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.
提出了一种新的基于壳向量的增量式支持向量机快速学习算法。
A new geometric fast incremental learning algorithm for support vector machines (SVM) was proposed.
目前SVM的研究热点主要有：svm的模型选择、快速学习算法研究等。
At present, there are some hot topics in SVM researches, for example, model selection, fast learning algorithms et al.
针对BP网络学习速度的缓慢性，本文提出了一种联合优化后的快速学习算法。
To accelerate the training speed of BP network, a joint-optimized fast BP learning algorithm is proposed.
本文介绍了一种具有快速学习算法、能够执行补偿模糊推理的补偿模糊神经网络。
The compensation fuzzy neural network (CFNN) with fast learning algorithm and compensation fuzzy inference is introduced in this paper.
神经网络的辨识采用变尺度二阶快速学习算法，利用二阶插值法来优化搜索学习速率。
In this paper, a kind of variable metric fast second order nonlinear optimization algorithm is proposed, where a second order interpolating method is used in the optimization of learning rate.
通过补偿模糊推理和快速学习算法的引入，使得补偿模糊神经网络在性能上优于一般的模糊神经网络。
Through the introduction of compensatory fuzzy inference and quick arithmetic, the property of compensatory fuzzy neural networks is superior to that of common fuzzy neutral networks.
因此，作者将非线性规划的平行切线算法用于神经网络的权值学习，提出了一种具有快速学习算法的神经网络。
This paper applies parallel tangents of nonlinear programming during the weights training of neural network and puts forward a neural network in view of fast learning algorithm.
针对交通流量混沌时间序列多步预测的问题，提出了一种基于混沌机理的小波神经网络(WNN)快速学习算法。
Aiming at the issue about multi-step prediction of the traffic flow chaotic time series, a fast learning algorithm of wavelet neural network (WNN) based on chaotic mechanism is proposed.
实现了一种新的基于遗传算法和梯度下降方法的快速模糊系统学习算法。
Moreover, a new fast learning method of fuzzy systems both based on genetic algorithms and gradient method is proposed.
设计有效的学习算法快速准确地对脑电信号进行连续预测是脑机接口研究的关键之一。
To develop effective learning algorithms for fast and accurate continuous prediction using Electroencephalogram (EEG) signal is a key issue in BrainComputer Interface (BCI).
本文提出了一种新的前向神经网络快速分层学习算法。
A new rapid layer-wise learning algorithm for feed-forward neural networks is proposed in this paper.
通过分析网络误差曲面特征，提出了快速BP算法，它可以大幅度地提高学习速度。
Based on analysing characteristics of error curved surface of the network, the authors have advanced the rapid BP algorithm, which can greatly raise the learning speed.
提出了一种快速、增量式的学习算法。
结果表明该算法能够对动态非线性系统的输入输出快速学习和建模，优于其它小波网络的学习算法。
The results show that this algorithm can model input and output learning kernel of dynamic nonlinear system quickly, which is superior to other learning methods of wavelet network.
模糊神经网络的学习算法采用的是快速的粒子群优化算法。
A fast stochastic global optimization algorithm, particle group optimization algorithm, was used for training the fuzzy neural network.
由于评价人工神经网络最终学习效果是通过累积误差来进行的，从而我们直接瞄准累积误差来研究多层人工神经网络快速学习的算法。
Since we value the learning effect of neural networks by cumulative error, the paper pay direct attention to it to study the BP algorithm.
经过对实际算例的模拟，结果表明本文给出的快速BP算法是一种适用于多层神经网络的、性能忧良的学习算法。
The examples show that the Quick BP Algorithm has good capability and is applicable to the multilayer neural network.
经过对实际算例的模拟，结果表明本文给出的快速BP算法是一种适用于多层神经网络的、性能忧良的学习算法。
The examples show that the Quick BP Algorithm has good capability and is applicable to the multilayer neural network.
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