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公开(公告)号：US5402522A
公开(公告)日：1995-03-28
申请号：US80860
申请日：1993-06-22
CPC分类号： G06N3/04
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.
摘要翻译： 动态稳定的关联学习神经网络系统包括多个突触（122,22-28），非线性函数电路（30）和自适应加权电路（150），用于根据本信号调整每个突触的重量 以及施加到特定突触的输入的信号的先前历史和当前信号以及施加到预定的其它突触组的输入的信号的先前历史。 条件信号神经元电路（100）的实施例从条件刺激接收输入信号，无条件信号神经元电路（110）从无条件刺激接收输入信号。 神经网络（200）由一组通过流通突触连接的条件信号和无条件信号神经元电路形成，以在每个输入（215）和相应的输出（245）之间形成分离的路径。 在一个实施例中，通过改变来自条件刺激的输入信号的权重来初始化神经网络（200），直到达到动态平衡。
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2.发明授权Dynamically stable associative learning neural system with one fixed weight 失效动态稳定关联学习神经系统具有一个固定的权重
公开(公告)号：US5222195A
公开(公告)日：1993-06-22
申请号：US864337
申请日：1992-04-06
IPC分类号： G06N3/04
CPC分类号： G06N3/04
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 collateral synapses. A flow-through neuron circuit (1110) embodiment includes a flow-through synapse (122) having a predetermined fixed weight. A neural network is formed by a set of flow-through neuron circuits connected by flow-through synapses to form separate paths between each input (215) and a corresponding output (245). In one embodiment (200), the neuron network is initialized by setting the adjustable synapses at some value near the minimum weight and setting the flow-through neuron circuits at some arbitrarily high weight. The neural network embodiments are taught by successively application of sets of inputs signals to the input terminals until a dynamic equilibrium is reached.
摘要翻译： 动态稳定的关联学习神经网络系统包括多个突触（122,22-28），非线性函数电路（30）和自适应加权电路（150），用于根据本信号调整每个突触的重量 以及施加到特定突触的输入和当前信号的信号的先前历史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（1110）实施例包括具有预定固定重量的流通突触（122）。 神经网络由一组通过流通突触连接的流通神经元电路形成，以在每个输入（215）和相应的输出（245）之间形成分离的路径。 在一个实施例（200）中，通过将可调节的突触设置在接近最小权重的某个值处来初始化神经元网络，并且将流通神经元电路设置在某种任意高的权重。 神经网络实施例通过将输入信号组连续地应用于输入端来教导，直到达到动态平衡。
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公开(公告)号：US5870493A
公开(公告)日：1999-02-09
申请号：US819142
申请日：1997-03-17
CPC分类号： G06K9/4628
摘要： An image recognition and classification system includes a preprocessor in which a "top-down" method is used to extract features from an image; an associative learning neural network system, which groups the features into patterns and classifies the patterns: and a feedback mechanism which improves system performance by tuning preprocessor scale, feature detection, and feature selection.
摘要翻译： 图像识别和分类系统包括其中使用“自顶向下”方法从图像中提取特征的预处理器; 一种联想学习神经网络系统，将特征组合成模式并对模式进行分类：通过调整预处理器规模，特征检测和特征选择来提高系统性能的反馈机制。
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公开(公告)号：US5822742A
公开(公告)日：1998-10-13
申请号：US331554
申请日：1995-02-24
CPC分类号： G06N3/04 , G06K9/4628 , G06K9/627 , G06K2209/01
摘要： A dynamically stable associative learning neural system includes a plurality of neural network architectural units. A neural network architectural unit has as input both condition stimuli and unconditioned stimulus, an output neuron for accepting the input, and patch elements interposed between each input and the output neuron. The patches in the architectural unit can be modified and added. A neural network can be formed from a single unit, a layer of units, or multiple layers of units.
摘要翻译： PCT No.PCT / US93 / 04364 Sec。 371日期：1995年2月24日 102（e）1995年2月24日PCT PCT 1993年5月13日PCT公布。 第WO93 / 23822号公报 日期：1993年11月25日动态稳定的关联学习神经系统包括多个神经网络建筑单元。 神经网络架构单元具有条件刺激和无条件刺激的输入，用于接受输入的输出神经元和插入在每个输入和输出神经元之间的补片元件。 可以修改和添加建筑单元中的修补程序。 神经网络可以由单个单元，单元层或多层单元形成。
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公开(公告)号：US5588091A
公开(公告)日：1996-12-24
申请号：US400124
申请日：1995-03-02
CPC分类号： G06K9/4628 , G06K9/627 , G06N3/04
摘要： A dynamically stable associative learning neural network system includes, in its basic architectural unit, at least one each of a conditioned signal input, an unconditioned signal input and an output. Interposed between input and output elements are "patches," or storage areas of dynamic interaction between conditioned and unconditioned signals which process information to achieve associative learning locally under rules designed for application-related goals of the system. Patches may be fixed or variable in size. Adjustments to a patch radius may be by "pruning" or "budding." The neural network is taught by successive application of training sets of input signals to the input terminals until a dynamic equilibrium is reached. Enhancements and expansions of the basic unit result in multilayered (multi-subnetworked) systems having increased capabilities for complex pattern classification and feature recognition.
摘要翻译： 动态稳定的关联学习神经网络系统在其基本架构单元中包括调节信号输入，无条件信号输入和输出中的至少一个。 在输入和输出元件之间插入“补丁”或在条件和非条件信号之间的动态交互的存储区域，其处理信息以在本地针对系统的应用相关目标设计的规则下本地实现关联学习。 补丁可能是固定的或可变的大小。 补丁半径的调整可能是“修剪”或“萌芽”。 通过将输入信号的训练集合连续应用到输入端子来教导神经网络，直到达到动态平衡。 基本单元的增强和扩展导致具有增加的复杂图案分类和特征识别能力的多层（多子网络）系统。
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6.发明授权Neural network with weight adjustment based on prior history of input signals 失效基于输入信号先前历史的神经网络与重量调整
公开(公告)号：US5119469A
公开(公告)日：1992-06-02
申请号：US448090
申请日：1989-12-12
IPC分类号： G06N3/04
CPC分类号： G06N3/04
摘要： 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.
摘要翻译： 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。
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