前馈神经网络在许多领域有着广泛的应用。
Feedforward neural networks have been widely used in many applications.
基于前馈神经网络的自适应回声消除方法。
A novel adaptive method based on feed-forward neural networks for echo cancellation is proposed in this paper.
本文提出一种前馈神经网络的快速学习算法。
In this paper, a novel fast learning algorithm for multilayered feedforward neural network is introduced.
并将该算法用来优化前馈神经网络的连接权值。
Applied to the problem of optimizing the connection weights of the feed-forward neural networks, the algorithm was feasible.
学习算法是BP前馈神经网络研究中的核心问题。
Learning algorithm is the core of the subject of studying BP feedforward neural networks.
将这些壳系数输入前馈神经网络簇，以识别该手写体数字。
The shell coefficients are used as features to input into a feed-forward neural network to recognize the handwritten numerals.
提出了一种利用多层前馈神经网络生成纹理图象的新方法。
In this paper, a new method to generate the texture image by use of Multi layer feed forward neural network is presented.
多层前馈神经网络是在实践中应用最为广泛的一种神经网络。
Multilayer feedforward neural network is the most popular one in practice.
此目标函数的优化速度快，大大提高了前馈神经网络的学习效率。
The target function's optimal velocity is high, so it can boost learning efficiency of feed forward neural networks.
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。
The convergence of the gradient algorithm for feedforward neural networks with one output unit has thoroughly studied.
采用混沌优化策略，提出一种前馈神经网络权参数的最优学习方案。
In this paper, the optimization design for feed-forward neural network is proposed based on chaos optimization.
采用遗传算法训练的多层前馈神经网络实现番茄成熟度的自动判别。
Automated identification of tomato maturation using multilayer feedforward neural network with GA can be realized.
提出了一种新的前馈神经网络(N - FNN)复值盲均衡算法。
In this paper, a new complex-valued blind equalization algorithm based on the feedforward neural network (N-FNN) is proposed.
本文提出了一个适用于统计模式识别任务的阶层式前馈神经网络模型。
A hierarchical feedforward neural network model particularly suited to practical statistical pattern recognition tasks is proposed in this paper.
然后研究了交流永磁同步电机控制系统及其基于前馈神经网络的系统辨识。
Then the PMSM control system and its identification based on neural network are researched.
本文采用多层前馈神经网络对遥感图像进行压缩，给出了具体的压缩算法。
This paper compresses remote sensing images with multilayer feedforward neural network and gives the compression algorithm in detail.
构造了局部连接的前馈神经网络的结构和基于递推预报误差的网络训练算法；
The structure of a partially connected feed forward neural network and the training algorithm based on the recursive prediction error are constructed.
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。
In this paper we prove a finite convergence of online BP algorithms for nonlinear feedforward neural networks when the training patterns are linearly separable.
给出一种基于多层前馈神经网络的中文文本分类模型，介绍了该模型的设计和实现。
This paper presents a text categorization model based on multilayered feedforward neutral network, and introduces the design and implementation of this model.
该文利用凸优化理论和约束优化理论为前馈神经网络构造出了一个新的优化目标函数。
The paper constructs a new optimal target function for feed forward neural networks according to convex optimization theory and constraint optimization theory.
采用平衡的倒摆小车所记录下来的数据，经处理后用有师学习方法来训练前馈神经网络。
Presents a method of training a feedforward neural network using supervised learning scheme to balance an inverted pendulum and cart system.
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。
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.
然后介绍了如何使用模糊聚类算法和等价的前馈神经网络从样本数据中辨识离散的TS模型。
Then we introduce how to identify the TS model from sample data using fuzzy clustering algorithm and equivalent feedforward neural network.
BP算法是前馈神经网络训练中应用最多的算法，但其具有收敛慢和陷入局部极值的严重缺点。
BP algorithm is the most popular training algorithm for feed forward neural network learning. But falling into local minimum and slow convergence are its drawbacks.
然后把分解后的图像作为多层前馈神经网络的输入节点，对自动喷漆线上的汽车车型进行识别。
The decomposed images are then used as the nodes on input-layer of multi-layer feed-forward network, which recognize the vehicle model on painting line.
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。
The research results show that, compared with standard feedforward neural networks, the ellipsoidal unit network is more reasonable and useful for fault diagnosis applications.
仿真结果表明，与一般BP网络相比较，分支前馈神经网络显著地减少了训练时间，且分类效果更好。
The simulation results show that the training time of Branched Feedforward Neural Network is obviously reduced and the classifying effect is much better as compared with general BP Network.
本文应用多层前馈神经网络和自组织特征映射神经网络分别对简单目标和复杂飞机目标进行了分类识别。
The classification of simple and complex objects is investigated using the multiple layer forward neural network and the self-organizing feature map network.
本文应用多层前馈神经网络和自组织特征映射神经网络分别对简单目标和复杂飞机目标进行了分类识别。
The classification of simple and complex objects is investigated using the multiple layer forward neural network and the self-organizing feature map network.
应用推荐