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    • 32. 发明授权
    • Neural network with weight adjustment based on prior history of input
signals
    • 基于输入信号先前历史的神经网络与重量调整
    • US5119469A
    • 1992-06-02
    • US448090
    • 1989-12-12
    • Daniel L. AlkonThomas P. VoglKim L. Blackwell
    • Daniel L. AlkonThomas P. VoglKim L. Blackwell
    • G06N3/04
    • G06N3/04
    • 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.
    • 动态稳定的关联学习神经网络系统包括多个突触和非线性函数电路,并且包括一个自适应加权电路,用于根据当前信号调整每个突触的重量,以及应用于输入的信号的先前历史 特定突触和当前信号以及施加到预定的一组其他附属突触的输入的信号的先前历史。 流过神经元电路实施例包括具有预定固定重量的流通突触。 使用上述类型的神经元电路形成神经网络。 一组流通神经元电路通过流通突触连接,以在每个输入端子和相应的输出端子之间形成单独的路径。 具有可调重量突触的其他神经元电路包括在网络内。 通过将可调节突触设置在接近最小重量的某个值来初始化该神经元网络。 通过将输入信号组连续地应用到输入端来教导神经网络,直到达到动态平衡。