An improved BP algorithm and a learning sample generation method based on statistics are performed.
提出了一种基于统计的学习样本生成方法，使样本生成问题规范化。
The part of neural network of the intelligent self-tuning PID controller based on BP network learns the learning sample.
由设计出的基于BP神经网络的智能自整定PID控制器的神经网络部分对学习样本进行学习。
Taking the 1975-2006 National passenger traffic data and other related indicators as a learning sample, then verify the validity of the training model after Parameter Optimization.
把1975到2006年全国的客运量数据和其他相关指标作为学习样本，验证寻优参数得到训练模型预测结果的可靠性。
Though choosing the experimental results as the learning sample, the performance predictive model of EDM micro-and-small holes is proposed, with the BP algorithm of artificial neural network.
采用人工神经网络的BP算法，以电火花微小孔加工工艺参数正交实验的结果作为神经网络的学习样本，建立电火花微小孔加工多目标工艺参数的预测模型。
According to the unified and standardization criterion, the optimal objectives are graded and combined to form learning sample set for FNN model, and its output is the integrated evaluated value.
采用统一、规范化准则，对各优化目标进行等级划分并进行不同的组合，形成FNN的学习样本集，其输出即决策解的综合性能的评估值。
Listing 1 is a sample learning overview topic from the pilot course.
清单1是来自示范课程的示例学习概述主题。
This sample graph is from a simple reinforcement learning application that USES Q learning.
这个示例图是从使用Q学习的一个简单增强式学习应用程序中得到的。
Learning by example is more fun, and this is where the Plug-in Project wizard can give us a hand by providing some sample code that we can then modify to our needs.
通过示例来学习会更有趣，这方面Plug - inProject向导可以提供帮助，它提供了一些样本代码，我们可以修改这些代码来满足我们的需要。
A comprehensive set of Documentation covering all aspects of the WSDK, including learning paths through Web services concepts, how to use the WSDK tools, and descriptions of the Sample applications.
一个综合文档，涵盖了WSDK的各个方面，包含了学习Web服务概念的整个过程、如何使用WSDK工具和对样本应用程序的描述。
Based on the analysis of the 188 manufacturers' sample, we established the influence mechanism of the inter-organization learning strategy.
通过对188个供应商样本的数据采集分析，我们构建了渠道成员组织间学习策略的影响机制模型。
Compared with statistical theory, statistical learning theory focuses on the machine learning of small sample size and can trade off between the complexity of models and generalization performance.
与传统统计学相比，统计学习理论是一种专门研究小样本情况下机器学习规律的理论。
This article describes a new type of fuzzy system with interpolating capability to extract MISO fuzzy rules from input output sample data through learning.
描述了一个通过学习从输入输出采样数据中提取MISO模糊规则的具有插值性能的新型模糊系统。
Computational learning theory provides formal framework for comparing the performance of two algorithms, and can determine the sample complexity and the computational complexity of some concept class.
计算学习理论为比较两算法的性能提供了形式化的框架，并能确定某概念类的计算复杂度和样本复杂度。
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。
The algorithm combines active learning, biased classification and incremental learning to model the small sample biased learning problem in relevance feedback process.
该算法将主动式学习、有偏分类和增量学习结合起来，对相关反馈过程中的小样本有偏学习问题进行建模。
A new adaptive variable neural network is developed according to the inner product feature of sample space. This method does not need to iterate learning.
根据样本空间的内积特性，提出一种无需迭代学习的自适应变结构神经网络。
As a new machine learning method, SVM can solve the small sample, nonlinear, high dimension and local minima, the actual problem.
作为一种新的机器学习方法，SV M能较好地解决小样本、非线性、高维数和局部极小点等实际问题。
By the simulation of a sample path, and the approximation to performance potentials via neural networks based on reinforcement learning (RL), the systems optimization methods are provided.
在样本轨道仿真的基础上，利用神经网络进行强化学习仿真逼近系统的性能势，进而对系统进行优化。
SVM solves the small sample problem mainly and finds the best compromise between the complexity of the model and the learning ability in order to obtaining the best generalization ability.
SVM主要解决小样本问题，在模型的复杂度和学习能力之间寻求最佳折衷，目的在于获得最好的泛化能力。
In inductive learning approach, how to discretize the sample data can directly affect the creation of the production rules and the recognizing result.
在归纳式学习方法中，对样本数据的不同分组会直接影响到所生成的反映变量间相互关系的规则，从而影响到对新样本的识别效果。
The algorithm selects training samples by local sample density, to reduce the training samples and thus to improve the speed of learning.
该算法根据样本的局部密度选择训练样本，减少参加训练的样本数量，提高学习速度。
Statistical Learning Theory is based on a solid theoretical foundation. It provides an unified framework for solving the small sample learning problem.
统计学习理论具有坚实的理论基础，为解决小样本学习问题提供了统一的框架。
Provided with the Algorithm to determine the pre-possessing of sample data, learning rate, momentum factor, the Number of Hidden Layer Nodes, etc.
给出了样本数据的预处理以及学习因子、动量系数、隐含层结点数等诚然定方法。
Numerical analysis testified that these fuzzy models can effectively control the influence of each sample on learning machine according to its confidence.
数值分析证明本文建立的模糊支撑向量机模型可根据数据置信度大小有效的控制各数据在学习机中的作用。
Then, giving the sample words from library, a categorization database is created and used for automatic categorization of Chinese journals by Bayesian learning.
其次，通过对图书馆的样本数据进行训练建立的分类库，本文使用贝叶斯分类器实现中文期刊的自动分类。
Because the nonlinear evolution of slope could be obtained by learning the sample repeatedly with neural network, the forecast effect of neural network is better than those of traditional methods.
由于神经网络可以通过对样本的反复学习来反映边坡复杂的非线性演化关系，其预测效果要优于传统的预测方法。
Support vector machine (SVM) based on the structural risk minimization of statistical learning theory is a method of machine learning for small sample set.
基于统计学习理论中结构风险最小化原则的支持向量机是易于小样本的机器学习方法。
Support vector machine (SVM) based on the structural risk minimization of statistical learning theory is a method of machine learning for small sample set.
基于统计学习理论中结构风险最小化原则的支持向量机是易于小样本的机器学习方法。
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