learning algorithm的例句大全

Neural network model, principle of learning algorithm, simulation curves and output results are discussed in detail.

对神经网络模型的建立、网络的训练方法、仿真曲线、神经网络的输出结果进行了详细的讨论。

Investigation on Description of Learning Space Based-on Interval Fuzzy Sets and Fuzzy Inductive Learning Algorithm

基于区间值模糊集的学习空间描述及其模糊归纳学习算法研究

presents the mathematical expressions of adding new learning sample and adding new redial basis function of relearning algorithm, studies how to get the new learning sample.

详细给出了径向基函数神经网络再学习算法中增加新训练样本和增加新基函数的数学公式，同时对如何获取新的训练样本进行了研究。

A class of neural networks containing direct controlled adaptive neuron and the corresponding fast learning algorithm method are proposed for developing this new humping speed control system.

提出了一种直接控制的自适应神经元模型和神经网络结构，以及一种快速学习算法（FLA），并把该神经网络和算法用于驼峰速度控制系统。

compared with relearning vocabulary, this transfer learning algorithm makes use of learned knowledge effectively, which can gain good performance especially in the situation when there are a few images in a novel category.

与重新学习视觉词典的分类算法相比，这种迁移算法有效地利用了已有知识，在新类别图像的训练样本较少的情况下，也能获得较好的分类效果。

A hybrid learning algorithm for neural network based distance protection

神经网络式距离保护的混合训练算法

Graph-based semi-supervised learning algorithm is drawing lots of attention in recent years due its some internal characteristics.

半监督学习中的基于图的算法由于自身的一些特殊性质，越来越引起人们的关注。

Implementation of the Fast Learning Algorithm lor Multilayer Feed-Forward Neural Networks

多层前馈神经网络快速学习算法的实现

The whole identification process takes much less CPU time than the fuzzy clustering method and the error-feedback learning algorithm.

整个辨识过程与模糊聚类方法[8]和误差反馈学习方法[5]相比所需的CPU时间最短。

A learning algorithm based on Lyapunov stability is derived to fuzzy relational model identification in this paper.

本文在李雅普诺夫稳定性意义下，提出了一种辨识模糊关系模型的学习算法。

The advantages and weaknesses of each type time delay compensation algorithm are analyzed, leading to the iterative learning algorithm. Thirdly, the effect of the network time delay to the linear system is analyzed and the time delay composition model is built.

分析每类时滞补偿算法的优点和不足，并引出迭代学习算法。再次，分析网络时滞对线性系统的影响，对时滞进行分析和建模。

To address the problem of multicomponent patterns that many researchers have forgotten to consider it in their classification systems, an adaptive boosting multi-label learning algorithm ( AdaBoost. MC) is developed based on decision trees, maximum a posteriori ( MAP) and the robust ranking principles.

为解决许多研究者在其分类系统中未予考虑的多成份模式问题，开发了一种基于决策树、最大后验（MAP）和鲁棒排序原则的自适应boosting多标记学习算法（AdaBoost.MC）。

The existing distributed machine learning algorithm library, Mahout, offers some classic classification algorithm, such as Bayes and decision trees.

现有的分布式机器学习算法库Mahout，提供了一些经典的分类挖掘算法，如贝叶斯、决策树等。

With the purpose of constructing a large scale of KBS for high performance learning algorithm, a multi-concept learning algorithm is realized in this paper. The learnable performance and complexity of the algorithm are analyzed and the advantages of this algorithm proved by experiments.

为满足大规模知识库系统建设对于高性能学习算法的需要，本文实现了一个面向通用问题的多概念学习算法，分析了算法的可学习性和复杂性，并通过实验验证了该算法的优点。

Improving the SA Learning Algorithm

BP网络局部学习速率自适应SA算法的改进

The reinforcement learning method is used in Agent learning, the TS type recurrent fuzzy neural network ( TSRFNN) is employed to realize the actor-critic elements. The agents in system are optimized coordinately by using the distributed learning algorithm.

在控制Agent中采用Actor-critic强化学习方法，运用TS回归模糊神经网络实现行动和评判模块，使用分布式学习算法对多个Agent协调优化。

furthermore, in order to overcome the disadvantage of the centralized computing of rpe learning algorithm, a parallel structure algorithm is derived.

进一步地，为了克服RPE算法集中运算的不足，设计完整的并行结构算法。

SCLA are soft competition schemes of the learning algorithms, in which the center vectors are trained based on the k means algorithm, and can remedy the problems of the dead node and the sensitivity to initial weight vectors that the latter algorithms have.

SCLA是RBF网络基于k均值方法训练中心向量的学习算法的软竞争格式，它可以克服后者对初始值敏感和死节点的问题。

This model uses support vector machines ( SVM) which has strong learning ability and needs only less number of samples as its learning algorithm, and uses the characteristic quantities which could characterize the furnace mouth flame spectral data to improve the efficiency of model training.

该模型采用学习能力强、对样本数目要求不高的支持向量机作为学习算法，以能表征炉口火焰光谱数据的特征量作为样本数据来提高模型的训练效率。

The Principle& Learning Algorithm of Nonlinear CC Neural Model

非线性CC神经元模型的原理及其学习算法