知识-模糊神经网络在复杂劳动计量中的应用

由于大脑自身结构的超复杂性和人们对大脑信息处理机制与过程认识的局限性,复杂劳动计量长期以来未能得到有效解决。文中首先系统地研究了复杂劳动过程的三要素,其次通过引入知识工程原理、模糊神经网络技术等,根据大脑处理信息在微观结构与宏观形式上的基本特点及复杂劳动面向对象的信息特征,提出基于知识－模糊神经网络的复杂劳动计量思路,以计量复杂劳动量,并给出计量方法与步骤及案例分析,案例分析结果表明,该文所提出的     

时滞对象的神经网络Smith预测控制

针对系统的时滞问题提出一种新的控制系统，将神经网络Smith预估器与神经元控制器通过预测误差有效结合在一起，起到预测控制目的。在神经网络Smith预估器中采用优于BP网络的IRN网络，仿真的结果表明系统的实时控制速度有了明显地提高。     

适用于全包线的航空发动机BP网络模型的动态辨识

为克服传统的发动机动态模糊辨识中存在的辨识精度低，辨识模型应用范围窄等不足，把对非线性系统具有高度逼近能力的神经网络应用于航空发动机动态特性的辨识，从而为发动机动态辨识开辟更为广阔的道路，采用均方差归一法的处理方法和BP算法的改进算法－输出端动量BP地，以某型发动机在飞行包线内某一飞行条件下的数据作为学习样本，辨识了发同的神经网络模型，在全包线范围内对该模型进行检验，结果表明，所得的发动机动态模型在全包线内都有很高的逼近精度，而且对噪声有很强的抑制能力。     

快速伺服刀架神经网络自适应PID控制

为了提高快速伺服刀架的控制性能,减小跟踪误差,实现正弦网格表面精密加工,提出了基于RBF和BP神经网络的自适应PID控制策略.由仿真结果可以看出,采用基于神经网络的自适应控制算法,使跟踪误差的最大值降低为1.37μm,跟踪误差的绝对均值降低为0.52μm.这两项指标相对与传统PID控制分别降低了28%和40%.     

An intelligentized and fast calibration method of SINS on moving base for planed missiles

In order to minish the error of inertial sensors, the technology of neural networks is attempted to on-line calibration of a slave inertial navigation system mounted on planed missiles. Based on the time-varied specialty of slave inertial navigation system on a moving base, an input–output sample structure method is proposed, and to automatically calibrate and revise the error of inertial sensors of inertial navigation system. When a missile is appended under the wing and in free-flight, in order to solve the inconsistent problem of measurement's character of the inertial sensors, the error angles between the master inertial navigation system and the slave inertial navigation system are estimated in advance, then, the input samples of a neural network can correctly simulate the free-flight state. Furthermore, in order to make a learning algorithm of neural networks can satisfy real-time calibrating on a moving base, the traditional Newton algorithm is improved by using first differential coefficient to replace the approximate matrix of second differential coefficients. As a result, the training speed and precision of neural network are enhanced. The simulation results indicate that the method and algorithm are feasible.     

利用试验设计法建立翼型气动特性的人工神经网络模型

建立了翼型气动特性预测的BP(Back Propagation)神经网络模型,重点研究了3种选取训练样本的试验设计(DOE)法:完全析因法、正交设计法和均匀设计法,对BP神经网络预测精度的影响,利用所建立的BP神经网络对FX63-137翼型几何型线进行了优化设计。研究结果表明:在因素数和水平数较少时,完全析因法、正交设计法及均匀设计法的平均测试误差分别为0.002%、0.029%、0.023%;在因素和水平数较多时,完全析因法的样本规模太大而不再适合,正交设计法和均匀设计法的平均测试误差分别为0.42%和0.15%,均匀设计法的预测精度更高,更适合于翼型气动特性预测的人工神经网络模型。优化后翼型的升阻比在迎角为0°~18°范围内均高于原始翼型,在迎角为1°、4°和15°时升阻比分别提高了4.38%、1.38%和5.51%。该研究方法及成果可以应用于翼型的多参数优化设计。     

流场特性预测的两类高效方法

在多因素多水平试验设计方法建立数据库的基础上,提出了预测流场特性的两类高效方法:一类是响应面方法;一类是小波神经网络方法.典型算例表明,两类方法都可以高效率的获取流场特性参数,而且在预测的精度与计算的效率上,小波神经网络方法较响应面方法更具有优势.      

Predicting the probability of occurrence of spread-F over Brazil using neural networks

The probability of occurrence of spread-F can be modeled and predicted using neural networks (NNs). This paper presents a feasibility study into the development of a NN based model for the prediction of the probability of occurrence of spread-F over selected equatorial stations within the Brazilian sector. The input space included the day number (seasonal variation), hour (diurnal variation), sunspot number (measure of the solar activity), magnetic index (measure of the magnetic activity) and magnetic position. Twelve years of spread-F data from Brazil (covering the period 1978–1989) measured at the equatorial site Fortaleza (3.9°S, 38.45°W) and low latitude site Cachoeira Paulista (22.6°S, 45.0°W) are used in the development of an input space and NN architecture for the model. Spread-F data that is believed to be related to plasma bubble developments (range spread-F) was used in the development of the model. The model results show the probability of spread-F occurrence as a function of local time, season and latitude. Results from the Brazilian Sector NN (BSNN) based model are presented in this paper, as well as a comparative analysis with a Brazilian model developed for the same purpose.     

无线传感网的分布式非测距三维定位算法

为解决三维空间无线传感器网络节点定位问题,基于区域立体网格化表示的思想,提出一种分布式非测距三维定位算法3D-DRL(three-Dimensional Distributed Range-free Localization).通过对立体网格投票,选取得票值最高的所有网格的质心作为未知节点的估计位置.3D-DRL无需未知节点间相互通信,具有较小的通信开销,不依赖于锚节点比例,且对网络拓扑结构具有鲁棒性.仿真结果表明,在无线传播环境理想、未知节点通信半径R=50 m、所有节点均随机部署在100 m×100 m×100 m三维区域的情况下,定位误差小于未知节点通信半径的8%,且通过调整VANR,能够实现所有节点的定位.     

无线传感器网络中的跨层路由协议

为了减少无线传感器网络中的干扰产生,降低由此引起多次重传后的分组丢弃从而导致的额外重传时延和能量消耗,改善网络的传输效率和能量效率,提出了一种基于跨层设计的干扰感知路由(IAR,Interference-Aware Routing)协议.与现有的基于竞争的路由协议不同,IAR协议引入节点干扰度和能量度作为选路代价,通过干扰和能量感知的路由选择机制实现路由建立.利用节点收到的发送请求、清除发送和应答分组的计数作为干扰度的计算依据,从而在路由选择中避开了干扰易发生区域;利用下一跳候选节点的初始和剩余能量作为能量度的计算依据,从而均衡了各节点的能量消耗,延长了整个网络的寿命.仿真结果表明,与动态码字路由和ad-hoc按需距离矢量路由协议相比,IAR协议提高了能量效率、改善了网络的吞吐量、分组投递率和时延等性能.