Self-organization and associative memory: 3rd edition
Self-organization and associative memory: 3rd edition
Introduction to the theory of neural computation
Introduction to the theory of neural computation
An introduction to neural and electronic networks
Parallel digital implementations of neural networks
Parallel digital implementations of neural networks
Digital neural networks
Neural Information Processing and VLSI
Neural Information Processing and VLSI
DARPA Neural Network Stdy
Polymorphic computing paradigms realized for a fpd-based multicomputer
Polymorphic computing paradigms realized for a fpd-based multicomputer
Parallel Implementation of Self-Organizing Map on the Partial Tree Shape Neurocomputer
Neural Processing Letters
Parallel implementation of self-organizing maps
Self-Organizing neural networks
Learning and decision-making in the framework of fuzzy lattices
New learning paradigms in soft computing
Adaptive filtering with the self-organizing map: a performance comparison
Neural Networks - 2006 Special issue: Advances in self-organizing maps--WSOM'05
A binary self-organizing map and its FPGA implementation
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
Evolvable block-based neural network design for applications in dynamic environments
VLSI Design - Special issue on selected papers from the midwest symposium on circuits and systems
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This paper begins with an overview of several competitive learning algorithms in artificial neural networks, including self-organizing feature maps, focusing on properties of these algorithms important to hardware implementations. We then discuss previously reported digital implementations of these networks. Finally, we report a reconfigurable parallel neurocomputer architecture we have designed using digital signal processing chips and field-programmable gate array devices. Communications are based upon a broadcast network with FPGA-based message preprocessing and postprocessing. A small prototype of this system has been constructed and applied to competitive learning in self-organizing maps. This machine is able to model slowly-varying nonstationary data in real time.