | 1573-773X |
Citations | PageRank | References |
24 | 0.92 | 6 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jose L. Bernier | 1 | 36 | 1.66 |
| J. Ortega | 2 | 940 | 73.05 |
| I. Rojas | 3 | 1750 | 143.09 |
| Eduardo Ros | 4 | 1100 | 86.00 |
| A. Prieto | 5 | 419 | 25.23 |