带知识库的高炉铁水含硅量的自适应预报系统

本文讨论自校正预报器与知识库系统配合使用时,对高炉铁水含硅量的在线预报问题. 自校正预报器按Box和Jenkins的原理构成,预报模型参数用递推近似极大似然法进行在线 估计.当炉况稳定时,自校正预报器的精度是满意的,而炉况木稳定时,由知识库系统的输出 对自校正预报进行检验和修正.实验表明,综合预报系统的预报精度超过熟练工长.因此,上 述系统可作为工长的操作指导.     

广义系统ARMA最优递推状态估值器

应用现代时间序列分析方法,基于ARMA新息模型和白噪声估值器,由非递推状 态估值器的递推变形,提出了广义系统的ARMA稳态最优递推状态估值器.它们具有 Wiener滤波器形式,可处理带奇异状态转移阵和/或带相关噪声的广义系统,可统一处理滤 波、平滑和预报问题,且可统一处理广义和非广义系统状态估计问题.仿真例子说明了其有效 性.     

广义离散随机线性系统最优递推预报方法及其渐近稳定性

讨论广义离散随机线性系统最优状态估计问题,运用新息理论和射影的方法提出了 广义离散随机线性系统最优递推预报器和滤波器,证明了递推预报器对于初始值的选取渐近 稳定.     

基于Kalman滤波的Wiener状态估值器

应用经典稳态Kalman滤波理论提出了设计Wiener状态估值器的新方法,其原理是: 基于在Wiener滤波器形式下的稳态Kalman滤波器和预报器及ARMA新息模型,由稳态最优非 递推状态估值器的递推变形引出Wiener状态估值器.所提出的Wiener状态估值器可统一处理状 态滤波、预报和平滑问题.它们具有ARMA递推形式,且具有渐近稳定性和最优性,仿真例子说 明了它们的有效性.     

带相关噪声异步采样系统的分布式最优线性递推融合估计

对带相关噪声的异步均匀采样线性离散系统, 研究了分布式最优线性递推融合预报和滤波问题. 通过引入 满足伯努利分布的随机变量将系统同步化, 给出了局部Kalman预报器和滤波器. 分别推导了局部估值间的互协方 差阵、分布式最优线性融合估值与局部估值间的互协方差阵. 提出了分布式最优线性递推融合预报器和滤波器. 与 局部估值按矩阵加权的分布式融合估计算法相比, 所提出的算法具有更高的估计精度, 但与集中式融合相比有精度 损失. 为了进一步提高估计精度, 又提出了带反馈的分布式最优线性递推融合预报器和滤波器, 证明了带反馈的融 合估计与集中式融合估计具有相同的精度. 仿真例子验证了所提算法的有效性.     

带白色和有色观测噪声系统解耦Wiener状态估值器

基于经典稳态Kalman滤波理论, 对带白色和有色观测噪声系统提出了设计最优Wiener状态估值器的新方法. 通过稳态Kalman滤波器建立ARMA新息模型, 由稳态最优非递推Kalman状态估值器的递推变形引出Wiener状态估值器, 可统一处理滤波、预报和平滑问题, 它们具有状态解耦的ARMA递推形式, 且具有渐近稳定性和最优性, 仿真结果表明了算法的有效性.     

一类非线性系统的自较正预报器及其应用

本文对一类较广泛的非线性随机系统,提出了两种自校正预报器(简记为STP)和一种自校正递推预报器(简记为STRP)。并且给出了在油田生产上的应用实例。     

带时间相关乘性噪声多传感器系统的分布式融合估计

研究带时间相关乘性噪声多传感器系统的分布式融合估计问题,其中时间相关的乘性噪声满足一阶高斯-马尔科夫过程.通过引入虚拟状态和虚拟过程噪声,构建了虚拟状态的递推方程.首先,基于新息分析方法,分别对系统状态和虚拟状态设计局部一步预报器.然后,基于一步预报器设计状态的局部线性滤波器、多步预报器和平滑器.推导了任意两个局部状态估计误差之间的互协方差矩阵.接着,基于线性最小方差意义下的矩阵加权、对角矩阵加权和标量加权融合算法,给出相应的分布式融合状态估值器.最后,分析算法的稳定性.仿真研究验证了该算法的有效性.     

基于分布式神经网络递推预报误差算法的非线性系统建模

采用基于递推预报误差算法的分布式神经网络 结构建立非线性系统模型．子神经网络模型及其连接权值均采用递推预报误差方法来进行训 练,将所有子网络融合得到的分布式神经网络模型在模型精确性和鲁棒性方面有显著地增加 ．该方法较好地应用于复杂非线性动态系统的建模．     

基于动态非线性逼近的非线性系统预测控制

对于一类常见多重时滞非线性离散系统．提出基于动态非线性逼近的增量型最小化递推预测模型、广义预测控制律、噪声估计器以及参数自适应递推预报算法。实现了对存在较大滞后的时滞非线性系统的广义预测控制．仿真结果表明了该算法的正确性和有效性．     

Cascaded RLS–LMS Prediction in MPEG-4 Lossless Audio Coding

This paper describes the cascaded recursive least square-least mean square (RLS-LMS) prediction, which is part of the recently published MPEG-4 Audio Lossless Coding international standard. The predictor consists of cascaded stages of simple linear predictors, with the prediction error at the output of one stage passed to the next stage as the input signal. A linear combiner adds up the intermediate estimates at the output of each prediction stage to give a final estimate of the RLS-LMS predictor. In the RLS-LMS predictor, the first prediction stage is a simple first-order predictor with a fixed coefficient value 1. The second prediction stage uses the recursive least square algorithm to adaptively update the predictor coefficients. The subsequent prediction stages use the normalized least mean square algorithm to update the predictor coefficients. The coefficients of the linear combiner are then updated using the sign-sign least mean square algorithm. For stereo audio signals, the RLS-LMS predictor uses both intrachannel prediction and interchannel prediction, which results in a 3% improvement in compression ratio over using only the intrachannel prediction. Through extensive tests, the MPEG-4 Audio Lossless coder using the RLS-LMS predictor has demonstrated a compression ratio that is on par with the best lossless audio coders in the field. In this paper, the structure of the RLS-LMS predictor is described in detail, and the optimal predictor configuration is studied through various experiments.     

ARMAX lattice algorithm for identification and prediction of dynamic systems

A new ARMAX lattic predictor is developed for identification and prediction of dynamic systems having unknown input and time delay from short records. It is based on the Levinson recursion scheme of the AR and ARMA lattice algorithms, without introducing stability problems or excessively increasing the computation. The cascaded structure of the lattice form, consisting of identical sections, is very convenient for implementation using special purpose hardware and microprocessors. The computational properties of the proposed predictor are discussed and compared with the well known extended recursive least-squares algorithm. The developed algorithm is tested on real, short records, obtained from an economic dynamic system.     

A D-step predictor in lattice and ladder form

We use the orthogonalizing property of the two-multiplier linear prediction lattice filter to construct ad-step ahead predictor in lattice form. The predictor generatesd-step forward and backward residuals in a recursive way and possesses most of the interesting properties of the basic one-step prediction lattice filter. An exact solution is presented first assuming a stationary observation process, using orthogonal projections in Hilbert space. Two adaptive implementations are also proposed for the case where the statistics of the signal process are unknown or time varying: a gradient method and a recursive least-squares scheme. Finally, we show how to construct an adaptived-step ahead predictor by adding a ladder part to thed-step lattice structure.     

An Evolving Fuzzy Predictor for Industrial Applications

A reliable and online predictor is very useful to a wide array of industries to forecast the behavior of time-varying dynamic systems. In this paper, an evolving fuzzy system (EFS) is developed for system state forecasting. An evolving clustering algorithm is proposed for cluster generation. Clusters are established and modified based on constraint criteria of mapping consistence and compatible measurement. A novel recursive Levenberg–Marquardt (R-LM) method is proposed for online training of nonlinear EFS parameters. The viability of the developed EFS predictor is evaluated based on both simulation from benchmark data and real-time tests corresponding to machinery condition monitoring and material property testing. Test results show that the developed EFS predictor is an effective and accurate forecasting tool. It can capture the system's dynamic behavior quickly and track the system's characteristics accurately. The proposed clustering algorithm is an effective structure identification method. The recursive training technique is computationally efficient, and can effectively improve reasoning convergence.      

Block-processing adaptive filter for human eye movements

Adaptive processing techniques can be divided into two categories: block processing and recursive methods. With block processing methods, incoming data are divided into blocks, and each block is processed as a whole to estimate predictor coefficients. With recursive methods, predictor parameters are updated as each new data point becomes available and computed thorugh a set of recursive algorithms. In this paper, five block processing adaptive filters are used in the prediction of the human eye movements. They are two-point-linear predictor (TPLP), five-point-quadratic predictor (FPQP), nine-point-cubic predictor (NPCP), polynomial-filter predictor 1 (PFP1), which is a linear convex combination of a TPLP and an FPQP, and polynomial-filter predictor 2 (PFP2), which is a linear convex combination of a TPLP, as FPQP, and an NPCP. These predictors were tested with various signals such as saccadic eye movements, sinusoidal, cubic, triangular, and parabolic signals. The results show that the TPLP is the best predictor for triangular signal and the NPCP is the best predictor for sinusoidal signal. Conversely, the FPQP is the best predictor for parabolic and cubic signals. The results also suggest that the PFP1 and PFP2 show significant improvement over that of the TPLP, FPQP, and NPCP in long-range prediction.     

Recursive identification of bilinear systems

Methods of identifying bilinear systems from recorded input-output data are discussed in this article. A short survey of the existing literature on the topic is given. ‘Standard’ methods from linear systems identification, such as least squares, extended least squares, recursive prediction error and instrumental variable methods are transferred to bilinear, input-output model structures and tested in simulation. Special attention is paid to problems of stabilizing the model predictor, and it is shown how a time-varying Kalman filter and associated parameter estimation algorithm can deal with this problem.     

P-SLAM: Simultaneous Localization and Mapping With Environmental-Structure Prediction

Traditionally, simultaneous localization and mapping (SLAM) algorithms solve the localization and mapping problem in explored regions. This paper presents a prediction-based SLAM algorithm (called P-SLAM), which has an environmental-structure predictor to predict the structure inside an unexplored region (i.e., look-ahead mapping). The prediction process is based on the observation of the surroundings of an unexplored region and comparing it with the built map of explored regions. If a similar environment/structure is matched in the map of explored regions, a hypothesis is generated to indicate that a similar structure has been explored before. If the environment has repeated structures, the mobile robot can use the predicted structure as a virtual mapping, and decide whether or not to explore the unexplored region to save the exploration time. If the mobile robot decides to explore the unexplored region, a correct prediction can be used to speed up the SLAM process and build a more accurate map. We have also derived the Bayesian formulation of P-SLAM to show its compact recursive form for real-time operation. We have experimentally implemented the proposed P-SLAM on a Pioneer 3-DX mobile robot using a Rao-Blackwellized particle filter in real time. Computer simulations and experimental results validated the performance of the proposed P-SLAM and its effectiveness in indoor environments     

Self-tuning predictors with application to river flow prediction

The paper presents developments of recursive self-adaptive prediction algorithms, called ‘self-tuning predictors’, using some common estimation techniques, and their application to prediction of flow discharge of the river Tigris at Baghdad, Iraq. Four kinds of predictors, viz the least-square predictor, the minimum-variance predictor, a predictor using stochastic approximation, and a two-stage predictor, have been developed. Using available data for the River Tigris, prediction results have been obtained for the average daily discharge, the average monthly discharge and the average yearly discharge. In each type of prediction a number of models have been tried. The various prediction results are presented in graphical and in tabular forms for comparison.     

Elitist teaching–learning-based optimization (ETLBO) with higher-order Jordan Pi-sigma neural network: a comparative performance analysis

An adaptive p-step prediction model for nonlinear dynamic processes is developed in this paper and implemented with a radial basis function (RBF) network. The model can predict output for multi-step-ahead with no need for the unknown future process output. Therefore, the long-range prediction accuracy is significantly enhanced and consequently is especially useful as the internal model in a model predictive control framework. An improved network structure adaptation is also developed with the recursive orthogonal least squares algorithm. The developed model is online updated to adapt both its structure and parameters, so that a compact model structure and consequently a less computing cost are achieved with the developed adaptation algorithm applied. Two nonlinear dynamic systems are employed to evaluate the long-range prediction performance and minimum model structure and compared with an existing PSC model and a non-adaptive RBF model. The simulation results confirm the effectiveness of the developed model and superior over the existing models.

     

非线性系统模糊神经网络变结构自适应控制

在非线性系统的模糊动力学模型基础上,提出一种模糊神经网络变结构自适应控制器;网络的结构根据非线性系统特性动态构成,基于该网络提出非线性预测器,基于梯度法提出了一种网络参数学习算法,并分析了收敛性及其性质。将网络预测器与参数学习算法相结合,构成自适应控制算法,证明了算法的收敛性。仿真结果证实了算法的有效性。