Training algorithm for radial basis function neural network based on quantum-behaved particle swarm optimization

Radial basis function (RBF) networks are widely applied in function approximation, system identification, chaotic time series forecasting, etc. To use a RBF network, a training algorithm is absolutely necessary for determining the network parameters. The existing training algorithms, such as orthogonal least squares (OLS) algorithm, clustering and gradient descent algorithm, have their own shortcomings respectively. In this paper, we propose a training algorithm based on a novel population-based evolutionary technique, quantum-behaved particle swarm optimization (QPSO), to train RBF neural network. The proposed QPSO-trained RBF network was tested on non-linear system identification problem and chaotic time series forecasting problem, and the results show that it can identify the system and forecast the chaotic time series more quickly and precisely than that trained by the particle swarm algorithm.     

An adaptive neural network switching control approach of robotic manipulators for trajectory tracking

In this paper, an adaptive neural network (NN) switching control strategy is proposed for the trajectory tracking problem of robotic manipulators. The proposed system comprises an adaptive switching neural controller and the associated robust compensation control law. Based on the Lyapunov stability theorem and average dwell-time approach, it is shown that the proposed control scheme can guarantee tracking performance of the robotic manipulators system, in the sense that all variables of the closed-loop system are bounded and the effect due to the external disturbance and approximate error of radical basis function (RBF) NNs on the tracking error can be converged to zero in an infinite time. Finally, simulation results on a two-link robotic manipulator show the feasibility and validity of the proposed control scheme.     

基于RBF神经网络的发酵过程菌体浓度的软测量

针对微生物发酵过程中菌体细胞浓度难以实时在线检测的问题,从生物细胞代谢角度分析了发酵过程中生物代谢参数的相关性,找出了能够表示生物变量大小的代谢参数,然后将这些参数作为输入变量,建立了菌体细胞浓度软测量的R B F神经网络模型,为复杂系统中生物量参数的检测提供了一条有效途径。仿真结果表明,该软测量方法具有较高的精度,能很好地应用于生物发酵过程。     

基于RBF神经网络的织物透气性能预测研究

建立了反映结构参数与织物透气性能之间关系的神经网络模型，采用了径向基函数神经网络（RBF）算法对织物透气性能进行预测研究。研究结果表明，此算法识别能力强，训练时间短，更能准确地预测织物的透气性能，具有一定的应用前景。     

基于RBF的模糊神经网络控制器设计与仿真分析

介绍一种基于RBF的模糊神经网络设计与仿真分析的实现方法。该方法利用MATLAB中的神经网络工具箱图形用户界面GUI结合模糊控制规则表给定的输入/输出样本数据设计、构建RBF模糊神经控制器,并在Simulink中建立系统仿真模型。通过对阶跃输入信号作用下系统动态性能的仿真分析,结果表明基于RBF的模糊神经控制器有良好的控制性能。     

Error estimates of local multiquadric‐based differential quadrature (LMQDQ) method through numerical experiments

In this article, we present an error estimate of the derivative approximation by the local multiquadric‐based differential quadrature (LMQDQ) method. Radial basis function is different from the polynomial approximation, in which Taylor series expansion is not applicable. So, the present analysis is performed through the numerical solution of Poisson equation. It is known that the approximation error of LMQDQ method depends on three factors, i.e. local density of knots h, free shape parameter c and number of supporting knots n_s). By numerical experiments, their contribution to the approximation error and correlation were studied and analysed in this paper. An error estimate ε ～ O((h/c)ⁿ) is thereafter proposed, in which n is a positive constant and determined by the number of supporting knots n_s. Copyright © 2005 John Wiley & Sons, Ltd.     

MR智能阻尼器试验研究及径向基网络模型

制作了MR智能阻尼器，并在不同的位移，速度和电流输入情况下，对自制的MR智能阻尼器进行了性能试验研究，建立了MR智能阻尼器的径向基函数网络模型。结果表明：神经网络模型能精确的描述阻尼器正向和逆向工作特性，并且对液体沉降、液体磁响应时间、蓄能器刚度等因素的影响也能很好的解决。     

神经网络在水质模型中的应用

将神经网络理论和算法应用于水环境评价中，基于RBF神经网络进行水质建模，应用国内河流的实测样本对模型进行训练和检验．该方法较之原有的水质数学模型以及其他全局逼近神经网络方法的结果更加精确，适用范围更广．     

基于RBF的传感器在线故障诊断和信号恢复

介绍利用径向基神经网络构造了一种在线故障诊断及信号恢复方法,给出了网络的连接结构和学习算法。采用RBF神经网络进行传感器在线故障诊断和信号恢复,其仿真结果表明,该方法具有收敛速度快、信号恢复准确度高、泛化能力强的特点,且可以诊断多种复杂工作系统的传感器在线故障信号,同时进行信号的恢复。实现传感器状态监测、故障诊断、分离和信号恢复。     

径向基函数(RBF)网络在入侵检测中的应用

近年来，BP神经网络因为技术成熟在入侵检测中得到了若干应用，但是其本身所具有的局部极小性质限制了检测性能的提高。针对RBF神经网络所具有的最优逼近性质，对其在入侵检测中的应用作了研究。实验证明，RBF网络能够提高入侵检测性能。