用遗传算法优化前馈神经网络的结构

本文就前馈网络的结构提出了一种新的编码方案———神经元编码法。该方案在对网络结构编码时 ,是围绕神经元进行的。然后采用遗传算法对其进行优化操作 ,并取得了预期的优化结果     

带前馈输入观测——状态反馈控制系统的CAD

带前馈输入观测——状态反馈控制系统的设计过程,涉及带符号变量的矩阵方程组求解,其通解一般难于求出,国内外大多数文献只能用递推法求其前几个采样值。借助MATLAB符号数学工具箱的有关函数,直接进行带符号变量矩阵运算和方程组求解,得到其准确的通解,系统设计全过程由编制的M文件自动完成。仿真结果表明,提出的设计方法和求解过程是正确的。     

一种改进的线性功放前馈设计法

根据表示功率放大器线性的参数——三阶互调的产生机理,提高功率放大器线性的方法有功率回退法、预失真法和前馈法。传统的前馈技术存在成本过高、功率损耗过大的问题,通过改变系统组成、增加环行器的方式可以改进传统前馈技术,通过实验对比,表明该方法能明显改善传统前馈技术中存在的问题。     

用遗化算法优化前馈神经网络的结构

本就前馈网络的结构提出了一种新的编码方案－神经元编码法。该方案在对网络结构编码时,是围绕神经元进行的。然后采用遗传算法对其进行优化操作,并取得了预期的优化结果。     

基于倒立摆系统前馈型神经网络实验的开发

介绍一个基于MATLAB/SIMULINK和直线一级倒立摆系统的前馈型人工神经网络的实验方案。设计了一级倒立摆系统BP神经网络控制器,并完成了仿真和实物控制实验。实验结果有利于对前馈型人工神经网络的本质认识。     

基于规则的前馈神经网络的混沌梯度下降耦合学习算法

提出用规则判断由于BP神经网络学习过程中因梯度搜索产生的局部极小，并利用具有全局寻优的特点的混沌优化算法，使学习过程能有效地逃离局部极小，即，梯度下降进研“粗搜索”、混沌优化法进行“细搜索”及建立规则将两者结合起来，就构成了本文的基本混沌梯度下降的前馈神经网络学习算法。这种算法有效地利用了梯度下降算法的快速性和混沌寻优的全局性，将该算法应用于铜锍吹炼过程智能决策支持系统模型库中的模型学习，产生了良好的应用效果。     

一种双向射频功率放大器设计

针对目前集成射频芯片前端存在发射功率较小和接收信号灵敏度较低的缺陷,设计了一种双向射频功率放大器。测试表明,该放大器能够有效放大射频信号的功率以及提高接收信号的灵敏度,并能灵活地进行发射和接收双向切换。此外为了帮助学生深入理解射频电路中传输线、Smith圆图、阻抗匹配和有源电路等的设计及其调试方法,详细介绍了利用安捷伦ADS2009软件中的Smith圆图工具对放大芯片的源端和负载端的阻抗匹配电路进行仿真和分析方法。     

一种新的全局优化前馈神经网络

将L-M算法与填充函数法相结合，提出一种训练前向网络的混合型全局优化新算法．L—M算法的收敛速度快，利用它先得到一个局部极小点，然后利用填充函数算法跳出局部最小，得到一个更低的局部极小点．重复计算即可得到全局最优点．经实验验证，该算法收敛速度很快，避免局部收敛，而且性能稳定．     

BP神经网络模型中平台现象的抑制

基于对BP算法执行过程中平台现象出现原因的剖析,提出了一种新的激活函数(f(x)=0.5sinλx+0.5),实验证明使用新的激活函数能有效地抑制平台现象,提高收敛速度。     

A maximum power point tracker for photovoltaic energy systems based on fuzzy neural networks

To extract the maximum power from a photovoltaic （PV） energy system, the real-time maximum power point （MPP） of the PV array must be tracked closely. The non-linear and time-variant characteristics of the PV array and the non-linear and non-minimum phase characteristics of a boost converter make it difficult to track the MPP for traditional control strategies. We propose a fuzzy neural network controller （FNNC）, which combines the reasoning capability of fuzzy logical systems and the learning capability of neural networks, to track the MPP. With a derived learning algorithm, the parameters of the FNNC are updated adaptively. A gradient estimator based on a radial basis function neural network is developed to provide the reference information to the FNNC. Simulation results show that the proposed control algorithm provides much better tracking performance compared with the filzzy logic control algorithm.     

用于功率放大器的阶梯记忆多项式预失真器(英文)

为了减少数字预失真系数的数量,提出了阶梯记忆多项式(SMP)预失真器.该预失真器通过改变传统记忆多项式预失真器中不同记忆深度对应的最大非线性阶数以降低系数数量.SMP预失真器系数的提取采用离线学习结构,直接通过功率放大器(PA)的输入、输出采样数据计算预失真系数.仿真结果表明了SMP预失真器的鲁棒性,它并不局限于某一特定PA模型.SMP预失真器的有效性分别在记忆多项式模型、并行Wiener模型、Wiener.Hammerstein模型、非单位延时记忆多项式模型和基于Freescale LDMOSFET MRF21030制作的实际PA上,通过仿真和实验的方式进行了验证.与传统MP预失真器相比,SMP预失真器可减少系数数量60%以上.     

LMI-based approach for global asymptotic stability analysis of continuous BAM neural networks

Studies on the stability of the equilibrium points of continuous bidirectional associative memory (BAM) neural network have yielded many useful results. A novel neural network model called standard neural network model (SNNM) is ad- vanced. By using state affine transformation, the BAM neural networks were converted to SNNMs. Some sufficient conditions for the global asymptotic stability of continuous BAM neural networks were derived from studies on the SNNMs’ stability. These co…     

Stability analysis of discrete-time BAM neural networks based on standard neural network models

To facilitate stability analysis of discrete-time bidirectional associative memory (BAM) neural networks, they were converted into novel neural network models, termed standard neural network models (SNNMs), which interconnect linear dynamic systems and bounded static nonlinear operators. By combining a number of different Lyapunov functionals with S-procedure, some useful criteria of global asymptotic stability and global exponential stability of the equilibrium points of SNNMs were derived. These stability conditions were formulated as linear matrix inequalities (LMIs). So global stability of the discrete-time BAM neural networks could be analyzed by using the stability results of the SNNMs. Compared to the existing stability analysis methods, the proposed approach is easy to implement, less conservative, and is applicable to other recurrent neural networks.     

共享功率放大器线性化平台的实验教学探索

为了使学生深入理解功率放大器(功放)的非线性特性对信号传输的影响,将共享功放线性化平台引入射频电路课程实验教学,将抽象难懂的非线性理论以及线性化过程转化为可视化实验,旨在提高学生的学习积极性,提升教学效果.该文对预失真模块、线性化系统、共享平台搭建以及测量实例等进行了具体分析和讲解,希望有助于学生对射频电路实际应用的理...     

基于RBF神经网络辨识的直接甲醇燃料电池电堆非成性建模与自适应模糊控制

1 Introduction Direct methanol fuel cell ( DMFC) is desirable toserve as the power systemfor portable devices such ascellular phones , portable computers ,etc. due to thetheoretically high energy density and the liquid fuelused that can be stored and tran…     

Robust exponential stability analysis of a larger class of discrete-time recurrent neural networks

The robust exponential stability of a larger class of discrete-time recurrent neural networks (RNNs) is explored in this paper. A novel neural network model, named standard neural network model (SNNM), is introduced to provide a general framework for stability analysis of RNNs. Most of the existing RNNs can be transformed into SNNMs to be analyzed in a unified way. Applying Lyapunov stability theory method and S-Procedure technique, two useful criteria of robust exponential stability for the discrete-time SNNMs are derived. The conditions presented are formulated as linear matrix inequalities (LMIs) to be easily solved using existing efficient convex optimization techniques. An example is presented to demonstrate the transformation procedure and the effectiveness of the results.     

一种使用混沌神经网络的电力负荷预测方法

本文对实际电力月负荷数据进行预处理,将其分为趋势序列和剩余序列两部分。计算剩余序列的最大Lyapunov指数、延迟时间等参数,发现其混沌特征。提出了一种使用混沌BP神经网络的电力月负荷预测方法。根据历史负荷序列预处理和Lyapunov指数估算,确定了网络的训练样本和网络参数,通过对实际电力负荷进行预测,结果显示该方法具有一定的可行性。     

Maximum power point tracking of a photovoltaic energy system using neural fuzzy techniques

In order to improve the output efficiency of a photovoltaic (PV) energy system, the real-time maximum power point (MPP) of the PV array should be tracked closely. The non-linear and time-variant characteristics of the photovoltaic array and the non-linear and non-minimum phase characteristics of a boost converter make it difficult to track the MPP as in traditional control strategies. A neural fuzzy controller (NFC) in conjunction with the reasoning capability of fuzzy logical systems and the learning capability of neural networks is proposed to track the MPP in this paper. A gradient estimator based on a radial basis function neural network is developed to provide the reference information to the NFC. With a derived learning algorithm, the parameters of the NFC are updated adaptively. Experimental results show that, compared with the fuzzy logic control algorithm, the proposed control algorithm provides much better tracking performance.     

低输出功率应用的E类CMOS功率放大器设计(英文)

利用标准的0.18μm6层金属混合信号/射频CMOS工艺设计了一种工作在2.4 GHz频段的全集成E类功率放大器.电路采用两级放大器级联结构,其中驱动级利用谐振技术生成高摆幅开关信号;输出级采用E类结构实现了信号的功率放大.在1.2 V电源电压下,设计的功率放大器最高输出功率为8.8 dBm,功率附加效率(PAE)达到44%.同时,提出了一种E类功率放大器功率控制方法.通过改变进入E类开关晶体管的信号幅度和占空比,在3位数字控制字的控制下,输出功率达到-3～8.8 dBm.所设计的功率放大器可以满足诸如无线传感网络(WSN)和生物遥测等低功率系统的应用.