共查询到20条相似文献，搜索用时 250 毫秒
1.
基于径向基函数网络的改进算法的股市数据预测 总被引：3，自引：0，他引：3
径向基函数网络的性能在很大程度上取决于径基函数中心位置的选取。本文通过结合输入输出矢量从而得到扩展矢量的方式改进了常用的确定径基函数中心的HCM算法。股市数据预测的实验结果表明：改进的HCM算法的网络的性能有了明显的改善。 相似文献
2.
数据挖掘技术在网络入侵检测中的应用 总被引：1，自引：0，他引：1
随着网络技术和网络规模的不断发展，网络入侵的风险和机会越来越多，网络安全已经成为无法回避的问题。因此为了保护越来越多的重要数据，入侵检测技术成为了一种非常关键的技术。本文陈述了入侵检测方法的基本思想，探讨了基于数据挖掘的入侵检测技术，建立了基于数据挖掘的网络入侵检测系统模型．最后通过一个实例说明了在该系统中入侵检测技术的应用。 相似文献
3.
前向神经网络改进预测方法及其在滑坡稳定性预测中的应用 总被引：3，自引：0，他引：3
本文提出一种改进了的前向神经网络预测方法，引入了衰减因子，以体现对前期效应的弱化，从而提高当前预测的精确度；在权值调整过程中，采用指数型能量函数，以改善学习收敛过程。最后将该方法应用于新滩滑坡的稳定性预测，取得了满意的结果 相似文献
4.
基于JAVA的数据库连接池方案 总被引：1，自引：0，他引：1
介绍了、java的数据库访问机制，针对数据库系统开发中存在的问题，提出了改善系统性能的连接池技术．深入分析了连接池的管理策略，并构造了一个连接池实例。 相似文献
5.
Asp.net中运用存储过程实现Web数据分页查询 总被引：1，自引：0，他引：1
随着网络的发展，利用asp．net进行Web程序的开发已越来越广泛．如何提高Web数据访问性能也一直是开发人员研究的重要问题，本文针对Web数据库记录的显示问题，讨论了在Aspnet框架下使用存储过程的方法，并用具体实例给出了用存储过程实现Web查询分页的技术。 相似文献
6.
油气生产数据仓库系统需要强大而灵活的多维数据查询功能与表现能力。对通过MDX和SQI．实现多维分析的优缺点进行了比较,在此基础上提出了应用于油气勘探开发数据的多维分析形式语言RM—MDX。该多维查询语言以MDX标准语法为基础,根据项目应用背景进行了简化和优化处理,并通过用户自定义函数（存储过程）技术对RM—MDX语言做进一步扩展和改善,使其在针对油气生产数据仓库上的多维分析功能更加强大和容易实现。 相似文献
7.
8.
9.
吴巍 《科技成果管理与研究》2009,(9):63-66
提出了预测函数控制（PFC）与PID控制结合的新方法．并针对典型工业过程控制．分析了预测函数控制的基本原理和特点。基于Smith预估器．给出了工业过程控制中改进的预测函数控制算法,并用MATLAB进行仿真实验．仿真结果也表明其对被控系统有良好的鲁棒性、抑制干扰能力和跟踪性能。 相似文献
10.
基于嵌入式Linux系统的网络编程技术 总被引：1，自引：0，他引：1
本文详细的介绍了基于Linux系统的嵌入式ARM板与RC机之间的网络通讯技术，并结合实例阐述了Linux操作系统下Socket套接字常用函数的用法，实现了客户机/服务器模型的网络编程。 相似文献
11.
Augmented reality is very useful in medical education because of the problem of having body organs in a regular classroom. In this paper, we propose to apply augmented reality to improve the way of teaching in medical schools and institutes. We propose a novel convolutional neural network (CNN) for gesture recognition, which recognizes the human's gestures as a certain instruction. We use augmented reality technology for anatomy learning, which simulates the scenarios where students can learn Anatomy with HoloLens instead of rare specimens. We have used the mesh reconstruction to reconstruct the 3D specimens. A user interface featured augment reality has been designed which fits the common process of anatomy learning. To improve the interaction services, we have applied gestures as an input source and improve the accuracy of gestures recognition by an updated deep convolutional neural network. Our proposed learning method includes many separated train procedures using cloud computing. Each train model and its related inputs have been sent to our cloud and the results are returned to the server. The suggested cloud includes windows and android devices, which are able to install deep convolutional learning libraries. Compared with previous gesture recognition, our approach is not only more accurate but also has more potential for adding new gestures. Furthermore, we have shown that neural networks can be combined with augmented reality as a rising field, and the great potential of augmented reality and neural networks to be employed for medical learning and education systems. 相似文献
12.
13.
BP神经网络被广泛应用于模式识别、信号处理和自动控制等领域,其广泛性是由于它能实现任何连续映射,但由于BP网络训练所固有的复杂性,目前尚没有任何一种完全的算法能适用于任何BP网络的训练。本文介绍了MATLAB神经网络工具箱中各种训练算法的特点及其函数的参数形式,并对它们的收敛速度和内存消耗情况进行了比较,说明了其各自适用的网络。 相似文献
14.
《Journal of The Franklin Institute》2023,360(13):10080-10099
In this paper, the quasi-synchronization problem of heterogeneous stochastic coupled neural networks (HSCNNs) is discussed. The effects of the mixed time-varying delay and diffusion phenomenon on the system are considered separately in time and space. Moreover, different from the previous distributed control, boundary control is introduced to realize network synchronization. This not only reduces the space cost of the controller, but also makes it easier to implement. Thus, the mean-square quasi-synchronization of HSCNNs is guaranteed by using matrix inequality and stochastic analysis tools. In addition to focusing on systems with Neumann boundary conditions, we briefly investigate HSCNNs with time-invariant delays and mixed boundary conditions respectively, and provide sufficient conditions to achieve the desired performance. Finally, the correctness of the conclusion is verified by several examples. 相似文献
15.
This paper studies the problem of adaptive neural network (NN) output-feedback control for a group of uncertain nonlinear multi-agent systems (MASs) from the viewpoint of cooperative learning. It is assumed that all MASs have identical unknown nonlinear dynamic models but carry out different periodic control tasks, i.e., each agent system has its own periodic reference trajectory. By establishing a network topology among systems, we propose a new consensus-based distributed cooperative learning (DCL) law for the unknown weights of radial basis function (RBF) neural networks appearing in output-feedback control laws. The main advantage of such a learning scheme is that all estimated weights converge to a small neighborhood of the optimal value over the union of all system estimated state orbits. Thus, the learned NN weights have better generalization ability than those obtained by traditional NN learning laws. Our control approach also guarantees the convergence of tracking errors and the stability of closed-loop system. Under the assumption that the network topology is undirected and connected, we give a strict proof by verifying the cooperative persisting excitation condition of RBF regression vectors. This condition is defined in our recent work and plays a key role in analyzing the convergence of adaptive parameters. Finally, two simulation examples are provided to verify the effectiveness and advantages of the control scheme proposed in this paper. 相似文献
16.
《Information processing & management》2016,52(2):217-257
Transductive classification is a useful way to classify texts when labeled training examples are insufficient. Several algorithms to perform transductive classification considering text collections represented in a vector space model have been proposed. However, the use of these algorithms is unfeasible in practical applications due to the independence assumption among instances or terms and the drawbacks of these algorithms. Network-based algorithms come up to avoid the drawbacks of the algorithms based on vector space model and to improve transductive classification. Networks are mostly used for label propagation, in which some labeled objects propagate their labels to other objects through the network connections. Bipartite networks are useful to represent text collections as networks and perform label propagation. The generation of this type of network avoids requirements such as collections with hyperlinks or citations, computation of similarities among all texts in the collection, as well as the setup of a number of parameters. In a bipartite heterogeneous network, objects correspond to documents and terms, and the connections are given by the occurrences of terms in documents. The label propagation is performed from documents to terms and then from terms to documents iteratively. Nevertheless, instead of using terms just as means of label propagation, in this article we propose the use of the bipartite network structure to define the relevance scores of terms for classes through an optimization process and then propagate these relevance scores to define labels for unlabeled documents. The new document labels are used to redefine the relevance scores of terms which consequently redefine the labels of unlabeled documents in an iterative process. We demonstrated that the proposed approach surpasses the algorithms for transductive classification based on vector space model or networks. Moreover, we demonstrated that the proposed algorithm effectively makes use of unlabeled documents to improve classification and it is faster than other transductive algorithms. 相似文献
17.
脱机手写体字符识别技术是当前的热点和难点问题,是解决目前大量已有的文档资料录入工作的关键,该领域目前还没有十分成熟的产品,还正处于研究阶段,特别是如何提高其识别率已成为一个亟待解决的问题。本文根据数字笔画特征值和环的数目构建一种神经网络特征输入的新的特征提取方案,参照汉字基本笔画定义,采用环、横、竖、撇、捺、竖定义数字的五种笔画,运用单字单网的12个并行BP神经网络进行数字识别,为解决手写体字符识别中所存在的困难找到一种切实可行的新途径。 相似文献
18.
Automatic modulation classification (AMC) is one of the core technologies in non-cooperative communication. In the complex wireless environment, it is not easy to quickly and accurately recognize the modulation styles of signals by conventional methods. The deep learning method (DLM) can deal with the problem and achieve good effects. In conventional DLMs, the length of input data is fixed. However, the signal length in communication is changing, which may not make full use of the DLMs’ input signal information to improve the recognition accuracy. In this paper, the deep multi-hop convolutional neural network (CNN) is employed to learn the time-domain signal features with different lengths. The proposed network includes the multi-hop connection rate and the receptive field extension scope to dispose of the limitation. The experiment shows that the proposed network can achieve better recognition results under the sparse multi-hop network structure. The reception field extension scope is also conducive to further improve the recognition effects. Finally, the proposed network has shorter training time and smaller parameters, which is more convenient for training the network and deploying in the existing communication system. 相似文献
19.
In this paper, a novel complex-valued neural network (CVNN) is proposed to investigate a nonlinear complex-variable nonconvex optimization problem (CVNOP) subject to general types of convex constraints, including inequality and bounded as well as equality constraints. The designed neural network is available to search the critical point set of CVNOP. In contrast with other related neural networks to complex-variable optimization problem, network herein contains fewer neurons and does not depend on exact penalty parameters. To our best knowledge, this is the first attempt to exploit the neural network to solve nonconvex complex-variable optimization problem. Furthermore, the presented network is also capable of solving convex or nonconvex real-variable optimization problem (RVNOP). Different from other existing neural networks for RVNOP, our network avoids the redundant computation of inverse matrix and relaxes some additional assumptions, comprising the objective function is bounded below over the feasible region or the objective function is coercive. Several numerical illustrations and practical results in beamforming provide the viability of the proposed network. 相似文献
20.
通过构建科技成果转化评估指标体系,并借助于BP人工神经网络方法,实现对高校科技成果转化指标体系的综合评价。主要应用主成分分析方法(PCAM)对神经网络的输入层数据进行处理,使用模拟退火算法(SA)与神经网络结合的方法提高评价的精确度,并通过实证分析证明BP神经元网络在高校科技成果评估领域的的适用性。 相似文献