基于Elman神经网络的非线性动态系统辨识

研究了应用动态递归神经网络实现动态系统辨识的原理和方法,在没有被辨识对象的先验知识情况下,通过改进的Elman网络实现了非线性动态系统的辨识。仿真结果表明,与前馈网络相比,Elman网络具有学习速度快、泛化能力强的特点,可用较小的网络结构实现高阶系统的辨识,适用于具有本质非线性动态系统的辨识。     

基于改进Elman神经网络的网络流量预测

针对网络系统非线性、多变量、时变性等特点,提出一种改进的Elman神经网络模型。在该模型的训练过程中引入了季节周期性学习方法,并对某高校主干网络出口流量进行实验检测。实验结果表明,该模型具有良好的预测效果,相对于传统线性模型、BP神经网络模型及标准Elman神经网络模型具有更高的预测精度和更好的自适应性。最后,通过自适应边界值方法进行检测,能够及时发现异常流量行为,说明该模型应用于网络流量预测是可行、有效的。     

Elman网络在Smith预测控制中的应用

Smith预测控制在实际应用中的难点在于很难得到实际系统精确的数学模型. 通过Elman网络拟合传统Smith估计器的模型误差, 并对其进行补偿. 实验结果表明, 这种基于Elman网络补偿模型的Smith预测控制充分利用了神经网络的非线性拟合能力, 只要对纯滞后环节精确建模, 就可以完全抵消纯滞后环节对控制品质及系统稳定性的不利影响. 这种方法使得Smith预测控制可以用于模型不易精确确定的系统.     

基于改进Elman网络的软测量建模方法

针对静态前馈网络和Elman网络在软测量建模中的不足,提出了一种新的改进的Elman网络模型,并将此模型应用于精馏塔出口成分含量的软测量建模中。实验模拟结果表明：改进的Elman网络模型具有更高的预测精度和较快的收敛速度,能够更好地实现精馏塔出口成分含量的软测量建模,为进一步实现产品质量控制提供了保证。     

Feedforward neural network and adaptive network-based fuzzy inference system in study of power lines

Over the past several decades, concerns have been raised over the possibility that the exposure to extremely low frequency electromagnetic fields from power lines may have harmful effects on human and living organisms. This paper presents novel approach based on the use of both feedforward neural network (FNN) and adaptive network-based fuzzy inference system (ANFIS) to estimate electric and magnetic fields around an overhead power transmission lines. An FNN and ANFIS used to simulate this problem were trained using the results derived from the previous research. It is shown that proposed approach ensures satisfactory accuracy and can be a very efficient tool and useful alternative for such investigations.     

基于Elman神经网络110警情预测研究

统计110警情数据,建立警情数据的时间序列,选定网络的输入、输出节点,创建Elman神经网络,利用样本数据对网络进行训练学习,测试数据的仿真实验结果表明,与BP神经网络相比,Elman神经网络对110警情进行短期预测,预测的精度更高。     

Comparison of conventional and soft computing-based power control methods in mobile communications systems

 In this paper, we give a comparison between the conventional power control scheme and soft computing-based approaches in a mobile communications application. At the base station, the `bang–bang' control strategy and a neural network-based prediction control method are employed. In addition, full power command transmission mode, single-bit command transmission mode, and fuzzy logic-based power command enhancement unit are considered. Based on simulation experiments, we quantitatively evaluate the performance of various combinations of these control methods and command transmission modes. Conclusions on the optimal configuration are finally drawn.     

Elman神经网络改进模型在脑膜炎诊断中的应用

为了客观地反映脑脊液（Cerebrospinal Fluid,CSF）所蕴涵的病理信息,研究CSF多变量非线性诊断指标和脑膜炎分类标准的内在联系,提出一种Elman神经网络改进模型辅助诊断脑膜炎疾病的方法。构建2层Elman神经网络改进模型网络训练和仿真的实验结构,分别把85和51例临床病例确诊数据作为训练样本和仿真数据的输入。仿真结果显示,采用Elman神经网络的改进模型应用于脑膜炎疾病的辅助诊断可以达到均方误差10-2精度。Elman神经网络改进模型针对CSF复杂数据关系辅助诊断脑膜炎疾病的智能计算是可行的。     

A new neural network-based approach for self-tuning control of nonlinear SISO discrete-time systems

This article presents a new neural network-based approach for self-tuning control of nonlinear single-input single-output (SISO) discrete-time dynamic systems. According to the approach, a neural network ARMAX (NN-ARMAX) model of the system is identified and continuously updated, using an online training algorithm. Control design is accomplished by solving an optimal discrete-time linear quadratic tracking problem using an observer-type linear state-space Kalman innovation model, which is built from the parameters of a local linear version of the NN-ARMAX model. The state-feedback control law is implemented using the Kalman state, which is calculated without estimating the noise covariance properties. The proposed control approach is shown to be very effective and outperforms the self-tuning control approach based on a linear ARMAX model on two simulation examples.     

Design and performance of an intelligent predictive controller for a six-degree-of-freedom robot using the Elman network

The aim of this paper was to propose a recurrent neural network-based predictive controller for robotic manipulators. A neural network controller for a six-joint Stanford robotic manipulator was designed using the generalized predictive control (GPC) and the Elman network. The GPC algorithm, which is a class of digital control method, requires long computational time. This is a disadvantage in real-time robot control; therefore, the Elman network controller was designed to reduce processing time by avoiding the highly mathematical and computational complexity of the GPC. The main reason for choosing the Elman network, amongst several neural network algorithms, was that the presence of feedback loops have a profound impact on the learning capability of the network. The designed neural network controller was able to recover quickly because of its significant generalization capability, which allowed it to adapt very rapidly to changes in inputs. The performance of the controller was also shown graphically using simulation software, including the dynamics and kinematics of the robot model.     

基于RENN 的柔性关节机械臂自适应动态面控制

针对面贴式永磁同步电机驱动的柔性关节机械臂动力学模型具有非线性、不确定性和未知外部扰动等特点,提出一种自适应动态面控制方法来实现其关节轨迹跟踪控制．控制律由动态面技术得到,降低了反推控制器的复杂性．模型不确定因素由递归Elman神经网络在线补偿,神经网络权值自适应律通过Lyapunov稳定性分析推导得到．仿真研究表明,该方法对于载荷不确定和外界扰动具有较强的鲁棒性,与传统动态面法相比,大大提高了柔性关节的位置跟踪精度．     

Nonlinear excitation controller design for power systems： an I＆I approach

The Immersion and Invariance(I&I) methodology provides a novel approach for nonlinear system control, which is distinct from the traditional feedback linearization and backstepping method.In this paper,a new excitation controller is designed for single machine infinite bus system(SMIBS) based on the I&I approach.Firstly the dynamic model of SMIBS is homeomorphously transformed to a specific form for which a stable lower-order target system is selected.Then the I&I excitation controller is designed by immersing the transformed system into the target system.Simulation results from PSCAD/EMTDC demonstrate that the proposed controller guarantees transient stability of the system after large disturbances.     

Direct adaptive neural network controller for a class of nonlinear systems based on fuzzy estimator of the control error

A new approach of direct adaptive control of single input single output nonlinear systems in affine form using single-hidden layer neural network (NN) is introduced. In contrast to the algorithms in the literature, the weights adaptation laws are based on the control error and not on the tracking error or its filtered version. Since the control error is being expressed in terms of the NN controller, hence its weights updating laws are obtained via back-propagation concept. A fuzzy inference system (FIS) with heuristically defined rules is introduced to provide an estimate of this error based on the past history of the system behaviour. The stability of the closed loop is studied using Lyapunov theory. A fixed structure is then proposed for the FIS and the design parameters reduce to the parameters of the NN. The method is reproducible and does not require any pre-training of the network weights.     

Double EWMA controller using neural network-based tuning algorithm for MIMO non-squared systems

The double exponentially weighted moving average (dEWMA) control method is a popular algorithm for adjusting a process from run to run in semiconductor manufacturing. For MIMO non-squared statistic systems, the singular value decomposition (SVD) method is used for decoupling and the SVD-based dEWMA control scheme is treated as a MIMO extension of dEWMA control design. To enhance the performance and robustness of the linear system in the presence of ramp disturbances and white noises, the neural network-based adaptive algorithm is used to automatically tune the dEWMA controller parameters. Under the specified input patterns, the early stop criterion for the training-validation neural networks, and the stability constraints added in the tuning mechanism, the simulations show that the proposed control technique can effectively improve the means and standard deviations of the process outputs.     

Bioreactor temperature profile controller using inverse neural network (INN) for production of ethanol

This paper presents the use of inverse neural networks (INN) for temperature control of a biochemical reactor and its effect on ethanol production. The process model is derived indicating the relationship between temperature, pH and dissolved oxygen. Using fundamental model obtained data sets; an inverse neural network has been trained using the back-propagation learning algorithm. Two types of temperature profile are used to compare the performance of the INN and conventional PID controllers. These controllers have been simulated in MATLAB for a quantitative comparison. The results obtained by the neural network based INN controller and by the PID controller are presented and compared. There is an improvement in the performance of INN controller in settling time and dead time and steady state error over the PID controller.     

一类纯反馈非线性系统的简化自适应神经网络动态面控制

针对一类完全非仿射纯反馈非线性系统,提出一种简化的自适应神经网络动态面控制方法.基于隐函数定理和中值定理将未知非仿射输入函数进行分解,使其含有显式的控制输入;利用简化的神经网络逼近未知非线性函数,对于阶SISO纯反馈系统,仅一个参数需要更新;动态面控制可消除反推设计中由于对虚拟控制反复求导而导致的复杂性问题.通过Lyapunov稳定性定理证明了闭环系统的半全局稳定性,数值仿真验证了方法的有效性.     

Adaptive tracking control of nonlinear systems with dynamic uncertainties using neural network

In this paper, an adaptive neural tracking control approach is proposed for a class of nonlinear systems with dynamic uncertainties. The radial basis function neural networks (RBFNNs) are used to estimate the unknown nonlinear uncertainties, and then a novel adaptive neural scheme is developed, via backstepping technique. In the controller design, instead of using RBFNN to approximate each unknown function, we lump all unknown functions into a suitable unknown function that is approximated by only a RBFNN in each step of the backstepping. It is shown that the designed controller can guarantee that all signals in the closed-loop system are semi-globally bounded and the tracking error finally converges to a small domain around the origin. Two examples are given to demonstrate the effectiveness of the proposed control scheme.     

Recursive estimation algorithms for power controls of wireless communication networks

Power control problems for wireless communication networks are investigated in direct-sequence codedivision multiple-access （DS/CDMA） channels. It is shown that the underlying problem can be formulated as a constrained optimization problem in a stochastic framework. For effective solutions to this optimization problem in real time, recursive algorithms of stochastic approximation type are developed that can solve the problem with unknown system components. Under broad conditions, convergence of the algorithms is established by using weak convergence methods.     

Design of power controller in CDMA system with power and SIR error minimization

In this paper, an uplink power control problem is considered for code division multiple access (CDMA) systems. A distributed algorithm is proposed based on linear quadratic optimal control theory. The proposed scheme minimizes the sum of the power and the error of signal-to-interference ratio (SIR). A power controller is designed by constructing an optimization problem of a stochastic linear quadratic type in Krein space and solving a Kalman filter problem.     

一种多电机同步传动模糊神经网络控制器的设计

设计了具有知识表达和自学习能力的模糊神经网络同步控制器．建立了模糊控制规则,进而提出了多电机同步控制的模糊神经网络学习算法．对四轴同步控制系统进行仿真实验．结果表明模糊神经网络同步控制器能有效实现多电机同步控制,收敛速度较快．鲁棒性较好。