功率放大器非线性特性及预失真建模研究

针对无记忆功率放大器的非线性特性及预失真建模的问题,首先建立了多项式模型、极坐标Saleh模型和基于正交三角函数的模型并利用MATLAB对其进行了求解,然后给出了无记忆多项式预失真处理器特性函数表达式及最小二乘解.针对记忆功率放大器的非线性特性及预失真建模的问题,首先建立了记忆多项式模型并对其进行了求解,然后建立了相应的有记忆多项式预失真模型并利用最小二乘法进行了求解,并提出了联合功率放大器特性和输入信号幅值范围的有记忆功放自适应预失真模型.最后求出所给输入信号、输出信号以及加入预失真后线性系统的输出信号的功率谱密度,并计算和比较了信道的带外失真参数ACPR;结果显示,加入预失真后大大提升了系统的性能,线性特性明显加强.     

功率放大器非线性特性及预失真建模

信号的功率放大器是电子通信系统的关键器件之一,功放的输出信号相对于输入信号可能产生非线性变形,这将带来无益的干扰信号,研究其机理并采取措施改善,具有重要意义.通过利用无记忆非线性功放和记忆非线性功放的实测数据用数学方法对其分别进行建模,而后使用前置预失真器的方法改善功放的非线性特性,并对其中预失真器的建模做了研究,采用间接学习型结构构建预失真器模型.仿真结果显示功放非线性模型结合预失真器模型能够很好地逼近实际情况,并且能很好地抑制带外频谱扩展.     

基于分段线性模型与记忆多项式的功率放大器非线性特性及预失真分析

功率放大器的非线性化导致信号的失真,是电子信息行业普遍存在的问题之一.在功率放大器的前端引入预失真模块能对失真有很好的改善,也是目前研究较多的一种方法.从这一点出发,研究了非记忆性功放和记忆性功放的输入输出特征曲线,分别利用分段线性模型以及记忆多项式模型进行了拟合与数学表示,并构建了预失真模型以及预失真函数,对引入预失真模块的整个系统进行了仿真实验和评价指标的计算.最后对有记忆功放进行了Burg法功率谱密度分析,计算出了ACPR值并进行分析.     

运用多目标规划优化功放的预失真模型

预失真技术是克服功率放大器非线性失真的一种非常有效的方法.许多研究者从数学建模的思想角度出发,建立了关于功放与预失真的各类级数模型,以实现线性增益输出.但是在应用通信系统中,这些模型不能有效地动态实现功放效率尽可能高的要求.在和记忆多项式模型的基础上,创新地运用多目标规划的方法优化功放的预失真模型.在满足一定的线性化输出的同时,尽可能地得到更高的功放效率,而且能同时计算出增益系数与模型参数.对于数据量为1000的无记忆功放得到增益倍数为1.82649,预失真模型的NMSE值为-41.5633.数据量为73920的有记忆功放得到增益倍数为9.5969,预失真模型的NMSE值为-24.3682.     

2013年研究生数模竞赛B题综述

宽带移动通信传输正在改变着人们的生活.作为信息传输的重要环节,信号的功率放大在无线通信中起着关键作用.第十届全国研究生数模竞赛B题:功率放大器非线性特性及预失真建模,利用数学建模的思路辨识功率放大器非线性失真行为模型并通过预畸变来减少失真.通信系统的发展需要有技术弥补功率器件的固有非线性特性,保证功率放大器的能量转换效率和线性度.随着器件的更新和信息传输速率的增加,解决此问题的数学模型—预失真算法一直在更新和发展.在研究生数模竞赛中引入此类赛题,可以开阔思维,激发研究生使用数学的兴趣,提高利用数学模型解决实际工程问题的能力,同时利用学生思维活跃的优势,提出有价值的创新点,丰富现有的算法类型,为国内相关企业研究解决此类问题提供新的思路方法,具有重要的意义.     

W-模型辨识及其统计特性

本文选用三位式伪随机序列作为输入 ,充分利用其激励信号的各种特性 ,辨识非线性系统 Wiener模型的线性子系统脉冲响应函数 ,对非线性增益环节 ,则由其奇数和偶数项多项式系数分别进行估计 .同时研究线性与非线性子系统辨识的统计特性 ,给出各部分参数估计的置信区间 .     

典型非线性模型的模糊辨识逼近精度分析

人们根据非线性系统的复杂特性归结了几种具有代表性的非线性模型.而模糊辨识方法是辨识非线性系统的有力工具,本文采用T-S模糊模型对三种常见的非线性模型:Hammerstein模型,Wiener模型和双线性模型进行逼近,并根据仿真数据研究不同的非线性结构对模糊模型逼近精度的影响.仿真实例是在训练和检验数据组数、模型阶数相同的情况下,采用三角形隶属函数,聚类型隶属函数和高斯型隶属函数分别对这三种非线性模型进行逼近能力的研究.     

失真风险保费的最优经验厘定

研究了贝叶斯模型中失真风险保费的经验厘定问题.通过引入分布函数的加权积分损失函数,利用信度理论的方法最小化期望损失得到分布函数的最优线性估计,进而得到失真风险保费的两个信度估计,并对信度估计的统计性质进行了比较.文章还讨论了失真函数和权重函数的选取问题,给出了结构参数的估计方法,证明了估计的无偏性和相合性.最后,利用数值模拟的方法验证了估计的收敛情况,并对不同失真函数下的收敛情况进行了比较.     

二个四参数增长曲线模型参数初始值的确定方法与模型应用

对非线性回归模型进行非线性最小二乘估计一般需要确定参数初始值.在非线性回归模型中,General Logistic模型和Von Bertalanffy模型是二个含有四参数的增长曲线模型,对数据的拟合有较强的适应性,应用较为广泛.本文给出这两个模型参数初始值的确定方法,并应用于实际拟合,得到很好的效果.     

基于不同核函数的非参数与参数利率模型的国债定价

以上海证券交易所的国债回购利率数据为样本,本文采用两种不同核函数:高斯核和抛物线核对非参数利率期限结构模型进行估计.结果显示:短期利率的密度函数是非正态的,扩散过程的漂移函数和扩散函数都是非线性的,高斯核比抛物线核对扩散函数拟合更平滑.然后,给出了基于非参数和参数利率模型的国债定价的方法,并对非参数利率模型、Vasicek模型、CIR模型、多项式样条静态模型进行国债定价预测比较与分析.     

普适双变量随机气候模式的研究

该文对海气耦合双变量随机气候模式进行了系统研究，研究结果表明：（1）无论是考虑只有大气子系统的随机作用还是同时考虑大气子系统和海洋子系统的双扰动，对给定的扰动频率，海气气候系统对大气的扰动作用相当于一个十分简单的线性放大器；对于不同的扰动频率，在能量上海温的变化将随大气的扰动做4次幂的非线性响应;（2）若同时考虑大气子系统和海洋子系统的随机扰动，在满足细致平衡条件下可以求出系统定态的概率分布。否则，只有当扰动为弱噪声时，才能用级数展开法近似求出系统的概率分布。     

INVESTIGATING TAX DISTORTIONS: AN APPLIED MODEL OF PETROLEUM EXPLORATION AND EXTRACTION DECISIONS

Abstract A numerical model is proposed for the testing of distortions caused by petroleum fiscal systems on the exploration and extraction activities of a profit‐maximizing firm. Traditional models have not been capable of testing for the distortions caused by the complex tax structures most often used by governments. Two tax combinations, as well as certain other taxes, are analyzed for distortions in the model. The model is parameterized using generic data because specific jurisdictions are not considered. The distortions due to rentals and royalties are as expected by theory. Property taxes show a new distortion result where production tilting is ambiguous.     

Discovering focal regions of slightly-aggregated sparse signals

The characteristic aspects of dynamic distortions on a lengthy time series of i.i.d. pure noise when embedded with slightly-aggregating sparse signals are summarized into a significantly shorter recurrence time process of a chosen extreme event. We first employ the Kolmogorov–Smirnov statistic to compare the empirical recurrence time distribution with the null geometry distribution when no signal being present in the original time series. The power of such a hypothesis testing depends on varying degrees of aggregation of sparse signals: from a completely random distribution of singletons to batches of various sizes on the entire temporal span. We demonstrate the Kolmogorov–Smirnov statistic capturing the dynamic distortions due to slightly-aggregating sparse signals better than does Tukey’s Higher Criticism statistic even when the batch size is as small as five. Secondly, after confirming the presence of signals in the pure noise time series, we apply the hierarchical factor segmentation (HFS) algorithm again based on the recurrence time process to compute focal segments that contain a significantly higher intensity of signals than do the rest of the temporal regions. In a computer experiment with a given fixed number of signals, the focal segments identified by the HFS algorithm afford many folds of signal intensity which also critically depend on the degree of aggregation of sparse signals. This ratio information can facilitate better sensitivity, equivalent to a smaller false discovery rate, if the signal-discovering protocol implemented within the computed focal regions is different from that used outside of the focal regions. We also numerically compute the specificity as the total number of signals contained in the computed collection of focal regions, which indicates the inherent difficulty in the task of sparse signal discovery.     

Global regularity of the elastic fields of a power‐law model on Lipschitz domains

In this paper, we study the global regularity of the displacement and stress fields of a nonlinear elastic model of power‐law type. It is assumed that the underlying domains are Lipschitz domains which satisfy an additional geometric condition near those points, where the type of the boundary conditions changes. The proof of the global regularity result relies on a difference quotient technique. Finally, a global regularity result for the stress fields of the elastic, perfect plastic Hencky model is derived. This model appears as a limit model of the power‐law model. Copyright © 2006 John Wiley & Sons, Ltd.     

Effect of superficial velocity on pressure fluctuations in a gas–solid fluidized bed: A stochastic analysis

The characteristics of a gas–solid fluidized bed are influenced by complicated and stochastic phenomena, e.g. jetting and the bubbling of the fluidizing medium and the motion of the fluidized particles. In this study pressure fluctuations due to the bubble movement were measured by means of a pressure probe over a range of time. The resultant time series have been analysed by determining their autocorrelation function and power spectrum with emphasis on the examination of the effect of the superficial velocity of the fluidizing gas. A stochastic model of bubble motion in a fluidized bed has been developed. This model visualizes the bubble motion in a fluidized bed to consist of the random movement, generating irregular signals, and the linear movement, generating wave-like signals. A theoretical autocorrelation function and a power spectral density function have been derived based on the model.     

Mathematical modeling of a steam generator for sensor fault detection

A dynamic model for a nuclear power plant steam generator (vertical, preheated, U-tube recirculation-type) is formulated as a sixth-order nonlinear system. The model integrates nodal mass and energy balances for the primary water, the U-tube metal and the secondary water and steam. The downcomer flow is determined by a static balance of momentum. The mathematical system is solved using transient input data from the Philippsburg 2 (FRG) nuclear power plant. The results of the calculation are compared with actual measured values. The proposed model provides a low-cost tool for the automatic control and simulation of the steam generating process. The “parity-space” algorithm is used to demonstrate the applicability of the mathematical model for sensor fault detection and identification purposes. This technique provides a powerful means of generating temporal analytic redundancy between sensor signals. It demonstrates good detection rates of sensor errors using relatively few steps of scanning time and allows the reconfiguration of faulty signals.