INTELLIGENT CONTROL SYSTEM OF PULSED MAG WELDING INVERTER BASED ON DIGITAL SIGNAL PROCESSOR

A fuzzy logic intelligent control system of pulsed MAG welding inverter based on digital signal processor （DSP） is proposed to obtain the consistency of arc length in pulsed MAG welding. The proposed control system combines the merits of intelligent control with DSP digital control. The fuzzy logic intelligent control system designed is a typical two-input-single-output structure, and regards the error and the change in error of peak arc voltage as two inputs and the background time as single output. The fuzzy logic intelligent control system is realized in a look-up table （LUT） method by using MATLAB based fuzzy logic toolbox, and the implement of LUT method based on DSP is also discussed. The pulsed MAG welding experimental results demonstrate that the developed fuzzy logic intelligent control system based on DSP has strong arc length controlling ability to accomplish the stable pulsed MAG welding process and controls pulsed MAG welding inverter digitally and intelligently.

A Fuzzy Neural Network for Drilling Tool Condition Monitoring

This paper presents a fuzzy neural network used for monitoring breakage and wear of tools by vibration sig-nal. Which describes the relationship betwee too conditons and the monitoring indices and expermental results indi-cate it is feasible to vibration signal for on-line drilling condition monitoring.

Study of Synthesis Identification in Cutting Process with Fuzzy Neural Network

With the development of industrial production modernization, FMS and CIMS will become more and more popularized. For its control system is increasingly modeled, intellectualized and automatized, in order to raise the reliability and stability in the manufacturing process, the comprehensive monitoring and diagnosis aimed at cutting tool wear and chatter become more and more important and get rapid development. The paper tried to discuss of the intellectual status identification method based on acoustics-vibra characteristics of machining process, and propose that the working conditions may be taken as a core, complex fuzzy inference neural network model based on artificial neural network theory, and by using various kinds of modernized signal processing method to abstract enough characteristics parameters which will reflect overall processing status from machining acoustics-vibra signal as information source, to identify different working condition, and provide guarantee for automation and intelligence in machining process. The complex network is composed of NNw and NNs, Each of them is composed of BP model network, NNw is weight network at rule condition, NNs is decision-making network of each status. Y out is final inference result which is to take subordinate degree as weight from NNw, to weight reflecting result from NNs and obtain status inference of monitoring system. In the process of machining, the acoustics-vibor signal were gotten by the acoustimeter and the acceleration piezoelectricity detector, the date is analysed by the signal processing software in time and frequency domain, then form multi feature parameter vector of criterion pattern samples for the different stage of cutting chatter and acoustics-vibra multi feature parameter vector. The vector can give a accurate and comprehensive description for the cutting process, and have the characteristic which are speediness of time domain and veracity of frequency domain. The research works have been practically applied in identification of tool wear, cutting chatter, experiment results showed that it is practicable to identify the cutting chatter based on fuzzy neural network, and the new method based on fuzzy neural network can be applied to other state identification in machining process.

Speed-Sensorless Control Using Elman Neural Network

This paper describes a modified speed-sensorless control for induction motor (IM) based on space vector pulse width modulation and neural network. An Elman ANN method to identify the IM speed is proposed, with IM parameters employed as associated elements. The BP algorithm is used to provide an adaptive estimation of the motor speed. The effectiveness of the proposed method is verified by simulation results. The implementation on TMS320F240 fixed DSP is provided.

基于模糊神经网络的电网消防预警算法

针对传统基于阈值判别方法的电网火灾预警系统预测精度低、抗干扰能力弱的问题,提出了一种基于模糊神经网络的电网消防预警算法。该算法利用神经网络学习大规模电网数据,使用模糊逻辑推理算法来提升预测结果的推理能力,并通过结合神经网络对大规模数据的学习能力和模糊逻辑算法的推理能力来分析电网线路参数,从而提升电网消防预警系统的精度和抗干扰能力。实验与仿真结果表明,所提出方法能显著提升电网火灾的预警精度,且使用模糊逻辑推理可以得到更符合实际情况的电网火灾预警结果。

基于模糊神经网络的数字媒体数据自动化采集系统设计

为了改善数字媒体数据传输效果,确保系统安全、稳定运行,设计基于模糊神经网络的数字媒体数据自动化采集系统。构建数字媒体数据自动化采集系统框架,数据采集层利用数据采集卡获取不同渠道的数字媒体数据,由数据处理层的数据融合模块调用改进模糊神经网络算法完成数字媒体数据的融合处理后,通过数据传输层的分层自组织无线网络将其传输至存储应用层,实现数字媒体数据的存储、查询、显示与输出。实验结果表明:该系统采集的音频信号波形规律、曲线平滑、功率均值波动误差在(0,0.14),满足允许误差范围;数据传输效果优于采用DSR协议的单点传输方式,当数字媒体数据源为3时,平均峰值信噪比指标最高;具有数字媒体数据查询功能。

数控机床工作台DSP定位误差系统设计及分析

为进一步优化数控机床对于测试误差的补偿功能,开发通过DSP硬件系统对误差进行准确预测并设置补偿措施。建立的定位误差模型预测补偿系统包含数控系统进给轴反馈结构、DSP建模预测系统以及数控系统。研究结果表明,采用Matlab软件运行得到的优化权值与阈值建立的GA-BP网络进行误差预测共需251μs;采用GA-BP网络构建的模型进行预测时达到了更高精度。该研究有助于提高数控机床加工精度,对提高加工参数的优化起到很好的指导意义以及控制效果。

基于小波阈值去噪与时频图像检测的信号调制识别技术

随着通信环境的日益复杂化,信号调制识别变得越来越重要。针对低信噪比下数字信号调制识别困难的问题,提出一种基于小波阈值去噪与时频图像检测的调制识别方法。该方法将接收到的实信号转换成解析信号,通过小波阈值法对解析信号进行去噪处理。引入时频重排技术将去噪后的一维信号转换成二维时频图像,通过双线性插值缩放图像,得到适应网络输入大小的时频图。将时频图输入VGG网络中训练识别。实验结果显示,提出的调制识别方法对于低信噪比下的调制识别问题表现优异。

基于DSP的整流管组件故障诊断技术研究

为了提高航空发电机整流管组件故障诊断系统的效果和可靠性,本文采用DSP芯片和神经网络方法设计了一个故障检测系统,可实现对发电机整流管组件整流管正常工作、单管断路和双管断路故障等三类模式的诊断。通过对实际航空发电机的数据进行分析,结果表明,对给定的数据,系统诊断正确率可达100%。

基于网络处理器的防火墙模型设计

设计一种基于机器学习的神经网络算法,评价网络处理器的防火墙模型,并测试评价算法效能。考虑网络处理器的防火墙设计模型的影响因素,分析网络处理器的数据结构,对防火墙模型的任务进行分解实现防火墙模型的设计,并在神经网络算法下完成算法的仿真测试。测试结果证实模糊神经网络对配网防火墙的稳定性预警能力优于传统的机器学习算法。该算法对防火墙模型设计的稳定性评价过程有积极意义。

基于模糊神经网络的电力通信信号异常检测研究

由于现有的异常检测方法检测准确率低,无法有效检测出异常信号,因此提出一种基于模糊神经网络的电力通信信号异常检测方法。将采集的样本数据输入网络,通过无监督的学习和训练获得稳定的网络参数,学习并提取重要的特征信息。对已知的数据特征指标进行编码,运用解调的方式提取异常信号特征,并将其输入模糊神经网络。结合粒子群算法对网络进行训练,获得全局最优值。通过快速傅里叶变换(Fast Fourier Transform,FFT)将训练数据变成信号幅度,并判断信号是否异常,从而完成信号异常检测。实验结果表明,利用所提方法的10个小组检测准确率均在99%以上,结果符合预期,异常检测效果良好。

基于GA-BP网络的数控机床工作台定位误差分析

为了提高数控机床对测试误差的补偿效果,开发一种通过遗传算法(GA)来完成BP网络的优化过程,并加入坐标参数、运动速度指标,建立工作台的定位误差仿真模型。先通过Matlab软件构建得到GA-BP模型,得到优化权值与阈值后,再将结果移植至DSP内开展建模与预测,由此促进预测速率的大幅提升。研究结果表明:以DSP构建的预测系统对各定位误差残差分布进行预测得到的范围是-0.69~0.51μm。采用GA-BP网络构建的模型进行预测时达到了更高精度。

Flatness predictive model based on T-S cloud reasoning network implemented by DSP

The accuracy of present flatness predictive method is limited and it just belongs to software simulation. In order to improve it, a novel flatness predictive model via T-S cloud reasoning network implemented by digital signal processor(DSP) is proposed. First, the combination of genetic algorithm(GA) and simulated annealing algorithm(SAA) is put forward, called GA-SA algorithm, which can make full use of the global search ability of GA and local search ability of SA. Later, based on T-S cloud reasoning neural network, flatness predictive model is designed in DSP. And it is applied to 900 HC reversible cold rolling mill. Experimental results demonstrate that the flatness predictive model via T-S cloud reasoning network can run on the hardware DSP TMS320 F2812 with high accuracy and robustness by using GA-SA algorithm to optimize the model parameter.

REALIZATION OF FUZZY-FILTER CONTROL ON A BRUSHLESS AC GENERATOR

The transistor voltage regulators have been widely adopted in the brushless AC generators in aircraft. This paper researches the digital voltage regulator. The paper presents the hardware platform of the digital voltage regulator, which is based on a DSP chip — TMS320C32. A novel fuzzy filter control structure is developed from normal fuzzy control strategy. And the fuzzy filter control algorithm is adopted in the hardware platform successfully. The computer simulation has been conducted. Some control parameters have been obtained through the simulation. The same parameters have been applied in the digital regulation experiments on a brushless AC generator. In the experiment, the digital voltage regulator results in good responses. From the experiment results, it can be seen that the new control algorithm is efficient for the digital voltage regulator.

模糊PID控制的恒星光谱模拟

针对目前基于数字微镜(Digital Micromirror Device,DMD)的光谱模拟中,光谱模拟单元具有不同偏态性质且存在非线性调制等特点,提出了一种适用于多种色温调制的基于模糊PID控制的恒星光谱模拟方法。构造了DMD工作矩阵、光谱调制权重矩阵、光谱分布函数矩阵以及目标光谱矩阵,建立了基于DMD的光谱模拟数学模型;然后,研究了基于遗传算法优化BP神经网络的光谱分布函数拟合算法,在400~800 nm的峰值波长内实现了光谱分布函数拟合;提出了一种基于模糊PID控制的光谱模拟算法,选择模糊集合与隶属度函数,制定了模糊推理以及解模糊化规则。仿真分析了模糊PID控制器性能,结果表明与PID控制相比,模糊PID控制的超调量减小90.7%,调节时间缩短了69.4%。最后,实验验证了3000~11000 K色温光谱分布曲线的模拟精度,结果表明:光谱模拟误差优于±4.21%;相较于PID控制,模糊PID控制在3000,6500以及11000 K色温的最大光谱模拟精度分别提高了2.31倍,1.71倍和2.02倍。所提出的方法可以进一步提升光谱模拟精度,为高精度星敏感器的地面标定提供理论与技术基础。

多核数字信号处理器矩阵乘卷积算法性能评测

矩阵乘卷积算法能够为各种卷积配置提供高性能基础实现,是面向给定芯片进行卷积性能优化的首要选择。针对国防科技大学自主研制的飞腾异构多核数字信号处理器(digital signal processor,DSP)芯片的特征以及矩阵乘卷积算法自身的特点,提出了一种面向多核DSP架构的高性能并行矩阵乘卷积实现算法ftmEConv。该算法由输入特征图转换、卷积核转换、矩阵乘以及输出特征图转换这四个均运行在通用多核DSP上的并行化部分构成,通过有效挖掘通用DSP核中功能单元的潜力来提升各个部分的性能。实验结果表明,ftmEConv实现了高达42.90%的计算效率,与芯片上的其他矩阵乘卷积算法实现相比,获得了高达7.79倍的性能加速。

基于生成对抗网络的数字音频信号多声道增强方法

基于生成对抗网络的数字音频信号多声道增强方法,提升数字音频清晰度、降低噪声对其的干扰。以结合傅里叶的双边语谱图滤波算法初步实现数字音频多声道信号去噪;构建基于对抗神经网络的数字音频信号多声道增强模型,将去噪后的数字音频多声道信号作为该模型的输入数据,输出增强的多声道数字音频信号,完成数字音频信号多声道增强。实验结果表明:该方法增强后的数字音频信号传输效果较好,同时信噪比和音频客观评判基准较高,且去噪后语谱图中噪点以及模糊区较少,能够有效去除信号中的噪声,提升数字音频信号质量。

音频传输中的瞬态频域扰动信号调制技术研究

音频信号传输时受到因噪声引起的瞬态频域扰动信号影响,导致整体调制效果较差。为此,文中研究具备去噪能力的音频瞬态频域扰动信号调制技术。此技术基于傅里叶变换去除音频信号传输中的噪声信息后,使用基于卷积过程神经网络的瞬态频域扰动信号识别方法,以信号识别的方式提取音频传输中的瞬态频域扰动信号。将提取到的瞬态频域扰动信号输入基于DDS的瞬态频域扰动信号调制器中,调制信号频率满足期望状态,完成瞬态频域扰动信号调制。实验结果显示:所提技术对数字电视系统传输的音频信号进行去噪、瞬态频域扰动信号识别和调制后,有效滤除了音频信号中的噪声信息,瞬态频域扰动信号识别结果准确,且具备有效调制音频传输中瞬态频域扰动信号数据传输速率的能力。

基于DSP的数字信号处理系统设计和实现

基于对语音、视频处理实时性和稳定性的高要求,以可编程数字信号处理器(Digital Signal Processor,DSP)为主体进行研究,通过分析DSP处理器的系统总体,介绍系统中不同环节的作用,设计出可以有效提高数据传输实时性和稳定性的数字信号处理系统,以提高语音和视频处理效率,助力企业实现可持续发展。