多目标神经空间映射算法优化设计微带滤波器

提出了一种多目标形式的空间映射算法,并将其应用于微带滤波器的优化设计问题.该算法没有采用ADS仿真软件进行粗糙模型的计算,而是采用多目标粒子群算法结合矩阵数值计算的形式,优化计算微带线的S参数,并应用神经网络映射粗糙模型与精确模型的仿真结果,由粗糙模型和神经网络推测出精确模型的帕累托最优解集.     

Neural-network-based adaptive matched filtering for QRS detection

We have developed an adaptive matched filtering algorithm based upon an artificial neural network (ANN) for QRS detection. We use an ANN adaptive whitening filter to model the lower frequencies of the ECG which are inherently nonlinear and nonstationary. The residual signal which contains mostly higher frequency QRS complex energy is then passed through a linear matched filter to detect the location of the QRS complex. We developed an algorithm to adaptively update the matched filter template from the detected QRS complex in the ECG signal itself so that the template can be customized to an individual subject. This ANN whitening filter is very effective at removing the time-varying, nonlinear noise characteristic of ECG signals. Using this novel approach, the detection rate for a very noisy patient record in the MIT/BIH arrhythmia database is 99.5%, which compares favorably to the 97.5% obtained using a linear adaptive whitening filter and the 96.5% achieved with a bandpass filtering method.     

Development of PSO-ANN Ensemble Hybrid Algorithm and Its Application in Compact Crown Circular Fractal Patch Antenna Design

The traditional methods of designing antennas are not suitable in case of fractal antennas due to non availability of accurate mathematical design expressions. Recently, ANN model relating the physical and electromagnetic parameters of the fractal antenna to be designed is used as objective function of the optimization algorithm and it has been shown as an effective approach. In presented paper, ANN ensemble model has been used as the objective function of a PSO algorithm to calculate the optimal dimensions of a circular fractal antenna for desired resonant frequency. It has been established that ANN ensemble has better performance than the constituent ANN models. The design accuracy of the proposed hybrid algorithm is validated through the simulation and experimental results of the designed antenna. The size reduction capability of the proposed fractal antenna is used to design an antenna for 5.8 GHz WLAN band with a size reduction of 41.64% compared to simple circular microstrip antenna. The miniaturization of the antenna will lead to the design of compact devices for wireless communication systems.     

神经网络在微带DGS结构设计中的应用

采用神经网络并结合逆向方法训练网络对微带DGS(Defected Ground Structure)结构进行建模分析。通过文中实例可以看出,人工神经网络产生的结果与数值仿真的结果吻合良好,证明该方法结合FDTD分析方法,作为电路设计辅助工具,具有缩短设计时间、降低设计成本的优点。为微带DGS结构分析和设计提供了一个新途径。     

Simple model for quantum-dot semiconductor optical amplifiers using artificial neural networks

A simple, accurate, and fast multilayer feedforward artificial neural network (ANN) model for quantum-dot semiconductor optical amplifiers (QD-SOAs) is developed. The developed ANN model has demonstrated excellent precision and accurately models the physical characteristics of QD-SOAs such as pulse amplification and four-wave mixing characteristics. Furthermore, the developed ANN model requires a very small computational time compared with numerical models, which is very attractive for computer-aided-design applications. The model is used to create very interesting design curves for QD-SOAs. In addition, the model is suitable for physical-parameter extraction from available measured data.     

动量VLBP神经网络在缺陷接地电路中的应用

采用一种改进的人工神经网络反向传播算法(BP算法),将动量方法和可变学习速度的BP算法(VLBP算法)结合,并且在每个样本点更新权值和偏置值,这种算法称为动量VLBP算法。用C语言实现该算法,并将其运用到对一种新型组合式非周期性缺陷接地结构(CNPDGS)低通滤波器的神经网络建模之中。以CNPDGS的结构尺寸和频率为输入样本,传输系数参数为输出样本,建模成功后,在样本范围内输入结构尺寸和频率能够很快得出准确的传输系数。结果表明应用动量VLBP算法的神经网络相对于FDTD分析方法可以节省大量的时间,并且与基本的BP算法相比,可以加速算法收敛、减少训练时间。     

人工神经网络基双频RFID读写器天线设计

给出了结合人工神经网络进行双频RFID读写器天线的优化设计方法,该天线由一层双边开槽贴片,厚空气层及接地层的加载缝隙耦合实现双频工作.以天线电磁仿真结果作为人工神经网络模型的训练数据,以天线贴片的四个关键结构尺寸作为神经网络模型的输入参数,以天线回波损耗作为输出参数,实现天线的快速优化设计.所设计天线的性能指标为:在驻波比小于2.0时,低频段(900 MHz)的工作带宽约为70 MHz(870～940 MHz),高频段(2.4 GHz)的工作带宽约为300MHz(2.235～2.535 GHz);天线尺寸为179.3 mm×120 mm.神经网络模型和电磁仿真结果吻合良好.     

Automatic Detection System for Cough Sounds as a Symptom of Abnormal Health Condition

The problem of attending to the health of the aged who live alone has became an important issue in developed countries. One way of solving the problem is to check their health condition by a remote-monitoring technique and support them with well-timed treatment. The purpose of this study is to develop an automatic system that can monitor a health condition in real time using acoustical information and detect an abnormal symptom. In this study, cough sound was chosen as a representative acoustical symptom of abnormal health conditions. For the development of the system distinguishing a cough sound from other environmental sounds, a hybrid model was proposed that consists of an artificial neural network (ANN) model and a hidden Markov model (HMM). The ANN model used energy cepstral coefficients obtained by filter banks based on human auditory characteristics as input parameters representing a spectral feature of a sound signal. Subsequently, an output of this ANN model and a filtered envelope of the signal were used for making an input sequence for the HMM that deals with the temporal variation of the sound signal. Compared with the conventional HMM using Mel-frequency cepstral coefficients, the proposed hybrid model improved recognition rates on low SNR from 5 dB down to –10 dB. Finally, a preliminary prototype of the automatic detection system was simply illustrated.      

Prediction of matching condition for a microstrip subsystem using artificial neural network and adaptive neuro-fuzzy inference system

In this paper, a subsystem consisting of a microstrip bandpass filter and a microstrip low noise amplifier (LNA) is designed for WLAN applications. The proposed filter has a small implementation area (49 mm²), small insertion loss (0.08 dB) and wide fractional bandwidth (FBW) (61%). To design the proposed LNA, the compact microstrip cells, an field effect transistor, and only a lumped capacitor are used. It has a low supply voltage and a low return loss (–40 dB) at the operation frequency. The matching condition of the proposed subsystem is predicted using subsystem analysis, artificial neural network (ANN) and adaptive neuro-fuzzy inference system (ANFIS). To design the proposed filter, the transmission matrix of the proposed resonator is obtained and analysed. The performance of the proposed ANN and ANFIS models is tested using the numerical data by four performance measures, namely the correlation coefficient (CC), the mean absolute error (MAE), the average percentage error (APE) and the root mean square error (RMSE). The obtained results show that these models are in good agreement with the numerical data, and a small error between the predicted values and numerical solution is obtained.     

基于BP神经网络与GA算法的信息过滤模型研究

信息过滤技术是当前信息技术研究的一个热点。本文对BP神经网络和遗传算法在信息过滤技术中的应用,包括BP神经网络、遗传算法、文本特征提取、学习方法等,进行了研究和探讨。提出了一种基于BP神经网络和GA算法的信息过滤模型,实验结果表明,此模型运行可靠,过滤效率有明显提高。     

矩形微带天线谐振频率的粒子群神经网络建模

谐振频率是微带天线设计过程中最重要的一个参数,直接决定设计的成败.文章提出一种改进的前向神经网络模型,该模型采用粒子群优化算法进行训练,网络结构可以根据被建模问题的复杂程度进行自适应调整,进而得到泛化能力好的神经网络.通过这种改进的粒子神经网络对矩形微带天线的谐振频率进行建模,得到的结果明显优于该问题已有文献的结果,可见这种粒子群神经网络对此问题的有效性.基于粒子群神经网络的矩形微带天线谐振频率模型可以明显提高微带天线的计算机辅助设计水平.     

支持向量机回归法在天线设计中的应用

提出了一种利用支持向量机回归进行天线设计的方法.以印刷偶极子天线为例,通过对3个主要的天线参数进行训练以捕捉天线结构尺寸和天线性能之间的关系,从而建立支持向量机回归模型.该模型在保证快速计算的前提下显示出比神经网络模型更高的计算精度,从而节省天线设计和优化时间.     

ANN based CMOS ASIC design for improved temperature-drift compensation of piezoresistive micro-machined high resolution pressure sensor

The paper investigates the temperature-drift compensation of a high resolution piezoresistive pressure sensor using ANN based on conventional neuron model as also the inverse delayed function model of neuron. Using the delayed neuron model, an improvement in temperature-drift compensation has been obtained compared to the conventional neuron model. The CMOS analog ASIC design of a feed forward neural network using the inverse delayed function model of self connectionless neuron for the precise temperature-drift compensation has been presented. The inverse tan-sigmoid function is realized in CMOS implementation by Gilbert multiplier, differential adder and a cubing circuit. The entire design of the circuit has been done using AMS 0.35 μm CMOS model and simulated using Mentor Graphics ELDO simulator. Using the inverse delayed function model of neuron a mean square error of the order of 10⁻⁷ of the neural network has been obtained against a mean square error of the order of 10⁻³ using conventional neuron model for the same architecture of ANN. This brings down the error from 9% for uncompensated sensor to 0.1% only for compensated sensor using the delayed model of neuron in the temperature range of 0-70 °C. Using conventional neuron based ANN compensation, the error is reduced to 1% error.     

一种神经网络控制器的容错设计与可靠性评估方法

本文以应用于电力控制系统中的人工神经网络快速汽门非线性适应控制器为例,详细论述了一种神经网络控制器的容错设计与可靠性评估方法,文中首先提出网络可靠性研究的重要性,以及本文所提出和容错设计与可靠性评估方法的通用性,然后详细介绍了一种神经网络快速汽门非线性适应控制器的设计结构和容错设计原理,最后根据设计的结果,得出了可靠性评估公式及其推广。     

Design of broadband array elements based on neural network and genetic algorithm

Coupled artificial neural network (ANN) and genetic algorithm (GA) models are developed for the design of broadband tapered slot antenna array elements. The ANN is employed to establish the complicated relationships between the key array performance indicator, i.e. active reflection coefficient, and its element parameters. The trained ANN models are combined with the GA to optimise the element parameters for a given operating frequency band without using time-consuming EM simulators. Optimisation results show the developed ANN-GA model can retain the accuracy obtainable from EM simulators and exhibit high computational efficiency.     

Study of complete interconnect reliability for a GaAs MMIC power amplifier

By combining the finite element analysis (FEA) and artificial neural network (ANN) technique, the complete prediction of interconnect reliability for a monolithic microwave integrated circuit (MMIC) power amplifier (PA) at the both of direct current (DC) and alternating current (AC) operation conditions is achieved effectively in this article. As a example, a MMIC PA is modelled to study the electromigration failure of interconnect. This is the first time to study the interconnect reliability for an MMIC PA at the conditions of DC and AC operation simultaneously. By training the data from FEA, a high accuracy ANN model for PA reliability is constructed. Then, basing on the reliability database which is obtained from the ANN model, it can give important guidance for improving the reliability design for IC.     

基于BP神经网络的网络智能诊断系统

随着计算机网络的发展，网络维护与管理日趋复杂。故障诊断就是对网络运行状态进行检测，并对出现的故障进行处理。随着人工智能理论的发展，要求把智能技术引入网络故障诊断领域，其中人工神经网络能比较准确地完成网络智能诊断任务，特别是目前广泛采用的前向多层神经网络模型(BP模型)。文章对BP神经网络故障诊断系统进行研究和设计，并通过实例进行仿真试验，结果证明该系统对网络故障的智能诊断有较高的实用价值。     

一类非线性时滞离散系统模糊H∞滤波器的设计

本文针对一类非线性、多时滞、干扰有界未知的离散系统进行H_∞模糊滤波器的设计.首先,利用T-S模糊模型来表达这个非线性、多时滞系统,并将建模误差,通过S-procedure,考虑到滤波器的设计中,建立基于T-S模糊模型的滤波器的存在条件,降低了滤波器设计的保守性.然后根据线性矩阵不等式(LMI)的性质将该模糊滤波器的设计转化为LMI的可行性问题,并采用凸优化技术的内点法求得LMI的可行解,设计出模糊滤波器.仿真结果表明,该方法是有效的.     

基于ADS的声表面波单端对谐振器建模

声表面波(SAW)滤波器的设计需对单端对谐振器建模。该文提出了一种新的基于ADS线性矩阵模型的声表面波单端对谐振器模型,并给出了主要参数的推导过程。借助新模型成功设计了一个中心频率为1GHz、带宽为30MHz的声表面波带通滤波器,其插入损耗小于4dB,带外抑制大于30dB。设计过程表明,采用新模型设计声表面波滤波器可做到操作性强、简单、快捷。     

基于DSP Builder的数字滤波器设计与实现

现场可编程门阵列（FPGA）器件广泛用于数字信号处理领域,而使用VHDL或VerilogHDL语言进行设计比较复杂。提出一种采用FDATOOL工具和DSP Builder实现FIR滤波器的设计方案,按照MATLAB/Simulink/DSP Builder/QuartusII设计流程,使用FDATOOL工具可以实时调整滤波器的参数,采用DSP Builder设计了一个16阶FIR低通滤波器模型,并完成了仿真与验证,将模型转换生成VHDL代码,实现了基于FPGA的数字滤波器的设计。结果表明,该方法简单易行,易修改与移植,可满足设计要求,它验证了采用DSP Builder实现数字滤波器设计的独特优势。