Feedforward neural networks have been widely used in many applications.
前馈神经网络在许多领域有着广泛的应用。
Learning algorithm is the core of the subject of studying BP feedforward neural networks.
学习算法是BP前馈神经网络研究中的核心问题。
The feedforward neural networks has been greatly used in the nonlinear signals processing.
前馈神经网已经被大量用于非线性信号处理。
The convergence of the gradient algorithm for feedforward neural networks with one output unit has thoroughly studied.
对于单输出前馈神经网络的梯度算法的收敛性已经有了详细的讨论。
Diagnosis models based on multilayer feedforward neural networks are widely used in the field of machinery fault diagnosis.
基于多层前向网络的诊断模型在设备故障诊断领域应用比较广泛。
It has payed great attention to effective training of feedforward neural networks when they are used for pattern classification.
前向网络在用于模式分类时，其网络的有效训练一直是一个受到关注的问题。
Based on weights analysis of feedforward neural networks, a hierarchic decomposition neural networks method for solving this problem is provided.
基于前馈神经网络的权重分析，提出一种基于神经网络的结构优化层次分解方法，较好地解决了这一问题。
The algorithm improves the generalization performance of feedforward neural networks through combining the regularization and pruning technology.
正则最小二乘算法将正则化网络和节点删除算法结合起来，大大提高了前馈网络的泛化性能。
This paper proposes a new global optimization technique in which combines the filled function method and BP algorithm for Training feedforward neural networks.
把填充函数法与BP算法相结合，提出一种训练前向神经网络的混合型全局优化新算法。
In this paper we prove a finite convergence of online BP algorithms for nonlinear feedforward neural networks when the training patterns are linearly separable.
当训练样本线性可分时，本文证明前馈神经网络的在线BP算法是有限次收敛的。
This paper analyzes the basic principles of the harmonic detection method based on multilayer feedforward neural networks, which has been studied extensively at present.
本文分析了目前文献中研究较多的基于多层前馈网络的谐波电流检测方法的基本原理。
In this paper, multi-layer feedforward neural networks are used to solve the nonlinear partial differential equation, and approach the centre manifold of the nonlinear system.
本文用多层前向神经网络求解该非线性偏微分方程，从而逼近非线性系统的中心流形。
The research results show that, compared with standard feedforward neural networks, the ellipsoidal unit network is more reasonable and useful for fault diagnosis applications.
研究结果表明，与标准的前馈神经网络相比，是比较合理的和有用的故障诊断应用椭球单元网络。
Results the properties of higher order feedforward neural networks model were superior to those of the traditional BP model when applied in the assessment of regional environmental quality.
结果高阶前馈神经网络模型应用于区域环境质量评价时，其性能指标优于传统BP网络。
A learning algorithm based on a hard limiter for feedforward neural networks (NN) is presented, and is applied in solving classification problems on separable convex sets and disjoint sets.
提出了基于硬限幅功能函数的前向神经网络的分类学习算法，并将其应用于可分凸集或不交集合的分类。
To investigate generalization capability of feedforward neural networks, the influencing factors of generalization capability of feedforward neural networks are analyzed according to function theory.
针对前向神经网络泛化问题，从函数论的角度分析了影响前向神经网络泛化性能的因素。
Neural networks have several different topologies, but the simplest is known as a feedforward network.
神经网络有几种不同的拓扑结构，但是最简单的一种是前馈网络。
To summarize, two kinds of improved feedforward fuzzy neural networks have some values in theory and application and is worth further extending.
综上所述，两种改进的模糊神经网络都具有一定的理论价值和应用价值，值得进一步推广运用。
Then two improved feedforward fuzzy neural networks are presented and applied to resolving the practical proWem in petroleum industry.
然后提出了两种改进的前馈模糊神经网络，并应用于解决石油工业中的实际问题。
A new mutual genetic operator based three stages feedforward neural network training method is proposed in this paper, which divides neural networks training procedure into three stages.
论文提出了一种新的基于互补遗传算子的前馈神经网络三阶段学习方法。该方法把神经网络的学习过程分为三个阶段。
Not only does the two spiral problem give a challenge to the statistic methods again, but also brings a doubt about the abilities of the general feedforward multi layered LBF neural networks.
双螺旋问题不仅使统计方法受到挑战，更使人们对一般前向多层神经网络的能力提出疑问。
Not only does the two spiral problem give a challenge to the statistic methods again, but also brings a doubt about the abilities of the general feedforward multi layered LBF neural networks.
双螺旋问题不仅使统计方法受到挑战，更使人们对一般前向多层神经网络的能力提出疑问。
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