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rbf nn
    Application of Incremental Genetic RBF NN to Hot Metal Desulfurization Pretreatment
    增量式遗传RBF神经网络在铁水脱硫预处理中的应用
    Safety mode of tower crane based on RBF NN
    基于RBF神经网络的塔式起重机安全状态模式识别
    A study of Modeling and Optimization Based on RBF NN and GA
    基于RBF神经网络和遗传算法的建模与优化研究
    The Simulative Model of the Tobacco Strips Product Line Based on RBF NN
    基于RBF神经网络的制丝生产线仿真模型
    Detection Method of Diesel Engine's Exhaust Temperature Sensor Based on RBF NN
    基于RBF神经网络的柴油机排气温度传感器检测方法的研究
    Direct Adptive Neuro Flight Controller Based on RBF NN
    基于RBF神经网络的直接自适应飞行控制器
    Smith-PID Control Based on RBF NN Tuning and Its Application
    基于RBF神经网络整定的Smith-PID控制及其应用
    A dynamic learning method of T-S fuzzy based RBF neural network is proposed on the basis of studying T-S fuzzy model and RBF neural network, by which the learning method of RBF NN is improved.
    在对T-S模糊模型与RBF神经网络进行深入研究的基础上,提出一种基于T-S模糊模型的RBF神经网络的动态学习算法,改进了RBF网络的学习方法。
    Here prediction models are built for BP NN and RBF NN separately, and simulated in test, with the result that BP model is inferior to RBF model in network practice speed and accuracy of prediction etc.
    本论文分别针对BP神经网络和RBF神经网络建立了预测模型,并对它们进行了仿真试验,发现BP预测模型在网络训练速度和预测精度等方面都较RBF模型差,所以在强度预测应用软件的开发中,预测模型采用RBF神经网络建立。
    Two soft sensor modeling methods based on Support Vector Machines (SVMs) and RBF Neural Networks (RBF NN) were proposed.
    给出了基于支持向量机(SVMs)和RBF神经网络的软测量建模方法.
    Handled annual flow series with RBF NN model and mix-regression model based on wavelet analysis, and threshold auto-regression model based on genetic simulation anneal algorithm to medium-and long-range forecast. Based on the theory of non-linearity combination forecast, established combination forecast model based on BP NN, obtained better practicability about flow forecast outcome, provided a probably new approach for runoff medium- and long-range forecast.
    本文运用基于小波分析的RBF神经网络模型、混合回归模型和基于遗传模拟退火算法的门限自回归模型对年径流序列进行了中长期预测,每个模型各具特色,最后根据非线性组合预测的原理,建立了基于BP神经网络的组合预测模型,得到了实用性较好的径流预测结果,为径流中长期预测提供了一种值得探讨和实践的新的可能途径。
    1. For uncertain robot this paper simplifies cost and structure by using PD+feedforword control structure based on robust adaptive control. The method identifies uncertain upper boundary function by using RBF NN, so the system has more adaptive ability.
    1. 在鲁棒自适应控制的基础上,采用 PD+前馈的控制结构,简化了控制器的成本和结构,用 RBF 神经网络对机器人的不确定性上界的函数进行辨识,使控制系统具有较强的自适应能力。
    and the approximate inversion of the flighter is provided by off-line trained neural networks; a direct adaptive self-repairing control reconfiguration approach based on inverse system is pretented,and the RBF NN on-line adaptive architecture is applied to compensate the inversion error due to modeling uncertainties and failures;
    设计了基于逆系统的RBF神经网络直接自适应重构控制方案,运用RBF神经网络在线自适应来补偿由于建模不准确及飞机发生故障而出现的逆误差:基于Lyapunov定理给出了神经网络的自适应调节律;
    Based on the above direct adaptive reconfigurable control method,the weights,centers and widths of the Gaussian function of RBF NN adptive adjust method,full adaptive control,is augmented;
    基于上述直接自适应重构控制方案,给出了对RBF神经网络外层权重、内层中心和宽度进行调节的完全自适应控制律重构;
    Comparedwith BP NN, RBF NN has more quick convergence velocity and can effectively avoid the problem of local minimum, but the selection of centers of RBF has become the bottleneck that retards the broader application of RBF NN.
    RBF神经网络相对BP网络具有收敛速度快、避免陷入局部极值等优点,但径向基函数中心的选取一直是阻碍BRFNN推广使用的瓶颈问题。
    On the basis of analyzing the existing diagnosis methods, this thesis presents using the RBF neural network as the core algorithm. The test on a 4-bus system prove that RBF NN is very suitable to fault section estimation.
    本文在分析现有诊断方法的基础上,提出采用RBF神经网络作为核心诊断算法,并对一个四母线系统进行测试,测试结果表明该算法适合用于故障诊断。
    In order to reduce the training difficult of RBF NN and enhance the reliability of diagnosis system, based upon the thought of “divide and conquer”, we first divide the grid into desired subnets, and then develop the distributed fault section estimation system. The test on the 118-bus system proves that this system is effective in fault section estimation.
    为了降低RBF神经网络的训练难度和提高诊断的可靠性,基于“分而治之”的思想,本文在将系统分割的基础上,采用多代理技术,开发出分布式故障诊断系统,并用IEEE118母线系统进行测试,结果表明该系统是有效的。
    The self-connection neural unit has been added to RBF NN which made RBF NN had the capacity of memorizing the past-time data .
    RRBF神经网络模型是在RBF神经网络的输入上加入了自反馈的神经元,使RRBF网络对过去时态的数据具有了记忆能力,其学习算法为在线的学习算法。
    The main contributions of the thesis are as follows:A prediction model of endpoint steel temperature (or the endpoint carbon content) is designed by the advantage of the RBF NN that has quick convergence velocity and can effectively avoid the problem of local minimum.
    利用RBF神经网络具有收敛速度快、避免陷入局部极值的优点,建立了终点钢水温度和终点碳含量的预报模型。
    Using RBF NN can restore the airplane to the normal state by online regulating the effect of the uncertainties and the error caused by fuzzy modeling.
    采用RBF神经网络在线补偿不确定项和模糊建模误差,能够使飞机获得满意的控制效果。
 

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