Input Values Function for Improving Generalization Capability of BP Neural Network

Input Values Function for Improving Generalization Capability of BP Neural Network

As is known to all, Back propagation (BP) neural network has two important advantage and disadvantage: learning speed and generalization capability (GC). In this paper, we propose a new method by adding input values function (IVF) to improve the generalization of BP neural network. The result shows:...

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Bibliographische Detailangaben
Hauptverfasser: Tongzhi He, Shijue Zheng, Ping Zhang, Ming Zou
Format: Tagungsbericht
Sprache:eng
Schlagworte:
Algorithm design and analysis
BP neural network
Computer science
Education
Feedforward systems
Generalization Capability
Helium
Input Values Function
Multi-layer neural network
Neural networks
Neurons
System testing
Wearable computers
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Beschreibung
Zusammenfassung:As is known to all, Back propagation (BP) neural network has two important advantage and disadvantage: learning speed and generalization capability (GC). In this paper, we propose a new method by adding input values function (IVF) to improve the generalization of BP neural network. The result shows: GC in a certain extent has been improved throught this method. But if we want to do much more promoting in various fields, there are still a lot of things that we must to be studied.
DOI:10.1109/APWCS.2010.64