A dynamic prediction method based on process neural networks is proposed for the process forecasting and prediction problem of dynamic system.
针对动态系统过程预测预报问题，提出了一种基于过程神经元网络的动态预测方法。
To solve the classification of dynamic signal, this paper proposed a feedback process neural networks model and classification methods based on this model.
针对动态信号模式分类问题，提出了一种反馈过程神经元网络模型和基于该模型的分类方法。
In this model, the continuous input-output mapping of the system is realized by nonlinear mapping capability to the time variable of process neural networks.
该模型利用过程神经元网络所具有的对时间变量的非线性映射能力，实现系统的输入、输出之间的连续映射关系。
The two different ways based on the time-domain feature expansion and the orthogonal decomposition feature expansion are used to establish process neural networks model.
采用基于时域特征扩展和基于正交分解特征扩展两种方式建立过程神经元网络模型，利用电力负荷数据进行网络训练和负荷预测。
In consideration of the complexity of the aggregation operation of time in process neural networks, a new learning algorithm based on function orthogonal basis expansion is proposed.
该文在考虑过程神经网络对时间聚合运算的复杂性的基础上，提出了一种基于函数正交基展开的学习算法。
The application shows that the algorithms simplify the computing complexity of process neural networks, and raise the efficiency of the network learning and the adaptability to real problem resolving.
应用表明，算法简化了过程神经网络的计算复杂度，提高了网络学习效率和对实际问题求解的适应性。
Then, the neural networks model is applied to identify and process input data, design suitable networks layer for the vehicle longitude dynamics system control.
利用神经网络模式识别能力对输入数据处理辨别，设计合适的控制网络层对汽车纵向动力学系统实施控制；
Against difficult convergence problem when the traditional neural networks are applied to complex process system, a hybrid expert neural networks model is brought forward.
针对传统神经网络用于复杂过程系统的控制时难于收敛的问题，文章提出了基于混合建模的模块化的神经网络模型。
The new development of application of Artificial Neural Networks on materials properties prediction and process parameters optimization is reviewed. The existing problems are indicated.
综述了人工神经网络在材料性能识别、预测及工艺优化方面应用的新进展，指出了存在的问题。
In essence, learning in artificial neural networks is an optimization process, that is, an artificial network adjusts the weights of the network on its concrete error information.
从本质上讲，人工神经网络的学习过程是一个优化的过程，即根据具体的误差信息来合理地选择网络的权重。
In order to achieve intelligent process state model output, the use of artificial neural networks for automatic reasoning capabilities.
为了实现过程状态模型的智能输出，运用人工神经网络实现自动推理的功能；
Logging curves and stratum parameters are changed to image pattern by image process technology. Neural networks are introduced to extract and remember pattern character of curves automatically.
利用图像处理技术将测井曲线和地质参数转化为图像模式，由神经网络自动提取和记忆曲线所表征的小层模式特征。
In this paper we propose an automatic sedimentary facies identifying method based on combing fuzzy ellipsoidal neural networks and image process technology.
提出了一种基于模糊超球神经网络聚类与图像处理技术相结合的沉积微相识别方法。
In this paper, the dynamic behaviors of continuous neural networks under structural variations in learning process are studied.
本文研究了连续神经网络在学习过程中结构摄动情况下网络的动态特性。
This paper puts forward a parameter self-tuning fuzzy controller based on two neural networks to actualize intelligent control of the activated sludge process.
本文提出一种基于两个神经元网络的模糊参数自校正控制器，实现活性污泥过程的智能控制。
To shorten the convergence time, the optimal search problem of disparity map is converted to an iterative convergence process of bi-valued neural networks.
为加快收敛速度，该算法将视差图的最优搜索问题转换为二值神经网络的迭代收敛过程。
Considering the complexity and the time variability of industrial process, an adaptive Supervised Distributed Neural Networks (SDNN) is proposed for modeling of industrial process.
针对工业生产过程的复杂性和时变性，提出一种用于工业生产过程建模的自适应监督式分布神经网络(SDNN)。
The method preprocesses power input of manual weld through rough neural networks, and judges lack of weld in a welding process. The system′s hardware is simple, cost is low and judgement is accurate.
该方法通过人工神经网络对采样的焊接热功输入进行处理，从而判断焊接过程是否漏焊。
Because of its unique ability to process information, the technology of neural networks is very appropriate for the computing of rolling force.
神经网络技术以其特有的信息处理能力，为轧制力的计算提供了一条很好的途径。
The high computational cost in the training process of neural networks is a major inconvenience.
神经网络训练过程中的高昂计算代价是有待克服的一个主要困难。
In this paper, a neural network controller is designed for ash content control of papermaking process, neural networks are trained by using the output error of the controlled plant directly.
对于非线性，大时延、变参数的造纸过程灰份含量的控制，本文给出了一种神经网络控制方法。
The constituting process and training method of the improved BP and RBF neural networks are put forward.
给出了改进的BP网络和RBF网络的构造过程和训练方法。
The engine vibration knowledge is automatically elicited from the training pattern sets in the off line learning process of neural networks.
神经网络的离线学习算法可以从训练样本中提取振动知识；
These methods however do not provide an effective feature extraction process, leading to difficulties and inefficiency in designing the neural networks.
但是，这些方法并未提出一个有效的特征抽取算法，导致神经网络的设计较困难、效率较低。
Neural networks technique is an important part of intelligent control theory. But, there are still many problems in the application of neural networks used for process identification and control.
神经网络技术是智能控制理论的重要组成部分，当前神经网络在系统辨识与控制中的应用还存在许多问题。
With parallel technology having become more matured, the neural networks' design process of hardware and software combination is getting more importance in these days.
随着并行技术的日益成熟，在并行集群上以软硬件相结合的方式设计神经网络的重要性也不断提高。
Artificial neural networks (ANN) can be used to simulate the visual thinking process of experts, and many of its advantages make itself have some natural connection and complementation with CBR.
人工神经网络(ANN)可以用来模仿专家的形象思维，它的许多优点使得CBR与ANN之间存在某种自然联系，在很多方面两者具有互补性。
Neural networks can implement fast parallel computation; meanwhile it has the characteristic of complex chaotic dynamical process, so it is one of the best choices to assist security chip design.
神经网络具备既能实现快速并行运算又有混沌动力学复杂行为的特征，是设计实现适用于实时安全通信应用的安全芯片最佳选择之一。
The ability of artificial neural networks to leam essential process of non linearities from plant data may provide a means by which to assist fermentation process in being controlled.
人工神经网络从植物数据中学习非线性基本过程的能力可有助于发酵过程的控制。本文旨在研究柠檬发酵过程，并建立了两个柠檬发酵过程的人工神经网络模型。
The ability of artificial neural networks to leam essential process of non linearities from plant data may provide a means by which to assist fermentation process in being controlled.
人工神经网络从植物数据中学习非线性基本过程的能力可有助于发酵过程的控制。本文旨在研究柠檬发酵过程，并建立了两个柠檬发酵过程的人工神经网络模型。
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