一类非线性系统结构适应式预报器及其应用

本文给出一种变结构式推广的递推梯度算法和一类非线性系统结构适应式预报器。这种算法能够自动改变模型的结构。通过油田预报实践,表明这种预报器能跟踪系统结构性变化,从而提高变结构系统的预报精度。     

Lifting transform via Savitsky-Golay filter predictor and application of denoising

The Savitsky-Golay filter is a smoothing filter based on polynomial regression. It employs the regression fitting capacity to improve the smoothing results. But Savitsky-Golay filter uses a fix sized window. It has the same shortage of Window Fourier Transform. Wavelet mutiresolution analysis may deal with this problem. In this paper, taking advantage of Savitsky-Golay filter＇s fitting ability and the wavelet transform＇s multiscale analysis ability, we developed a new lifting transform via Savitsky-Golay smoothing filter as the lifting predictor, and then processed the signals comparing with the ordinary Savitsky-Golay smoothing method. We useed the new lifting in noisy heavy sine denoising. The new transform obviously has better denoise ability than ordinary Savitsky-Golay smoothing method. At the same time singular points are perfectly retained in the denoised signal. Singularity analysis, multiscale interpolation, estimation, chemical data smoothing and other potential signal processing utility of this new lifting transform are in prospect.     

热轧恒压供水系统智能Smith预估多模控制策略研究

针对热轧恒压供水系统的非线性和时滞性特点,提出了一种智能Smith预估多模控制策略。当供水压力差绝对值大于等于设定值的20%时采用了PD控制,在设定值的10%到20%范围内采用PID控制,小于设定值的10%时采用了模糊自适应PID控制,通过模糊规则在线调整PID控制器的三个参数,并引入Smith预估器对供水系统的纯滞后现象进行补偿。基于推导出的典型恒压供水模型,利用MATLAB软件对常规Smith-PID、Smith-模糊PID、智能Smith预估多模控制器进行仿真分析。仿真结果及现场应用表明:相比常规的PID-Smith和Smith-模糊PID控制器,智能Smith预估多模控制器具有鲁棒性强、动态响应快及稳态精度高的优点。     

混沌信号的相空间重构和预测方法研究

基于复杂非线性系统的相空间重构理论和神经网络本质为非线性映射关系的特点,提出利用混沌时间序列重构相空间和BP神经网络构建其预测模型的方法。利用该方法对典型的Lorenz混沌时间序列进行了空间重构,研究了预测模型的预测效果,结果表明单步预测效果理想,多步预测在50步以内也能取得较小的预测误差,证明了混沌信号不同于随机噪声,具有短期可预测、长期不可预测的特征。该方法为具有混沌特性的时间序列如心电信号、电力负荷等预测模型的建立提供了理论基础。     

基于改进型Smith预估器的超临界机组主汽温模糊控制系统

介绍了超临界机组主汽温采用燃水比调整和二级喷水减温调节的控制策略.针对超临界机组主汽温对象时变性、非线性、大迟延等特点,提出了一种改进型Smith预估器对主汽温对象惰性区特性进行预估补偿,并将模糊控制理论与经典控制理论结合形成了主汽温综合控制方案.通过现场实际采集的历史数据,对系统进行仿真分析,结果表明该方案可以很好地解决主汽温系统的非线性和滞后性问题.     

极点配置自校正预估PID控制器

对于时滞过程,本文提出了一种新的自校正预估 PID 控制器,它实现了极点配置和静态抗干扰性能,克服了文献[2]提出的自校正预估 PI 控制器和文献[5]提出的自校正预估 PID 控制器不能在线校正控制器参数的缺点.仿真例子说明了本文结果的有效性.     

General Data Analysis Supported SPC

The issue of autocorrelation in statistical process control (SPC) data has been and continues to be a much discussed and researched topic. In nearly all published studies and in SPC training modules, the predominate models for autocorrelation come from a class of time-series models known as autoregressive-integrated moving average (ARIMA) models. However, little attention is given to a variety of systematic patterns pervasive in SPC applications that are not adequately modeled by the ARIMA framework. In this paper, we suggest the need for an even broader perspective than defined by ARIMA models. Furthermore, we argue that effective SPC training not only requires attention to data analysis tools for modeling systematic patterns but also to tools for diagnostically checking the distributional assumptions of variable and attribute charts.     

一种收敛于全局无偏最优解的非线性自回归（NAR）预测

本文研究了基于Ｖｏｌｔｅｒｒａ级数展开的非线性自回归预测模型的参数估计问题，从线性自适应ＩＩＲ波滤输出误差算法的观点，研究ＮＡＲ模型的参数估计问题，利用平均收敛条件，提出一可收敛于全局无偏最优解的新算法，解决了一般的预测误差算法的有偏解问题。     

二维溃坝洪水波演进的数值模拟

采用基于MacCormack预测-校正技术的隐式数值格式求解控制水流运动的二维浅水方程，建立了模拟大坝瞬间全溃或局部溃倒所致的洪水演进过程数学模型．应用该模型对矩形明渠中缓、急流过渡的水面曲线进行了数值计算，并与理论解进行了比较。最后用该模型预测了矩形河道中大坝瞬间局部溃倒时下游有多个障碍物的洪水演进过程，算例说明该数学模型对模拟溃坝洪水波很有效。     

关于布朗运动过程的最佳非线性预测

本文将研究非线性预测理论中的一些问题。首先,取标准布朗运动过程X(t)为基础过程,然后用满足一定条件的Borel可测函数f,把X(t)进行变换而得到新的随机过程f(X(t))。这样,实际能够观测到的过程就是形如Y(t)=f(X(t))的随机过程。本文的主要目的就是,利用观测值Y(f),r≤t来求Y(t+τ)、τ>0的最佳非线性预测量Y(t,τ)。其次,取带漂移布朗运动过程为基础过程,而后用如上的方法作出新的随机过程。从而,讨论怎样求这种过程的最佳非线性预测量问题。