This paper suggests a new structure-learning algorithm called TANC-CBIC, makes experiment in MBNC experiment platform with programming TANC-CBIC algorithm.
文中提出了一种新的结构学习TANC - CBIC算法。并在贝叶斯分类器实验平台MBNC上编程实现。
This system, possesses the structure of neural net and learning algorithm, addressed as fuzzy neural net FNN.
该系统具有神经网络的结构和学习算法，称模糊神经网络FNN。
The topologic structure and learning algorithm of the rough neural network are given, and the approximation theorem of the rough neural network is presented.
给出了粗糙神经网络的拓朴结构和学习算法以及粗糙神经网络的逼近定理。
Based on expatiated the basic structure model and some general improved algorithms of BP neural network, this paper brings forward a new self-organization learning algorithm.
介绍了BP网络的基本结构模型与常见改进算法，在此基础上提出了一种新型的结构自组织BP网络算法。
A learning algorithm of subtractive clustering for RBF network is used to obtain the parameters of radial basis function so as to optimize network structure.
在RBF网络中采用了一种减聚类的学习算法来确定径向基函数的相应参数，使网络结构得到优化。
A learning algorithm of subtractive clustering method for RBFNN is used to obtain the parameters of radial basis function, so that RBFNN has an optimized structure.
在RBF神经网络中采用了一种减聚类的学习算法来确定径向基函数的相应参数，从而使神经网络结构得到优化。
A new neural network controller is proposed based on the PID controller structure. Its basic structures and learning algorithm are analysed.
根据PID控制结构提出了一种新型神经网络控制器，对其基本结构和学习算法等进行了分析。
The principle of the approach, the structure of neural networks'and hew learning algorithm are interpreted.
文中阐述了这种方法的原理、神经网络的结构及学习算法。
In many papers on learning BN structure, the Crossing Entropy was used as an indicator of measuring the learning accuracy of an algorithm.
在许多关于信度网结构的学习文献中，都将交叉熵作为检验算法学习效果的一个指标。
The network structure, learning algorithm, feature extraction and synthetic decision are described.
文中阐述了其网络结构、学习算法、特征提取及综合决策方法。
The new algorithm is different from the algorithms of iterative learning control proposed recently, and is with nonlinear structure.
这类新算法与目前所有迭代学习控制算法不同，具有非线性结构。
We first discuss the structure and principle of the CMAC neural network. Using competitive learning, we develop a new adaptive quantization algorithm.
首先阐述了CMAC神经网络的原理、结构和学习算法，提出了一种新的采用竞争学习原理的非等距自适应量化算法。
Finally, taking data from CAE as samples; the BP neural network of warping-shrinkage prediction model is established by designing the network structure and selection of learning algorithm.
最后以数值仿真得到的数据为样本数据，通过设计网络结构和选用学习算法，建立并得到基于BP人工神经网络的翘曲——收缩预测模型。
The Thesis analyses many kinds of Algorithm about Bayesian network structure learning, and then Setting-up a new Algorithm about structure learning Foundation on hydro-electrical simulation system.
本文在分析了多种贝叶斯网络结构学习算法的基础上，并且根据水电仿真的应用背景，提出了一种根据多专家提供的规则库进行贝叶斯网络结构学习的新算法。
Since basic TAN learning algorithm choice tree structure rooted randomly, that makes it unable to express the dependence among attributes accurately.
但传统TAN的构造算法中树的根结点是随意选择的，这使得其无法精确表达属性间的依赖关系。
The fuzzy space structure of system and the number of fuzzy rules based on fuzzy competitive learning algorithm are determined and the fitness degree of each rule contrast to each sample is obtained.
基于竞争学习算法的模糊分类器确定系统的模糊空间和模糊规则数，并得出每个样本对每条规则的适用程度。
After exploratory researches for the structure and learning algorithm of neural network, an algorithm based on adaptive gain coefficient is presented.
对神经网络的结构和学习算法进行了探索性研究，引入一种基于自适应增益系数改进的学习算法。
This paper proposes to overcome those problems by incorporating the improved RPCL (rival penalized competitive learning)algorithm and the EM(expectation maximization)algorithm into the EBF structure.
本文提出用结合改进的RPCL算法和EM算法的EBF网络结构来解决上述问题。
Given the structure of position regulator of linear servo system and open-closed loop iterative learning position regulation algorithm with forgetting factor.
给出直线伺服系统位置调节器结构及带有遗忘因子的开闭环迭代学习位置调节算法。
The structure and algorithm of artificial neural network model were described, and the model and learning-procedure of mechanical fault diagnosis neural network were designed.
阐述了人工神经网络模型的一般结构和算法，并设计了机械故障诊断神经网络的模型和学习过程。
Structure, learning rule and recognition algorithm of fuzzy ART is described and designed.
提出并设计了模糊art神经网络的结构、学习规则和识别算法。
The PNN structure was optimized based on statistical results from the PCA for the training samples. A learning algorithm was introduced into the PNN to reduce uncertainties parameter.
以概率乘法公式为理论依据，根据训练样本的PCA结果对PNN进行结构优化，并引入学习算法减小PNN的参数不确定性。
FCMAC based controller structure and a simple learning algorithm were also proposed. In the learning algorithm only small parts of parameters of the FCMAC were adjusted at each learning iteration.
给出一种基于FCMAC的自学习控制器的结构及合适的学习算法，这种网络每次学习少量参数，算法简单。
This paper presents a kind of software faults prediction model based on artificial neural network and the structure of the feed-forward multi-layer network with backpropagation learning algorithm.
该文介绍了一种基于人工神经网络的软件失效预测模型，给出了基于反向传播算法的多层前向网络的网络结构。
The algorithm used weighted templates to structure each weak learning classifier, which overcame the shortcoming of structuring classifier by using a single feature.
在该演化算法中，采取训练正反类样本加权模板的方法来构造各个弱学习分类器，克服了常规的基于单一特征构造弱分类器的不足。
The algorithm can change network's learning rate followed by network's convergence state, and can adjust network's structure based on the neurons' change and their relationship.
该自组织BP网络算法能够根据当前收敛状态自动调整学习率，使得网络收敛速度与学习率变化保持一致。
For the complexity of data and the deep relationship of semantic, a Lie Group deep structure learning algorithm is proposed.
针对数据的复杂性和语义深层关系，提出一种李群深层结构学习算法。
A parameter self-learning algorithm is presented after defining data structure and variable array to improve the prototype's adaptability to different size of workpieces.
在定义了数据结构和变量数组的基础上，给出了参数自学习过程算法，改善了模型样机对不同规格样本工件的适应性。
A parameter self-learning algorithm is presented after defining data structure and variable array to improve the prototype's adaptability to different size of workpieces.
在定义了数据结构和变量数组的基础上，给出了参数自学习过程算法，改善了模型样机对不同规格样本工件的适应性。
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