Self-organizing learning array and its application to economic and financial problems

Authors:
Z. Zhu;H. He;J. A. Starzyk;C. Tseng
Affiliations:
School of Electrical Engineering and Computer Science, Ohio University, Athens, OH 45701, USA;School of Electrical Engineering and Computer Science, Ohio University, Athens, OH 45701, USA;School of Electrical Engineering and Computer Science, Ohio University, Athens, OH 45701, USA;Department of Computer Science and Information Systems, Texas A&M University - Commerce, Commerce, TX 75429, USA
Venue:
Information Sciences: an International Journal
Year:
2007

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Abstract

A new self-organizing learning array (SOLAR) system has been implemented in software. It is an information theory based learning machine capable of handling a wide variety of classification problems. It has self-reconfigurable processing cells (neurons) and an evolvable system structure. Entropy based learning is performed locally at each neuron, where neural functions and connections that correspond to the minimum entropy are adaptively learned. By choosing connections for each neuron, the system sets up the wiring and completes its self-organization. SOLAR classifies input data based on weighted statistical information from all neurons. Unlike artificial neural networks, its multi-layer structure scales well to large systems capable of solving complex pattern recognition and classification tasks. This paper shows its application in economic and financial fields. A reference to influence diagrams is also discussed. Several prediction and classification cases are studied. The results have been compared with the existing methods.