The embedded training algorithm is highlighted in this part.
嵌入式训练算法是这部分的重点。
This initializing method can also be used as fast training algorithm for H-MLP network.
该初始化方法同时可作为H-MLP网络的一种快速训练算法。
A novel algebraic training algorithm for a three-layered MLP is proposed in this paper.
本文针对三层前馈网络提出一种全新的学习算法。
The authors introduce the operating principle, network structure and training algorithm.
介绍了工作原理，网络结构和训练算法。
A Complex-valued Functional Link Neural Network (CFLNN) with its training algorithm is proposed.
提出了一种复值函数型连接神经网络(CFLNN)结构及其学习算法。
This paper presents a training algorithm for probabilistic neural networks using the MCE criterion.
提出了一种基于最小分类错误准则的概率神经网络的训练算法。
A structure and training algorithm for quasi-diagonal recurrent neural network (QDRNN) is presented.
提出一种准对角递归神经网络(QDRNN)结构及学习算法。
The scale factor training algorithm has been brought forward to accelerate the training of BP network.
提出了比例系数BP网络训练算法，提高了网络的训练速度。
Now we introduce trajectory model in speaker recognition and improve the codebook training algorithm for VQ.
文章把轨线模型应用于说话人识别，同时对VQ模型的电码本训练算法进行了改进。
After analysis of em algorithm, we presented a new cooperative training algorithm based on incremental learning.
本文在分析了EM算法的基础上，提出了一种新的协同训练算法。
This paper presents an efficient training algorithm for probabilistic neural networks using the minimum classification error criterion.
提出了一种基于最小分类错误准则的概率神经网络的训练算法。
Secondly, the hybrid training algorithm is proposed on the base of the Error Back-propagation algorithm's disadvantage analysis in the paper.
其次，从误差反传算法在预测中存在的问题入手，提出一种混合训练算法。
Then an end-point forecast model was built based on the RBF neural network by analyzing the melting technology and the RBF training algorithm.
然后通过对真空感应冶炼工艺机理的深入分析以及对神经网络算法的研究，建立了基于RBF神经网络的终点预报模型。
A new training approach for the training algorithm of a fully connected recurrent neural network based on the digital filter theory is proposed.
一种新的基于数字滤波器理论的全互连复值递归神经网络训练方法被提出。
The structure of a partially connected feed forward neural network and the training algorithm based on the recursive prediction error are constructed.
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；
A maximum entropy model of feature extraction for the gas disaster information is established, with the training algorithm of the maximum entropy presented.
以最大熵原理为基础，建立了最大熵瓦斯灾害信息特征提取模型，提出了最大熵模型的参数训练算法。
Through analyzing CDHMM training algorithm, this system is implemented successfully in using high performance float-point DSP TMS320C6711 of TI incorporated.
在分析了CDHMM训练算法运算特性的基础上，采用TI高性能浮点DSP芯片TMS320C 6711成功实现了口令式语音的CDHMM的训练。
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.
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。
Adopts a classifier support vector machine which is a very good training algorithm, which can acquire very good generalization when the training datum are very few.
文中采用的分类器——支持向量机是一种能在训练样本数很少的情况下达到很好分类推广能力的学习算法。
Neural network BP training algorithm based on gradient descend technique may lead to entrapment in local optimum so that the network inaccurately classifies input patterns.
基于梯度下降的神经网络训练算法易于陷入局部最小，从而使网络不能对输入模式进行准确分类。
An information fusion model of integrated navigation system based on neurons is proposed, and also an on line adaptive training algorithm of the weights of neuron is given.
提出了一种基于神经元状态融合的组合导航系统信息融合模型，给出了神经元融合权重在线自适应学习算法。
And two kinds of training algorithm of equalizer in time domain which are used widely in multi-carrier modulation technique were compared in the underwater acoustic environment.
并对两种多载波调制技术中应用较为广泛的时域均衡器训练算法在水声环境下进行了比较。
In the training algorithm of the network an algorithm with adaptive step correction and momentum factor is put forward which obviously improves the convergence speed of training.
在网络训练算法中，提出一种自适应修正步长和矩量因子的算法，显著提高了训练的收敛速度。
Then it introduces the basic principle of BP neural network, topology structure, and the mapping relationship, analyses the training algorithm of BP neural network and its ideas.
然后介绍了BP神经网络的基本原理，拓扑结构和映射关系；分析了BP神经网络的训练算法及算法构成思想。
A scale training algorithm of BP neural network is used, and sample reorganization method is proposed. Its advantage is the fast training speed and good feature extraction ability.
作者使用比例训练的BP算法，提出对训练模式进行样本重组的方法，其特点是训练速度快、特征抽取能力强。
It is also indicated that current WNN has a poor convergence performance because of adopting the random initialization method and gradient training algorithm of traditional BP NET.
还指出由于当前的连续小波神经网络主要使用传统BP神经网络的随机初始化方法和基于梯度的训练算法，因此存在收敛性差的缺点。
To combine the attribute reduction algorithm and the incremental training algorithm of support vector machine, a support vector machine classifier based on rough set is constructed.
将属性约简算法和支持向量机增量训练算法相结合，构造基于粗糙集数据预处理的支持向量机分类器。
The simulation results show the presented quick training algorithm can speed up the learning process of MLP, and improve the learning properties on convergence and robust performance.
仿真结果表明，改进的BP算法可显著加速网络训练速度，学习过程具有较好的收敛性和较强的鲁棒性。
Compared to the standard Balm-Welch training algorithm, this algorithm has lower complexity while prediction performance is better than or at least comparable to other existing methods.
同标准训练算法相比，该算法具有时间复杂度低，预测精度高等优点。
Compared to the standard Balm-Welch training algorithm, this algorithm has lower complexity while prediction performance is better than or at least comparable to other existing methods.
同标准训练算法相比，该算法具有时间复杂度低，预测精度高等优点。
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