基于熵值法径向基神经网络的清扫车吸尘口垃圾颗粒驻留时间预测

针对清扫车吸尘口内垃圾颗粒驻留时间受到结构类、固相类及气相类因素多类型、非线性作用预测难的问题,提出了基于熵值法的径向基神经网络(RBFNN)的垃圾颗粒驻留时间预测方法,该方法考虑了吸尘负压、滚刷转速、颗粒质量、颗粒密度、颗粒流量和吸尘管直径6种因素,将采用熵值法求解的因素权重作为输入扰动变量,建立了垃圾颗粒驻留时间的预测模型。结果表明：与采用传统RBFNN的预测方法相比,所提方法具有预测精度高的优点,可较好地解决清扫车吸尘口垃圾颗粒驻留时间预测难的问题,有助于提升清扫车吸尘系统设计水平。     

基于径向基神经网络的明渠流量软测量方法

为克服现有明渠流量测量方法在监测自动化、测量准确度、测量成本和适用范围等方面存在的不足,在新兴的大尺度粒子图像测速(large-scale particle image velocimetry,LSPIV)技术的框架下,设计了一种基于径向基神经网络(radial basis function neural networ...     

A definition of partial derivative of random functions and its application to RBFNN sensitivity analysis

Considering the inputs of a feed-forward neural network as random variables, this paper proposes a definition of partial derivative of a function with respect to a random variable in the probability measure space. The mathematical expectation of the mean square or absolute value of the partial derivative is regarded as a type of measure of the network's sensitivity, which extends Zurada's sensitivity definition of networks in Zurada et al, [Perturbation method for deleting redundant inputs of perceptron networks, Neurocomputing 14 (1997) 177–193] from the certain environment to the stochastic environment. Furthermore, for the purpose of network's redundant feature deletion or feature selection, the new sensitivity measure is applied to the sensitivity analysis of Radial Basis Function Neural Networks (RBFNNs). The feasibility and the effectiveness of the sensitivity approach to redundant feature deletion are illustrated.     

基于改进径向基函数神经网络的激光陀螺温度补偿

传统的径向基神经网络(RBFNN)在激光陀螺零偏的温度补偿过程中会由于随机选取中心不合适而导致算法效率降低和数值病态,故本文提出了一种基于Kohonen网络和正交最小二乘(OLS)算法的RBFNN温度补偿方法。介绍了该方法的原理及建模步骤,设计了常温和变温环境下激光陀螺的数据采集试验及其温度补偿试验。由于结合了Kohonen网络的模式分类能力和OLS的优化选择能力,该方法可以快速、准确地辨识出受温度影响的激光陀螺零偏。利用逐步回归法、RBFNN法及其改进方法对多种温变环境影响的激光陀螺零偏进行了辨识与补偿试验,试验结果表明,在常温环境下,三者的辨识能力相当;随着温变速率的上升,改进RBFNN法不仅节省了时间,其补偿后的零偏也均小于5×10-4(°)/h(1σ),提高精度均能达86%以上。得到的结果表明改进RBFNN法提高了辨识精度且稳定、有效,适用于多种温度变化环境下激光陀螺零偏的温度补偿。     

一种基于车体侧向动力学的自主车定位方法

定位是车辆自主驾驶研究过程中的关键问题.从车辆的侧向动力学模型出发,研究并分析了其稳态响应结果,以此建立了车辆定位模型.由于定位模型具有非线性、多自由度、多变元的特点,在实际定位中很难应用.因此,在定位模型中采用了一种动态自适应网络即改进型的RBFNN,它能够动态调节网络的规模和参数,具有较强的逼近能力以及自学习能力;并利用Kalman滤波器对输入的前轮摆角以及车速信号进行滤波处理,减小误差,提高定位精度.实车实验中,定位方法能够得到较高的定位结果,东、北向位置误差均不超过1米.实验结果表明,是一种可行、有效的定位方法,可以在实车上使用.     

新品上市的CBR动态预测

针对新品上市数据匮乏、不确定性高所带来的预测难点,提出了关于案例推理和改进粒子群神经网络的动态预测方法.首先考虑产品属性及销售序列特征,提出两阶段综合聚类算法划分案例集合;其次采用核心案例的相似性搜索机制确定RBF神经网络模型的训练集,并通过动态聚类和改进的粒子群算法进行网络训练及参数优化;最后采用相异距离的聚类方法保留预测结果,实现模型的动态扩展.企业实例及公共数据集的仿真结果表明,CBR-IPRBF动态预测方法能够适用于任何类型新品上市的销量预测,且对于数据量不足等非理想状况具有较优的性能.模型预测精度高,算法扩展性强,具有广泛适用性,能够为企业提供实际、有效的决策支持.     

基于ARM嵌入式系统的软测量应用

在基于Windows CE操作系统的ARM嵌入式系统上实现了一套通用工业过程软仪表。选用ARM嵌入式系统作为硬件平台,基于普通PC机上的嵌入式应用软件开发包Embeded VC 开发,应用双重RBF神经网络的模型作为软测量的数学模型。该软件包采用面向对象的软件体系结构,根据模块细化原则可以划分为系统调度、数据预处理、系统组态、核心算法、通讯和人机界面等6个模块。应用该软仪表对某化工厂加压甲醇精馏塔塔顶甲醇浓度模型进行仿真测试,取得了较好的结果。     

基于神经网络的污水出水COD预测模型

研究了在污水处理过程中基于BP和RBF神经网络的活性污泥法污水处理系统建模方法,利用重庆唐家桥污水处理厂的监测数据对基于BP和RBF神经网络建立的污水处理厂出水COD预测模型进行了仿真分析,讨论了2种模型的学习效率和影响预测精度的诸多因素,同时对污水处理出水COD浓度预测模型进行了优化,最终确定了基于神经网络的污水处理厂出水水质预测模型.最后还使用重庆唐家桥污水处理厂的监测数据对确定的模型进行了应用实证研究.     

基于专家系统的汽车衡故障传感器判别

传感器是自动化设备的核心部件,传感器故障检测显得尤为重要。针对目前汽车衡维护、检修的困难,为有效准确判断故障传感器,提出了以径向基函数神经网络（RBFNN）预估值的初始化数据库专家系统判别方法。经现场测试准确率达到96%以上,从而有效简单地判定传感器好坏和识别故障传感器的位置。     

Modeling, analysis and optimization of aircyclones using artificial neural network, response surface methodology and CFD simulation approaches

The pressure drop is an important performance parameter to evaluate and design cyclone separators. In order to accurately predict the complex non linear relationships between pressure drop and geometrical dimensions, a radial basis neural network (RBFNN) is developed and employed to model the pressure drop for cyclone separators. The neural network has been trained and tested by experimental data available in literature. The result demonstrates that artificial neural networks can offer an alternative and powerful approach to model the cyclone pressure drop. Four mathematical models (Muschelknautz method “MM”, Stairmand, Ramachandran and Shepherd & Lapple) have been tested against the experimental values. The residual error (the difference between the experimental value and the model value) of the MM model is the lowest. The analysis indicates the significant effect of the vortex finder diameter D_x and the vortex finder length S, the inlet width b and the total height H_t. The response surface methodology has been used to fit a second order polynomial to the RBFNN. The second order polynomial has been used to get a new optimized cyclone for minimum pressure drop using the Nelder-Mead optimization technique. A comparison between the new design and the standard Stairmand design has been performed using CFD simulation. CFD results show that the new cyclone design is very close to the Stairmand high efficiency design in the geometrical parameter ratio, and superior for low pressure drop at nearly the same cut-off diameter. The new cyclone design results in nearly 75% of the pressure drop obtained by the old Stairmand design at the same volume flow rate.