共查询到20条相似文献,搜索用时 31 毫秒
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Yonghua Fang Yagoub M.C.E. Fang Wang Qi-Jun Zhang 《Microwave Theory and Techniques》2000,48(12):2335-2344
A new macromodeling approach is developed in which a recurrent neural network (RNN) is trained to learn the dynamic responses of nonlinear microwave circuits. Input and output waveforms of the original circuit are used as training data. A training algorithm based on backpropagation through time is developed. Once trained, the RNN macromodel provides fast prediction of the full analog behavior of the original circuit, which can be useful for high-level simulation and optimization. Three practical examples of macromodeling a power amplifier, mixer, and MOSFET are used to demonstrate the validity of the proposed macromodeling approach 相似文献
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For the first time, we propose a robust algorithm for automating the neural-network-based RF/microwave model development process. Starting with zero amount of training data and then proceeding with neural-network training in a stage-wise manner, the algorithm can automatically produce a neural model that meets the user-desired accuracy. In each stage, the algorithm utilizes neural-network error criteria to determine additional training/validation samples required and their location in model input space. The algorithm dynamically generates these new data samples during training, by automatic driving of simulation tools (e.g., OSA90, Ansoft-HFSS, Agilent-ADS). Initially, fewer hidden neurons are used, and the algorithm adjusts the neural-network size whenever it detects under-learning. Our technique integrates all the subtasks involved in neural modeling, thereby facilitating a more efficient and automated model development framework. It significantly reduces the intensive human effort demanded by the conventional step-by-step neural modeling approach. The algorithm inherently distinguishes nonlinear and smooth regions of model behavior and uses relatively fewer samples in smooth subregions. It automatically deals with large data errors that can occur during dynamic sampling by using a Huber quasi-Newton technique. The algorithm is demonstrated through practical microwave device and circuit examples 相似文献
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神经网络动态逆在歼击机安全着陆中的控制 总被引:1,自引:1,他引:0
给出了基于神经网络动态逆的自适应跟踪控制方法,用以解决飞机着陆过程中的复杂非线性和出现舵机故障的情况.应用神经网络直接对非线性系统故障模型求逆,使得所设计的逆系统能够包含故障信息,克服了传统的控制设计中将过程模型线性化,从而将不可忽视的非线性关系用线性关系代替或忽略的弊端.对由于建模误差、不确定性因素等引起的非线性系统逆误差,通过自组织模糊小脑模型关节控制器(SOFCMAC)神经网络在线进行修正.并在此基础上对3个通道分别设计了参考模型和线性控制器,以实现对伪线性系统进行跟踪控制.通过将这种方法用于某型歼击机在着陆过程中发生平尾卡死故障控制的过程仿真,验证了该方法的可行性. 相似文献
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针对使用CAD软件设计射频微波电路繁琐且耗时长等缺点,提出一种新颖的带外部输入的非线性自回归(NARX)神经网络逆向建模方法。此方法采用具有激励函数的NARX 神经网络(DAFNN)为模型以提高网络的泛化能力,利用支持向量机(SVM)替代模型的前馈部分完成数据分类,解决设计中的多解问题。然后应用于可以覆盖多个频段的可重构功率放大器中,实验表明,该方法在精度方面分别优于直接逆向建模方法和自适应浊逆向建模方法99.86%和81.32%,计算速度方面优于直接逆向建模方法31.72%,可以降低射频微波可重构功率放大器的设计复杂度、缩短其设计时间。 相似文献
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Z. Marinković O. Pronić-Rančić V. Marković 《Journal of Communications Technology and Electronics》2014,59(11):1303-1309
A new method for accurate determination of noise parameters of microwave transistors for various bias conditions is proposed in this paper. The proposed model consists of a transistor empirical noise model (modification of Pospieszalski’s noise model) and two artificial neural networks. With the aim to avoid extraction of the empirical model parameters for each bias point, an artificial neural network is used to introduce bias-dependence of the equivalent circuit parameters. Accuracy of such bias-dependent model is further improved by using an additional neural network aimed to correct the noise parameters’ values. The proposed modeling approach is exemplified by modelling of a MESFET device in packaged form. The noise parameters obtained by the simulation agree well with the measured data. 相似文献
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The trend of using accurate models such as physics-based FET models, coupled with the demand for yield optimization results in a computationally challenging task. This paper presents a new approach to microwave circuit optimization and statistical design featuring neural network models at either device or circuit levels. At the device level, the neural network represents a physics-oriented FET model yet without the need to solve device physics equations repeatedly during optimization. At the circuit level, the neural network speeds up optimization by replacing repeated circuit simulations. This method is faster than direct optimization of original device and circuit models. Compared to existing polynomial or table look-up models used in analysis and optimization, the proposed approach has the capability to handle high-dimensional and highly nonlinear problems 相似文献
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随着微波器件结构复杂度的增长和产品性能要求的提高,微波器件建模不仅要能够描述其理想电磁特性,还要能快速准确反映多物理参数对器件性能的影响。虽然神经网络已经被引入到微波器件领域,但是将其应用于器件的多物理特性建模的研究还比较少。文章提出了一种基于人工神经网络的多物理参数建模方法来表示输入输出变量之间的非线性关系。提出了一种高效的神经网络多物理参数模型,并针对该模型引入了一种新的训练算法。所提出的模型可以快速准确地预测微波器件的多物理响应,如滤波器的S参数特性曲线、离子敏感场效应晶体管的输出特性曲线等。与有限元方法相比,此方法可以节省约98%的计算成本与99%的计算时间,为实现快速高效的微波器件行为级建模提供一种可行方法。 相似文献
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近年来基于深度神经网络(Deep Neural Network,DNN)的全差异空间建模方法(Total Variability, TV)在语种识别领域得到了广泛研究。本文提出了一种基于DNN的改进TV方法,既利用了DNN对数据的音素状态对齐效果,又充分考虑了语种任务的相关性。该方法首先利用带有瓶颈层的深层神经网络(Deep Bottleneck Network, DBN)对语种数据特征按照音素状态进行聚类,得到语种任务相关通用背景模型(Universal Background Model, UBM),然后利用该UBM模型并结合深度瓶颈特征(Deep Bottleneck Feature, DBF)进行TV建模。实验表明,与经典的TV方法相比,该方法能够显著的提升系统性能和效率,并且融合后性能得到了进一步提升。 相似文献
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针对无源定位中噪声统计特性不准确和对多源信息的综合利用,提出一种利用深度神经网络(DNN)的无源定位方法,该方法将训练集数据输入到深度神经网络中进行学习训练,利用随机失活这一正则化方法提高了模型的泛化能力,对模型的超参数选择进行二维搜索,最终得到深度神经网络模型的最优参数设置。将其和传统的无源定位方程解算方法以及单层神经网络模型进行对比,仿真结果表明提出的方法能有效降低噪声对无源定位的精度影响,增强了系统鲁棒性,同时也证明了深度神经网络对多源信息的综合利用能力。 相似文献
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In this paper, systematic neural network modeling techniques are presented for microwave modeling and design using the concept of inverse modeling where the inputs to the inverse model are electrical parameters and outputs are geometrical parameters. Training the neural network inverse model directly may become difficult due to the nonuniqueness of the input-output relationship in the inverse model. We propose a new method to solve such a problem by detecting multivalued solutions in training data. The data containing multivalued solutions are divided into groups according to derivative information using a neural network forward model such that individual groups do not have the problem of multivalued solutions. Multiple inverse models are built based on divided data groups, and are then combined to form a complete model. A comprehensive modeling methodology is proposed, which includes direct inverse modeling, segmentation, derivative division, and model combining techniques. The methodology is applied to waveguide filter modeling and more accurate results are achieved compared to the direct neural network inverse modeling method. Full electromagnetic simulation and measurement results of Ku-band circular waveguide dual-mode pseudoelliptic bandpass filters are presented to demonstrate the efficiency of the proposed neural network inverse modeling methodology. 相似文献
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Jian Chen Song Lin 《IEEE transactions on systems, man and cybernetics. Part C, Applications and reviews》2004,34(2):219-225
A new neural-network-based approach to assess the preference of a decision-maker (DM) for the multiple objective decision making (MODM) problem is presented in this paper. A new neural network structure with a "twin-topology" is introduced in this approach. We call this neural network a decision neural network (DNN). The characteristics of the DNN are discussed, and the training algorithm for DNN is presented as well. The DNN enables the decision-maker to make pairwise comparisons between different alternatives, and these comparison results are used as learning samples to train the DNN. The DNN is applicable for both accurate and inaccurate comparisons (results are given in approximate values or interval scales). The performance of the DNN is evaluated with several typical forms of utility functions. Results show that DNN is an effective and efficient way for modeling the preference of a decision-maker. 相似文献
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Identification and control of continuous-time nonlinear systems via dynamic neural networks 总被引:1,自引:0,他引:1
Ren X.M. Rad A.B. Chan P.T. Wai Lun Lo 《Industrial Electronics, IEEE Transactions on》2003,50(3):478-486
In this paper, we present an algorithm for the online identification and adaptive control of a class of continuous-time nonlinear systems via dynamic neural networks. The plant considered is an unknown multi-input/multi-output continuous-time higher order nonlinear system. The control scheme includes two parts: a dynamic neural network is employed to perform system identification and a controller based on the proposed dynamic neural network is developed to track a reference trajectory. Stability analysis for the identification and the tracking errors is performed by means of Lyapunov stability criterion. Finally, we illustrate the effectiveness of these methods by computer simulations of the Duffing chaotic system and one-link rigid robot manipulator. The simulation results demonstrate that the model-based dynamic neural network control scheme is appropriate for control of unknown continuous-time nonlinear systems with output disturbance noise. 相似文献
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《Electron Devices, IEEE Transactions on》1971,18(12):1168-1174
A comprehensive overview of recent approaches to microwave transistor modeling and simulation is presented. Three basic approaches to semiconductor device modeling are compared: the linear two-port model, the device-physics model, and the equivalent circuit model. Equivalent circuit models are discussed in detail with examples. Good solutions to the problem of linear modeling have been found and several authors have been able to predict the noise and gain of microwave transistors in the linear operating regions. However solutions for nonlinear operation of transistors at microwave frequencies have only recently been implemented and much room for improvement remains. 相似文献
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针对传统PID控制自适应和抗扰能力欠佳的问题,提出了一种具有强抗扰动能力的NLPID神经网络控制方法。该方法通过扩张状态观测器对系统建模中不确定性因素以及系统的外部扰动实时观测进行前馈补偿,并与非线性PID神经网络控制相结合,实现对非线性、时变、不确定性、受未知外扰系统的最优PID自适应抗扰控制。通过Matlab仿真结果与传统PID控制对比分析,表明该方法具有优良的动态品质和静态性能,在非线性系统控制领域拥具有重要的应用价值。 相似文献
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针对非光滑器件/电路的非光滑特性,提出了一种基于标准数学规划问题的建模及仿真。首先将非光滑器件建模为具有可能发生状态跳变的分段线性函数;其次给出了由这些非光滑器件构成的电路的非光滑线性特性的动态系统方程;然后对这些非光滑动态系统方程进行时间离散化得到各种类型的一步非光滑问题,如(线性)互补问题或具有等式-不等式约束的非线性(或二次)规划,进而进行数值求解。仿真实验结果表明,本文所提出的数值建模方法对于具有大量事件的非光滑系统是有效的,对于模型参数的变化具有鲁棒性。 相似文献