A hypercube compact neural network
A major problem facing implementation of neural networks is the connection problem. One popular tradeoff is to remove connections. Random disconnection severely degrades the capabilities. The hypercube based Compact Neural Network (CNN) has structured architecture combined with a rearrangement of the memory vectors gives a larger input space and better degradation than a cost equivalent network with more connections. The CNNs are based on a Hopfield network. The changes from the Hopfield net include states of -1 and +1 and when a node was evaluated to 0, it was not biased either positive or negative, instead it resumed its previous state. L = PEs, N = memories and t/sub ij/s is the weights between i and j.
- Research Organization:
- Dept. of Electrical Engineering, Univ. of Washington, Seattle, WA (US)
- OSTI ID:
- 6024078
- Report Number(s):
- CONF-8809132-
- Journal Information:
- Neural Networks; (United States), Journal Name: Neural Networks; (United States) Vol. 1:1; ISSN NNETE
- Country of Publication:
- United States
- Language:
- English
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990210* -- Supercomputers-- (1987-1989)
ALGORITHMS
COMPUTER ARCHITECTURE
COMPUTER NETWORKS
COMPUTERIZED SIMULATION
COMPUTERS
ELECTRONIC CIRCUITS
ELEMENTS
HYPERCUBE COMPUTERS
IMPLEMENTATION
INTEGRATED CIRCUITS
MATHEMATICAL LOGIC
MEMORY DEVICES
MICROELECTRONIC CIRCUITS
PERFORMANCE
PROGRAMMING
SEMIMETALS
SILICON
SIMULATION
VECTOR PROCESSING