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针对神经网络在预测复合镀层性能方面的应用情况,以及传统的BP神经网络存在缺陷;通过对RBF神经网络的基本原理和特点的研究,建立了利用RBF神经网络对Ni-TiN纳米复合镀层显微硬度进行预测的模型。通过实验数据验证了所建立的RBF神经网络模型具有很高的精确度,其最小相对误差可达0.62%,而且所建立的预测模型具有优化工艺参数的功能,对复合镀层的其它性能进行预测具有指导意义。 相似文献
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在介绍RBF神经网络基本思想的基础上,建立了爆破振动预测模型,用RBF神经网络方法对质点振幅、主振频率及振动持续时间进行预测。用阳泉煤矿主井爆破开挖工程中所监测到的振动数据对模型进行了训练,并对27组数据进行了预测,实测结果和模型预测结果的对比表明,RBF神经网络预测模型能反映影响因素与特征量之间的非线性关系,适用于爆破振动特征参量预测。 相似文献
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针对水下复杂工作环境下机械臂控制性能易受影响,而传统控制方法效果不佳的问题,提出了一种基于模糊RBF(radial basis function,径向基函数)神经网络的智能控制器,用于精确、稳定地控制水下机械臂。考虑到在水扰动环境下,机械臂通常受到附加质量力、水阻力和浮力的影响,运用拉格朗日法和Morison方程,建立包含水动力项的二杆机械臂动力学模型,通过模糊RBF神经网络对水下机械臂动力学方程中的水动力不确定项进行总体识别并拟合,利用模糊系统启发式搜索和RBF神经网络推理速度较快的优点,使水下机械臂系统具有较高的控制精度和较强的自适应性。考虑到水动力项,采用Lyapunov稳定性理论验证了水下机械臂系统的稳定性。最后利用MATLAB对二杆机械臂进行轨迹跟踪控制仿真实验,并对比模糊RBF神经网络与常规RBF神经网络识别方法和传统模糊控制方法的控制效果。仿真结果表明:与常规RBF神经网络识别方法相比,模糊RBF神经网络控制下二杆机械臂关节1的响应时间缩短了91%,相对误差减小了88%,关节2的响应时间缩短了92%,相对误差降低了77%;与传统模糊控制方法相比,关节1的相对误差减小了65%,关节2的相对误差减小了10%。研究结果表明模糊RBF神经网络的控制效果优于常规RBF神经网络识别方法和传统模糊控制方法,可为水下机械臂的控制提供一种精度较高、较有效的方法。 相似文献
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林跃东 《制冷与空调(北京)》2022,(1):90-94
商场建筑夏季空调能耗占总能耗的50%以上,鉴于空调能耗较高,对空调能耗进行预测有利于提升运行经济性.针对商场建筑空调系统非线性、多变量等问题,提出一种基于RBF神经网络空调系统能耗预测模型.该方法将日最高温度、日最低温度、日平均温度、日最高湿度、日最低湿度、日平均风速和空调能耗作为RBF神经网络的输入,建立空调系统能耗... 相似文献
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径向基函数神经网络(RBFNN)具有最优逼近和全局逼近的特性,在函数拟合方面优于传统的BP网络,将在化工领域广泛使用的软测量技术应用于电机系统的转矩测量,该方法的可行性进行了论证,并运用RBF神经网络建立转矩的软测量模型。同时建立了基于BP神经网络的软测量模型,用改进的kvenberg—Marquardt算法对BP神经网络进行学习和训练,并对两种网络进行了对比。该方法只需要电流信息,辨识方法简单。研究表明,RBF神经网络辨识效果优于BP神经网络。 相似文献
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This study presents a hybrid learning neural fuzzy system for accurately predicting system reliability. Neural fuzzy system learning with and without supervision has been successfully applied in control systems and pattern recognition problems. This investigation modifies the hybrid learning fuzzy systems to accept time series data and therefore examines the feasibility of reliability prediction. Two neural network systems are developed for solving different reliability prediction problems. Additionally, a scaled conjugate gradient learning method is applied to accelerate the training in the supervised learning phase. Several existing approaches, including feed‐forward multilayer perceptron (MLP) networks, radial basis function (RBF) neural networks and Box–Jenkins autoregressive integrated moving average (ARIMA) models, are used to compare the performance of the reliability prediction. The numerical results demonstrate that the neural fuzzy systems have higher prediction accuracy than the other methods. Copyright © 2005 John Wiley & Sons, Ltd. 相似文献
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基于RBF网络和NIRS的绿茶水分含量分析模型 总被引:4,自引:4,他引:4
基于径向基函数(RBF)和反向传播(BP)神经网络分别建立了绿茶水分含量的近红外光谱分析模型.结果表明:RBF网络预测模型的相关系数r(p)=0.933,预测标准误RMSEP=0.528%;BP网络预测模型的相关系数r(p)=0.914,预测标准误RMSEP=0.598%.RBF网络模型优于BP网络模型. 相似文献
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Amin Bemani Alireza Baghban Shahaboddin Shamshirband Amir Mosavi Peter Csiba Annamaria R. Varkonyi-Koczy 《计算机、材料和连续体(英文)》2020,63(3):1175-1204
In the present work, a novel machine learning computational investigation is
carried out to accurately predict the solubility of different acids in supercritical carbon
dioxide. Four different machine learning algorithms of radial basis function, multi-layer
perceptron (MLP), artificial neural networks (ANN), least squares support vector machine
(LSSVM) and adaptive neuro-fuzzy inference system (ANFIS) are used to model the
solubility of different acids in carbon dioxide based on the temperature, pressure, hydrogen
number, carbon number, molecular weight, and the dissociation constant of acid. To
evaluate the proposed models, different graphical and statistical analyses, along with novel
sensitivity analysis, are carried out. The present study proposes an efficient tool for acid
solubility estimation in supercritical carbon dioxide, which can be highly beneficial for
engineers and chemists to predict operational conditions in industries. 相似文献
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This paper explores the applicability of neural networks for analyzing the uncertainty spread of structural responses under the presence of one-dimensional random fields. Specifically, the neural network is intended to be a partial surrogate of the structural model needed in a Monte Carlo simulation, due to its associative memory properties. The network is trained with some pairs of input and output data obtained by some Monte Carlo simulations and then used in substitution of the finite element solver. In order to minimize the size of the networks, and hence the number of training pairs, the Karhunen–Loéve decomposition is applied as an optimal feature extraction tool. The Monte Carlo samples for training and validation are also generated using this decomposition. The Nyström technique is employed for the numerical solution of the Fredholm integral equation. The radial basis function (RBF) network was selected as the neural device for learning the input/output relationship due to its high accuracy and fast training speed. The analysis shows that this approach constitutes a promising method for stochastic finite element analysis inasmuch as the error with respect to the Monte Carlo simulation is negligible. 相似文献
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给出一种基于逆问题求解的人-车-路闭环系统操纵性能优化的方法.利用径向基函数神经网络建立了汽车侧向位移与方向盘转角及其它响应之间的映射关系,由跟踪路径反求出方向盘转角及汽车的其它响应,进而计算闭环系统的操纵性能评价指标并进行优化.该方法是在不同汽车方案具有相同实际行驶路径的基础上对操纵性能进行分析并优化,从而得到的最优汽车方案在跟踪某一典型路径时具有最好的操纵性能. 相似文献