Neural network with weight adjustment based on prior history of input signals
US5119469A · kind A · utility
Assignees
Inventors
Key dates
| Filing date | Dec 12, 1989 |
| Grant date | Jun 2, 1992 |
| Priority date | — |
| Expiry date | Dec 12, 2009 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06N3/0499
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
A dynamically stable associative learning neural network system include a plurality of synapses and a non-linear function circuit and includes an adaptive weight circuit for adjusting the weight of each synapse based upon the present signal and the prior history of signals applied to the input of the particular synapse and the present signal and the prior history of signals applied to the input of a predetermined set of other collateral synapses. A flow-through neuron circuit embodiment includes a flow-through synapse having a predetermined fixed weight. A neural network is formed employing neuron circuits of both the above types. A set of flow-through neuron circuits are connected by flow-through synapses to form separate paths between each input terminal and a corresponding output terminal. Other neuron circuits having only adjustable weight synapses are included within the network. This neuron network is initialized by setting the adjustable synapses at some value near the minimum weight. The neural network is taught by successively application of sets of inputs signals to the input terminals until a dynamic equilibrium is reached.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.