A context layered locally recurrent neural network for dynamic system identification

This paper puts forward a novel recurrent neural network (RNN), referred to as the context layered locally recurrent neural network (CLLRNN) for dynamic system identification. The CLLRNN is a dynamic neural network which appears in effective in the input–output identification of both linear and nonlinear dynamic systems. The CLLRNN is composed of one input layer, one or more hidden layers, one output layer, and also one context layer improving the ability of the network to capture the linear characteristics of the system being identified. Dynamic memory is provided by means of feedback connections from nodes in the first hidden layer to nodes in the context layer and in case of being two or more hidden layers, from nodes in a hidden layer to nodes in the preceding hidden layer. In addition to feedback connections, there are self-recurrent connections in all nodes of the context and hidden layers. A dynamic backpropagation algorithm with adaptive learning rate is derived to train the CLLRNN. To demonstrate the superior properties of the proposed architecture, it is applied to identify not only linear but also nonlinear dynamic systems. The efficiency of the proposed architecture is demonstrated by comparing the results to some existing recurrent networks and design configurations. In addition, performance of the CLLRNN is analyzed through an experimental application to a dc motor connected to a load to show practicability and effectiveness of the proposed neural network. Results of the experimental application are presented to make a quantitative comparison with an existing recurrent network in the literature.     

一种新的对角回归神经网络快速学习算法

提出一种新的动态对角回归神经网络学习算法－局部动态误差反传算法（LDBP），该算法定义了一种新的局部均方差函数，并为回归单元建立一种新的学习结构。如果估计出各层的期望输出值，多层回归网络便可分解成一组自适应单元（Adaline），而每个单元可通过二次优化方法进行训练。采用可在有限步人找出全局最优解的共轭梯度法（CG）进行寻优。由于学习过程采用超线性搜索，大大减少了循环步数和计算时间。     

Neighborhood based modified backpropagation algorithm using adaptive learning parameters for training feedforward neural networks

The major drawbacks of backpropagation algorithm are local minima and slow convergence. This paper presents an efficient technique ANMBP for training single hidden layer neural network to improve convergence speed and to escape from local minima. The algorithm is based on modified backpropagation algorithm in neighborhood based neural network by replacing fixed learning parameters with adaptive learning parameters. The developed learning algorithm is applied to several problems. In all the problems, the proposed algorithm outperform well.     

A diagonal recurrent neural network-based hybrid direct adaptiveSPSA control system

A direct adaptive simultaneous perturbation stochastic approximation (DA SPSA) control system with a diagonal recurrent neural network (DRNN) controller is proposed. The DA SPSA control system with DRNN has simpler architecture and parameter vector size that is smaller than a feedforward neural network (FNN) controller. The simulation results show that it has a faster convergence rate than FNN controller. It results in a steady-state error and is sensitive to SPSA coefficients and termination condition. For trajectory control purpose, a hybrid control system scheme with a conventional PID controller is proposed     

连续搅拌反应釜( CSTR)的神经网络动态建模

基于广义性能指标,提出一种神经网络学习算法－广义递推预报误差学习算法（ＧＲＰＥ）,该算法具有二阶收敛阶次。同时讨论了学习速率的选择问题,利用所提出方法对ＣＳＴＲ动态建模结果表明,基于ＧＲＰＥ训练的ＤＲＮＮ比基于ＢＰ训练的ＭＬＰ模型精度高,收敛速度快。     

聚合物分子量分布的多变量动态系统模型

为了解决聚合产品分子量分布控制的难题,将神经网络引入对其进行了无需任何系统内部先验知识的黑箱建模。所使用的神经网络是由B样条神经网络和非线性递归神经网络(DRNN)组合而成,并使用误差反传算法对网络进行训练和学习,从而建立了多变量动态系统的分子量分布模型。在模型建立中将控制变量与分布参数的函数关系利用非线性递归神经网络描述,分子量分布函数使用B样条神经网络表示,仿真研究结果证明该方法取得了预期的建模效果,具有一定的推广实用价值。     

一种基于PSO的自适应神经网络预测控制

针对非线性系统,提出了一种基于微粒群优化(PSO)的自适应神经网络预测控制方法.采用对角递归网络(DRNN)对非线性系统进行建模,并利用扩展卡尔曼滤波(EKF)递推估计算法在线计算网络模型参数的Jacobian矩阵以实现模型参数的自适应.利用PSO算法在线优化求解非线性系统的预测控制律,以克服传统基于梯度法的非线性规划方法求解预测控制律时对初始条件非常敏感的缺点.生化发酵过程的仿真结果表明,所提出的控制方法具有良好的跟踪能力和抗干扰能力.     

Adaptive hybrid control for linear piezoelectric ceramic motor drive using diagonal recurrent CMAC network

This work presents an adaptive hybrid control system using a diagonal recurrent cerebellar-model-articulation-computer (DRCMAC) network to control a linear piezoelectric ceramic motor (LPCM) driven by a two-inductance two-capacitance (LLCC) resonant inverter. Since the dynamic characteristics and motor parameters of the LPCM are highly nonlinear and time varying, an adaptive hybrid control system is therefore designed based on a hypothetical dynamic model to achieve high-precision position control. The architecture of DRCMAC network is a modified model of a cerebellar-model-articulation-computer (CMAC) network to attain a small number of receptive-fields. The novel idea of this study is that it employs the concept of diagonal recurrent neural network (DRNN) in order to capture the system dynamics and convert the static CMAC into a dynamic one. This adaptive hybrid control system is composed of two parts. One is a DRCMAC network controller that is used to mimic a conventional computed torque control law due to unknown system dynamics, and the other is a compensated controller with bound estimation algorithm that is utilized to recover the residual approximation error for guaranteeing the stable characteristic. The effectiveness of the proposed driving circuit and control system is verified with hardware experiments under the occurrence of uncertainties. In addition, the advantages of the proposed control scheme are indicated in comparison with a traditional integral-proportional (IP) position control system.     

Robust air/fuel ratio control with adaptive DRNN model and AD tuning

Current production engines use look-up table and proportional and integral (PI) feedback control to regulate air/fuel ratio (AFR), which is time-consuming for calibration and is not robust to engine parameter uncertainty and time varying dynamics. This paper investigates engine modelling with the diagonal recurrent neural network (DRNN) and such a model-based predictive control for AFR. The DRNN model is made adaptive on-line to deal with engine time varying dynamics, so that the robustness in control performance is greatly enhanced. The developed strategy is evaluated on a well-known engine benchmark, a simulated mean value engine model (MVEM). The simulation results are also compared with the PI control.     

应用DRNN和ARIMA组合模型的时空集成预测方法

在评析目前时空预测研究现状的基础上,提出基于动态回归神经网络(DRNN)和自回归集成移动平均(ARIMA)组合模型的时空集成预测方法.该方法先用ARIMA模型对时空数据的时序进行预测,再用DRNN捕获时空数据间隐藏的空间关系,最后用线性回归将二者整合起来,得到集成预测结果.案例实验结果表明:该方法比不考虑空间影响的预测方法或单一的预测方法有更高的精度;该方法具有良好的动态处理和计算能力,对跨空间的动态过程的预测有效可行.     

Neural network control of nonlinear dynamic systems using hybrid algorithm

In this paper, a hybrid method is proposed to control a nonlinear dynamic system using feedforward neural network. This learning procedure uses different learning algorithm separately. The weights connecting the input and hidden layers are firstly adjusted by a self organized learning procedure, whereas the weights between hidden and output layers are trained by supervised learning algorithm, such as a gradient descent method. A comparison with backpropagation (BP) shows that the new algorithm can considerably reduce network training time.     

基于动态递归神经网络的自适应PID控制

提出一种基于动态递归神经网络的自适应PID控制方案,该控制系统由神经网络辨识器和神经网络控制器组成。辨识器采用单隐层的动态递归神经网络,网络结构为2-4-1;辨识算法为动态BP算法;控制器采用两层线性结构的神经网络,输入为系统偏差及其一阶、二阶微分,因此具有增量型PID控制结构。应用该控制系统对一非线性时变系统进行仿真研究,仿真结果表明该控制方案不仅具有良好的跟踪特性,而且对系统参数变化具有较强的鲁棒性。     

基于神经网络的弹性连杆机构振动主动控制

研究基于神经网络的弹性连杆机构振动主动控制方法.介绍了双隐层动态递归神经 网络的数学模型,利用实验数据离线设计了神经网络辨识器与神经网络控制器.采用基于神 经网络的间接自适应控制策略对弹性连杆机构实施了振动主动控制,机构的动力学品质得到 显著改善.实验结果证明了该方法的有效性.     

基于混沌和二阶对角递归网络的船舶横摇的直接多步预测方法

在对船舶横摇预测研究的基础上,提出一种基于混沌和在隐层具有2个反馈权值的二阶对角递归神经网络的直接多步预测模型;给出了易于实现的动量梯度学习算法,并对其收敛性进行了验证.仿真结果表明,直接多步预测不依赖于单步预测的结果,对比单步预测模型能快速、准确地预测船舶横摇运动时间序列,具有更好的预测精度及较长的预测时间.     

Development of a neural network based integrated control system of 120 ton/h capacity boiler

An integrated control system based on artificial neural network (ANN) is presented in this paper to control a 120 ton/h capacity boiler of the Zia Fertilizer Company Limited (ZFCL), Ashuganj, Bangladesh. The process inverse dynamic modelling technique is applied to design the proposed controller. A multilayer feed-forward neural network is trained to identify the unknown inverse dynamic model of the boiler plant by a well known learning algorithm called backpropagation. The training data were collected from the history file of ZFCL. A new software controller is then developed for integrated control system of the ZFCL boiler using the weights of the trained network. Both the training mode and running mode of the developed controller are presented in this paper. The controller output is also converted into electrical signal using pulse width control technique. The generated signal is used for on-line regulation of the control valve through the parallel port of the computer. The developed controller is tested by using the boiler input–output data that are not used during the training. The output response and performance of the developed controller is compared with those of the existing PID controller of the plant.     

A transformation strategy for implementing distributed, multilayer feedforward neural networks: Backpropagation transformation

Most artificial neural networks (ANNs) have a fixed topology during learning, and often suffer from a number of shortcomings as a result. Variations of ANNs that use dynamic topologies have shown ability to overcome many of these problems. This paper introduces location-independent transformations (LITs) as a general strategy for implementing distributed feed forward networks that use dynamic topologies (dynamic ANNs) efficiently in parallel hardware. A LIT creates a set of location-independent nodes, where each node computes its part of the network output independent of other nodes, using local information. This type of transformation allows efficient support for adding and deleting nodes dynamically during learning. In particular, this paper presents a LIT that supports both the standard (static) multilayer backpropagation network, and backpropagation with dynamic extensions. The complexity of both learning and execution algorithms is O(q(Nlog M)) for a single pattern, where q is the number of weight layers in the original network, N the number of nodes in the widest node layer in the original network, and M is the number of nodes in the transformed network (which is linear in the number hidden nodes in the original network). This paper extends previous work with 2-weight-layer backpropagation networks.     

A Proposed Hybrid Recurrent Neural Control System for Two Co-operating Robots

This paper discusses a model refernce adaptive (MRAC) position/force controller using proposed neural networks for two co-operating planar robots. The proposed neural network is a recurrent hybrid network. The recurrent networks have feedback connections and thus an inherent memory for dynamics, which makes them suitable for representing dynamic systems. A feature of the networks adopted is their hybrid hidden layer, which includes both linear and nonlinear neurons. On the other hand, the results of the case of a single robot under position control alone are presented for comparison. The results presented show the superior ability of the proposed neural network based model reference adaptive control scheme at adapting to changes in the dynamics parameters of robots.     

Lur'e systems with multilayer perceptron and recurrent neuralnetworks: absolute stability and dissipativity

Sufficient conditions for absolute stability and dissipativity of continuous-time recurrent neural networks with two hidden layers are presented. In the autonomous case this is related to a Lur'e system with multilayer perceptron nonlinearity. Such models are obtained after parametrizing general nonlinear models and controllers by a multilayer perceptron with one hidden layer and representing the control scheme in standard plant form. The conditions are expressed as matrix inequalities and can be employed for nonlinear H_∞ control and imposing closed-loop stability in dynamic backpropagation     

Towards the Optimal Learning Rate for Backpropagation

A backpropagation learning algorithm for feedforward neural networks withan adaptive learning rate is derived. The algorithm is based uponminimising the instantaneous output error and does not include anysimplifications encountered in the corresponding Least Mean Square (LMS)algorithms for linear adaptive filters. The backpropagation algorithmwith an adaptive learning rate, which is derived based upon the Taylorseries expansion of the instantaneous output error, is shown to exhibitbehaviour similar to that of the Normalised LMS (NLMS) algorithm. Indeed,the derived optimal adaptive learning rate of a neural network trainedby backpropagation degenerates to the learning rate of the NLMS for a linear activation function of a neuron. By continuity, the optimal adaptive learning rate for neural networks imposes additional stabilisationeffects to the traditional backpropagation learning algorithm.