提出了一种发动机多变量神经网络控制方法。
A multivariable neural network adaptive control is provided for a turbofan engine.
由于这些变量具有非线性时间序列数据，用人工神经网络(ANN)将使用反向传播算法作为学习算法。
Since these variables are characterized as nonlinearities time series data, Artificial Neural networks (ANN) will be employed using back propagation algorithm as learning algorithm.
本文根据变量泵的具体情况，为其设计了自适应的神经网络模糊控制器，实现了变量泵的智能控制。
This paper describes that the combination of neural networks and fuzzy controller make the designing an intelligent controller for variable piston pumps become feasible and realistic.
同时，应用神经网络的学习和记忆功能，对控制变量的隶属函数和控制规则进行优化，使控制方案更趋于合理。
At the same time, using the study and memory ability of neural networks, optimize subject function and control rules of control variables, which makes the control scheme more reasonable.
神经网络的输入变量为表征火焰图像的燃烧参数，本文给出了这些参数的定义即其求解算法。
The input variables of neural network are the combustion parameters that symbolize flame image. In this paper, we give out the definitions for these parameters and their solution method.
提出了一种基于混沌变量的多层模糊神经网络优化算法设计。
An optimization algorithm design based on chaotic variable is proposed for multilayer fuzzy neural network.
通过分析影响股价变动的各种因素，选取了两组变量作为神经网络的输入。
By analyzing the factors that affect stock values, two groups of variables are chosen as the input of the neural network.
该模型对预测财务困境的神经网络模型的输入变量进行了优化。
The model optimizes the input variables of neural networks for predicting financial distress.
应用神经网络技术可以较少的试验工作量，建立高精度的变量间非线性映射模型，取得了满意的效果。
Using the neural networks technique and small test work, a high precision model for the non linear reflection of variables has been established and achieved satisfactory results.
运用云神经网络学习变量间的云映射关系，从中生成云决策树。
It adopts cloud neural network to study the cloud mapping relationship between variables, so as to generate cloud decision tree.
用神经网络为非线性软测量建模，用于推断估计不可在线测量的变量。
Modeling for nonlinear soft measuring by means of neural network can be used to estimate variables that can not be measured on line.
基于解的充分必要条件，讨论了一类变量变分不等式的神经网络方法。
Neural networks for solving a class of variant variational inequalities are studied under the necessary and sufficient conditions of the solution.
基于混沌变量，提出一种神经网络自适应控制系统的优化设计方案。
In this paper, the optimization design for self-adaptive control system of feed-forward neural network is proposed based on chaotic variable.
神经网络技术以较少的实验工作量，建立高精度的变量间的非线性映射模型。
Neural network technique establishes the high accuracy variable's non-linear reflecting model with less experiment workload.
分析了神经网络导向控制器的设计方法，选择了神经网络导向控制器的输入、输出变量，并建立了神经网络导向控制器的结构。
The design method of the new controller was analyzed, the input and output variables of the neural network navigation controller were selected, the structure of the controller was also established.
本文提出了利用人工神经网络进行地质数据多变量分析的方法。
This paper proposes to use artificial neural network on multivariate analysis of geological data.
评价功能是基于决策者(专家)的知识和模糊神经网络实现的，适用于以语言型变量为主的系统的评价问题。
The evaluation system is realized based on the decisionmakers' knowledge and the fuzzy neural networks, and suitable for the problems in which most variables are linguistic variables.
该控制器利用神经网络在线学习具有多变量耦合、非线性及不确定性的复杂的焊接动态过程的控制规则，实现PID参数的自动整定。
The controller can learn, on line, the control rules of the complex dynamic process with multivariable coupling, nonlinear and uncertainty, so that the PID parameters are tuned automatically.
应用单层神经网络可以学习多变量模糊控制规则中的未知参数．还可由它来实现多变量模糊推理过程。
The parameters of me fuzzy control rules of me controller can be learned by the learning slgorithm of the neural netowrk. and the inference process can be realized by the network.
针对多变量非线性离散时间系统设计多模型神经网络解耦控制器。
A multiple models neural network decoupling controller is designed to control the multivariable nonlinear discrete time system.
采用径向基函数（RBF）神经网络进行多变量系统的建模研究。
The modelling problem of multivariable system using radial basis function (RBF) neural networks is studied.
针对可控受限多变量耦合系统，提出了一种基于对角递归神经网络(DRNN)整定的PID混合解耦控制。
According to the limited controllability of the multivariable coupling system, a PID self-tuning mixed decoupling control method based on DRNN is put forward.
文章从单变量模型、多变量模型、条件概率模型以及神经网络模型等最新模型介绍了国外对财务困境预测模型的研究；
The paper introduces the new studies on single-variable model, multi-variable model, conditioning probability model and nerve network model in foreign countries.
依据BP神经网络系统能够利用人工智能的方法，准确分析多变量非线性系统的特性，采用多层向前BP神经网络系统建立起了橡胶老化预报模型。
Based on characteristics of BP neural network which can precisely analyze multi-variable nonlinear systems using artificial intelligence, in this paper, we proposed model for predicting rubber aging.
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.
由于人工神经网络好的非线性的多变量校正特点，预测结果是准确的。
Owing to good nonlinear multivariate calibration nature of ANN, the predicted results was reliable.
针对现代带钢热连轧中活套系统的解耦控制问题，提出了基于神经网络简单自适应多变量解耦控制方法(NNMSAC)。
A neural network multivariable simple adaptive control (NNMSAC) is proposed, disposing of decoupling control with looper system in hot strip mill.
将此分析方法用于处理神经网络的输入变量，提取其主要成分，使结构大为简化。
Based on this method, the main components are gotten, and then simplified network structure is designed.
结果表明：对于矩形平面厅堂，选择少数与厅堂声级相关性高的几何、物理参量作为神经网络模型的输入变量，可以准确地预测厅堂声级。
It shows that the good agreements between measured and calculated results can be obtained if the basic parameters used as inputs to the first layer of the neutral network are reasonable.
针对复杂多变量系统难以建模的问题，采用多层局部回归神经网络离线建立其预测模型。
Aiming at the difficulties in modeling the complex MIMO system, the multilayer local recurrent neural network is used to build the predictive model of the process off-line.
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