自适应性，人工神经网络可以通过训练适应各种情况。把目标输入与目标输出样

自适应性，人工神经网络可以通过训练适应各种情况。把目标输入与目标输出样本输入到网络中，网络根据预定的学习算法，调整网络各层神经元之间的连接权值，这就通过神经元之间的连接权值的形式把输入输出数据的基本信息存储在网络中，直至网络达到稳定状态。（5）综合推理能力，一个已经训练好的网络，当有新的输入数据时，网络可以识别新信息，并根据己有的知识对信息进行分类，从而输出正确的结果。所以利用神经网络进行备件需求预测有着得天独厚的优势

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源语言: -

目标语言: -

结果 (英语) 1: [复制]

复制成功！

Adaptive, artificial neural network can adapt to various situations through training. Input the target input and target output samples into the network, and the network adjusts the connection weights between the neurons in each layer of the network according to the predetermined learning algorithm, which converts the input and output data in the form of the connection weights between the neurons Basic information is stored in the network until the network reaches a stable state. (5) Comprehensive reasoning ability, a trained network, when there is new input data, the network can identify new information, and classify the information according to its own knowledge, so as to output correct results. Therefore, the use of neural network for spare parts demand forecasting has a unique advantage

正在翻译中..

结果 (英语) 2:[复制]

复制成功！

Self-adaptable, artificial neural networks can be trained to adapt to a variety of situations. The target input and target output samples are entered into the network, and according to the predetermined learning algorithm, the network adjusts the connection weights between the neurons in each layer of the network, which stores the basic information of the input and output data in the network in the form of the connection weight between neurons until the network reaches a stable state. (5) Comprehensive reasoning ability, a well-trained network, when there is new input data, the network can identify new information, and according to their own knowledge to classify the information, so as to output the correct results. Therefore, the use of neural networks for spare parts demand prediction has a unique advantage.

正在翻译中..

结果 (英语) 3:[复制]

复制成功！

Adaptive, artificial neural network can adapt to various situations through training. The target input and output samples are input into the network. According to the predetermined learning algorithm, the network adjusts the connection weights between the neurons in each layer of the network, which stores the basic information of the input and output data in the network in the form of the connection weight between the neurons, until the network reaches a stable state. (5) When there are new input data, the network can recognize the new information and classify the information according to the existing knowledge, so as to output the correct results. Therefore, using neural network to forecast the demand of spare parts has a unique advantage

正在翻译中..

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