Modeling power and intermodulation behavior of microwave transistors with unified small‐signal/large‐signal neural network models

This article presents a detailed procedure to learn a nonlinear model and its derivatives to as many orders as desired with multilayer perceptron (MLP) neural networks. A modular neural network modeling a nonlinear function and its derivatives is introduced. The method has been used for the extraction of the large‐signal model of a power MESFET device, modeling the nonlinear relationship of drain‐source current I_ds as well as gate and drain charge Q_g and Q_d with respect to intrinsic voltages V_gs and V_ds over the whole operational bias region. The neural models have been implemented into a user‐defined nonlinear model of a commercial microwave simulator to predict output power performance as well as intermodulation distortion. The accuracy of the device model is verified by harmonic load‐pull measurements. This neural network approach has demonstrated to predict nonlinear behavior with enough accuracy even if based only on first‐order derivative information. © 2003 Wiley Periodicals, Inc. Int J RF and Microwave CAE 13: 276–284, 2003.     

Stabilization of electromechanical systems via interconnection and damping assignment

In this article, we propose a general controller structure for asymptotic position regulation of electromechanical systems derived using the Interconnection and Damping Assignment Passivity‐Based Control methodology recently proposed in the literature. The controller is applicable to arbitrary fully actuated electromechanical systems with linear magnetic materials consisting of inductances, permanent magnets, and one mechanical co‐ordinate. We assume linear magnetic materials and fully actuated electrical dynamics; however, no restrictions are imposed on the particular form of the parameters that define the system dynamics, i.e. the inductance matrix, the magnetic coupling or the potential energy function. This allows us to treat—in a unified framework and without any additional simplifying assumptions—very diverse applications, including magnetic suspensions, and stepper and permanent magnet synchronous motors. Instrumental for our developments is the inclusion of ‘virtual’ couplings between the electrical and the mechanical subsystem, which is naturally suggested in this control methodology. Copyright © 2003 John Wiley & Sons, Ltd.     

A nonlinear set‐membership filter for on‐line applications

A guaranteed estimator for a general class of nonlinear systems and on‐line usage is developed and analysed. This filter bounds the linearization error, then applies a linear set‐membership filter such that stability guarantees hold for nonlinear systems. A tight bound on the linearization error is found using interval analysis. This filter recursively estimates an ellipsoidal set in which the true state lies. General assumptions include the use of bounded noises and twice continuously differentiable dynamics. When the system is uniformly observable, it is proven that the nonlinear set‐membership filter is stable. In addition, if no noise is present and the initial error is small, the error between the centre of the estimated set and the true value converges to zero. The result is an estimator which is computationally attractive and can be implemented robustly in real‐time. The proposed method is applied to a two‐state example to demonstrate the theoretical results. Copyright © 2003 John Wiley & Sons, Ltd.     

On Velocity‐Based Local Model Networks For Nonlinear Identification

This paper exposes the strengths and weaknesses of the recently proposed velocity‐based local model (LM) network. The global dynamics of the velocity‐based blended representation are directly related to the dynamics of the underlying local models, an important property in the design of local controller networks. Furthermore, the sub‐models are continuous‐time and linear providing continuity with established linear theory and methods. This is not true for the conventional LM framework, where the global dynamics are only weakly related to the affine sub‐models. In this paper, a velocity‐based multiple model network is identified for a highly nonlinear dynamical system. The results show excellent dynamical modelling performances, highlighting the value of the velocity‐based approach for the design and analysis of LM based control. Three important practical issues are also addressed. These relate to the blending of the velocity‐based local models, the use of normalised Gaussian basis functions and the requirement of an input derivative.     

基于RBF神经网络的一类不确定非线性系统自适应H控制

基于RBF神经网络提出了一种H_∞ 自适应控制方法.控制器由等效控制器和H_∞ 控制器两部分组成.用RBF神经网络逼近非线性函数,并把逼近误差引入到网络权值的自适应律中用以改善系统的动态性能.H_∞ 控制器用于减弱外部干扰及神经网络的逼近误差对跟踪的影响.所设计的控制器不仅保证了闭环系统的稳定性,而且使外部干扰及神经网络的逼近误差对跟踪的影响减小到给定的性能指标.最后给出的算例验证了该方法的有效性.     

Multi-symplectic methods for the coupled 1D nonlinear Schrödinger system

In the paper, the multi-symplectic formulation of the coupled 1D nonlinear Schrödinger system (CNLS) is considered. For the multi-symplectic formulation, a new six point scheme, which is equivalent to the multi-symplectic Preissman integrator, is derived. We also present numerical experiments, which show that the multi-symplectic scheme has excellent long-time numerical behaviour and energy conservation property.     

Ant colony search algorithms for optimal polygonal approximation of plane curves

This paper presents a new polygonal approximation method using ant colony search algorithm. The problem is represented by a directed graph such that the objective of the original problem becomes to find the shortest closed circuit on the graph under the problem-specific constraints. A number of artificial ants are distributed on the graph and communicate with one another through the pheromone trails which are a form of the long-term memory guiding the future exploration of the graph. The important properties of the proposed method are thoroughly investigated. The performance of the proposed method as compared to those of the genetic-based and the tabu search-based approaches is very promising.     

二维对流扩散方程基于三角形网格的特征差分格式

引言 对流扩散方程描述了众多的物理现象,其数值算法研究一直受到重视11一叩3一‘4].在这方面,特征差分方法和特征有限元方法是非常有效的两种方法[1一6}.特征差分方法计算简单,但适应区域不够灵活.特征有限元方法则对矩形区域或一般区域均可得到令人满意的结果,但特征有限     

基于T-S模糊逻辑推理的非线性预测模型

建立了基于Takagi-Sugeno模糊逻辑推理的局部多元回归模糊模型(LMRF模型)，将其用于具有小样本数据和非线性特点的宏观非线性系统预测。在多维空间下用C-聚类方法划分模糊子空间，采用带3参数的广义高斯隶属函数．提高了预测的精度，后件参数的辨识采用最小二乘法将减少计算复杂度，交通流量预测实例表明，基于T-S模糊推理的局部多元回归模型用于交通系统能准确地表达交通流量与社会经济的复杂关系，相对于多元线性回归预测模型精度要高得多。     

一种新型非线性偏最小二乘方法研究及应用--基于Chebyshev多项式改进的PLS方法

在实际生产过程中，过程变量之间往往存在大量相关关系，甚至非线性相关关系。过程变量间存在线性相关时，可采用偏最小二乘方法(Partial Least Squares，PLS)计算模型参数，但由于PLS方法采用线性关系来联系输入和输出因子，因而并不能有效地应用于非线性较强的过程。在这种情况下要对变量进行有效的压缩维数．需要采用非线性PLS方法。基于Chebyshev多项式改进的多元多项式PLS方法，是一种新的非线性PLS方法。该方法利用Chebyshev多项式的正交性质和递推性质，将过程输入变量正交化、线性化后，再应用PLS方法计算模型参数并还原，从而得到比较精确的模型。由于该方法综合考虑了输入变量的自相关和输入变量间的协相关关系。能够更有效地表达过程变量间的非线性关系，因此其对非线性过程的研究提供了新的思路。