【作者】 李明玉；

【导师】 虞厥邦；

【作者基本信息】 电子科技大学 ， 电路与系统， 2003， 硕士

【摘要】 神经网络在微波电路CAD和微波电路优化中的应用是目前非线性微波电路研究领域中的前沿课题之一。神经网络的出现为一类输入输出关系呈高度非线性的系统提供了建模的有效工具。通过对射频微波电路测量或仿真，可得到微波电路的输入和输出数据，利用该数据可以对神经网络进行训练，而神经网络一旦训练成功，就能够瞬时地对在微波电路设计所学习的任务提供准确答案。经过训练后的神经网络模型可广泛用于解决射频微波电路的各种问题。如用于微波电路CAD，可用所建立的模型结构来描述这么一类微波电路的非线性行为特征；如用于微波电路设计，则可进行如共面波导、晶体管、传输线、滤波器和放大器等的设计；如用于微波电路优化，则可用所建立的电路模型优化电路参数，进行阻抗匹配等。本文工作正是在这样的应用背景下，基于电子科技大学青年科技基金《无线非线性信道建模及特性分析方法研究》，对神经网络在非线性微波电路建模和优化中的应用，展开了一些研究工作，作者的主要贡献和创新如下：1） 将神经网络应用于微波电路的时域非线性特性建模，通过分析了一个功率放大器的时域瞬态和稳态的非线性特性，并用HP-ADS对其非线性特性进行了仿真，然后把其仿真输入和输出数据来训练神经网络。计算机仿真结果表明所建立的神经网络模型和非线性放大器的输出基本一致。2） 研究了一个建立在Volterra级数基础上的一般的分析方法，包括非线性参数和交调、频率中的谱分量之间的相互影响。对于Volterra级数而言，用于实际系统建模时的主要困难是其核函数的辨识。本文通过神经网络和Volterra级数的等价性，用训练后的神经网络确定出Volterra级数的核函数。3） 把基于小波的神经网络训练方法用于微波电路的优化，通过对由微带线组成的低通滤波器参数的仿真和优化，表明了在得到较好优化结果的情况下，其优化时间大大降低。更多还原

【Abstract】 Researching the applications of neural network techniques in the area of microwave circuits CAD and optimization design is one of the front edge issues in nonlinear microwave electronics domains. The emergence of neural network provide effective tools for a system with highly nonlinear input-output relations. A neural network model for a device/circuit can be developed by learning and abstracting from measured-simulated microwave data, through a process called training. Once be trained, the neural network model can be used during microwave design to provide instant answers to any pre-assigned task. The trained neural network model can be used to solve a variety of problems emerged in RF/microwave circuit design, such as in microwave circuit CAD, the established model structure can be used to characterize the nonlinear behavior of microwave circuits. And in microwave circuit design, the model can be used for the design of coplanar waveguides, transistors, transmission line, filters and amplifiers, etc. Besides, after in microwave circuit optimization, a neural network model can be used to optimize circuit parameters and match the microwave impedances, etc.Under this background, with the support of the project entitled "Modeling of Wireless Nonlinear Channel and Analysis of Its Characteristics" of of UESTC Youth Funddation, this dissertation is intended to develop some techniques for application of neural networks in the fields of nonlinear microwave circuit modeling and optimization. The main contributions of the dissertation are as following:1. Modeling time-domain nonlinear characteristics of microwave circuits by using the neural network techniques, then analyzing the time-domain transient and steady states of a power-amplifier and in collarboration with the HP-ADS. The results of computer simulation show that the output of the neural network model output is concidede with the output of a real nonlinear power-amplifier.2. The nonlinear input-output characteristics of a GaAs MESFET is modeled by the use of Volterra series in the frequency domain. Based on this frequency domain model, after an MLP network training, the input-output characteristics of the device as well as the Volterra series Kernal function, which is hard to be determined by traditional approaches, is obtained with satisfactory accuracy.3. A wavelet neural network training method is used for the optimization of a microwave circuit. This technique is used for the simulation and optimization<WP=6>of the low-pass filter parameter. Simulation results indicate that this training algorithm is able to give solutions with high quality and remarkably reduced computation time.更多还原