共查询到20条相似文献,搜索用时 15 毫秒
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针对如何分析校园无线网络数据,挖掘数据中蕴藏的学生行为,更好地辅助教学管理,本文提出了在Hadoop平台构建基于自组织神经网络(SOFM)的模糊C-均值(FCM)聚类算法。该算法采用自组织神经网络与模糊C-均值聚类算法相结合,避免了模糊C-均值聚类算法初始化不当带来的误差,目标函数中采用马氏距离,自适应的调整了数据的几何分布。考虑到无线用户数据规模庞大,采用了Hadoop平台并行运行聚类算法。实验结果表明,本文提出的算法提高了聚类结果的准确性,有效地降低了时间复杂度,分析平台为学校管理层快速有效的做出决策提供了依据,研究分析方法对其它高校有较大地参考价值。 相似文献
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自组织特征映射神经网络SOM(Self-Organizing Feature Maps)是一种优良的聚类工具,但其存在着一些限制,如需要预先定义网络大小、网络的收敛性较差和结构不灵活等.为了克服这些不足,在自组织神经网络理论的指导下,提出了一种基于生长型自组织神经网络的聚类方法.在无监督的情况下,该方法采用阈值控制的触发机制实现网络中神经元的生长和删除,并通过神经元权值的有效调整,以期得到数据对象的聚类结果.实验以二维空间中的数据对象为输入样本,验证了该方法的有效性和优越性. 相似文献
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A Novel Self-Organizing Neural Network for Motion Segmentation 总被引:2,自引:2,他引:2
Many computer vision techniques, above all for structure from motion problems, require a segmentation of the images captured by one or more cameras. This paper deals with the segmentation based on the motion information, but can be easily extended to other cases (color, texture and so on). A new neural network, the EXIN Segmentation Neural Network (EXIN SNN) is here introduced. It is incremental, self-organizing and considers its task as the solution of a pattern recognition problem. This original approach overcomes the limits of the traditional segmentation techniques, namely the need of a spatial support for the image objects and the translation parallel to the image plane for the objects in the scene. Examples are given both for synthetic and real images. 相似文献
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A novel hybrid learning algorithm based on a genetic algorithm to design a growing fuzzy neural network, named self-organizing fuzzy neural network based on genetic algorithms (SOFNNGA), to implement Takagi-Sugeno (TS) type fuzzy models is proposed in this paper. A new adding method based on geometric growing criterion and the epsiv-completeness of fuzzy rules is first used to generate the initial structure. Then a hybrid algorithm based on genetic algorithms, backpropagation, and recursive least squares estimation is used to adjust all parameters including the number of fuzzy rules. This has two steps: First, the linear parameter matrix is adjusted, and second, the centers and widths of all membership functions are modified. The GA is introduced to identify the least important neurons, i.e., the least important fuzzy rules. Simulations are presented to illustrate the performance of the proposed algorithm 相似文献
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一种用于非线性控制的神经网络模糊自组织控制器 总被引:5,自引:0,他引:5
本文提出一种神经网络自组织控制器,并应用于非线性跟踪控制中,为了加快模糊控制器的在线学习,文中给出了一种变的最速梯度下降学习算法,仿真结果表明,该控制是有效的。 相似文献
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《IEEE transactions on systems, man, and cybernetics. Part B, Cybernetics》2008,38(5):1326-1346
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This paper presents a self-organizing transient chaotic neural network to solve the channel assignment problem, one of NP-complete problems. The proposed neural network consists of two parts. The first part is the self-organizing evolution stage, which based on the mutual inhibition mechanisms of bristle differentiation and the problem's heuristic information. The second part is the transient chaotic neural network executing stage. A significant property of the TCNN model is that the chaotic neurodynamics is temporarily generated for searching and self-organizing in order to escape the local minima. In the proposed neural network, the first part is used to improve the quality of the obtained solutions. The simulating results have shown that the self-organizing transient chaotic neural network improves greatly performance through solving the well-known benchmark problems, especially for the Sivarajan's and Kunz's benchmark problems, while the performance is comparable with existing algorithms. 相似文献
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针对模糊神经网络学习算法计算量过大,在预测模型设计中提出了基于改进T-S模糊推理的模糊神经网络学习算法。主要工作如下:首先,改进T-S模糊推理方法,定义基于偏移率的T-s模糊推理方法;然后,通过将此模糊推理方法与基于合成规则的模糊推理方法及距离型模糊推理方法相比较可以看出,所提方法有较少的计算量,且比较有效;最后,在此基础上改善了模糊神经网络学习算法,并将其应用于天气预测与安全态势预测。测试结果表明,该方法明显改善了学习效率,减少了预测模型设计中的学习次数与时间复杂度,并降低了学习误差。 相似文献
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A self-organizing and self-evolving agents (SOSENs) neural network is proposed. Each neuron of the SOSENs evolves itself with a simulated annealing (SA) algorithm. The self-evolving behavior of each neuron is a local improvement that results in speeding up the convergence. The chance of reaching the global optimum is increased because multiple SAs are run in a searching space. Optimum results obtained by the SOSENs are better in average than those obtained by a single SA. Experimental results show that the SOSENs have less temperature changes than the SA to reach the global minimum. Every neuron exhibits a self-organizing behavior, which is similar to those of the self-organizing map (SOM), particle swarm optimization (PSO), and self-organizing migrating algorithm (SOMA). At last, the computational time of parallel SOSENs can be less than the SA 相似文献
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《IEEE transactions on systems, man, and cybernetics. Part B, Cybernetics》2008,38(6):1537-1548
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Fuzzy Inference Neural Network for Fuzzy Model Tuning 总被引:1,自引:0,他引:1
Keon-Myung Lee Dong-Hoon Kwak Hyung Lee-Kwang 《IEEE transactions on systems, man, and cybernetics. Part B, Cybernetics》1996,26(4):637-645
In fuzzy modeling, it is relatively easy to manually define rough fuzzy rules for a target system by intuition. It is, however, time-consuming and difficult to fine-tune them to improve their behavior. This paper describes a tuning method for fuzzy models which is applicable regardless of the form of fuzzy rules and the used defuzzification method. For this purpose, this paper proposes a fuzzy neural network model which can embody fuzzy models. The proposed model provides the functions to perform fuzzy inference and to tune the parameters for the shape of antecedent linguistic terms, the relative importance degrees of rules, and the relative importance degrees of antecedent linguistic terms in rules. In addition, to show its applicability, we perform some experiments and present the results 相似文献
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联合模糊逻辑和神经网络的网络选择算法 总被引:1,自引:0,他引:1
在网络优化选择问题的研究中,针对异构网络环境下的网络选择的问题,由于网络性能存在差异,提出一种联合模糊逻辑和神经网络的自适应网络选择算法.由于新方法具有学习训练的能力,所以能够根据输出误差对模糊神经网络的隶属度函数的参数进行动态的在线调整,从而使用户选择最优的网络.最后将联合模糊逻辑和神经网络的网络选择算法与基于模糊逻辑的网络选择算法进行了比较.仿真结果表明,改进方法能有效的保证用户舒适度比率趋于期望的理想值,实现了最优的网络接入选择,减少了乒乓效应发生的次数,并且相较于不自适应调整的模糊逻辑算法有更高的用户舒适度比率. 相似文献
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Evolutionary Algorithm‐Based Radial Basis Function Neural Network Training for Industrial Personal Computer Sales Forecasting 下载免费PDF全文
Forecasting is one of the crucial factors in applications because it ensures the effective allocation of capacity and proper amount of inventory. Because Box–Jenkins models using linear forecasting have their constraint to predict complexity in the real world, other nonlinear approaches are developed to conquer the challenge of nonlinear forecasting. With the same goal, we are proposing a hybrid of genetic algorithm and artificial immune system (HGAI) algorithm with radial basis function neural network learning for function approximation and further applying it to conduct an industrial personal computer sales forecasting exercise. In addition, five well‐known benchmark problems were used to evaluate the results in the experiment, and the newly proposed HGAI algorithm has returned better results than the Box–Jenkins models and other algorithms. 相似文献
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Fuzzy Neural Network Models for Classification 总被引:2,自引:0,他引:2
In this paper, we combine neural networks with fuzzy logic techniques. We propose a fuzzy-neural network model for pattern recognition. The model consists of three layers. The first layer is an input layer. The second layer maps input features to the corresponding fuzzy membership values, and the third layer implements the inference engine. The learning process consists of two phases. During the first phase weights between the last two layers are updated using the gradient descent procedure, and during the second phase membership functions are updated or tuned. As an illustration the model is used to classify samples from a multispectral satellite image, a data set representing fruits, and Iris data set. 相似文献
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一种广义模糊神经网络的参数解耦学习算法 总被引:3,自引:0,他引:3
对于强非线性系统采用分段建模十分有效,广义模糊神经网络能实现这种思想。在此基础上,给出一种模糊规则前、后件参数可分别进行学习的算法,仿真结果表明该方法拟合能力强、学习效率高。 相似文献
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提出了基于DNA计算和遗传算法的DNA遗传算法,给出了DNA遗传算法的结构,讨论了遗传操作算子,利用DNA遗传算法对FNN进行学习,比采用梯度型算法和遗传算法有更高的学习精度和更快的收敛速度,该算法有全局收敛性避免了采用梯度型学习算法训练FNN时固有的局部收敛问题,同样,该算法加速了FNN的训练,能够在线应用. 相似文献
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本文提出了用于SCARA机器人运动控制的自组织模糊聚类神经网络控制器.该控制器基于模糊聚类方法在学习模糊规则之前先优化训练数据,去除冗余数据并解决数据冲突问题,不但减少了神经网络的计算负担,而且生成的规则更加适合机器人运动控制.控制器主要特点是能够动态地自组织结构,学习速度快,鲁棒性强.仿真结果表明控制效果很好. 相似文献
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本文提出一种用自组织自学习适应思想解决非线性动力系统控制问题的新方法。在每个小区域感受野,可以把非线性系统近似展开为线性,由神经元执行控制。各神经元的凝视点,感受野和功能由自组织自学习自适应方法进行调节。大量仿真结果验证了本方法的正确性和实用性。 相似文献