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研究神经网络在计算机网络安全评价中的应用价值。分析神经网络的特点及其发展历程,计算机网络安全的概念、影响因素及计算机系统的脆弱性,按照可行性、简要性、独立性、完备性和准确性的设计原则建立计算机网络安全评价体系,利用粒子群优化算法对BP神经网络进行优化,然后应用BP神经网络设计计算机网络安全评价模型,按照安全评价步骤对计算机网络安全进行评价。在计算机网络安全评价中应用神经网络可以降低计算机网络安全风险,降低由于安全问题造成的各项损失。神经网络是一种具有自适应、自组织、自学习能力的智能人工算法技术,在计算机网络安全评价中具有极大的应用价值。 相似文献
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目前,空间目标中约6%为正在工作的航天器,而约94%的空间目标为太空垃圾,严重干扰和限制了航天器发射、运行等正常的太空活动轨道,在有效清除空间碎片之前,必须对其进行有效识别。本文基于散射光谱,使用卷积神经网络对空间碎片四种材质进行分类识别,并与BP神经网络的识别结果分析比较。鉴于试验所得的材质的原始光谱信噪比低、特征信息弱等特点,需要对光谱信号进行预处理包括去噪、BRDF计算和归一化处理。然后各取四种材质的200帧样本数据进行训练,另各取50帧数据预测,结果表明:卷积神经网络的总体精度比BP神经网络低2%,耗时少101 s;而增加训练样本数据量达到每个材质各500帧时,卷积神经网络的总体精度仅比BP神经网络低0.05%,耗时则少了891 s,卷积神经网络极大的体现了其时间的优越性。该方法对大数据量的空间碎片材质的分类,具有较大的实用性和借鉴意义。 相似文献
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模拟数字电路故障诊断新方法 总被引:1,自引:0,他引:1
利用小波变换与神经网络相结合的方法,采用能量分布特征提取方法和改进BP算法,给出了一种基于小波变换和BP神经网络相结合的模拟电路故障诊断方法.用正弦信号仿真模拟电路,应用小波变换对模拟电路的采样信号进行多尺度分解,再进行能量分布特征提取,然后利用神经网络对各种状态下的特征向量进行分类识别,实现模拟电路故障诊断.在用神经网络诊断模拟电路的基础上,进行了将神经网络用于数字电路单故障诊断的研究.对两者的实例电路仿真结果表明,神经网络可以有效、方便地实现电路的故障检测和定位,准确率高,为故障诊断的研究提供了一种新思路. 相似文献
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社区时序数据建模是世界各国的学者研究的新型热点课题,人工神经网络环境下复杂非线性物联网技术社区时序数据系统得到了海量实践的应用。本课题对非线性物联网技术社区时序数据预测神经网络中存在的几个瓶颈进行分析探讨,基于提出人工神经网络非线性视角下物联网技术社区时序数据预测中的应用研究来优化预测神经网络环境下中的瓶颈。因此,通过人工神经网络社区数据的仿真实验表明该算法的高效性和实用性。 相似文献
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应用多层次前馈网络构造模糊变量隶属度函数和模糊推理控制模型,使神经网络不再表现为黑箱式映射,其所有节点和参数都具有模糊系统等价意义。将模糊规则与隶属度函数用神经网络表现出来。利用神经网络的自学习特性,实现隶属度函数和模糊规则的自动提取,可优化调整隶属度函数,同时模糊系统也弥补了神经网络运算速度慢的缺点。 相似文献
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基于模糊神经网络智能预测模型的设计与实现 总被引:1,自引:0,他引:1
针对智能决策支持系统中经常遇到的预测类问题,根据人工神经网络和模糊逻辑系统的各自特点,设计一种模糊神经网络模型,将模糊系统用类似于神经网络的结构表示,再用相应的学习算法训练模糊系统实现模糊推理.并对此模型进行预测验证和编程实现. 相似文献
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本文提出了一种新的ATM组播交换(Multicast Switching)神经网络调度方法。ATM组播交换的神经网络调度方法国外已有文献提出,但其神经网络的能量函数结构复杂难以用VLSI实现。我们提出的神经网络能量函数结构简单,其神经网络控制器容易实现。文中给出了神经网络的调度算法、能量函数及其神经网络控制器的实现方法。通过计算机模拟,证明该方法是非常有效的。 相似文献
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建立多层人工神经网络的最优化求解方法,以多层神经网络总体平均误差为目标函数,以权值和阈值作为设计变量,采用共轭梯度法对网络的权值和阈值进行优化计算,完成神经网络训练的方法.将优化原理应用到多层神经网络权值和阈值的计算问题,实现网络权值和阈值的快速计算,为分析神经网络的合理结构提供了必要条件. 相似文献
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Widrow-Hoff神经网络学习规则的应用研究 总被引:1,自引:0,他引:1
基于线性神经网络原理,提出线性神经网络的模型,并利用Matlab实现Widrow-Hoff神经网络算法。分析Matlab人工神经网络工具箱中有关线性神经网络的工具函数,最后给出线性神经网络在系统辨识中的实际应用。通过对线性神经网络的训练,进一步验证Widrow-Hoff神经网络算法的有效性,以及用其进行系统辨识的高精度拟合性。 相似文献
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Yong Li Zheng Tang GuangPu Xia RongLong Wang 《IEEE transactions on circuits and systems. I, Regular papers》2005,52(1):200-206
We propose a positively self-feedbacked Hopfield neural network architecture for efficiently solving crossbar switch problem. A binary Hopfield neural network architecture with additional positive self-feedbacks and its collective computational properties are studied. It is proved theoretically and confirmed by simulating the randomly generated Hopfield neural network with positive self-feedbacks that the emergent collective properties of the original Hopfield neural network also are present in this network architecture. The network architecture is applied to crossbar switching and results of computer simulations are presented and used to illustrate the computation power of the network architecture. The simulation results show that the Hopfield neural network architecture with positive self-feedbacks is much better than the previous works including the original Hopfield neural network architecture, Troudet's architecture and maximum neural network for crossbar switching in terms of both the computation time and the solution quality. 相似文献
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This paper proposes a method to design a robust controller by use of a neural network. The trained neural network functions as a sliding mode controller which is robust against uncertainties. From the analysis of the neural network, it is proved that the switching surface is not the same as the sliding surface like conventional sliding mode control theory. The neural network shows that the switching surface should be a nonlinear surface because of a hard limitation on control inputs, even if the designed sliding surface is linear. From the result of estimating the robustness of neural networks, we propose that generalization of neural networks which are used as controllers should be measured by the robustness. Numerical simulations show that the controller is robust against uncertainties and robustness can be improved by the proposed method. 相似文献
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基于BP神经网络的社团分类研究 总被引:1,自引:1,他引:0
社团分类对研究复杂网络及其特性具有重要意义,为此提出将神经网络的分类特性应用于社团分类中,以BP神经网络为模型,以Zachary研究的俱乐部成员关系网络为测试数据,利用社团节点与神经网络节点之间关系的相似性模拟社团分类结果,根据成员关系图建立邻接矩阵,利用邻接矩阵关系进行实验设计建立BP神经网络,通过已知样本学习和检验样本检验,实现成员关系网络的分类模拟,数值实验结果表明文章运用的仿真方法是可行有效的. 相似文献
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Liu G.P. Kadirkamanathan V. Billings S.A. 《IEEE transactions on systems, man and cybernetics. Part C, Applications and reviews》1999,29(1):34-43
This paper is concerned with the adaptive control of continuous-time nonlinear dynamical systems using neural networks. A novel neural network architecture, referred to as a variable neural network, is proposed and shown to be useful in approximating the unknown nonlinearities of dynamical systems. In the variable neural networks, the number of basis functions can be either increased or decreased with time, according to specified design strategies, so that the network will not overfit or underfit the data set. Based on the Gaussian radial basis function (GRBF) variable neural network, an adaptive control scheme is presented. The location of the centers and the determination of the widths of the GRBFs in the variable neural network are analyzed to make a compromise between orthogonality and smoothness. The weight-adaptive laws developed using the Lyapunov synthesis approach guarantee the stability of the overall control scheme, even in the presence of modeling error(s). The tracking errors converge to the required accuracy through the adaptive control algorithm derived by combining the variable neural network and Lyapunov synthesis techniques. The operation of an adaptive control scheme using the variable neural network is demonstrated using two simulated examples 相似文献
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Based on the growing demand for neural network technologies, various neural network inference engines are being developed. However, each inference engine has its own neural network storage format. There is a growing demand for standardization to solve this problem. This study presents interworking techniques for ensuring the compatibility of neural networks and data among the various deep learning frameworks. The proposed technique standardizes the graphic expression grammar and learning data storage format using the Neural Network Exchange Format (NNEF) of Khronos. The proposed converter includes a lexical, syntax, and parser. This NNEF parser converts neural network information into a parsing tree and quantizes data. To validate the proposed system, we verified that MNIST is immediately executed by importing AlexNet's neural network and learned data. Therefore, this study contributes an efficient design technique for a converter that can execute a neural network and learned data in various frameworks regardless of the storage format of each framework. 相似文献