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Hyperbolic tangent sigmoid multi-layered perceptron, and the adaptive radial basis function networks. (2000).
| Content Provider | CiteSeerX |
|---|---|
| Author | Tsai, Kuo-Ming Wang, Pei-Jen |
| Abstract | Predictions on the surface finish of work-pieces in electrical discharge machining (EDM) based upon physical or empirical models have been reported in the past years. However, when the change of electrode polarity has been considered, very few models have given reliable predictions. In this study, the comparisons on predictions of surface finish for various work materials with the change of electrode polarity based upon six different neural-networks models and a neuro-fuzzy network model have been illustrated. The neural-network models are the Logistic Sigmoid Multi-layered Perceptron (LOGMLP), the Hyperbolic |
| File Format | |
| Publisher Date | 2000-01-01 |
| Access Restriction | Open |
| Subject Keyword | Adaptive Radial Basis Function Network Hyperbolic Tangent Sigmoid Multi-layered Perceptron Surface Finish Electrode Polarity Different Neural-networks Model Reliable Prediction Various Work Material Logistic Sigmoid Multi-layered Perceptron Empirical Model Neural-network Model Electrical Discharge Machining Neuro-fuzzy Network Model |
| Content Type | Text |
