Data Flow Design for the Backpropagation Algorithm
@inproceedings{Liou2002DataFD, title={Data Flow Design for the Backpropagation Algorithm}, author={Cheng-Yuan Liou and Yen-Ting Kuo}, year={2002} }
We report a data flow [1] design for the multilayer network. Both back-propagation(BP) [2] learning and feedforward computing are constructed with a single basic module where each neuron is regarded as a module. This design can be extended to various networks [3][4] .
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References
SHOWING 1-8 OF 8 REFERENCES
Learning representations by back-propagating errors
- Computer ScienceNature
- 1986
Back-propagation repeatedly adjusts the weights of the connections in the network so as to minimize a measure of the difference between the actual output vector of the net and the desired output vector, which helps to represent important features of the task domain.
Neurons with graded response have collective computational properties like those of two-state neurons.
- BiologyProceedings of the National Academy of Sciences of the United States of America
- 1984
A model for a large network of "neurons" with a graded response (or sigmoid input-output relation) is studied and collective properties in very close correspondence with the earlier stochastic model based on McCulloch - Pitts neurons are studied.
Self-organized formation of topologically correct feature maps
- PhysicsBiological Cybernetics
- 2004
In a simple network of adaptive physical elements which receives signals from a primary event space, the signal representations are automatically mapped onto a set of output responses in such a way that the responses acquire the same topological order as that of the primary events.
Dataflow machine architecture
- Computer ScienceCSUR
- 1986
It appears that the overhead due to fine-grain parallelism can be made acceptable by sophisticated compiling and employing special hardware for the storage of data structures, and some of the objections raised against the dataflow approach are discussed.
Neural networks and physical systems with emergent collective computational abilities.
- Computer ScienceProceedings of the National Academy of Sciences of the United States of America
- 1982
A model of a system having a large number of simple equivalent components, based on aspects of neurobiology but readily adapted to integrated circuits, produces a content-addressable memory which correctly yields an entire memory from any subpart of sufficient size.
Lecture Notes on Neural Networks
- 2001
Committee Machines. Handbook for Neural Network Signal Processing, Yu Hen and Jenq-Neng
- 2001