发明授权
- 专利标题: Dynamically stable associative learning neural system
- 专利标题(中): 动态稳定关联学习神经系统
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申请号: US80860申请日: 1993-06-22
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公开(公告)号: US5402522A公开(公告)日: 1995-03-28
- 发明人: Daniel L. Alkon , Thomas P. Vogl , Kim L. Blackwell
- 申请人: Daniel L. Alkon , Thomas P. Vogl , Kim L. Blackwell
- 申请人地址: DC Washington MI Ann Arbor
- 专利权人: The United States of America as represented by the Department of Health and Human Services,Environmental Research Institute of Michigan
- 当前专利权人: The United States of America as represented by the Department of Health and Human Services,Environmental Research Institute of Michigan
- 当前专利权人地址: DC Washington MI Ann Arbor
- 主分类号: G06N3/04
- IPC分类号: G06N3/04 ; G06F15/18
摘要:
A dynamically stable associative learning neural network system include a plurality of synapses (122,22-28), a non-linear function circuit (30) and an adaptive weight circuit (150) 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 synapses. An embodiment of a conditional-signal neuron circuit (100) receives input signals from conditional stimuli and an unconditional-signal neuron circuit (110) receives input signals from unconditional stimuli. A neural network (200) is formed by a set of conditional-signal and unconditional-signal neuron circuits connected by flow-through synapses to form separate paths between each input (215) and a corresponding output (245). In one embodiment, the neural network (200) is initialized by varying the weight of the input signals from conditional stimuli, until a dynamic equilibrium is reached.
公开/授权文献
- US6121808A DLL calibrated phase multiplexer and interpolator 公开/授权日:2000-09-19