基于稀疏最小二乘支持向量回归的非线性自适应波束形成

该文基于最小二乘支持向量回归(LS-SVR)模型提出一种非线性自适应波束形成算法,以提高模型失配、小样本数、复杂多干扰等情况下的自适应波束形成器的鲁棒性。推导了高维矩阵逆矩阵求解的递推快速算法,实现了回归参数的实时求解。采用奇异性准则实时寻找输入样本集的具有较小信息冗余度的子集,并在该子集上完成波束形成计算,使得LS-SVR波束形成的求解得以稀疏化,提高了学习效率,降低了计算复杂度与系统存储空间需求。对比仿真结果验证了所提算法的正确性和有效性。     

自适应旁瓣对消在数字阵列雷达中的工程实现

自适应旁瓣对消是雷达抗有源干扰的有效方法。文中简要介绍了自适应旁瓣对消的基本原理,旁瓣对消模块在某雷达的应用,推导出便于工程实现的理论公式。在实际工作中能满足雷达系统抗干扰性能指标的要求。     

基于改进混洗蛙跳算法的冷连轧轧制规程优化

 轧制规程优化是使轧制过程达到最佳状态的重要保证。综合考虑影响冷连轧轧制过程的多种因素,以等功率裕度和克服划痕为目标函数,建立了轧制规程多目标优化模型,并采用自适应混沌变异蛙跳算法对轧制规程进行优化设计。该算法是在基本蛙跳算法中,加入自适应混沌变异操作,具有较好的寻优精度和收敛速度。对某钢厂1370mm冷连轧机轧制规程进行优化的结果表明,优化后的规程较好地实现了各机架等功率负荷分配,降低了划痕出现的概率和程度,大大地提高了产品质量,增加了经济效益。     

变步长自适应滤波算法探讨与改进

对自适应滤波器变步长算法进行了研究和仿真,并对算法进行了改进,使改进后的变步长算法控制效果更好。     

基于Alpha稳定分布噪声环境下的两步分离自适应算法

自适应滤波理论技术在很多领域有广泛应用,若实际应用中随机信号和噪声有显著的脉冲特性,则Alpha稳定分布能更好地进行描述。文章给出了在分数低阶Alpha稳定分布噪声环境下的一种两步分离自适应算法TSLMP。实验表明,该算法相对以往的LMP算法计算复杂度大大降低,有良好的收敛特性。     

Extinction conditions of a premixed flame in a channel

A local refinement method is used to numerically predict the propagation and extinction conditions of a premixed flame in a channel considering a thermodiffusive model. A local refinement method is employed because of the numerous length scales that characterize this phenomenon. The time integration is self adaptive and the solution is based on a multigrid method using a zonal mesh refinement in the flame reaction zone. The objective is to determine the conditions of extinction which are characterized by the flame structure and its properties. We are interested in the following properties: the curvature of the flame, its maximum temperature, its speed of propagation and the distance separating the flame from the wall. We analyze the influence of heat losses at the wall through the thermal conductivity of the wall and the nature of the fuel characterized by the Lewis number of the mixture. This investigation allows us to identify three propagation regimes according to heat losses at the wall and to the channel radius. The results show that there is an intermediate value of the radius for which the flame can bend and propagate provided that its curvature does not exceed a certain limit value. Indeed, small values of the radius will choke the flame and extinguish it. The extinction occurs if the flame curvature becomes too small. Furthermore, this study allows us to predict the limiting values of the heat loss coefficient at extinction as well as the critical value of the channel radius above which the premixed flame may propagate without extinction. A dead zone of length 2-4 times the flame thickness appears between the flame and the wall for a Lewis number (Le) between 0.8 and 2. For small values of Le, local extinctions are observed.     

交流伺服系统无速度传感器矢量控制研究

为保证控制精度,降低制造成本,研究了一种无速度传感器矢量控制交流伺服系统。该控制系统采用id=0矢量控制策略,PWM采用空间矢量PWM(SVPWM)技术以提高控制性能;同时设计了模型参考自适应系统(MRAS)对电机转子速度/位置进行估算,实现无速度传感器控制。数字仿真试验验证了所设计的伺服系统跟踪定位能力强,鲁棒性好,速度/位置估算精度高。     

新型无速度传感器直接转矩控制系统

无速度传感器技术中最常用的方法是自适应观测器,观测器的增益和速度自适应律决定了观测器的性能。传统采用极点配置方法的自适应观测器存在区域不稳定的问题,针对此缺陷提出了一种基于线性矩阵不等式的新型观测器。仿真结果表明:新型观测器克服了传统方法存在的区域不稳定的缺点,能够在全速范围内准确辨识转速。     

全程S曲线加减速控制的自适应分段NURBS曲线插补算法

为满足现代数控加工的高速度、高精度要求,提出基于7段式S曲线加减速全程规划的NURBS曲线自适应分段插补算法。该算法根据NURBS曲线几何形状将其自适应分段,并计算曲线段各项参数值、对应S曲线加减速规划(速度规划为17种类型)中加减速类型和自适应调整速度曲线加减速时间。在固定插补周期下,与单独自适应算法、5段式S曲线加减速控制方法的仿真结果相比,在满足加速度与加加速度限制条件,且最大弦高误差不超过0.5μm时,该算法插补精度高于单独自适应算法,与5段式S曲线加减速控制方法近似,且其全程平均进给速度比5段式S曲线加减速控制方法平均进给速度提高21.7%,达到594mm/s。     

Statistical process control with intelligence using fuzzy ART neural networks

With the automation development of manufacturing processes, artificial intelligence technology has been gradually employed to increase the automation and intelligence degree in quality control using statistical process control (SPC) method. In this paper, an SPC method based on a fuzzy adaptive resonance theory (ART) neural network is presented. The fuzzy ART neural network is applied to recognize the special disturbance of the manufacturing processes based on the classification on the histograms, which shows that the fuzzy ART neural network can adaptively learn the features of the histograms of the quality parameters in manufacturing processes. As a result, the special disturbance can be automatically detected when a feature of the special disturbance starts to appear in the histograms. At the same time, combined with spectrum analysis of the autoregressive model of quality parameters, the fuzzy ART neural network can also be utilized to adaptively detect the abnormal patterns in the control chart.