将人工神经网络的非线性特性和信息的分布性用于煤低温自燃实验炉的模型辩识。
The nonlinearity and distribution of information processing of the artificial neural network are utilized in the indentification of model of coal spontaneous combustion stove at low temperature.
其主要特点是能够提供一个跟踪网络来辩识系统模型，进而确定控制器的网络参数，实现间接自适应神经网络控制。
Its major feature is that it can provide a tracing network to identify system model so as to determine the network parameters of the controller and realize an indirect adaptive neural network control.
细胞神经网络由于其连续时间的特性，因此在图象处理和图形辩识方面有着潜在的应用。
Cellula Neural Network has potential applications in image processing and recognizing because of its property of continuous time.
采用新型对角回归神经网络来辩识系统模型，可对PID控制器参数进行整定，实现多变量解耦控制。
Adopting new type diagonal regression neural network to identify the system model, the parameters of PID controller have been set, and the multi -variable decoupling control being realized.
没能辩识指定的主机名，网络失败。
基于反问题中的模型参数辩识理论，通过人工神经网络，建立了考虑应力路径影响的粘土的神经网络本构模型。
Based on the model parameter identification theory in inverse problem, a neural network model for constitutive law of clay under multiple stress paths is set up through artificial neural network.
本文利用递归神经网络来建立异步电机转速辩识模型，其网络学习采用实时递归学习算法。
This paper presents a model for identifying induction motor speed using the recurrent neural network, which is trained by a real time recurrent learning algorithm.
该方法采用一个三层BP网络辩识交流调速系统的特性，用另一个CMAC神经网络作为自适应控制器。
By using BP neural networks to oltain the model of AC speed adjustable system; another CMAC is used for adaptive controller.
在力控制环建立了基于BP网络的自适应pid控制器，该网络可辩识所接触环境的动力学特性。
An adaptive PID controller Which utilizes a BP network is also devised in the force control loop. This network can identify the dynamics of the contacting environment.
这就使网络辩识结果不再受输入信号时延的影响。
Therefore the identification results can not be influenced by the input signal time-delay.
网络危机信息具有隐蔽程度高、获取渠道分散、积聚性、难辩识等特点，直接影响了企业危机预警的效果与危机决策。
The network crisis information have the characteristics of high degree of hiding, dispersion source, gathering and difficult to identify.
该方法基于均匀设计获得的样本进行神经网络学习，用模式–遗传–神经网络进行岩体流变参数的最优辩识。
The samples produced by uniform design are used to train NN whose architecture is determined in global optimization by pattern-genetic algorithm(PGA).
该方法基于均匀设计获得的样本进行神经网络学习，用模式–遗传–神经网络进行岩体流变参数的最优辩识。
The samples produced by uniform design are used to train NN whose architecture is determined in global optimization by pattern-genetic algorithm(PGA).
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