Neural network electrothermal modeling approach for microwave active devices

This article presents an artificial neural network (ANN) approaches for small‐ and large‐signal modeling of active devices. The small‐signal characteristics were modeled by S‐parameters based feedforward NN models. The models have been implemented to simulate the bias, frequency and temperature dependence of measured S‐parameters. Feedback NN based large‐signal model was developed and implemented to simulate the drain current and its inherent thermal effect due to self‐heating and ambient temperature. Both small‐ and large‐signal models have been validated by measurements for 100‐μm and 1‐mm GaN high electron mobility transistors and very good agreement was obtained.     

Neural network modeling of microwave FETs based on third‐order distortion characterization

A new method for characterization of HEMT distortion parameters, which extracts the coefficents of a Taylor series expansion of I_ds(V_gs, V_ds), including all cross‐terms, is developed from low‐frequency harmonic measurements. The extracted parameters will be used either in a Volterra series model around a fixed bias point for 3^rd‐order characterization of small‐signal I_ds nonlinearity, or in a large‐signal model of I_ds characteristic, where its partial derivatives are locally characterized up to the 3^rd order in the whole bias region, using a novel neural‐network representation. The two models are verified by one‐tone and two‐tone intermodulation distortion (IMD) tests on a PHEMT device. © 2006 Wiley Periodicals, Inc. Int J RF and Microwave CAE, 2006.     

Automated time domain modeling of linear and nonlinear microwave circuits using recurrent neural networks

In this article, a recurrent neural network (RNN) method is employed for dynamic time‐domain modeling of both linear and nonlinear microwave circuits. An automated RNN modeling technique is proposed to efficiently determine the training waveform distribution and internal RNN structure during the offline training process. This technique extends a recent automatic model generation (AMG) algorithm from frequency‐domain model generation to dynamic time‐domain model generation. Two types of applications of the algorithm are presented, transient electromagnetic (EM) behavior modeling of microwave structures, and time‐domain envelope modeling of power amplifiers (PA). For transient EM modeling, we consider EM structures with varying material and geometrical parameters. AMG automatically varies the EM structural parameters during training and drives time‐domain EM simulators to generate necessary amount of data for RNN to learn. AMG aims to model the transient behavior with minimum RNN order while satisfying accuracy requirements. In modeling PA behavior, an envelope formulation is used to specifically learn the AM/AM and AM/PM distortions due to third‐generation (3G) digital modulation input. The RNN PA model is able to model these time domain distortions after training and can accurately model the amplifier behavior in both time (AM/AM, AM/PM) and frequency (spectral re‐growth). © 2008 Wiley Periodicals, Inc. Int J RF and Microwave CAE, 2008.     

Generalized nonlinear FET/HEMT modeling

A modeling procedure which provides an accurate large-signal response for variation in bias, input power level, and fundamental frequency for FET/HEMT transistors is designed. A procedure for measuring the large-signal input response on an easily implemented system is presented. The technique is illustrated by designing a nonlinear PHEMT model, which includes an accurate large-signal input response and works with variations in the aforementioned input conditions. © 2004 Wiley Periodicals, Inc. Int J RF and Microwave CAE 14, 122–133, 2004.     

Neural network methods for the modeling and control of welding processes

While welding processes are of great importance in manufacturing, their modeling and control is still subject of research. The highly nonlinear, strongly coupled, and multivariable nature of these processes renders the use of analytical tools practically impossible. In this article a novel approach is presented which employs networks of simple nonlinear units: a neural network. A widely used welding process, the Gas Tungsten Arc Welding is presented and the problem of its modeling and control is exhibited. A very brief introduction to neural networks is followed by presenting the experimental results for modeling the static and dynamic behavior of the process, as well as some practical recommendations regarding the use of the neural network techniques for controlling these processes.     

Wavelet networks for nonlinear system modeling

This study presents a nonlinear systems and function learning by using wavelet network. Wavelet networks are as neural network for training and structural approach. But, training algorithms of wavelet networks is required a smaller number of iterations when the compared with neural networks. Gaussian-based mother wavelet function is used as an activation function. Wavelet networks have three main parameters; dilation, translation, and connection parameters (weights). Initial values of these parameters are randomly selected. They are optimized during training (learning) phase. Because of random selection of all initial values, it may not be suitable for process modeling. Because wavelet functions are rapidly vanishing functions. For this reason heuristic procedure has been used. In this study serial-parallel identification model has been applied to system modeling. This structure does not utilize feedback. Real system outputs have been exercised for prediction of the future system outputs. So that stability and approximation of the network is guaranteed. Gradient methods have been applied for parameters updating with momentum term. Quadratic cost function is used for error minimization. Three example problems have been examined in the simulation. They are static nonlinear functions and discrete dynamic nonlinear system.     

Neural networks implementation for modeling and control design of manufacturing systems

This paper presents a dynamic neural network implementation for the modeling and control design of a class of manufacturing systems. The evolution of the considered systems is supposed to be continuous and non-stochastic. A separate implementation of the system elements is detailed. These elements are then connected together in order to obtain a global net that simulates the behavior of the real system. The obtained model is modular and can be adapted easily for any modification of the system. Permanent correction rules are developed to control the speed of the machines according to a desired profile and to take into consideration the buffers limited capacities. The convergence of the control design is proved. The proposed approach is applied on an exhaust valves assembly workshop.     

Constitutive relations for nonlinear modeling of Si/SiGe HBTs using an ANN model

In this article, we demonstrate how the constitutive relations for the nonlinear modeling of hetro‐junction bipolar transistors (HBTs) can be based on an artificial neural network (ANN) model representation.. The model is implemented using a commercial microwave simulator, and has been validated by DC and nonlinear measurements. Excellent agreement is obtained as compared with the results of the DC measurements, and the model predicts well the higher‐order harmonics in a single tone test.. © 2005 Wiley Periodicals, Inc. Int J RF and Microwave CAE, 2005.     

Nonlinear modeling of class‐E microwave power amplifiers

Verification of the Materka–Kacprzak model suitable for highly saturated MESFET operation is presented. To examine the validity of the model, a broadband microwave class‐E power amplifier was designed and fabricated using a Siemens CLY5 MESFET transistor. A 200 MHz bandwidth (22%) from 0.82 to 1.02 GHz with a power‐added efficiency greater than 60% was measured at an input power level of 15 dBm with a constant output power of 24 dBm. ©1999 John Wiley & Sons, Inc. Int J RF and Microwave CAE 9: 93–103, 1999     

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.     

Application of artificial neural network models to linear and nonlinear RF circuit modeling

Typical RF and wireless circuits comprise a large number of linear and nonlinear components. The complexity of the RF portion of a wireless system continues to increase in order to support multiple standards, multiple frequency bands, the need for higher bandwidth, and stringent adjacent channel specifications. The time required to carry out a virtual prototyping of such complex circuits and their trade‐off analysis with the baseband circuitry can be unacceptably long, because both the circuit simulation and optimization procedures can be very time consuming. Typically, one divides the task into those of designing the nonlinear elements or subcircuits that can be accurately analyzed by using RF simulators, and uses circuit level analysis for simulating the circuits at module level. In this article, we will review some approaches to modeling both the linear RF elements as well as nonlinear subcircuits (amplifiers, mixers, VCOs), and will emphasize on the application of the artificial neural networks (ANNs). Furthermore, we will demonstrate the use of the ANN to the design of RF circuits and illustrate their application to wireless types of problems of practical interest. © 2001 John Wiley & Sons, Inc. Int J RF and Microwave CAE 11: 231–247, 2001.     

Training wavelet networks for nonlinear dynamic input–output modeling

In the framework of nonlinear process modeling, we propose training algorithms for feedback wavelet networks used as nonlinear dynamic models. An original initialization procedure is presented that takes the locality of the wavelet functions into account. Results obtained for the modeling of several processes are presented; a comparison with networks of neurons with sigmoidal functions is performed.     

一种基于动态人工神经网络的Wiener模型辨识

提出了一种新的辨识模型对Ｗｉｅｎｅｒ模型进行辨识,该模型 线性动态神经元串联一静态网络模型组成,利用线性动态神经元对Ｗｉｅｎｅｒ模型的线性动态部分建模,利用静态ＢＰ网络逼近模型的静态非线性部分,并且给出了统一的ＢＰ辨识算法,仿真结果表明了该方法的有效性。     

Parametric modeling of microwave passive components using combined neural networks and transfer functions in the time and frequency

A novel parametric modeling technique is proposed to develop combined neural network and transfer function models for both time and frequency (TF) domain applications of passive components, where the neural network is trained to map geometrical variables to the coefficients of transfer functions. Built on our previous work, a new order‐changing module is developed to enforce stability of transfer functions and simultaneously guarantee continuity of coefficients. A constrained optimization strategy is introduced to enforce passivity of transfer functions through a neural network training process. A general equivalent circuit for two‐port passive components is generated directly from coefficients of arbitrary‐order transfer functions. Once trained, the parametric model can provide accurate and fast prediction of the electromagnetic behavior of passive components with geometrical parameters as variables. Compared to our previous work, the proposed method enables models to work well in the time domain providing good accuracy in challenging modeling applications. Two parametric modeling examples of spiral inductors and interdigital capacitors, and their application in both time and frequency domain simulations of a power amplifier are examined to demonstrate the validity of the proposed technique. © 2012 Wiley Periodicals, Inc. Int J RF and Microwave CAE , 2013.     

Neural networks stabilization and disturbance attenuation for nonlinear switched impulsive systems

In this paper, we address the problem of neural networks (NNs) stabilization and disturbance rejection for a class of nonlinear switched impulsive systems. An adaptive NN feedback control scheme and an impulsive controller for output tracking error disturbance attenuation of nonlinear switched impulsive systems are given under all admissible switched strategy based on NN. The NN is used to compensate for the nonlinear uncertainties of switched impulsive systems, and the approximation error of NN is introduced to the adaptive law in order to improve the tracking attenuation quality of the switched impulsive systems. Impulsive controller is designed to attenuate effect of switching impulse. Under all admissible switching law, impulsive controller and adaptive NN feedback controller can guarantee asymptotic stability of tracking error and improve disturbance attenuation level of tracking error for the overall nonlinear switched impulsive system. Finally, a numerical example is given to demonstrate the effectiveness of the proposed control and stabilization methods.     

混沌机制在T-S模型模糊神经网络的系统辨识研究

提出一种T－S模型的模糊神经网络,在通常BP算法的基础上,引进混沌机制来训练模糊神经网络的权值参数。将混沌BP算法应用于非线性系统建模,以求获得全局意义下的最优逼近。仿真研究说明了其有效性和良好的性质。     

Modeling of microstrip antennas using neural networks techniques: A review

Artificial neural networks modeling have recently acquired enormous importance in microwave community especially in analyzing and synthesizing of microstrip antennas (MSAs) due to their generalization and adaptability features. A trained neural model estimates response very fast, which is nearly equal to its measured and/or simulated counterpart. Thus, it completely bypasses the repetitive use of conventional models as these models need rediscretization for every minor changes in the geometry, which itself is a time‐consuming exercise. The purpose of this article is to review this emerging area comprehensively for both analyzing and synthesizing of the MSAs. During reviewing process, some untouched cases are also observed, which are essentially required to be resolved for antenna designers. Unique and efficient neural networks‐based solutions are suggested for these cases. The proposed neural approaches are validated by fabricating and characterizing of the prototypes too. © 2015 Wiley Periodicals, Inc. Int J RF and Microwave CAE 25:747–757, 2015.     

基于神经网络的非线性系统多步预测控制

针对离散非线性系统,利用非线性激励函数的局部线性表示,提出一种可用于非线性过程的神经网络多步预测控制方法,并给出了控制律的收敛性分析．该方法将非线性系统处理成简单的线性和非线性两部分,对复杂的非线性多步预测方程给出了直观而有效的线性形式,并用线性预测控制方法求得控制律,避免了复杂的非线性优化求解．仿真结果表明了该算法的有效性．     

基于神经网络的非线性系统预测函数控制

针对离散非线性系统,利用神经网络非线性激励函数的局部线性表示,提出一种可用于非线性过程的神经网络预测函数控制方法并给出了控制律的收敛性分析.该方法将复杂的神经网络非线性预测方程转化成直观而有效的线性形式,同时利用线性预测函数方法求得解析的控制律,避免了复杂的非线性优化求解,仿真结果表明了算法的有效性.