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Averaged learning equations of error-function-based multilayer perceptrons
- Source :
- Neural Computing and Applications. 25:825-832
- Publication Year :
- 2014
- Publisher :
- Springer Science and Business Media LLC, 2014.
-
Abstract
- The multilayer perceptrons (MLPs) have strange behaviors in the learning process caused by the existing singularities in the parameter space. A detailed theoretical or numerical analysis of the MLPs is difficult due to the non-integrability of the traditional log-sigmoid activation function which leads to difficulties in obtaining the averaged learning equations (ALEs). In this paper, the error function is suggested as the activation function of the MLPs. By solving the explicit expressions of two important expectations, we obtain the averaged learning equations which make it possible for further analysis of the learning dynamics in MLPs. The simulation results also indicate that the ALEs play a significant role in investigating the singular behaviors of MLPs.
- Subjects :
- business.industry
Numerical analysis
Computer Science::Neural and Evolutionary Computation
Activation function
Process (computing)
Parameter space
Perceptron
Error function
Singularity
Artificial Intelligence
Applied mathematics
Gravitational singularity
Artificial intelligence
business
Software
Mathematics
Subjects
Details
- ISSN :
- 14333058 and 09410643
- Volume :
- 25
- Database :
- OpenAIRE
- Journal :
- Neural Computing and Applications
- Accession number :
- edsair.doi...........1e47b903dc892cea00a0559998f1dde8
- Full Text :
- https://doi.org/10.1007/s00521-014-1557-5