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Prediction of ultimate bearing capacity of Tubular T-joint under fire using artificial neural networks
Ultimate bearing capacity Tubular T-joint Artificial neural network Finite element analysis Fire
2012/3/23
An artificial neural network (ANN) model is developed for the prediction of the ultimate bearing capacity of tubular T-joint under fire. The input parameters of the network are composed of the diamete...
Prediction of ultimate bearing capacity of Tubular T-joint under fire using artificial neural networks
Ultimate bearing capacity Tubular T-joint Artificial neural network Finite element analysis Fire
2012/3/20
An artificial neural network (ANN) model is developed for the prediction of the ultimate bearing capacity of tubular T-joint under fire. The input parameters of the network are composed of the diamete...
We introduce a new procedure for training of artificial neural networks by using the approximation of an objective function by arithmetic mean of an ensemble of selected randomly generated neural netw...
Performance Improvement of GPS GDOP Approximation Using Recurrent Wavelet Neural Network
Rapid and Precise Calculation GPS GDOP RWNN
2013/3/5
One of the most important factors affecting the precision of the performance of a GPS receiver is the relative positioning of satellites to each other. Therefore, it is essential to choose appropriate...
An Improved Neural Architecture for Gaze Movement Control in Target Searching
An Improved Neural Architecture Gaze Movement
2013/7/24
This paper presents an improved neural architecture for gaze movement control in target searching. Compared with the four-layer neural structure proposed in [14], a new movement coding neuron layer...
Effect of moisture content on thermodynamic characteristics of grape: mathematical and artificial neural network modelling
sorption thermodynamic drying, neural network grape
2014/2/25
Artificial neural networks (ANNs) and four empirical mathematical models, namely Henderson, GAB, Halsey, and Oswin were used for the estimation of equilibrium moisture content (EMC) of the dried grape...
Application of Artificial Neural Networks Model as Analytical Tool for Groundwater Salinity
Groundwater Salinity Artificial Neural Networks Modeling Analytical Tool
2013/3/12
The main source of water in Gaza Strip is the shallow coastal aquifer. It is extremely deteriorated in terms of salinity which influenced by many variables. Studying the relation between these variabl...
Application of artificial neural network and wavelet transform for vibration analysis of combined faults of unbalances and shaft bow
Rotor test rig Rotor faults vibration analysis, unbalance
2010/9/26
The vibration analysis of rotating machinery can give an indication of the condition of potential faults such as unbalance, bent shaft, shaft crack, bearing clearance, rotor rub, misalignment, loosene...
Verification of filter efficiency of horizontal roughing filter by Weglin's design criteria and Artificial Neural Network
Verification filter efficiency horizontal roughing filter Artificial Neural Network
2010/9/26
The general objective of this study is to estimate the performance of the Horizontal Roughing Filter (HRF) by using Weglin's design criteria based on 1/3–2/3 filter theory. The main objective of the p...
基于BP神经网络的果蔬热导率预测模型（Prediction Model of Thermal Conductivities of Fruits and Vegetables Based on BP Neural Networks）
果蔬 热导率 BP神经网络
2010/12/29
通过微热探针法测试装置研究了30个品种的果蔬热导率与可溶性固形物含量、含水率、密度和硬度等因素的变化关系，提出了一种基于BP神经网络的果蔬热导率预测模型，并根据误差比较分析进行了模型优化。结果表明，该优化网络模型具有较好的热导率预测效果，平均相对误差为1.11%，平均绝对误差为0.0057W/(m?K)，可以用于果蔬贮藏加工业中果蔬热传递过程的计算。
基于改进BP神经网络的排种器充种性能预测（Prediction for Performance of Seed-filling Process Based on Improved BP Neural Network）
排种器 充种 性能预测 BP神经网络
2010/12/29
充种性能直接影响排种器排种质量，应用Matlab神经网络工具箱建立了排种器充种单粒率η1和空穴率η2的改进BP神经网络预测模型。选取转速n、种子当量直径d、充种角β和型孔直径D作为试验因素进行充种性能试验，获得64组单粒率和空穴率的试验结果。选取55组结果作为训练样本，采用Levenberg-Marquardt训练方法对建立的网络进行训练，并选取剩余的9组结果对训练好的网络进行仿真预测。其中，n、...
基于神经网络的车辆排气噪声声音品质预测技术（Sound Quality Prediction of Vehicle Exhaust Noise Based on Neural Network）
车辆 声音品质 排气噪声 神经网络
2010/12/29
通过评审团成对比较法测试得到18种车辆排气噪声的满意度评价，考察并选取响度、尖锐度、粗糙度、波动度和峭度作为描述车辆排气噪声声音品质的客观心理声学参数，使用BP神经网络理论建立车辆排气噪声声音品质神经网络预测模型，对排气噪声样本的满意度进行预测，并与使用多元线性回归模型所得的预测值进行了比较。结果表明，神经网络模型预测值更接近实测值，误差在10%范围以内，对于单一噪声样本满意度的预测精度高于多元线...
基于神经网络的离心泵能量性能预测（Energy Characteristics Prediction of Centrifugal Pumps Based on Artificial Neural Network）
离心泵 性能预测 神经网络
2010/12/29
总结了BP网络和RBF网络在离心泵能量性能预测中的应用现状，介绍了这两种网络的结构及特点。分别采用BP网络和RBF网络建立了离心泵能量性能预测模型。用57组数据对这两个预测模型进行了训练，并用6组数据对两种网络结构的性能预测模型进行了仿真。研究结果表面：两种网络结果的预测模型预测精度比较接近且预测结果的趋势也相同，BP网络预测精度略高于RBF网络；BP网络扬程平均预测误差为3.85%，效率平均预测...
基于遗传模糊神经网络的植物病斑区域图像分割模型（Image Segmentation Model of Plant Lesion Based on Genetic Algorithm and Fuzzy Neural Network）
植物病害 遗传算法 模糊神经网络
2010/12/29
针对植物病斑区域图像边界的模糊性和不确定性因素，利用模糊逻辑的推理规则和神经网络的自适应性，提出全规则的自适应模糊神经网络模型作为植物病叶图像像素归属的决策系统，并利用遗传算法对系统的可调整参数初始值进行全局优化，提高了网络训练速度，避免了传统BP算法的局部最小值。通过对马铃薯早疫病病斑图像分割的实验表明，该模型速度快且稳定，精度高且鲁棒性好，简单易于实现。
静液驱动履带车辆转向神经网络PID控制仿真（Steering Neural Network PID Control for Tracked Vehicle with Hydrostatic Drive）
履带车辆 静液驱动 转向 神经网络
2010/12/29
根据履带车辆转向运动学和动力学分析，提出转向控制策略，可在满足系统压力限制以及保证车辆转向安全条件下自动降低平均车速以保证驾驶员期望转向半径的准确实现。转向控制器由神经网络PID控制器和泵马达排量控制器组成。运用Matlab/Simulink对系统进行神经网络转向控制仿真分析，仿真结果表明，与传统PID控制相比较，神经网络控制输出超调量由10.5%降至4.1%，控制响应时间由4.8s降至2.2...