Self-organizing learning array and its application to economic and financial problems
Information Sciences: an International Journal
Time sequence data mining using time-frequency analysis and soft computing techniques
Applied Soft Computing
A Hierarchical Self-organizing Associative Memory for Machine Learning
ISNN '07 Proceedings of the 4th international symposium on Neural Networks: Advances in Neural Networks
Online Dynamic Value System for Machine Learning
ISNN '07 Proceedings of the 4th international symposium on Neural Networks: Advances in Neural Networks
Hybrid Pipeline Structure for Self-Organizing Learning Array
ISNN '07 Proceedings of the 4th international symposium on Neural Networks: Part II--Advances in Neural Networks
Scaling analysis of a neocortex inspired cognitive model on the Cray XD1
The Journal of Supercomputing
A context switching streaming memory architecture to accelerate a neocortex model
Microprocessors & Microsystems
A binary self-organizing map and its FPGA implementation
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
Applied Intelligence
Associative learning in hierarchical self organizing learning arrays
ICANN'05 Proceedings of the 15th international conference on Artificial Neural Networks: biological Inspirations - Volume Part I
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A new machine learning concept-self-organizing learning array (SOLAR)-is presented. It is a sparsely connected, information theory-based learning machine, with a multilayer structure. It has reconfigurable processing units (neurons) and an evolvable system structure, which makes it an adaptive classification system for a variety of machine learning problems. Its multilayer structure can handle complex problems. Based on the entropy estimation, information theory-based learning is performed locally at each neuron. Neural parameters and connections that correspond to minimum entropy are adaptively set for each neuron. By choosing connections for each neuron, the system sets up its wiring and completes its self-organization. SOLAR classifies input data based on the weighted statistical information from all the neurons. The system classification ability has been simulated and experiments were conducted using test-bench data. Results show a very good performance compared to other classification methods. An important advantage of this structure is its scalability to a large system and ease of hardware implementation on regular arrays of cells.