射频功率放大器的建模

提出了运用模糊神经网络对射频功放电路进行建模的方法,模糊神经网络是近年来发展起来的一种新型的网络结构,具有万能函数逼近器的功能,文中用MATALAB中自带的自适应神经模糊系统ANFIS对仿真得到的数据进行建模,并利用得到的模型计算功放的频谱,功率压缩曲线,功率增益曲线,与ADS仿真的结果进行比较,取得了较好的结果,证明了建模方法的有效性.     

基于神经网络的GaAs微波及高速集成电路CAD技术

介绍了基于神经网络的 Ga As微波与高速电路 CAD方面的研究与开发工作 ,其中包括用神经网络进行器件与电路建模的原理与方法及这一模型在优化与统计分析中的应用。     

基于模糊逻辑的射频功放建模

提出了一种基于模糊逻辑的射频功放行为模型的建模方法.利用了模糊逻辑的万能逼近能力,对射频功放电路外部端口动态特性进行了准确的建模,并将模型计算得到的稳态输出电压,功率压缩特性和增益压缩特性曲线与ADS软件仿真结果进行了比较,取得了较好的效果,证明建模方法的有效性.     

基于模糊逻辑的两级运算放大器宏模型

运算放大器是最常用的模拟集成电路功能块,本文提出了一种基于模糊逻辑建立的运算放大器宏模型。基于运放电路结构分解的模糊逻辑建模方法利用了电路内部的结构特点与工作特性等先验知识。根据电路的结构知识确定模型整体结构;根据电路的工作特性确定模糊逻辑模型的规则（包括条数、前提与结论函数的形式）及初始参数。这就大大简化了模型构造过程,使一开始构造出来的初始模型就有较好的逼近精度,后续模型参数的学习训练只需经过少量的迭代,相对于传统的系统辨识方法具有很大的优势。     

基于模糊逻辑的数字驱动器宏模型

在芯片设计或板级设计中都需要准确的数字驱动器模型来进行信号完整性分析,本文提出了一种基于模糊逻辑建立的驱动器宏模型。基于驱动器结构分解的模糊逻辑建模方法根据电路的结构知识确定模型整体结构;根据电路的工作特性确定模糊逻辑模型的规则(包括条数、前提与结论函数的形式)及初始参数。这就大大简化了模型构造过程,使一开始构造出来的初始模型就有较好的逼近精度,后续模型参数的学习训练只需经过少量的迭代,相对于传统的系统辨识方法具有很大的优势。     

层次模糊系统的全局逼近性质及其与多子波神经网络关系研究

该文首先讨论了B样条基函数的特性,在此基础上证明了基于B样条隶属函数的层次模糊系统(HBFS)是全局逼近器这一重要结论。与此同时,根据基于B样条隶属函数的层次模糊系统与多子波神经网络在逼近问题上的等价性,我们对于多子波神经网络是否为全局逼近器这一问题给出了一种全新的证明方法,以上工作为HBFS的实际应用提供了坚实的的理论基础。     

折线模糊神经网络对Chonquet可积函数的泛逼近性

首先定义了Choquet积分模的概念,然后分析了折线模糊神经网络在Choquet积分模意义下对模糊值函数的泛逼近性,证明了当模糊值函数满足相容性时,折线模糊神经网络能够以任意精度逼近该Choquet可积模糊值函数.     

一种新的气动参数计算方法

理论上已证明模糊神经网络可以充当万通逼近器,能以任意精度逼近任何非线性函数。鉴于气动参数通常是攻角α、侧滑角β、马赫数M、高度H等的非线性函数,用模糊神经网络对气动参数进行高精度拟合。在飞行器轨迹计算中用拟合得到的计算公式计算气动参数,可节省计算机时和存贮单元,提高计算精度,仿真实验也表明了这一方法的有效性和可行性。     

模糊神经网络的交叉研究

介绍模糊神经网络交叉的基础与途径，从基于神经网络的模糊逻辑系统和用模糊逻辑增强的的神经网络两个方面介绍了模糊神经网络交叉研究的具体实现，最后指出模糊神经网络交叉的未来方向。     

用基函数神经网络实现多阈值图象分割

本文介绍了一种用基函数神经网络实现多阈值图象分割的新方法。它从函数逼近的角度研究基于灰度直方图的多阈值分割问题，提出了一种模糊反向传播学习算法，采用该算法的高斯基函数网络能够准确检测直方图中包含的子区域和它们的分布函数，而且速度很快。实验表明本文的方法在实际图象分割中是有效的。     

Equivalent-circuit interconnect modeling based on the fifth-order differential quadrature methods

This paper introduces an efficient and passive discrete modeling technique for estimating signal propagation delays through on-chip long interconnects that are represented as distributed RLC transmission lines. The proposed delay model is based on a less frequently used numerical approximation technique, called the differential quadrature method (DQM). The DQM can compute the partial derivative of a function at any arbitrary point located within a prespecified closed domain of the function by quickly estimating the weighted linear sum of values of the function at a relatively small set of well-chosen grid points within the domain. By using the fifth-order DQM, a new approximation framework is constructed in this paper for discretizing the distributed RLC interconnect and thereafter modeling its delay. Due to high efficiency of DQM approximation, the proposed framework requires only few grid points to achieve good accuracy. The presented equivalent-circuit model appears like the ones derived by the finite difference (FD) method. However, it has higher accuracy and less internal nodes than generated by the FD-based modeling. The fifth-order DQM modeling technique is shown to preserve passivity. It has linear forms that are compatible with the passive order-reduction algorithm for linear network. Numerical experiments show that the proposed modeling approach leads to high accuracy as well as high efficiency.     

A new SPICE subcircuit model of power p-i-n diode

A new modeling approach for the power p-i-n diode is proposed. The base region is represented with a two-port network, obtained by solving the ambipolar diffusion equation with the Laplace transform method, and by approximating the resulting transcendental functions in the s-domain with rational approximations. Two different networks have been obtained. The first one, based on Taylor-series approximation is shown to be a generalization of a two-port model already proposed in the literature for the nonquasi-static modeling of bipolar transistors. The second network representation is based on Pade' approximation and is shown to be more accurate than the Taylor-series approach, The obtained RLC networks are easily implemented in a PSPICE subcircuit which also takes into account the emitter recombination effects and the dynamic of the space-charge voltage build-up. Good agreement has been obtained by comparing the results of the proposed model with numerical device simulations     

Macromodeling of nonlinear digital I/O drivers

In this paper, a modeling technique using spline functions with finite time difference approximation is discussed for modeling moderately nonlinear digital input/output (I/O) drivers. This method takes into account both the static and the dynamic memory characteristics of the driver during modeling. Spline function with finite time difference approximation includes the previous time instances of the driver output voltage/current to capture the output dynamic characteristics of digital drivers accurately. In this paper, the speed and the accuracy of the proposed method is analyzed and compared with the radial basis function (RBF) modeling technique, for modeling different test cases. For power supply noise analysis, the proposed method has been extended to multiple ports by taking the previous time instances of the power supply voltage/current into account. The method discussed can be used to capture sensitive effects like simultaneous switching noise (SSN) and cross talk accurately when multiple drivers are switching simultaneously. A comparison study between the presented method and the transistor level driver models indicate a computational speed-up in the range of 10-40 with an error of less than 5%. For highly nonlinear drivers, a method based on recurrent artificial neural networks (RNN) is discussed.     

利用遗传优化算法的混沌时间序列建模

提出一种利用遗传算法对混沌数据序列建模的全局建模方法，可获得接近原始混沌系统的映射。该方法利用遗传算法同时进行函数拟和与参数优化。实验结果表明该方法有较好的建模效果，对用于短波通信的有关电离层参数预测显示了满意的结果。     

有理B样条函数在传感器线性化中的应用

提出一种基于有理B样条函数的传感器线性化方法。B样条函数具有良好的光滑性及逼近性,而非均匀有理B样条进一步引入权因子以及函数节点的非均匀化,使得每个控制点对整个曲线的影响更加灵活,曲线的连接、光滑更容易实现。因此,非均匀有理B样条能够保证建模精度,适用于传感器建模以实现线性化。详细介绍了基于非均匀有理B样条的传感器线性化方法,利用实际传感器输入/输出数据进行了实验研究,并与神经网络方法做了比对。理论和实验结果表明提出的方法在传感器的线性化应用中具有计算简便,校正精度高的优点。     

Landmark‐based device calibration and region‐based modeling for RSS‐based localization

During the past decades, many fingerprint‐based indoor positioning systems have been proposed and have achieved great success. However, uncontrolled effects of device diversity, signal noise, and dynamic obstacles could recognizably degrade the performance of modern fingerprint‐based indoor localization systems. In this paper, to amend the variations in radio signal strengths (RSSs) caused by device diversity, we proposed an automatic device calibration process. Because of device diversity, the sensed RSS would deviate from the trained radio map and thus leads to poor positioning. An RSS transform function could be adopted to calibrate the RSS variation between different devices and overcome the device diversity problem. However, to train the transform function, a data collection process is required. Unlike conventional calibration methods requiring manual data collection, we proposed a landmark‐based automatic collection process. Based on the detection of Wi‐Fi landmarks, our system could automatically collect pair‐wise RSS samples between devices and train the RSS transform function without extra human power. In addition, to well represent the effects of signal noise and dynamic obstacles, a region‐based RSS modeling method was also proposed. The proposed modeling method allows our system to perform region‐based target localization and utilize more robust region information for localization. Experiments in various environments demonstrate that our system could give a better positioning performance by properly handling the RSS variation caused by signal noise, dynamic environment, and device diversity. Copyright © 2015 John Wiley & Sons, Ltd.     

Performance of optical bit rate limiters with pre- or post-optical amplification

A communication system incorporating a bit rate limiter (BRL) device is analyzed using recently proposed statistical models for power splitters and combiners. We also consider the possible optical attenuation and amplification before and after BRL device. We obtain the total output moment generating function (MGF) of the number of photoelectrons at the receiver end for two possible synchronization procedures. Furthermore, we use saddle-point approximation method to evaluate the system performance considering the effects of shot-noise, thermal noise, and source extinction ratio.     

Parametric manufacturing yield modeling of GaAs/AlGaAs multiplequantum well avalanche photodiodes

GaAs/AlGaAs multiple quantum well (MQW) avalanche photodiodes (APD's) are of interest as an ultra-low noise image capture mechanism for high-definition systems. Since literally millions of these devices must be fabricated for imaging arrays, it is critical to evaluate potential performance variations of individual devices in light of the realities of semiconductor manufacturing. Specifically, even in a defect-free manufacturing environment, random variations in the fabrication process will lead to varying levels of device performance, Accurate device performance prediction requires precise characterization of these variations. This paper presents a systematic methodology for modeling the parametric performance of GaAs MQW APD's. The approach described requires a model of the probability distribution of each of the relevant process variables, as well as a second model to account for the correlation between this measured process data and device performance metrics. The availability of these models enables the computation of the joint probability density function required for predicting performance using the Jacobian transformation method. The resulting density function can then be numerically integrated to determine parametric yield. Since they have demonstrated the capability of highly accurate function approximation and mapping of complex, nonlinear data sets, neural networks are proposed as the preferred tool for generating the models described above. In applying this methodology to MQW APD's, it is shown that using a small number of test devices with varying active diameters, barrier and well widths, and doping concentrations enables prediction of the expected performance variation of APD gain and noise in larger populations of devices. This approach compares favorably with Monte Carlo techniques and allows device yield prediction prior to high volume manufacturing in order to evaluate the impact of both design decisions and process capability     

Improved one-band self-consistent effective mass methods for hole quantization in p-MOSFET

An improved one-band self-consistent effective mass approximation (EMA) for hole quantization in p-MOSFET is presented. It is developed by extracting empirically a set of hole-effective masses based on the rigorous self-consistent six-band EMA. It is found that the self-consistent model using such improved one-band effective masses can provide accurate hole quantization characteristics. For further simplification, the triangular well approximation is also assessed. Fairly accurate MOS electrostatics is also obtained if introducing an effective field in the inversion layer in triangular well approximation. However, the triangular well approximation has its limitation in describing the hole centroid. In essence, the shorter computing time of the proposed improved one-band methods without sacrificing the accuracy of MOS electrostatics provides its potential in device modeling for hole quantization.     

Polynomial-Approximation-Based Control for Nonlinear Systems

This paper is concerned with the stabilization problem for nonlinear systems. A new polynomial-approximation-based approach for modeling nonlinear systems is first proposed. The nonlinearity is approximated by polynomials, and the approximation errors are treated as modeling uncertainties. The original nonlinear systems are converted into polynomial systems with modeling uncertainties. In order to highlight the approximation accuracy, the piecewise polynomial approximation functions are utilized. A novel polynomial state-feedback controller is designed to solve the stabilization problem. Furthermore, switched polynomial state-feedback controllers are designed to improve the performance. The stabilization conditions are presented in terms of sum of squares, which can be numerically solved via SOSTOOLS. Finally, simulation examples are provided to demonstrate the feasibility of the proposed method and show its advantage over the polynomial-fuzzy-model-based approach.