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A New Adaptive Neural Network Model for Financial Data Mining


Xu, S and Zhang, M, A New Adaptive Neural Network Model for Financial Data Mining, Proceedings part 1, 4th International Symposium on Neural Networks, 3-7 June 2007, Nanjing, China, pp. 1265-1273. ISBN 3-540-72382-X (2007) [Refereed Conference Paper]

DOI: doi:10.1007/978-3-540-72383-7_147


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.

Item Details

Item Type:Refereed Conference Paper
Research Division:Information and Computing Sciences
Research Group:Machine learning
Research Field:Neural networks
Objective Division:Information and Communication Services
Objective Group:Information systems, technologies and services
Objective Field:Information systems, technologies and services not elsewhere classified
UTAS Author:Xu, S (Dr Shuxiang Xu)
ID Code:50035
Year Published:2007
Deposited By:Computing
Deposited On:2007-08-01
Last Modified:2012-02-27

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