Application of two different neural network techniques to lateral outflow over rectangular side weirs located on a straight channel

Side weirs are structures often used in irrigation techniques, sewer networks and flood protection. This study aims to obtain sharp-crested rectangular side weirs discharge coefficients in the straight channel by using artificial neural network model for a total of 843 experiments. The performance of the feed forward neural networks (FFNN) and radial basis neural networks (RBNN) are compared with multiple nonlinear and linear regression models. Root mean square errors (RMSE), mean absolute errors (MAE) and correlation coefficient (R) statistics are used for the evaluation of the models’ performances. Comparison results indicated that the neural computing techniques could be employed successfully in modeling discharge coefficient. The FFNN is found to be better than the RBNN. It is found that the FFNN model with RMSE of 0.037 in test period is superior in estimation of discharge coefficient than the multiple nonlinear and linear regression models with RMSE of 0.054 and 0.106, respectively.     

H∞ output tracking control of uncertain and disturbed nonlinear systems based on neural network model

The work presented in this paper seeks to address the tracking problem for uncertain continuous nonlinear systems with external disturbances. The objective is to obtain a model that uses a reference-based output feedback tracking control law. The control scheme is based on neural networks and a linear difference inclusion (LDI) model, and a PDC structure and H_∞ performance criterion are used to attenuate external disturbances. The stability of the whole closed-loop model is investigated using the well-known quadratic Lyapunov function. The key principles of the proposed approach are as follows: neural networks are first used to approximate nonlinearities, to enable a nonlinear system to then be represented as a linearised LDI model. An LMI (linear matrix inequality) formula is obtained for uncertain and disturbed linear systems. This formula enables a solution to be obtained through an interior point optimisation method for some nonlinear output tracking control problems. Finally, simulations and comparisons are provided on two practical examples to illustrate the validity and effectiveness of the proposed method.     

Use of artificial neural networks for prediction of discharge coefficient of triangular labyrinth side weir in curved channels

Side weirs have been extensively used in hydraulic and environmental engineering applications. The discharge coefficient of the triangular labyrinth side weirs is 1.5-4.5 times higher than that of rectangular side weirs. This study aims to estimate the discharge coefficient (C_d) of triangular labyrinth side weir in curved channel by using artificial neural networks (ANN). In this study, 7963 laboratory test results are used for determining the C_d. The performance of the ANN model is compared with multiple nonlinear and linear regression models. Root mean square errors (RMSE), mean absolute errors (MAE) and correlation coefficient (R) statistics are used as comparing criteria for the evaluation of the models’ performances. Based on the comparisons, it was found that the neural computing technique could be employed successfully in modeling discharge coefficient from the available experimental data. There were good agreements between the measured values and the values obtained using the ANN model. It was found that the ANN model with RMSE of 0.1658 in validation stage is superior in estimation of discharge coefficient than the multiple nonlinear and linear regression models with RMSE of 0.2054 and 0.2926, respectively.     

基于神经网络的非线性系统稳定自适应控制

本文针对一类具有未知非线性函数和未知虚拟系数非线性函数的二阶非线性系统 ,提出了一种基于神经网络的稳定自适应输出跟踪控制方法 .用李雅普诺夫稳定性分析方法证明了本文的神经网络自适应控制器能够使受控系统稳定 ,并使输出跟踪误差随时间趋于无穷而收敛到零 .仿真算例证明了该算法的有效性     

Artificial Neural Network Approach to Data Matrix Laser Direct Part Marking

Certain applications have recently appeared in industry where a traditional bar code printed on a label will not survive because the item to be tracked has to be exposed to harsh environments. Laser direct-part marking is a manufacturing process used to create permanent marks on a substrate that could help to alleviate this problem. In this research, artificial neural networks were employed to model the laser direct-part marking process of Data Matrix symbols on carbon steel substrates. Several experiments were conducted to study the laser direct-part marking process and to generate data to serve as training, validation and testing data sets in the artificial neural networks modeling process. Two performance measures, mean squared error and correlation coefficient, were utilized to assess the performance of the artificial neural network models. Single-output artificial neural network models corresponding to four performance measures specific to the Data Matrix bar code symbology were found to have good learning and predicting capabilities. The single-output artificial neural network models were compared to equivalent multiple linear regression models for validation purposes. The prediction capability of the single-output artificial neural network models with respect to laser direct-part marking of Data Matrix symbols on carbon steel substrates was superior to that of the multiple linear regression models.     

基于概率模型的实时修正IMM目标跟踪算法

针对传统的IMM算法采用固定测量噪声协方差矩阵和Markov转移概率矩阵导致模型切换缓慢,跟踪精度下降的问题,提出了一种具有模型概率实时修正的IMM机动目标跟踪算法。该算法在监控区域上建立无线电指纹库,利用支持向量回归算法训练得到观测模型。引入模糊神经网络,在模型交互输出阶段自适应地调整测量误差协方差矩阵。根据IMM子模型中连续时间点之间的模型概率的比值,对Markov转移概率进行修正。仿真结果表明,提出的方法在实时性、跟踪精度方面具有良好的性能。     

Neural networks for estimation of discharge capacity of triangular labyrinth side-weir located on a straight channel

Side-weirs are flow diversion devices widely used in irrigation, land drainage, and urban sewage systems. It is essential to correctly predict the discharge coefficient for hydraulic engineers involved in the technical and economical design of side-weirs. In this study, the discharge capacity of triangular labyrinth side-weirs is estimated by using artificial neural networks (ANN). Two thousand five hundred laboratory test results are used for determining discharge coefficient of triangular labyrinth side-weirs. The performance of the ANN model is compared with multi nonlinear regression models. Root mean square errors (RMSE), mean absolute errors (MAE) and correlation coefficient (R) statistics are used as comparing criteria for the evaluation of the models’ performances. Based on the comparisons, it was found that the neural computing technique could be employed successfully in modelling discharge coefficient from the available experimental data. There were good agreements between the measured values and the values obtained using the ANN model. It was found that the ANN model with RMSE of 0.0674 in validation stage is superior in estimation of discharge coefficient than the multiple nonlinear and linear regression models with RMSE of 0.1019 and 0.1507, respectively.     

非线性多变量零阶接近有界系统的多模型自适应控制

针对一类多变量非线性离散时间系统,提出一种新的基于神经网络的多模型自适应控制方法.为了将非线性系统的高阶非线性项的限制条件放宽到零阶接近有界,该方法引入了一种新的非线性模型.该模型在传统线性回归模型基础上增加了非线性补偿项,使模型的估计误差有界.一个神经网络模型与非线性模型同时被用来对系统进行辨识.基于性能指标的切换机构选择性能较好的模型对应的控制器 对系统进行控制. 理论分析证明了零阶接近有界多模型自适应控制系统的有界输 入和有界输出稳定性. 仿真实验说明了提出的多模型自适应控制方法的有效性.     

Robust backstepping control of nonlinear systems using neuralnetworks

A controller is proposed for the robust backstepping control of a class of general nonlinear systems using neural networks (NNs). A tuning scheme is proposed which can guarantee the boundedness of tracking error and weight updates. Compared with adaptive backstepping control schemes, we do not require the unknown parameters to be linear parametrizable. No regression matrices are needed, so no preliminary dynamical analysis is needed. One salient feature of our NN approach is that there is no need for the off-line learning phase. Three nonlinear systems, including a one-link robot, an induction motor, and a rigid-link flexible-joint robot, were used to demonstrate the effectiveness of the proposed scheme     

基本积分型李亚普诺夫函数的直接自适应神经网络控制

针对一类具有下三角形函数控制增益矩阵的非线性系统,基于滑模控制原理,并利用 多层神经网络的逼近能力,提出了一种直接自适应神经网络控制器设计的新方案.通过引入积 分型李亚普诺夫函数及残差与逼近误差和的上界函数的自适应补偿项,证明了闭环系统是全局 稳定的,跟踪误差收敛到零.     

Prescribed performance adaptive neural tracking control for strict-feedback Markovian jump nonlinear systems with time-varying delay

A prescribed performance adaptive neural tracking control problem is investigated for strict-feedback Markovian jump nonlinear systems with time-varying delay. First, a new prescribed performance constraint variable is proposed to generate the virtual control that forces the tracking error to fall within prescribed boundaries. Combining with the approximation capability of neural networks and backstepping design, the adaptive tracking controller is designed. The designed controller is independent on time delay by constructing appropriate Lyapunov functions to offset the unknown time-varying delays. It is proved that the closed-loop system is uniformly ultimately bounded in probability, and that both steady-state and transient-state performances are guaranteed. Finally, simulation results are given to illustrate the effectiveness of the proposed approach.     

基于神经网络的一类非线性系统自适应跟踪控制

提出一种非线性系统的自适应神经跟踪控制方案。通过利用RBF神经网络对未知非线性系统建模,并用一个滑模控制项消除网络建模误差和外部干扰的影响,从而能够保证闭环系统的全局稳定性和输出跟踪误差渐近收敛于零。     

基于神经网络的严反馈块非线性系统的鲁棒控制

针对非匹配不确定性的严反馈块非线性系统,基于神经网络提出一种鲁棒控制方法．利用Lyapunov稳定性定理推导出RBF神经网络的全调节律,用于处理系统中的非线性参数不确定性,提高了神经网络的在线逼近能力;采用神经网络和鲁棒控制方法,利用已知信息的同时,对控制系数矩阵未知时的设计问题进行处理,避免了控制器可能的奇异问题;引入非线性跟踪微分器,解决了Backstepping设计中的“计算膨胀”问题．运用Lyapunov稳定性定理证明了闭环系统的所有信号均最终一致有界．     

Adaptive tracking H-infinity control for switched nonlinear systems with unknown control gain sign

This paper addresses the adaptive tracking control scheme for switched nonlinear systems with unknown control gain sign. The approach relaxes the hypothesis that the upper bound of function control gain is known constant and the bounds of external disturbance and approximation errors of neural networks are known. RBF neural networks (NNs) are used to approximate unknown functions and an H-infinity controller is introduced to enhance robustness. The adaptive updating laws and the admissible switching signals have been derived from switched multiple Lyapunov function method. It’s proved that the resulting closed loop system is asymptotically Lyapunov stable such that the output tracking error performance and H-infinity disturbance attenuation level are well obtained. Finally, a simulation example of Forced Duffing systems is given to illustrate the effectiveness of the proposed control scheme and improve significantly the transient performance.     

基于核岭回归的非线性内模控制

提出一种基于核蛉回归(KRR)建模的内模控制策略.该方法充分利用基干结构风险最小化为学习规则的回归方法的非线性拟合性能,建立内模控制系统,从理论上分析了内模控制系统的稳定性和稳态误差同逆模与内模估计误差的关系问题.仿真表明,在训练样本有限和有噪声污染情况下,该系统较神经网络方法具有更好的控制性能.     

不确定非线性系统神经网络自适应控制

针对一类不确定非线性系统,利用神经网络可逼近任意非线性函数的能力,以及误差滤波理论,提出了一种基于径向基神经网络的自适应控制器设计方案,以使非线性系统在存在不确定项或受到未知干扰时,其输出为期望输出.根据Lyapunov理论,给出了系统稳定的充分条件,并进行了详细证明.该设计方法能够保证跟踪误差收敛,从而进一步说明该控制器的有效性.最后,用Sumulink对设计方案进行仿真,仿真结果表明了其实用性.     

Dynamic option hedging with transaction costs: A stochastic model predictive control approach

This paper proposes stochastic model predictive control as a tool for hedging derivative contracts (such as plain vanilla and exotic options) in the presence of transaction costs. The methodology combines stochastic scenario generation for the prediction of asset prices at the next rebalancing interval with the minimization of a stochastic measure of the predicted hedging error. We consider 3 different measures to minimize in order to optimally rebalance the replicating portfolio: a trade‐off between variance and expected value of hedging error, conditional value at risk, and the largest predicted hedging error. The resulting optimization problems require solving at each trading instant a quadratic program, a linear program, and a (smaller‐scale) linear program, respectively. These can be combined with 3 different scenario generation schemes: the lognormal stock model with parameters recursively identified from data, an identification method based on support vector regression, and a simpler scheme based on perturbation noise. The hedging performance obtained by the proposed stochastic model predictive control strategies is illustrated on real‐world data drawn from the NASDAQ‐100 composite, evaluated for a European call and a barrier option, and compared with delta hedging.     

路灯节能调控系统矢量传感器误差修正方法

针对路灯节能调控系统中传感器的固有误差和系统误差,使其测量值易受非线性干扰而引起矢量误差问题,提出一种路灯节能控制系统矢量传感器误差修正方法.利用BP神经网络的非线性映射能力,建立补偿逆模型,为传感器提供理想的线性特性,同时根据网络层次间取值结果,引入梯度下降法,依据结果调整权值和阈值取值,令其无限接近非线性函数,考虑...     

基于RBF神经网络的摩擦补偿建模与控制

机械系统摩擦的精确数学模型很难建立,因此,尝试采用RBF神经网络系统在线逼近摩擦模型并将辨识结果作为控制算法的补偿项。在控制方法上,采用了基于RBF神经网络系统补偿的PD算法。在系统证明上,从李雅普诺夫函数中导出了自适应参数并且分析了闭环系统跟踪误差的有界性。利用Matlab对提出的方法及证明的有效性进行了验证。     

Risk-neutral density extraction from option prices: improvedpricing with mixture density networks

One of the central goals in finance is to find better models for pricing and hedging financial derivatives such as call and put options. We present a new semi-nonparametric approach to risk-neutral density extraction from option prices, which is based on an extension of the concept of mixture density networks. The central idea is to model the shape of the risk-neutral density in a flexible, nonlinear way as a function of the time horizon. Thereby, stylized facts such as negative skewness and excess kurtosis are captured. The approach is applied to a very large set of intraday options data on the FTSE 100 recorded at LIFFE. It is shown to yield significantly better results in terms of out-of-sample pricing accuracy in comparison to the basic and an extended Black-Scholes model. It is also significantly better than a more elaborate GARCH option pricing model which includes a time-dependent volatility process. From the perspective of risk management, the extracted risk-neutral densities provide valuable information for value-at-risk estimations.