Neural networks and fuzzy systems: a dynamical systems approach to machine intelligence
Neural networks and fuzzy systems: a dynamical systems approach to machine intelligence
A New Two-Level Associative Memory for Efficient Pattern Restoration
Neural Processing Letters
CITWORKSHOPS '08 Proceedings of the 2008 IEEE 8th International Conference on Computer and Information Technology Workshops
Commodity-grid based distributed pattern recognition framework
AusGrid '08 Proceedings of the sixth Australasian workshop on Grid computing and e-research - Volume 82
One shot associative memory method for distorted pattern recognition
AI'07 Proceedings of the 20th Australian joint conference on Advances in artificial intelligence
Morphological associative memories
IEEE Transactions on Neural Networks
A Hierarchical Graph Neuron Scheme for Real-Time Pattern Recognition
IEEE Transactions on Neural Networks
AI '09 Proceedings of the 22nd Australasian Joint Conference on Advances in Artificial Intelligence
Multi-wheel graph neuron: a distributed associative memory for structured P2P networks
Proceedings of the 11th International Conference on Information Integration and Web-based Applications & Services
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Pattern recognition involving large-scale associative memory applications, generally constitutes tightly coupled algorithms and requires substantial computational resources. Thus these schemes do not work well on large coarse grained systems such as computational grids and are invariably unsuited for fine grained environments such as wireless sensor networks (WSN). Distributed Hierarchical Graph Neuron (DHGN) is a single-cycle pattern recognising algorithm, which can be implemented from coarse to fine grained computational networks. In this paper we describe a two-level enhancement to DHGN, which enables it to act as a standard binary image recogniser. This paper demonstrates that our single-cycle learning approach can be successfully applied to denser patterns, such as black and white images. Additionally we are able to load-balance the pattern recognition processes, irrespective of the granularity of the underlying computational network.