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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Bibliographic Details
Main Authors: Tongzhi He, Shijue Zheng, Ping Zhang, Ming Zou
Format: Conference Proceeding
Language:English
Subjects:
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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Summary: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