网络中各个神经元接受前一级的输入，并输出到下一级，网络中没有反馈，可以

网络中各个神经元接受前一级的输入，并输出到下一级，网络中没有反馈，可以用一个有向无环路图表示。这种网络实现信号从输入空间到输出空间的变换，它的信息处理能力来自于简单非线性函数的多次复合。网络结构简单，易于实现。BP网络是一种典型的前馈网络。（2）反馈网络：网络内神经元间有反馈，可以用一个无向的完备图表示。这种神经网络的信息处理是状态的变换，可以用动力学系统理论处理。系统的稳定性与联想记忆功能有密切关系。Hopfield网络、波耳兹曼机均属于这种类型

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源语言: -

目标语言: -

结果 (英语) 1: [复制]

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Each neuron in the network accepts the input of the previous stage and outputs to the next stage. There is no feedback in the network and can be represented by a directed acyclic graph. This kind of network realizes the transformation of the signal from the input space to the output space, and its information processing ability comes from the multiple recombination of simple nonlinear functions. The network structure is simple and easy to implement. BP network is a typical feedforward network. <br>(2) Feedback network: There is feedback between neurons in the network, which can be represented by an undirected complete graph. The information processing of this kind of neural network is the transformation of state, which can be processed by dynamic system theory. The stability of the system is closely related to the associative memory function. Hopfield network and Boltzmann machine belong to this type

正在翻译中..

结果 (英语) 2:[复制]

复制成功！

Each neuron in the network receives the input from the previous level and outputs to the next level, and there is no feedback in the network, which can be represented by a loopless graph. This network realizes the transformation of signal from input space to output space, and its information processing ability comes from the compounding of simple nonlinear functions. The network structure is simple and easy to implement. The BP network is a typical feed-forward network.<br>(2) Feedback network: there is feedback between neurons within the network, can be represented by an undirectional complete diagram. This kind of neural network information processing is the transformation of state, which can be processed by dynamic system theory. The stability of the system is closely related to the associative memory function. Hopfield networks and Boltzmann machines fall into this category.

正在翻译中..

结果 (英语) 3:[复制]

复制成功！

Each neuron in the network receives the input from the previous stage and outputs to the next level. There is no feedback in the network and can be represented by a directed acyclic graph. This kind of network realizes the transformation of signal from input space to output space, and its information processing ability comes from multiple compounding of simple nonlinear functions. The network structure is simple and easy to implement. BP network is a typical feedforward network.<br>(2) Feedback network: there is feedback between neurons in the network, which can be represented by an undirected complete graph. The information processing of this neural network is a state transformation, which can be dealt with by the theory of dynamic system. The stability of the system is closely related to the function of associative memory. Hopfield network and Boltzmann machine belong to this type<br>

正在翻译中..

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