首页 | 本学科首页   官方微博 | 高级检索  
相似文献
 共查询到20条相似文献,搜索用时 0 毫秒
1.
Previously neural networks have shown interesting performance results for tasks such as classification, but they still suffer from an insufficient focus on the structure of the knowledge represented therein. In this paper, we analyze various knowledge extraction techniques in detail and we develop new transducer extraction techniques for the interpretation of recurrent neural network learning. First, we provide an overview of different possibilities to express structured knowledge using neural networks. Then, we analyze a type of recurrent network rigorously, applying a broad range of different techniques. We argue that analysis techniques, such as weight analysis using Hinton diagrams, hierarchical cluster analysis, and principal component analysis may be useful for providing certain views on the underlying knowledge. However, we demonstrate that these techniques are too static and too low-level for interpreting recurrent network classifications. The contribution of this paper is a particularly broad analysis of knowledge extraction techniques. Furthermore, we propose dynamic learning analysis and transducer extraction as two new dynamic interpretation techniques. Dynamic learning analysis provides a better understanding of how the network learns, while transducer extraction provides a better understanding of what the network represents.  相似文献   

2.
神经网络已经在模式识别、自动控制及数据挖掘等领域取得了广泛的应用,但学习方法的速度不能满足实际需求。传统的误差反向传播方法(BP)主要是基于梯度下降的思想,需要多次迭代;网络的所有参数都需要在训练过程中迭代确定,因此算法的计算量和搜索空间很大。ELM(Extreme Learning Machine,ELM)是一次学习思想使得学习速度提高很多,避免了多次迭代和局部最小值,具有良好的泛化性能、鲁棒性与可控性。但对于不同的数据集和不同的应用领域,无论ELM是用于数据分类或是回归,ELM算法本身还是存在问题,所以本文对已有方法深入对比分析,并指出极速学习方法未来的发展方向。  相似文献   

3.
针对Internet上的信息过载问题,提出了一种基于内容分析的信息推荐方法。该方法使用神经网络作为知识表示和推理机制来建立用户兴趣模型,然后以用户模型为基础来预测用户对信息的偏好程度,并据此做出信息推荐。提出的方法通过一个仿真试验进行验证。  相似文献   

4.
In multi-instance learning, the training examples are bags composed of instances without labels, and the task is to predict the labels of unseen bags through analyzing the training bags with known labels. A bag is positive if it contains at least one positive instance, while it is negative if it contains no positive instance. In this paper, a neural network based multi-instance learning algorithm named RBF-MIP is presented, which is derived from the popular radial basis function (RBF) methods. Briefly, the first layer of an RBF-MIP neural network is composed of clusters of bags formed by merging training bags agglomeratively, where Hausdorff metric is utilized to measure distances between bags and between clusters. Weights of second layer of the RBF-MIP neural network are optimized by minimizing a sum-of-squares error function and worked out through singular value decomposition (SVD). Experiments on real-world multi-instance benchmark data, artificial multi-instance benchmark data and natural scene image database retrieval are carried out. The experimental results show that RBF-MIP is among the several best learning algorithms on multi-instance problems.  相似文献   

5.
BP神经网络模型是一种典型的前向型神经网络,具有良好的自学习、自适应、联想记忆、并行处理和非线形转换的能力,是目前应用最为广泛的一种神经网络模型。本文介绍了BP神经网络的实现以及其在数据挖掘分类方面的应用。  相似文献   

6.
This article describes our experience with designing and using a module architecture assistant, an intelligent tool to help human software architects improve the modularity of large programs. The tool models modularization as nearest-neighbor clustering and classification, and uses the model to make recommendations for improving modularity by rearranging module membership. The tool learns similarity judgments that match those of the human architect by performing back propagation on a specialized neural network. The tool's classifier outperformed other classifiers, both in learning and generalization, on a modest but realistic data set. The architecture assistant significantly improved its performance during a field trial on a larger data set, through a combination of learning and knowledge acquisition.  相似文献   

7.
复交替投影神经网络   总被引:2,自引:1,他引:2  
首先对MarksII等人提出的交替投影神经网络进行了研究,将其应用范围从实数域拓广到复数域,并证明了其稳态收敛性,接着研究了复数域的中替投影神经网络(不妨称此情形下的网络为复交替投影响神经网络),得到了几个有用结论,并从数学上对这些结构进行了严格证明,最后,为了验证文中物理分析的正确性,设计了一个仿真实验,仿真实验结果表明文中的理论与验结果完全吻合,实际上,复交替投影神秒仅可用于信号处理中的带限信号外推,还可用于选频,陷波等场合。  相似文献   

8.
基于Bayes法则和BP神经网络的高速动态情形下车型识别   总被引:2,自引:0,他引:2  
针对在高速动态情形下的车型识别,介绍了一种对汽车提取特征、基于红外线检测的汽车分类仪;阐述了采用汽车特征参数作为样本向量训练BP网络的方法和识别车型原理;采用共轭梯法修正BP网络,提高了训练速度和全局收敛性;对于样本向量存在的数据“噪声”,则以Bayes法则对大量样本去除“噪声”,使特征样本向量更有代表性,理论与实际证明,这样得到BP网有强容错能力,能识别没有看过的汽车样本,从而提高了车型识别精度。  相似文献   

9.
提出了基于Levenberg-Marquardt(LM)算法的BP神经网络对蛋白质序列进行家族分类的新方法.该方法采用二肽含量对蛋白质序列进行特征提取,根据影响因子评价特征的相对重要性,用改进的BP神经网络LM优化算法构造一个三层人工神经网络,通过对PIR数据库中三类家族的学习,该网络对未知蛋白质序列分类的准确率分别达到了98.9%.98.1%,97.8%。  相似文献   

10.
增量学习是在原有学习成果的基础上,对新信息进行学习,以获取新知识的过程,它要求尽量保持原有的学习成果.文章先简述了基于覆盖的构造型神经网络,然后在此基础上提出了一种快速增量学习算法.该算法在原有网络的分类能力基础上,通过对新样本的快速增量学习,进一步提高网络的分类能力.实验结果表明该算法是有效的.  相似文献   

11.
12.
The ensemble of evolving neural networks, which employs neural networks and genetic algorithms, is developed for classification problems in data mining. This network meets data mining requirements such as smart architecture, user interaction, and performance. The evolving neural network has a smart architecture in that it is able to select inputs from the environment and controls its topology. A built-in objective function of the network offers user interaction for customized classification. The bagging technique, which uses a portion of the training set in multiple networks, is applied to the ensemble of evolving neural networks in order to improve classification performance. The ensemble of evolving neural networks is tested by various data sets and produces better performance than both classical neural networks and simple ensemble methods.  相似文献   

13.
Labeled graphs are an appropriate and popular representation of structured objects in many domains. If the labels describe the properties of real world objects and their relations, finding the best match between two graphs turns out to be the weakly defined, NP-complete task of establishing a mapping between them that maps similar parts onto each other preserving as much as possible of their overall structural correspondence. In this paper, former approaches of structural matching and constraint relaxation by spreading activation in neural networks and the method of solving optimization tasks using Hopfield-style nets are combined. The approximate matching task is reformulated as the minimization of a quadratic energy function. The design of the approach enables the user to change the parameters and the dynamics of the net so that knowledge about matching preferences is included easily and transparently. In the last section, some examples demonstrate the successful application of this approach in classification and learning in the domain of organic chemistry.  相似文献   

14.
神经网络极速学习方法研究   总被引:57,自引:0,他引:57  
单隐藏层前馈神经网络(Single-hidden Layer Feedforward Neural Network,SLFN)已经在模式识别、自动控制及数据挖掘等领域取得了广泛的应用,但传统学习方法的速度远远不能满足实际的需要,成为制约其发展的主要瓶颈.产生这种情况的两个主要原因是:(1)传统的误差反向传播方法(Back Propagation,BP)主要基于梯度下降的思想,需要多次迭代;(2)网络的所有参数都需要在训练过程中迭代确定.因此算法的计算量和搜索空间很大.针对以上问题,借鉴ELM的一次学习思想并基于结构风险最小化理论提出一种快速学习方法(RELM),避免了多次迭代和局部最小值,具有良好的泛化性、鲁棒性与可控性.实验表明RELM综合性能优于ELM、BP和SVM.  相似文献   

15.
ABSTRACT This paper investigates the function approximation problem by using Walsh functions to establish a Walsh‐basis‐function neural network (WBFNN). The proposed novel system avoids the possible heavy computation problem of a controller usually encountered in adaptive neural controller design. With the developed adaptation scheme combined with the sliding mode control strategy for a class of nonlinear systems, the proposed WBFNN‐based controller can guarantee global stability of the closed‐loop system in the Lyapunov sense. The output tracking error then converges to zero asymptotically, and boundedness of all the signals in the whole system is ensured. Simulation validation for a nonlinear unstable system was performed to verify the effectiveness of the proposed controller design.  相似文献   

16.
神经网络集成   总被引:175,自引:2,他引:175  
神经网络集成通过训练多个神经网络并将成结论进行合成,可以显著地提高学习系统的泛化能力。它不仅有助于科学家对机器学习和神经的深入研究,还有助于普通工程技术人员利用神经网络技术来解决真实世界中的问题。因此,它被视为一种广阔应用前景的工程化神经计算技术,已经成为机器学习和神经计算领域的研究热点。该文从实现方法、理论分析和应用成果等三个方面综述了神经网络集成的国际研究现状,并对该领域值得进一步研究的一些问题进行了讨论。  相似文献   

17.
一种基于神经网络集成的规则学习算法   总被引:8,自引:0,他引:8  
将神经网络集成与规则学习相结合,提出了一种基于神经网络集成的规则学习算法.该算法以神经网络集成作为规则学习的前端,利用其产生出规则学习所用的数据集,在此基础上进行规则学习.在UCl机器学习数据库上的实验结果表明,该算法可以产生泛化能力非常强的规则.  相似文献   

18.
Radial Basis Neural Networks have been successfully used in a large number of applications having in its rapid convergence time one of its most important advantages. However, the level of generalization is usually poor and very dependent on the quality of the training data because some of the training patterns can be redundant or irrelevant. In this paper, we present a learning method that automatically selects the training patterns more appropriate to the new sample to be approximated. This training method follows a lazy learning strategy, in the sense that it builds approximations centered around the novel sample. The proposed method has been applied to three different domainsan artificial regression problem and two time series prediction problems. Results have been compared to standard training method using the complete training data set and the new method shows better generalization abilities.  相似文献   

19.
基于对目前神经网络存在问题的具体分析,认为将启发性信息引入神经网络训练将是提高网络学习能力\质量以及效率的重要途径。进而讨论了启发知识的来源与种类,将启发性知识分成诱导性约束和强制性约束两类,进而建立了引入网络训练的相应策略,给出了启发性知识引入与选择的具体原则,并建立了两种基于导数关系的启发知识模型。最后建立了神经网络的具体训练算法。具体应用结果证明了所提出策略与方法的有效性。  相似文献   

20.
进化神经网络研究进展   总被引:11,自引:0,他引:11  
进化神经网络是将进化算法应用于神经网络的构造、学习而得到的神经网络,具有很强的鲁棒适应性。综述了进化神经网络方法及其应用研究新进展,对研究中出现的一些问题进行了讨论与展望。  相似文献   

设为首页 | 免责声明 | 关于勤云 | 加入收藏

Copyright©北京勤云科技发展有限公司  京ICP备09084417号