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A New Adaptive Neural Network Model for Financial Data Mining
Data Mining is an analytic process designed to explore data (usually large amounts of data - typically business or market related) in search of consistent patterns and/or systematic relationships between variables, and then to validate the findings by applying the detected patterns to new subsets of data. One of the most commonly used techniques in data mining, Artificial Neural Networks provide non-linear predictive models that learn through training and resemble biological neural networks in structure. This paper deals with a new adaptive neural network model: a feed-forward higher order neural network with a new activation function called neuron-adaptive activation function. Experiments with function approximation and stock market movement analysis have been conducted to justify the new adaptive neural network model. Experimental results have revealed that the new adaptive neural network model presents several advantages over traditional neuron-fixed feed-forward networks such as much reduced network size, faster learning, and more promising financial analysis. © Springer-Verlag Berlin Heidelberg 2007.
History
Publication title
Proceedings part 1, 4th International Symposium on Neural NetworksEditors
Derong Liu, Shumin Fei, Zeng-Guang Hou, Huaguang Zhang & Changyin SunPagination
1265-1273ISBN
3-540-72382-XDepartment/School
School of Information and Communication TechnologyPublisher
Springer-VerlagPlace of publication
Berlin, GermanyEvent title
International Symposium on Neural Networks (ISNN)Event Venue
Nanjing, ChinaDate of Event (Start Date)
2007-06-03Date of Event (End Date)
2007-06-07Repository Status
- Restricted