Research on runoff variations based on wavelet analysis and wavelet neural network model： A case study of the Heihe River drainage basin （1944-2005）

The Heihe River drainage basin is one of the endangered ecological regions of China. The shortage of water resources is the bottleneck, which constrains the sustainable development of the region. Many scholars in China have done researches concerning this problem. Based on previous researches, this paper analyzed characteristics, tendencies, and causes of annual runoff variations in the Yingluo Gorge （1944-2005） and the Zhengyi Gorge （1954-2005）, which are the boundaries of the upper reaches, the middle reaches, and the lower reaches of the Heihe River drainage basin, by wavelet analysis, wavelet neural network model, and GIS spatial analysis. The results show that： （1） annual runoff variations of the Yingluo Gorge have principal periods of 7 years and 25 years, and its increasing rate is 1.04 m^3/s.10y; （2） annual runoff variations of the Zhengyi Gorge have principal periods of 6 years and 27 years, and its decreasing rate is 2.25 m^3/s.10y; （3） prediction results show that： during 2006-2015, annual runoff variations of the Yingluo and Zhengyi gorges have ascending tendencies, and the increasing rates are respectively 2.04 m^3/s.10y and 1.61 m^3/s.10y; （4） the increase of annual runoff in the Yingluo Gorge has causal relationship with increased temperature and precipitation in the upper reaches, and the decrease of annual runoff in the Zhengyi Gorge in the past decades was mainly caused by the increased human consumption of water resources in the middle researches. The study results will provide scientific basis for making rational use and allocation schemes of water resources in the Heihe River drainage basin.

Wavelet Neural Network Based on NARMA-L2 Model for Prediction of Thermal Characteristics in a Feed System

Research of thermal characteristics has been a key issue in the development of high-speed feed system. Most of the work carried out thus far is based on the principle of directly mapping the thermal error against the temperature of critical machine elements irrespective of the operating conditions. But recent researches show that different sets of operating parameters generated significantly different error values even though the temperature of the machine elements generated was similar. As such, it is important to develop a generic thermal error model which is capable of evaluating the positioning error induced by different operating parameters. This paper ultimately aims at the development of a comprehensive prediction model that can predict the thermal characteristics under different operating conditions （feeding speed, load and preload of ballscrew） in a feed system. A novel wavelet neural network based on feedback linearization autoregressive moving averaging （NARMA-L2） model is introduced to predict the temperature rise of sensitive points and thermal positioning errors considering the different operating conditions as the model inputs. Particle swarm optimization（PSO） algorithm is brought in as the training method. According to ISO230-2 Positioning Accuracy Measurement and ISO230-3 Thermal Effect Evaluation standards, experiments under different operating conditions were carried out on a self-made quasi high-speed feed system experimental bench HUST-FS-001 by using Pt100 as temperature sensor, and the positioning errors were measured by Heidenhain linear grating scale. The experiment results show that the recommended method can be used to predict temperature rise of sensitive points and thermal positioning errors with good accuracy. The work described in this paper lays a solid foundation of thermal error prediction and compensation in a feed system based on varying operating conditions and machine tool characteristics.

Wavelet neural network aerodynamic modeling from flight data based on pso algorithm with information sharing and velocity disturbance

For the accurate description of aerodynamic characteristics for aircraft,a wavelet neural network (WNN) aerodynamic modeling method from flight data,based on improved particle swarm optimization (PSO) algorithm with information sharing strategy and velocity disturbance operator,is proposed.In improved PSO algorithm,an information sharing strategy is used to avoid the premature convergence as much as possible;the velocity disturbance operator is adopted to jump out of this position once falling into the premature convergence.Simulations on lateral and longitudinal aerodynamic modeling for ATTAS (advanced technologies testing aircraft system) indicate that the proposed method can achieve the accuracy improvement of an order of magnitude compared with SPSO-WNN,and can converge to a satisfactory precision by only 60 120 iterations in contrast to SPSO-WNN with 6 times precocities in 200 times repetitive experiments using Morlet and Mexican hat wavelet functions.Furthermore,it is proved that the proposed method is feasible and effective for aerodynamic modeling from flight data.

Backstepping sliding mode control with self recurrent wavelet neural network observer for a novel coaxial twelve-rotor UAV

The robust attitude control for a novel coaxial twelve-rotor UAV which has much greater payload capacity,higher drive capability and damage tolerance than a quad-rotor UAV is studied. Firstly,a dynamical and kinematical model for the coaxial twelve-rotor UAV is designed. Considering model uncertainties and external disturbances,a robust backstepping sliding mode control( BSMC) with self recurrent wavelet neural network( SRWNN) method is proposed as the attitude controller for the coaxial twelve-rotor. A combinative algorithm of backstepping control and sliding mode control has simplified design procedures with much stronger robustness benefiting from advantages of both controllers. SRWNN as the uncertainty observer is able to estimate the lumped uncertainties effectively.Then the uniformly ultimate stability of the twelve-rotor system is proved by Lyapunov stability theorem. Finally,the validity of the proposed robust control method adopted in the twelve-rotor UAV under model uncertainties and external disturbances are demonstrated via numerical simulations and twelve-rotor prototype experiments.

Prediction of Al(OH)_3 fluidized roasting temperature based on wavelet neural network

The recycle fluidization roasting in alumina production was studied and a temperature forecast model was established based on wavelet neural network that had a momentum item and an adjustable learning rate. By analyzing the roasting process, coal gas flux, aluminium hydroxide feeding and oxygen content were ascertained as the main parameters for the forecast model. The order and delay time of each parameter in the model were deduced by F test method. With 400 groups of sample data (sampled with the period of 1.5 min) for its training, a wavelet neural network model was acquired that had a structure of {7 211}, i.e., seven nodes in the input layer, twenty-one nodes in the hidden layer and one node in the output layer. Testing on the prediction accuracy of the model shows that as the absolute error ±5.0 ℃ is adopted, the single-step prediction accuracy can achieve 90% and within 6 steps the multi-step forecast result of model for temperature is receivable.

Self-Constructing Neural Network Modeling and Control of an AGV

Tracking precision of pre-planned trajectories is essential for an auto-guided vehicle (AGV). The purpose of this paper is to design a self-constructing wavelet neural network (SCWNN) method for dynamical modeling and control of a 2-DOF AGV. In control systems of AGVs, kinematical models have been preferred in recent research documents. However, in this paper, to enhance the trajectory tracking performance through including the AGV’s inertial effects in the control system, a learned dynamical model is replaced to the kinematical kind. As the base of a control system, the mathematical models are not preferred due to modeling uncertainties and exogenous inputs. Therefore, adaptive dynamic and control models of AGV are proposed using a four-layer SCWNN system comprising of the input, wavelet, product, and output layers. By use of the SCWNN, a robust controller against uncertainties is developed, which yields the perfect convergence of AGV to reference trajectories. Owing to the adaptive structure, the number of nodes in the layers is adjusted in online and thus the computational burden of the neural network methods is decreased. Using software simulations, the tracking performance of the proposed control system is assessed.

基于自回归小波神经网络的机械臂自适应滑模控制

针对机械臂存在模型不确定和未知扰动的问题,提出一种动力学模型参数分块逼近的神经网络非奇异终端滑模(nonsingular terminal sliding mode, NTSM)控制方法.为加快系统跟踪误差的收敛速度,避免传统终端滑模存在的奇异性问题,采用一种非奇异终端滑模面.利用多组自回归小波神经网络(self-recurrent wavelet neural network, SRWNN)分块逼近系统未知的动力学模型参数,并采用自适应更新律调整权重.通过积分控制项补偿SRWNN的逼近误差,并使用Lyapunov稳定性理论证明了系统稳定性.使用MATLAB进行仿真分析,分块SRWNN滑模控制与滑模控制、整体SRWNN滑模控制相比,关节角度跟踪误差的平均稳态误差分别降低了31.9%、76.5%,表明此方法是一种可靠、有效的轨迹跟踪控制方法.

基于神经网络模型的朝阳市生猪价格预测

选取2020年1月至2023年6月朝阳市生猪日价格和猪饲料中豆粕的日价格作为研究数据,分别建立BP神经网络模型和小波神经网络模型对朝阳市生猪价格进行预测.将前160周的价格数据作为BP神经网络模型和小波神经网络模型训练集数据,161~180周的价格数据作为预测数据,通过图形显示预测值和实际值变化,计算2种神经网格模型的平均绝对误差和均方根误差.通过误差比较分析得出,在朝阳市生猪价格波动领域,小波神经网络模型优于BP神经网络模型,建议推广应用.

基于整体退火遗传小波网络的计量终端可靠性预测

为了解决小波神经网络初值敏感性及收敛稳定性问题,以提高计量终端软件可靠性预测建模的效率及准确性。文章完善了整体退火遗传算法(WAGA),并验证了其具有极强的整体收敛和全局优化能力,利用其全局寻优能力,优化小波神经网络(WNN)的参数,提出基于整体退火遗传小波神经网络(WAGA-WNN)的建模方法;用该方法建立计量终端的软件可靠性预测模型。实验结果表明,该方法可以解决小波神经网络初值敏感性及收敛稳定性难题,建立的软件可靠性预测模型效率和准确度较高。

基于SCADA告警数据的电网故障类型判断方法

为了提高电网故障诊断效果,提出基于SCADA告警数据的电网故障类型判断方法。构建引入动量项、自适应学习率的改进小波神经网络故障识别模型,采用提升小波对元器件两端线路数正序信号进行分裂、预估、调整等过程分解,获取不同尺度正序信号输入神经网络,输出结果即为细化的电网故障类型。实验结果表明,分解尺度为3时,故障录波信号均方误差最小,小波神经网络性能更稳定。该方法根据相间两相电流突变量情况判断故障类型及故障相,可精准判断电网故障类型及故障原因。

基于交通小区短时交通生成预测的城市道路网短时动态交通预测研究

城市道路周边用地性质的不同导致短时发生和吸引交通量的差异,直接影响城市道路网上的交通流。论文对城市路网中各地块的发生和吸引交通量进行短时预测,将预测的交通量通过动态交通分配到城市路网中,以此实现城市路网短时动态交通预测。将单一的BP神经网络模型为对比模型,对模型进行训练及参数标定,并检验模型的预测效果。实验结果表明,论文所提的预测方法与BP神经网络相比,预测精度最高可提升84.28%。

A Wavelet Neural Network Based Non-linear Model Predictive Controller for a Multi-variable Coupled Tank System

In this paper, a novel real time non-linear model predictive controller(NMPC) for a multi-variable coupled tank system(CTS) is designed. CTSs are highly non-linear and can be found in many industrial process applications. The involvement of multi-input multi-output(MIMO) system makes the design of an effective controller a challenging task. MIMO systems have inherent couplings,interactions in-between the process input-output variables and generally have an complex internal structure. The aim of this paper is to design, simulate, and implement a novel real time constrained NMPC for a multi-variable CTS with the aid of intelligent system techniques. There are two major formidable challenges hindering the success of the implementation of a NMPC strategy in the MIMO case. The first is the difficulty of obtaining a good non-linear model by training a non-convex complex network to avoid being trapped in a local minimum solution. The second is the online real time optimisation(RTO) of the manipulated variable at every sampling time.A novel wavelet neural network(WNN) with high predicting precision and time-frequency localisation characteristic was selected for an MIMO model and a fast stochastic wavelet gradient algorithm was used for initial training of the network. Furthermore, a genetic algorithm was used to obtain the optimised parameters of the WNN as well as the RTO during the NMPC strategy. The proposed strategy performed well in both simulation and real time on an MIMO CTS. The results indicated that WNN provided better trajectory regulation with less mean-squared-error and average control energy compared to an artificial neural network. It is also shown that the WNN is more robust during abnormal operating conditions.

光伏电站直流输电线路早期绝缘故障识别与定位

直流输电线路发生早期绝缘故障时电流波动小、故障现象不明显,难以快速识别以采取保护措施,光伏电站线路拓扑结构复杂,不易准确定位故障发生位置。该文提出一种连续小波变换和混合神经网络模型结合的方法,可在尽可能短的时间完成故障识别与定位。该方法首先利用连续小波变换对暂态零模电流信号提取二维时频矩阵特征,压缩为彩色图像;然后,将图像送入神经网络模型中进行训练。该混合神经网络模型通过结合卷积神经网络和门控循环单元,提高识别精度并减少训练时间。最后,为验证本方法的优势,在高噪声环境下选取4条直流输电线路分别进行4种时频分析方法、3种神经网络模型仿真对比后,又对早期绝缘故障单独进行识别仿真试验,结果表明该方法可有效识别出早期绝缘故障并定位至发生线路,且具有较强的抗噪能力。

基于改进小波神经网络的大数据网络安全态势预测

为提升网络安全态势预测的精度,提出使用动量因子(Momentum Factor,MF)改进小波神经网络(Wavelet Neural Network,WNN),形成MF-WNN的网络安全态势要素提取模型,通过MF对WNN的权值进行精度优化。实验结果表明,与其他3种常用的预测模型相比,基于改进WNN的预测模型具有较高的精确度。

一种组合模型的电离层总电子含量预报方法

针对电离层总电子含量(TEC)的非线性、非平稳等多种因素影响会导致全球导航定位服务数据的高噪声问题,提出一种小波分解、长短期记忆(LSTM)网络模型、埃尔曼(Elman)神经网络模型组合的方法:采用国际全球卫星导航系统服务组织(IGS)中心提供的不同纬度、不同时间段的TEC格网点数据,利用db4小波分解对前20 d的TEC样本序列进行分解得到高频信息与低频信息;再分别利用LSTM模型和Elman模型对高频信息和低频信息进行预报;然后将2种模型的预报值进行重构;最后利用滑动窗口预测连续多个2 d数据进行分析研究。实验结果表明,组合模型在春、夏、秋、冬不同季节的电离层预报的均方根误差分别为0.85、0.68、0.84和0.84个总电子含量单位(TECu),平均绝对值残差分别为0.66、0.55、0.60和0.69个TECu,平均相对精度分别为97.1%、97.1%、96.7%、95.9%,与2种单一模型相比可有大幅度提升。

基于WNN的隧道交织区车辆换道持续距离预测

车辆换道行为往往被当作瞬时交通行为而忽视其换道过程的行为特征。采用城市连续隧道交织区换道行为作为研究对象,探究其换道过程中换道持续距离的选择行为,提出了一种引入小波变换和人工神经网络的组合预测模型。首先,对提取到的换道数据进行一定程度的降噪处理,将影响换道持续距离的主要因素作为神经网络的输入变量,以南京市“九华山-西安门”连续隧道交织区轨迹数据为例,通过训练模型来提高对车辆换道持续距离的预测精度。结果表明:与机器学习模型和神经网络模型进行对比分析,发现所提出的小波神经网络模型具有较高的预测性能,对该交织段典型换道行为进行特征分析研究,可以为城市连续隧道交织区管理方案的制定奠定理论基础。

施工场景下灰色小波神经网络短时交通量预测模型研究

在城市道路施工场景下应用短时交通量预测对提高施工区域交通效率及安全水平至关重要。考虑到施工场景下短时交通量历史样本量小且样本呈现非线性的特点,引入灰色预测模型,构建施工场景下的灰色小波神经网络短时交通量预测模型。以行宫西大街由西向东断面的交通量数据为例,分别基于小波神经网络短时交通量预测模型、灰色小波神经网络短时交通量预测模型,利用Matlab进行训练。结果显示,灰色小波神经网络短时交通量预测结果的平均绝对误差、平均相对误差和均方误差相较于小波神经网络短时交通量预测模型,分别降低了74.14%、75.21%和92.70%,该模型对城市道路施工场景下的短时交通量预测精确度更高。

横浪作用下LNG船运动量小波神经网络预测模型

港口码头设计中主要考虑的问题之一是系泊船舶的运动量。首先开展了26.6万m 3LNG船舶系泊运动物理模型试验,测量了船舶在试验条件下的六自由度运动量;同时,考虑船舶运动响应的主要影响因素,根据横浪作用下26.6万m 3LNG船运动六分量的试验数据,构建了189组容量的训练集,基于小波分析和神经网络,研究确定三层隐含层神经元数分别为8、12和22,进而建立系泊LNG船舶运动量的预测模型。预测结果表明,小波神经网络模型具有输出多参数的算法优势,能够综合考虑系泊船非线性系统中不易量化的众多影响因素(波浪波高、周期、波长以及船舶自身特性等),给出相对精确的预测结果。小波神经网络模型在研究系泊船舶运动量预测方面具有良好适用性,可以有效地预测船舶的运动量,为实际工程设计提供参考。