multilayer feedforward 在 数学 分类中 的翻译结果

  本文关键词：一种多层前馈网络计量经济建模方法，由笔耕文化传播整理发布。

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    Sensitivity Analysis of Econometric Model Using Multilayer Feedforward Network

    基于多层前馈网络的计量经济模型敏感性分析方法

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    Multilayer feedforward network approach for econometric modeling

    一种多层前馈网络计量经济建模方法

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    Application of Multilayer Feedforward Neural Network in Customer Loss

    多层前馈神经网络在客户流失分析中的应用

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    Econometric modeling using multilayer feedforward networkmethod is employed to fit historical data to approximate the underlyingproduction function, solving the problem of selecting the form of productionfunction.

    其中,利用多层前馈网络计量经济建模方法,拟合历史数据,逼近潜在的生产函数,解决了如何选择生产函数形式的问题;

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    The optimal control problem for the FCC process is solved using the MFNN (multilayer feedforward neural network) for system recognition and modeling, periodogram analysis for model testing, and the advanced Frank Wolfe algorithm for steady state optimization computation. Steady stable state data was used from the production process at Dagang Oil Refinery Works to train and test the neural network. The results prove that the neural network is effective for the system recognition, modeling and stable state optimal control of complex non linear production processes.

    为解决催化裂化过程的优化控制问题 ,采用多层前馈神经网络进行辨识、建模 ,用周期图检验法对模型检验 ,用改进的 Frank- Wolfe算法进行稳态优化计算 ,并以大港炼油厂实际生产过程的稳态数据进行试验和验证 ,说明神经网络适合于解决非线性复杂生产过程的辨识、建模和稳态优化控制问题

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    Multilayer feedforward neural networks have been widely used in many applications, for which the back-propagation (BP) is the most popular training algorithm.

    多层感知器前传神经网络已广泛应用于很多领域,其中BP算法是应用最普遍的训练算法。

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    The input representation of multilayer feedforward neural networks is very important and has been thoroughly studied, while the output representation is hardly considered.

    输入表示对BP网络解决分类问题时的性能非常重要,很多研究者在这方面作了不少的工作,而对于输出表示的研究却很少。

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    The approximation ability of multilayer feedforward neural networks is studied. The problem of choosing input stimulating signals and the effect of increasing the number of hidden layers and the number of nets in each layer on the approximation precision of nonlinear functions by multilayer feed forward BP networks are discussed.

    对多层前向神经网络的函数逼近能力进行了研究，讨论了用多层前向BP网络来逼近非线性函数时，输入激励信号的选择和增加隐层层数和每层神经元个数对逼近精度的影响。

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    A fast learning algorithm for multilayer feedforward perception based on the Kalman filter theory is presented.

    提出基于卡尔曼滤波前向多层感知器快速学习算法，对此算法进行了详细的推证。

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    A systematic analysis is given to those main respects related to the global optimization of multilayer feedforward networks, some fundamental conditions which need be met by any algorithm of global optimization are presented, a practicable algorithm of global optimization is suggested and a theoretical proof of the reasonableness and convergence of the present algorithm is presented.

    在对多层前向网络全局最优化问题所涉及的几个主要方面进行深入剖析的基础上,给出了全局最优化算法应具备的基本条件和一种算法格式,对所给算法格式的收敛性做了理论证明。

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  multilayer feedforward

The simulation results have shown that multilayer feedforward neural network models with two hidden layers provide sufficiently accurate prediction of the concentration profile of the process.

Both the recall and the learning phases of the multilayer feedforward with backpropagation ANN model are considered.

This approach utilizes a multilayer feedforward neural network to compensate for model uncertainty associated with the robotic operation.

This approach utilizes a multilayer feedforward neural network to compensate for model uncertainty associated with the robotic operation.

The HSOM is shown to form arbitrarily complex clusters, in analogy with multilayer feedforward networks.

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The approximation ability of multilayer feedforward neural networks is studied. The problem of choosing input stimulating signals and the effect of increasing the number of hidden layers and the number of nets in each layer on the approximation precision of nonlinear functions by multilayer feed forward BP networks are discussed. Under constraints of limited hidden lapers and the number of nets in each layer the learning speed of neural networks and the approximation precsision are improved by using...

The approximation ability of multilayer feedforward neural networks is studied. The problem of choosing input stimulating signals and the effect of increasing the number of hidden layers and the number of nets in each layer on the approximation precision of nonlinear functions by multilayer feed forward BP networks are discussed. Under constraints of limited hidden lapers and the number of nets in each layer the learning speed of neural networks and the approximation precsision are improved by using the prior knowledge of approxunated functions and adding one layer before the hidden layers Simulation results are given to verify the above statements.

对多层前向神经网络的函数逼近能力进行了研究，讨论了用多层前向ＢＰ网络来逼近非线性函数时，输入激励信号的选择和增加隐层层数和每层神经元个数对逼近精度的影响。为了在隐层层数、每层神经元个数有限的情况下，加快网络学习速度，改善逼近效果，本文提出了利用对被逼近函数的先验知识，在隐层前加一函数层的思想，并通过仿真证明了其有效性。

There is a set of one-input nonlinear affine systems which can be linearized by state and input transformation of the form (x)and u=α(x)+β(x)v.In this paper, a multilayer feedforward neural network is used to realize. the state transformation and then the system is stabilized by the implementation of Liyapunov method. It is shown through simulation that this method is feasible.

在用状态方程描述的单输入仿射非线性系统中存在一类可以通过非线性状态变换（ｘ）和输入变换ｕ＝α（ｘ）＋β（ｘ）ｖ实现输入──状态线性化的系统，用多层前馈神经网络通过实时学习实现此状态变换，并在此基础上用李亚普诺夫方法设计进行系统镇定的反馈控制器，仿真表明，学习在很短时间内可收敛，且系统对外干扰和未建模动态有一定鲁棒性。

A New fast learning algorithm for training multilayer feedforward neural networks by using variable time--varying forgetting factor technique and U-D factorization based fading memory extended Kalman filter is proposed in this paper. In comparison with BP and ex tended Kalman filter (EKF ) based learning algorithm, the new algorithm can not only obviously improve the convergency rate,numerical stability. but also provide much more accurate learning results in fewer iterations with fewer hidden nodes. In...

A New fast learning algorithm for training multilayer feedforward neural networks by using variable time--varying forgetting factor technique and U-D factorization based fading memory extended Kalman filter is proposed in this paper. In comparison with BP and ex tended Kalman filter (EKF ) based learning algorithm, the new algorithm can not only obviously improve the convergency rate,numerical stability. but also provide much more accurate learning results in fewer iterations with fewer hidden nodes. In addition, it is less affected by the choice of initial weights and initial covariance matrix as well as other setup parameters. The results of simulated computations of nonlinear dynamic system modelling and identification applications show that the new algorithm proposed here is an effective and efficient learning algorithm for feedforward neural networks.

本文针对前馈神经网络ＢＰ算法所存在的收敛速度慢区常遇局部极小值等缺陷，提出一种基于Ｕ－Ｄ分解的渐消记忆推广卡尔曼滤波学习新算法．与ＢＰ和ＥＫＦ学习算法相比，，新算法不仅大大加快了学习收敛速度、数值稳定性好，而且需较少的学习次数和隐节点数即可达到更好的学习效果，对初始权值，初始方差阵等参数的选取不敏感，便于工程应用．非线性系统建模与辨识的仿真计算表明，该算法是提高网络学习速度、改善学习效果的一种非常有效的方法．

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  本文关键词：一种多层前馈网络计量经济建模方法，由笔耕文化传播整理发布。