A New Searching Strategy for the Lost Plane Based on RBF Neural Network Model and Global Optimization Model

In this paper, we construct two models for the searching task for a lost plane. Model 1 determines the searching area. We predict the trajectory of floats generated after the disintegration of the plane by using RBF neural network model, and then determine the searching area according to the trajectory. With the pass of time, the searching area will also be constantly moving along the trajectory. Model 2 develops a maritime search plan to achieve the purpose of completing the search in the shortest time. We optimize the searching time and transform the problem into the 0-1 knapsack problem. Solving this problem by improved genetic algorithm, we can get the shortest searching time and the best choice for the search power.

Calibration Method Based on RBF Neural Networks for Soil Moisture Content Sensor

Temporal and spatial variation of soil moisture content is significant for crop growth,climate change and the other fields.In order to overcome shortage of non-linear output voltage of TDR3 soil moisture content sensor and increase soil moisture content data collection and computational efficiency,this paper presents a RBF neural network calibration method of soil moisture content based on TDR3 soil moisture sensor and wireless sensor networks.Experiment results show that the calibration method is effective...

Neural Network Robust Control Based on Computed Torque for Lower Limb Exoskeleton

The lower limb exoskeletons are used to assist wearers in various scenarios such as medical and industrial settings.Complex modeling errors of the exoskeleton in different application scenarios pose challenges to the robustness and stability of its control algorithm.The Radial Basis Function(RBF)neural network is used widely to compensate for modeling errors.In order to solve the problem that the current RBF neural network controllers cannot guarantee the asymptotic stability,a neural network robust control algorithm based on computed torque method is proposed in this paper,focusing on trajectory tracking.It innovatively incorporates the robust adaptive term while introducing the RBF neural network term,improving the compensation ability for modeling errors.The stability of the algorithm is proved by Lyapunov method,and the effectiveness of the robust adaptive term is verified by the simulation.Experiments wearing the exoskeleton under different walking speeds and scenarios were carried out,and the results show that the absolute value of tracking errors of the hip and knee joints of the exoskeleton are consistently less than 1.5°and 2.5°,respectively.The proposed control algorithm effectively compensates for modeling errors and exhibits high robustness.

Research on the Correlation Between Physical Examination Indexes and TCM Constitutions Using the RBF Neural Network

Objective To establish correlation models between various physical examination indexes and traditional Chinese medicine(TCM)constitutions,and explore their relationships based on the radial basis function(RBF)neural network.Methods The raw data of physical examination indexes and TMC constitutions of 650 subjects who underwent a physical examination were cleaned,classified and sorted,on the basis of which valid data were retrieved and categorized into a training dataset and a test dataset.Subsequently,the RBF neural network was applied to the valid samples in the training set to establish correlation models between various physical examination indexes and TCM constitutions.The accuracy and the error margin of the correlation model were then verified using the valid samples in the test set.Results Of all selected samples,the highest accuracy rates were 80% for the blood lipid index-TCM constitution model;100% for the renal function index-TCM constitution model;100% for the blood routine(male)index-TCM constitution model;88.8% for the blood routine(female)index-TCM constitution model;84.1%for the urine routine index-TCM constitution model;and 100% for the blood transfusion index-TCM constitution model.Conclusions The samples selected in this study suggested that there is a strong correlation between physical examination indexes and TCM constitutions,making it feasible to apply the established correlation models to TCM constitution identification.

Application of Nonlinear Predictive Control Based on RBF Network Predictive Model in MCFC Plant

This paper described a nonlinear model predictive controller for regulating a molten carbonate fuel cell (MCFC). A detailed mechanism model of output voltage of a MCFC was presented at first. However, this model was too complicated to be used in a control system. Consequently, an off line radial basis function (RBF) network was introduced to build a nonlinear predictive model. And then, the optimal control sequences were obtained by applying golden mean method. The models and controller have been realized in the MATLAB environment. Simulation results indicate the proposed algorithm exhibits satisfying control effect even when the current densities vary largely.

Neural-networks-based Modelling and a Fuzzy Neural Networks Controller of MCFC

Molten Carbonate Fuel Cells (MCFC) are produced with a highly efficient and clean power generation technology which will soon be widely utilized. The temperature characters of MCFC stack are briefly analyzed. A radial basis function (RBF) neural networks identification technology is applied to set up the temperature nonlinear model of MCFC stack, and the identification structure, algorithm and modeling training process are given in detail. A fuzzy controller of MCFC stack is designed. In order to improve its online control ability, a neural network trained by the I/O data of a fuzzy controller is designed. The neural networks can memorize and expand the inference rules of the fuzzy controller and substitute for the fuzzy controller to control MCFC stack online. A detailed design of the controller is given. The validity of MCFC stack modelling based on neural networks and the superior performance of the fuzzy neural networks controller are proved by Simulations.

基于RBF神经网络的光伏并网系统自适应等效建模方法

针对广义负荷建模中的光伏并网系统模型难以适应不同逆变器控制和频率扰动的动态响应问题,提出了一种基于径向基函数(radialbasisfunction,RBF)神经网络的光伏并网系统自适应等效建模方法。首先,建立了光伏并网逆变器不同控制策略响应波形的检测判据。然后,构建了以电压-频率扰动为输入,有功功率和无功功率为输出的光伏并网系统RBF神经网络模型。最后,在Matlab/Simulink中搭建了光伏并网系统模型,并将其接入IEEE14节点配电网进行仿真验证。结果表明,构建的光伏并网自适应等效模型能够有效辨识电压频率给定控制、有功无功给定控制、下垂控制策略类型,能够准确反映光伏并网系统在不同电压、频率扰动下的有功功率、无功功率的动态响应特性。

基于自适应RBF神经网络具有模型不确定性的四旋翼无人机指定时间预设性能控制方法

四旋翼无人机具有强耦合和欠驱动的特点,在飞行过程中很容易受到外界干扰,进而影响整个无人机系统的稳定性和精度。为此,提出了一种基于RBF神经网络的指定时间预设性能约束控制策略。首先,针对四旋翼无人机的不确定数学模型难以精确建立,并且在执行任务过程中存在外部未知扰动问题,提出了一种基于指定时间预设性能控制方法,将四旋翼无人机的轨迹跟踪问题转换为对位置子系统和姿态子系统的期望指令跟踪问题;其次,在设计控制器过程中,为了解决“微分爆炸”问题产生的滤波器误差,引入一种新型滤波误差补偿方法,通过RBF神经网络逼近外部未知扰动,并将预测结果补偿给控制器以提高轨迹跟踪的鲁棒性。最后,应用仿真模拟方法验证无人机控制系统稳定性和性能优势,通过飞行试验验证,微风聚拢环境下实际飞行轨迹与仿真模拟结果趋于一致,自主轨迹跟踪起降位置偏差小于1 cm,证明了所提出算法的有效性。

智能汽车轨迹跟踪MPC-RBF-SMC协同控制策略研究

针对自动驾驶车辆行驶过程中模型失配以及外部环境干扰导致车辆轨迹跟踪环节精确性不高的问题,提出了一种结合车辆运动学模型预测控制(MPC)、径向基(RBF)神经网络和滑模控制(SMC)的轨迹跟踪控制策略。通过建立车辆运动学MPC模型计算当前状态车辆期望横摆角速度,并将其与实际横摆角速度的偏差输入RBF-SMC控制器,利用RBF快速逼近非线性模型的特点,结合滑模控制输出前轮转角,实现车辆的横向轨迹跟踪控制。仿真结果表明,与传统的控制器相比,该方法轨迹跟踪精度显著提高,并在不同行驶工况下表现出较好的鲁棒性。

Application of BP NN and RBF NN in Modeling Activated Sludge System

Based on the operation data from a certain wastewater treatment plant(WWTP) in northeast China, the models of back propagation neural network(BP NN) and radial basis function neural network(RBF NN) have been designed respectively and the ability of convergence and generalization has been analyzed separately. As for BP NN, the effects of numbers of layers and nodes have been studied; as for RBF NN, the influences of the number of nodes and the RBF′s width have been studied. It is concluded that BP NN has converged much slowly in comparison with RBF NN. The conclusion that the RBF NN is suitable for modeling activated sludge system has been drawn. An automatically optimum design program for RBF NN has been developed, through which the RBF NN model of traditional activated sludge system has been established.

基于BP-ANN与RBF-ANN的钢筋与混凝土黏结强度预测模型研究

为研究神经网络对钢筋与混凝土黏结强度的预测能力以及神经网络的输出性能,基于大量的试验数据,提出一种基于改进神经网络的变形钢筋与混凝土黏结强度预测模型,对混凝土结构的研究与实际工程应用均有着重要的意义。收集290组黏结锚固试验数据,引入基于反向传播人工神经网络(BP-ANN)与径向基函数神经网络(RBF-ANN)算法,揭示混凝土强度、保护层厚度、钢筋直径、锚固长度及配箍率对变形钢筋与混凝土黏结性能的影响规律,建立基于改进神经网络算法的钢筋与混凝土黏结强度预测模型。对比分析不同数据预处理方法和训练神经元个数对建议模型预测结果的影响,评估各经典模型与建议模型的预测精度和离散性,提出临界锚固长度计算公式。结果表明:BP-ANN预测值与试验值比值的均值、标准差及变异系数分别为1.009、0.188、0.86,其预测精度略高于RBF-ANN;建议模型能够更准确、更稳定地预测钢筋与混凝土的黏结强度,该方法为解决钢筋与混凝土黏结问题提供了新思路。

Dynamic modeling and RBF neural network compensation control for space flexible manipulator with an underactuated hand

In space operation,flexible manipulators and gripper mechanisms have been widely used because of light weight and flexibility.However,the vibration caused by slender structures in manipulators and the parameter perturbation caused by the uncertainty derived from grasping mass variation cannot be ignored.The existence of vibration and parameter perturbation makes the rotation control of flexible manipulators difficult,which seriously affects the operation accuracy of manipulators.What’s more,the complex dynamic coupling brings great challenges to the dynamics modeling and vibration analysis.To solve this problem,this paper takes the space flexible manipulator with an underactuated hand(SFMUH)as the research object.The dynamics model considering flexibility,multiple nonlinear elements and disturbance torque is established by the assumed modal method(AMM)and Hamilton’s principle.A dynamic modeling simplification method is proposed by analyzing the nonlinear terms.What’s more,a sliding mode control(SMC)method combined with the radial basis function(RBF)neural network compensation is proposed.Besides,the control law is designed using a saturation function in the control method to weaken the chatter phenomenon.With the help of neural networks to identify the uncertainty composition in the SFMUH,the tracking accuracy is improved.The results of ground control experiments verify the advantages of the control method for vibration suppression of the SFMUH.

基于数据同化方法修正RBF神经网络的高维气动力建模预测

通过数据同化方法修正径向基函数(radial basis function,RBF)神经网络,以提高高维气动力的建模精度。通过在传统RBF神经网络的核函数中引修正量γ,使用EnKF滤波数据同化算法修正该矫正因子,并将其应用于CRA309旋翼翼型的高维气动力建模预测中。结果发现数据同化方法采用非侵入的方式,在不破坏神经网络整体架构的情况下仅对核函数的矫正因子进行修正,大幅减少优化参数与变量,显著提升了RBF神经网络的建模精度和效率。将修正后的RBF神经网络模型应用于高维气动力建模中,用仿真数据替代对气动力参数进行预测。设计结果验证了预测模型的可行性,在风洞试验数据较少的情况下对提高试验数据的利用效率具有一定的工程实用价值。

改进GA-RBF神经网络的水厂混凝投药预测

为了提高水厂混凝剂投加量预测准确性,针对投药系统易受多种水质因素影响,且投药后净水过程存在高度非线性的特点,通过改进遗传算法(GA)优化径向基函数神经网络(也称为RBF神经网络)的权值ω_i和高斯基函数中心宽度向量σ_i,构建GA-RBF神经网络净水厂投药量预测模型。Matlab仿真结果表明,GA-RBF神经网络预测模型可通过实现全局逼近来回避极值陷阱,提高了稳定性和全局寻优能力,相较于单一RBF神经网络预测模型,GA-RBF神经网络预测模型的拟合优度提高5.474%,平均绝对误差降低了4.14%,根均方误差降低3.392%,迭代速度和预测精度都有所提高,数据拟合能力更强。

基于动力学模型优化PSO-RBF神经网络的水下机械臂控制

随着我国海洋资源开发与利用的增加,对海洋资源开发能力的要求也日益提高。然而,我国在海洋探测方面的研究仍处于起步阶段,面临着复杂的海洋环境和海洋主权保护的挑战。研究聚焦于智能化水下机器人-机械臂系统UVMS的研究。基于Lagrange法和Morison方程,精确建立了六自由度水下机械臂的动力学模型。为了提高系统的稳定性和轨迹跟踪的准确性,采用了适应值优化的PSO粒子群算法结合RBF神经网络,并将其应用于水下机械臂的动力学模型中。仿真实验结果表明,改进的PSO-RBF神经网络自适应滑模控制算法较传统PID及RBF神经网络算法提前约0.3 s和0.1 s确定控制参数,提前达到稳定状态。

基于PSO-RBF组合模型的长江集装箱运价指数预测

长江集装箱运价指数作为长江航运市场的晴雨指向标,能够有效反映中国长江航运的经济情况,同时侧面反映出中国内河航运的发展动态。通过对长江集装箱运价指数的预测,可以为沿岸航运企业经营决策和政府宏观经济制定提供重要依据。选取影响长江集装箱运价指数的8个指数,运用BP神经网路、RBF神经网络对2017年至2022年5月长江集装箱运价指数进行预测,提出了一种改进的PSO-RBF组合模型,获得的预测误差较小。结果表明:粒子群算法能对RBF神经网络的输出权重、隐单元中心等关键参数取值进行寻优,使其能够更好地收敛,结果优于其他算法;PSO-RBF组合模型是预测长江集装箱运价指数的一种有效方法。

基于GM-RBF组合模型的BDS-3卫星钟差短期预报

针对卫星钟差具有趋势项和随机项变化的特征问题,提出了GM-RBF组合模型的方法。该模型首先用GM(1,1)提取预处理后的卫星钟差趋势项部分并进行建模预报,得到相应的残差序列,通过RBF神经网络训练用灰色模型预报所获得的残差序列,然后将GM(1,1)模型的钟差后续预报值与RBF神经网络的残差预报值对应相加可得组合模型的预报结果。为验证组合模型的有效性和可行性,将组合模型预报结果与GM(1,1)模型、ARIMA模型、RBF神经网络模型预报结果进行对比实验。实验结果表明:组合模型预报精度要高于其他单一模型,其在不同时段的平均预报精度可提高46.4%~86.2%。

基于RBF神经网络模型对污泥减量优化的研究

研究了温度、pH值、反应时间、高铁酸钾投加量等因素对污泥溶胞效果和分解效果的影响,并通过建立RBF神经网络模型对实验进行优化。研究结果表明,温度为60℃、反应时间为2~4 h、pH值为12、高铁酸钾投加量5.5mg/(gSS)的条件下,污泥减量处理效果显著且较为经济。此外,RBF神经网络模型计算的污泥溶胞率、污泥分解率值与实验得出的结果,两者相对误差均小于5%,验证了该模型的良好拟合性。