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The main reason for using neural network in time series analysis and system identification is that a trained neural network has the capability to approximate a arbitrary bounded continuous function.

在时间序列分析和系统辨识中,利用神经网络的一个主要原因就是前向神经网络逼近任意连续有界函数的能力。

Through above study, the theoretical analysis of the inherent fault tolerance of feed-forward neural networks has been extended from hard-limit activation functions to differentiable activation functions; 2. By using above method, fault tolerance of discrete Hopfield feedback neural networks have been also analyzed. It means that the above method can be applied not only for feed-forward neural networks but also for feedback neural networks; 3. For the feed-forward neural network with the Sigmoid activation function, Chebyshev Inequality method was presented.

这项研究将以前仅限于对硬限幅作用函数前向神经网络固有容错性能的理论分析推广到具有可微作用函数的前向神经网络; 2 针对离散型Hopfield反馈神经网络,利用上面提出的方法,对其同样进行了容错性分析,得出许多有用的结论和计算公式,说明上面提出的方法不仅适用于前向神经网络,同时也适用于反馈神经网络; 3 针对具有Sigmoid作用函数的前向神经网络提出了一种切比雪夫不等式法。

Using BP algorithm of feed-forward neural network in fuzzy neural network, the small disjunction samples are further processed.

采用模糊神经网络中的前向神经网络BP算法对小分裂样本进行进一步处理。

The stability of the network is given by the state equation of the network.

根据动态前向神经网络的状态方程,本文给出该神经网络的稳定条件。

The method of multiple models approximation using self-organizing map neural network is analyzed. The mechanism of this method is different to that of approximation method using other kinds of feedforward neural networks.

针对基于自组织映射神经网络的非线性函数逼近,研究其方法和原理,指出它与一般前向神经网络在逼近原理上的不同。

Feedforward Multilayer Neural Network has been extensively studied due to its importance and wide application.

摘要作为神经网络重要模型之一的前向神经网络一直是人们研究的热点。

We use the filtered variables to build Ridge estimation equation, and calculate the multiple correlation coefficient, at the same time passed the correlation test of the Ridge estimation equation; we built forward neural network model based on SCG method and momentum method under the MATLAB 6.5 situation, when estimating the fuel load of the sampled plots, the average precision of the artificial neural network model is 89.51%.

利用筛选出的自变量构建岭估计方程,并通过了岭估计方程的相关检验;在MATLAB6.5环境下构建基于SCG法和动量法的前向神经网络模型,在对所设置实验样地可燃物估测中人工神经网络模型估测的平均精度为89.5%。

BP neural network based on adaptive UKF is introduced in this paper for the standard BP neural network has slow converges,local minimum value and weak generalization ability.It can train weight of BP and improve the efficiency of BP without linearization by using the frame of Kalman filters and adaptive factor to adjust the variance of dynamic model.

1引言1986年,Rum elhart和M cC lelland等人在多层前向神经网络模型的基础上,提出了误差反向传播学习算法(BackPropagation简写BP),该算法是目前在神经网络中应用最广泛的算法,它解决了多层前向神经网络的学习问题[1]。

Before transecting inject 1ml Sodium Chloride in the epineurium. Annular tensile the proximal of epineurium with microscopic forceps annular shear the epineurium 7mm before broken ends with microscopic shear and stretch the epineurium until the two ends distance is 5mm.Use 8-0 suture line to bridge the nerve gap and make a 5 mm nerve tube at the proximal nerve end. Interrupted suture the epineurium tube and the distal nerve end make a 5 mm nerve mamuro for the injury nerve.Results: Postperative observation lasted for 6 weeks regeneration nerve through the 5 mm nerve mamuro.

在大鼠的坐骨神经距股方肌下缘15mm处,用1ml注射器向神经外膜下注入1ml生理盐水,然后于此处切断坐骨神经,用显微镊环形拉伸近端神经外膜,在距断端约7mm处用显微剪环形剪开神经外膜,并继续拉伸外膜使外膜两断端间距约5mm停止,用8-0的无损伤缝线桥接神经外膜两端,在神经近端形成约5mm的神经外膜管,将神经外膜管与神经远端间断缝合,使断伤的神经束间留有5mm的小间室。

Then the self-recurrent neural network structure, one kind of simplified structure of internal recurrent network, and the leaming algorithm, dynamic backpropogation algorithm of self-recurrent neural network, is proposed.

本文将神经网络作为传统的线性模型的非线性推广进行了分析,证实了多层前向神经网络与非线性自回归模型的等价意义,及带有反馈的循环神经网络与非线性自回归移动平均模型的等价意义。

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