Temperature‐dependent models of low‐noise microwave transistors based on neural networks

Neural networks are proposed for efficient temperature‐dependent modeling of small‐signal and noise performances of low‐noise microwave transistors over a wide temperature range. The proposed models can be based either on neural networks only or on a combination of neural networks and empirical transistor models. © 2005 Wiley Periodicals, Inc. Int J RF and Microwave CAE, 2005.     

Estimation of radiation characteristics of different slotted microstrip antennas using a knowledge‐based neural networks model

In this article, a common neural model incorporated with prior knowledge is suggested for estimating radiation characteristics (i.e., resonance frequencies, gains, directivities, antenna efficiencies, and radiation efficiencies) of four‐slotted microstrip antennas with inserted air‐gap for dual‐frequency operation. By incorporating prior knowledge in the existing neural networks, the required numbers of training patterns are drastically reduced. Further, the proposed approach is capable for accurately estimating the radiation characteristics in extrapolation region too. The proposed neural approach is also validated with measured results. A very good agreement is achieved in simulated, estimated, and measured results. © 2014 Wiley Periodicals, Inc. Int J RF and Microwave CAE 24:673–680, 2014.     

Microstrip antenna design using artificial neural networks

Neural‐network computational modules have recently gained recognition as an unconventional and useful tool for RF and microwave modeling and design. Neural networks can be trained to learn the behavior of passive/active components/circuits. This work describes the fundamental concepts in this emerging area aimed at teaching RF/microwave engineers what neural networks are, why they are useful, when they can be used, and how to use them to model microstrip patch antenna. This work studies in‐depth different designs and analysis methods of microstrip patch antenna using artificial neural‐network and different network structure are also described from the RF/microwave designer's perspective. This article also illustrates two examples of microstrip antenna design and validating the utility of ANN in the area of microstrip antenna design. © 2009 Wiley Periodicals, Inc. Int J RF and Microwave CAE, 2010.     

Signal‐noise support vector model of a microwave transistor

In this work, a support vector machines (SVM) model for the small‐signal and noise behaviors of a microwave transistor is presented and compared with its artificial neural network (ANN) model. Convex optimization and generalization properties of SVM are applied to the black‐box modeling of a microwave transistor. It has been shown that SVM has a high potential of accurate and efficient device modeling. This is verified by giving a worked example as compared with ANN which is another commonly used modeling technique. It can be concluded that hereafter SVM modeling is a strongly competitive approach against ANN modeling. © 2007 Wiley Periodicals, Inc. Int J RF and Microwave CAE, 2007.     

Microwave noise modeling for PHEMT using artificial neural network technique

An improved noise model for pseudomorphic high electron mobility transistors (PHEMT) based on the combination of the artificial neural network (ANN) and conventional equivalent circuit modeling technique is presented. The frequency dispersion of the gate noise model parameter P, drain noise model parameter R, and the correlation coefficient C have been taken into account by using an ANN model. The influence of the gate leakage current can be accommodated by using the proposed noise model. The noise model parameters are determined directly from on wafer noise parameters measurement based on the noise correlation matrix technique. Good prediction for noise parameters and significant improvements of the accuracy of noise parameters are obtained up to 26 GHz for 2 × 40 μm gate width (number of gate fingers × unit gate width) 0.25 μm Double Heterojunction δ‐doped PHEMTs over a wide range of bias points. © 2008 Wiley Periodicals, Inc. Int J RF and Microwave CAE, 2009.     

Predicting grinding burn using artificial neural networks

This paper introduces a method for predicting grinding burn using artificial neural networks (ANN). First, the way to model grinding burn via ANN is presented. Then, as an example, the prediction of grinding burn of ultra-strength steel 300M via ANN is given. Very promising results were obtained.     

利用人工鱼群算法优化前向神经网络

人工鱼群算法(AFSA)是一种最新提出的新型的寻优策略，文中尝试将人工鱼群算法用于三层前向神经网络的训练过程，建立了相应的优化模型，进行了实际的编程计算，并与加动量项的BP算法、演化算法以及模拟退火算法进行比较，结果表明AFSA具有鲁棒性强，全局收敛性好，以及对初值的不敏感性等特点。     

On the small signal modeling of advanced microwave FETs: A comparative study

Although many successful techniques have been proposed in the last decades for extracting the small signal equivalent circuit for microwave transistors from scattering parameter measurements, small signal modeling is still object of intense research. Further improvement and development of the proposed methods are incessantly required to take into account the continuous and rapid evolution of the transistor technology. The purpose of this article is to facilitate the choice of the most appropriate strategy for each particular case. For that, we present a brief but thorough comparative study of analytical techniques developed for modeling different types of advanced microwave transistors: GaAs HEMTs, GaN HEMTs, and FinFETs. It will be shown that a crucial step for a successful modeling is to adapt accurately the small signal equivalent circuit topology under “cold” condition to each investigated technology. © 2008 Wiley Periodicals, Inc. Int J RF and Microwave CAE, 2008.     

基于人工神经网络的CCD数码相机颜色特征化

阐述了基于BP神经网络的数码相机特征化方法。采用不同的神经网络结构,建立了数码相机记录的RGB信息和原影像C IEXYZ色度信息之间的非线性对应关系。对NIKON D200数码相机进行了研究,通过实验得到了合理的神经网络结构为3—10—10—3。测试不同的训练样本和测试样本,达到的C IELAB平均色差和最大色差分别为1.9~2.2和6.7~7.4个色差单位。讨论了实验设备的重复性,同时,分析了样本数量对实验结果的影响。实验结果表明:对数码相机的特征化,可采用BP神经网络技术实现较高的精度。     

Multi‐objective memetic algorithm: comparing artificial neural networks and pattern search filter method approaches

In this work, two methodologies to reduce the computation time of expensive multi‐objective optimization problems are compared. These methodologies consist of the hybridization of a multi‐objective evolutionary algorithm (MOEA) with local search procedures. First, an inverse artificial neural network proposed previously, consisting of mapping the decision variables into the multiple objectives to be optimized in order to generate improved solutions on certain generations of the MOEA, is presented. Second, a new approach based on a pattern search filter method is proposed in order to perform a local search around certain solutions selected previously from the Pareto frontier. The results obtained, by the application of both methodologies to difficult test problems, indicate a good performance of the approaches proposed.     

Application of artificial neural networks for the development of a signal monitoring system

A prototype of a Signal Monitoring System (SMS) utilizing artificial neural networks is developed in this work. The prototype system is unique in: 1) its utilization of state-of-the-art technology in pattern recognition such as the Adaptive Resonance Theory family of neural networks, and 2) the integration of neural network results of pattern recognition and fault identification databases.
The system is developed in an X-windows environment that offers an excellent Graphical User Interface (GUI). Motif software is used to build the GUI. The system is user-friendly, menu-driven, and allows the user to select signals and paradigms of interest. The system provides the status or condition of the signals tested as either normal or faulty. In the case of faulty status, SMS, through an integrated database, identifies the fault and indicates the progress of the fault relative to the normal condition as well as relative to the previous tests.
Nuclear reactor signals from an Experimental Breeder Reactor are analyzed to closely represent actual reactor operational data. The signals are both measured signals collected by a Data Acquisition System as well as simulated signals.     

基于人工神经网络对sEMG信号的手势动作识别

针对利用表面肌电信号(sEMG)对手势动作的肌电信号的研究较少和sEMG信号处理过于复杂的问题,提出了利用人工神经网络和sEMG信号对人的手势动作进行识别研究,引入了MYO硬件设备对新的手势动作sEMG信号采集.利用MYO从手臂上获取每一个手势动作的sEMG信号,提取信号特征值,作为算法的训练数据和测试数据.采用人工神经网络中的反向传递神经网络算法来进行对4种不同手势动作分类,对应目标手指识别率在90.35%.研究结果可以被用来做临床诊断和生物医学的应用以及用于现代硬件的发展和更现代化的人机交互的发展.     

人工神经网络在传感器数据融合中的应用

针对压力传感器对温度的交叉灵敏度，采用BP人工神经网络法对其进行数据融合处理。消除温度对压力传感器的影响，大大提高了传感器的稳定性及其精度，效果良好。     

最佳拟合与神经网络相结合实现传感器特性线性化

提出了一种传感器特性线性化的方法.该方法把传感器特性分为线性和非线性段,用一种改进的BP神经网络映射传感器特性非线性段的反函数作为校正环节,用最佳拟合方法得到线性段的直线方程,从而实现传感器特性的线性化.经过仿真试用表明,这种方法可使传感器的非线性误差减小近十倍.最后,给出了一些仿真实验和仿真结果.     

人工神经网络在预测服装企业安全库存的应用

安全库存是一种额外持有的库存，它作为一种缓冲器用来补偿在订货提前期内实际需求超过期望需求量或实际提前期超过期望提前期所产生的需求。在服装企业中一般凭经验来设定安全库存，但实际效果不佳，应用人工神经网络，建立BP神经网络模型，用多个影响安全库存的指标及安全库存对网络进行训练，以达到对安全库存量预测的目的，经验证和预测效果十分理想。     

Recurrent neural networks and prior knowledge for sequence processing: a constrained nondeterministic approach

The paper focuses on methods for injecting prior knowledge into adaptive recurrent networks for sequence processing. In order to increase the flexibility needed for specifying partially known rules, a nondeterministic approach for modelling domain knowledge is proposed. The algorithms presented in the paper allow time-warping nondeterministic automata to be mapped into recurrent architectures with first-order connections. These kinds of automata are suitable for modeling temporal scale distortions in data such as acoustic sequences occurring in problems of speech recognition. The algorithms output a recurrent architecture and a feasible region in the connection weight space. It is demonstrated that, as long as the weights are constrained into the feasible region, the nondeterministic rules introduced using prior knowledge are not destroyed by learning. The paper focuses primarily on architectural issues, but the proposed method allows the connection weights to be subsequently tuned to adapt the behavior of the network to data.     

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.     

Milling force prediction using regression and neural networks

This study focuses on developing a good empirical relationship between the cutting force in an end milling operation and the cutting parameters such as speed, feed and depth-of-cut, by using both multiple regression and neural network modeling processes. A regression model was first fitted to experimentally collected data and any abnormal data points indicated by this analysis were filtered out. By repeating this process several times, a final set of filtered data was obtained and analyzed using neural networks to yield a good, final model. This study shows that analyzing milling force data using conventional regression can lead to a more accurate neural networks model for force prediction.     

Enhancement of time domain analysis and optimization through neural networks

An efficient computational approach to time domain microwave design and optimization is presented. In particular, artificial neural networks are coupled with a full‐wave time domain simulator in order to model and optimize microwave structures. Furthermore, neural networks are used to predict the late time response from the early time response of a structure to accelerate the convergence of time domain simulations, particularly in the case of high‐Q structures such as filters and resonators. The combination of neural networks with a time domain TLM solver is demonstrated by means of a design example of an iris‐coupled band pass filter. The results demonstrate the dramatic gain in speed and numerical efficiency enabled by this approach to optimizing and modeling microwave devices. © 2007 Wiley Periodicals, Inc. Int J RF and Microwave CAE, 2007.     

Applications of artificial neural networks to the nonlinear combination of forecasts

Abstract: This paper proposes artificial neural networks (ANN) as a tool for nonlinear combination of forecasts. In this study, three forecasting models are used for individual forecasts, and then two linear combining methods are used to compare with the ANN combining method. The comparative experiment using real-world data shows that the prediction by the ANN method outperforms those by linear combining methods. The paper suggests that the ANN method can be used as an alternative to conventional linear combining methods to achieve greater forecasting accuracy.