Improved Algorithm of Variable Bandwidth Kernel Particle Filter

Aiming at the large cost of calculating variable bandwidth kernel particle filter and the high complexity of its algorithm, a self-adiusting kernel function particle filter is presented. Kernel density estimation is facilitated to iterate and obtain new particle set. And the standard deviation of particle is introduced in the kernel bandwidth. According to the characteristics of particle distribution, the bandwidth is dynamically adjusted, and the particle distribution can thus be more close to the posterior probability density model of the system. Meanwhile, the kernel density is used to estimate the weight of updating particle and the system state. The simulation results show the feasibility and effectiveness of the proposed algorithm.     

一类增量式支持向量机的分析

针对训练数据发生增量改变时,标准一类支持向量机的批处理算法需要重新进行训练,不适合在线增量环境学习的问题,提出一种详细的增量式标准一类分类向量机算法,并通过理论分析对该算法的可行性和有限收敛性进行了证明,确保该算法的每步调整都是可靠的,并确保该算法通过有限步调整最终收敛到问题的最优解。在标准数据集上的实验结果验证了理论分析的正确性。     

基于小波神经网络的航空发动机建模研究

提出将多个多输入单输出小波神经网络(WNN)组合构造多输入多输出(MIMO)的WNN来逼近MIMO非线性动态系统的快速而简单的实现方法，并采用高效率的初始化方法缩短了训练时间。采用某型航空发动机在飞行包线内均匀分布的工作点参数来训练，建立了全包线适用的动态小波神经网络航空发动机模型，用交叉验证的方法检验表明在全包线内有较高的精度及泛化能力。与反传算法神经网络(BPNN)、径向基函数神经网络(RBFNN)建立的动态模型在精度及泛化能力等方面做比较，结果表明WNN建立的模型训练精度高而且泛化能力强。     

基于支撑域的单分类器和密度估计的本质关系

单类支持向量机和支持向量数据描述是两种流行的基于支撑域的单分类器。为揭示采用高斯核后他们与密度估计之间的关系,首先将基于支撑域的单分类器统一到密度估计的框架下;其次证明了基于支撑域的单分类器诱导的密度估计和真实密度一致,同时也能减小积分平方误差。最后通过人工数据集实验验证了上述关系。     

Multi-target Collaborative Combat Decision-Making by Improved Particle Swarm Optimizer

A decision-making problem of missile-target assignment with a novel particle swarm optimization algorithm is proposed when it comes to a multiple target collaborative combat situation.The threat function is established to describe air combat situation.Optimization function is used to find an optimal missile-target assignment.An improved particle swarm optimization algorithm is utilized to figure out the optimization function with less parameters,which is based on the adaptive random learning approach.According to the coordinated attack tactics,there are some adjustments to the assignment.Simulation example results show that it is an effective algorithm to handle with the decision-making problem of the missile-target assignment(MTA)in air combat.     

基于ELMD与改进SMSVM的机械故障诊断方法

机械振动信号携带大量重要的机械状态信息,然而机械故障振动信号在复杂工作状态下通常呈现非平稳、非线性特性。因此,从振动信号抽取和选择有效的机械故障特征、提高故障识别性能,成为机械故障诊断研究的热点。针对上述问题,本文提出了基于集成局部均值分解(Ensemble local means decomposition,ELMD)与改进的稀疏多尺度支持向量机(Sparse multiscale support vector machine,SMSVM)的机械故障诊断方法。该方法首先使用自适应非线性、非平稳信号处理方法 ELMD把多模态调制故障信号分解成为多个单模态解调信号,有效地增强了故障特征。把压缩感知和多尺度分析技术融合于故障模式分类中,提出改进SMSVM旋转机械故障识别方法,提高多类机械微弱故障数据模式识别性能。该方法融合稀疏表示、多尺度分析和SVM的优点,无需求解复杂的优化问题,易于推广至更多尺度SVM,具有计算量少、泛化性与鲁棒性好、物理意义明显等优点。人工数据和实验设备数据验证了本文算法的优越性。     

Warehouse Environment Parameter Monitoring System and Sensor Error Correction Model Based on PSO-BP

The warehouse environment parameter monitoring system is designed to avoid the networking and high cost of traditional monitoring system.A sensor error correction model which combines particle swarm optimization(PSO)with back propagation(BP)neural network algorithm is established to reduce nonlinear characteristics and improve test accuracy of the system.Simulation and experiments indicate that the PSO-BP neural network algorithm has advantages of fast convergence rate and high diagnostic accuracy.The monitoring system can provide higher measurement precision,lower power consume,stable network data communication and fault diagnoses function.The system has been applied to monitoring environment parameter of warehouse,special vehicles and ships,etc.     

Aero-engine Thrust Estimation Based on Ensemble of Improved Wavelet Extreme Learning Machine

Aero-engine direct thrust control can not only improve the thrust control precision but also save the operating cost by reducing the reserved margin in design and making full use of aircraft engine potential performance.However,it is a big challenge to estimate engine thrust accurately.To tackle this problem,this paper proposes an ensemble of improved wavelet extreme learning machine(EW-ELM)for aircraft engine thrust estimation.Extreme learning machine(ELM)has been proved as an emerging learning technique with high efficiency.Since the combination of ELM and wavelet theory has the both excellent properties,wavelet activation functions are used in the hidden nodes to enhance non-linearity dealing ability.Besides,as original ELM may result in ill-condition and robustness problems due to the random determination of the parameters for hidden nodes,particle swarm optimization(PSO)algorithm is adopted to select the input weights and hidden biases.Furthermore,the ensemble of the improved wavelet ELM is utilized to construct the relationship between the sensor measurements and thrust.The simulation results verify the effectiveness and efficiency of the developed method and show that aero-engine thrust estimation using EW-ELM can satisfy the requirements of direct thrust control in terms of estimation accuracy and computation time.     

基于NRS-SVM模型的航空弹药消耗预测研究

结合航空弹药训练消耗的特点,研究邻域粗糙集(Neighborhood rough sets,NRS)与支持向量机(Support vector machines,SVM)融合的航空弹药训练消耗预测问题。通过邻域粗糙集将5个初始影响因素约简为3个核心影响因素,并以此训练集对支持向量机进行回归优化。通过参数寻优得到最优的惩罚参数和核参数,进而构建NRS-SVM组合预测模型来预测航空弹药消耗。实证研究表明,该模型预测结果与实际数据吻合度较高,且与其他预测模型相比具有更好的预测性能。     

支持向量机的正定核(英文)

探讨并论证了支持向量机中Mercer核,再生核与正定核这几种核函数的关系及它们在支持向量机中各自的角色.通过核矩阵的正定性检验了核函数的构造方法.基于Bochner定理,在Fourier域验证了许多平移不变核函数.基于Schoenberg定理验证了一些旋转不变径向核.最后讨论了离散尺度与小波核函数的构造,核函数选择与核参数学习.     

Threshold Selection Method Based on Reciprocal Gray Entropy and Artificial Bee Colony Optimization

Since the logarithmic form of Shannon entropy has the drawback of undefined value at zero points,and most existing threshold selection methods only depend on the probability information,ignoring the within-class uniformity of gray level,a method of reciprocal gray entropy threshold selection is proposed based on two-dimensional（2-D）histogram region oblique division and artificial bee colony（ABC）optimization.Firstly,the definition of reciprocal gray entropy is introduced.Then on the basis of one-dimensional（1-D）method,2-D threshold selection criterion function based on reciprocal gray entropy with histogram oblique division is derived.To accelerate the progress of searching the optimal threshold,the recently proposed ABC optimization algorithm is adopted.The proposed method not only avoids the undefined value points in Shannon entropy,but also achieves high accuracy and anti-noise performance due to reasonable 2-D histogram region division and the consideration of within-class uniformity of gray level.A large number of experimental results show that,compared with the maximum Shannon entropy method with 2-D histogram oblique division and the reciprocal entropy method with 2-D histogram oblique division based on niche chaotic mutation particle swarm optimization（NCPSO）,the proposed method can achieve better segmentation results and can satisfy the requirement of real-time processing.     

改进的粒子群算法求解背包问题

本文提出了改进的粒子群算法求解背包问题,阐明了该算法求解背包问题的具体实现过程。通过与其他文献中实例的计算结果比较,表明该算法切实可行,有较高的搜索效率。     

一种新的基于粒子群算法的聚类方法

建立了聚类分析问题的数学优化模型,提出了一种新的粒子群算法解决聚类问题。对基本粒子群优化算法作了改进,思路是将K-均值方法的结果作为一个粒子和利用新的分类中心调整粒子位置。对Iris植物样本数据的测试结果表明:4种粒子群算法的效果都比较好,特别是第3种改进的粒子群算法的效果更好,粒子群优化聚类技术很有潜力．     

基于自适应容积粒子滤波的车辆状态估计

针对车辆状态估计中由模型的强非线性、噪声的非高斯分布等相关因素导致估计精度下降甚至发散的问题,本文提出了基于自适应容积粒子滤波(Adaptive cubature particle filter,ACPF)的车辆状态估计器。首先基于非稳态动态轮胎模型,构建高维度非线性八自由度车辆模型。其次利用自适应容积卡尔曼滤波(Adaptive cubature Kalman filter,ACKF)算法更新基本粒子滤波(Particle filter,PF)算法的重要性密度函数,以完成自适应容积粒子滤波算法设计。利用车载传感器信息,运用ACPF算法实现对车辆的侧倾角、质心侧偏角等关键状态变量高精度在线观测。搭建Simulink-Carsim联合仿真平台进行了算法的验证,结果表明该算法状态估计精度高于传统无迹粒子滤波(Unscented particle filter,UPF)算法,且算法运算效率高于UPF算法,而传统PF估计值发散。研究结果为实现车辆动力学精准控制提供了理论支持。     

Solving Job-Shop Scheduling Problem Based on Improved Adaptive Particle Swarm Optimization Algorithm

An improved adaptive particle swarm optimization(IAPSO)algorithm is presented for solving the minimum makespan problem of job shop scheduling problem(JSP).Inspired by hormone modulation mechanism,an adaptive hormonal factor(HF),composed of an adaptive local hormonal factor(H l)and an adaptive global hormonal factor(H g),is devised to strengthen the information connection between particles.Using HF,each particle of the swarm can adjust its position self-adaptively to avoid premature phenomena and reach better solution.The computational results validate the effectiveness and stability of the proposed IAPSO,which can not only find optimal or close-to-optimal solutions but also obtain both better and more stability results than the existing particle swarm optimization(PSO)algorithms.     

协同多目标攻击空战决策的启发式粒子群优化算法

利用协同多目标攻击战术的特定知识，并结合粒子群算法，提出了一种用于空战决策的启发式粒子群算法。该算法利用粒子群算法对解空间探索能力强，容易跳出局部最优陷井及启发式算法局部搜索能力强的优点，快速、高效地对全局最优值进行搜索。该算法通过求解友机导弹对目标的最优分配来确定空战决策方案。仿真实验结果表明。本文算法对最优空战决策方案的搜索性能明显优于普通粒子群算法及其他两种遗传算法。     

AC-PSO算法在无人机任务规划中的应用

无人机飞行中合理的路线规划可以减小飞行时间、降低油耗,减小被敌方发现、攻击的可能,从而提高了完成任务的概率.鉴于大部分无人机是以一个相对固定的高度进行侦察和任务飞行,故可将无人机的飞行任务规划视为二维平面的TSP问题.本文进一步将地面防空威胁与飞行距离统一量化,通过求解TSP求取最优无人机任务规划.文中通过分析蚁群算法与粒子群算法,提出了一种新的混合方法AC-PSO算法解决TSP求解问题.算法借鉴了蚁群算法的路线构造方法和粒子群算法的进化策略思想,同时给出了提升算法效率的一些措施.实验验证,该算法和威胁建模方法相结合,能有效地满足无人机飞行任务规划的要求.     

基于改进PSO算法的导弹控制参数优化

研究了将粒子群算法(PSO)应用于空对空导弹控制参数自动设计的方法,解决导弹控制参数手工设计中遇到的困难与问题.标准PSO算法在导弹静稳定工作点参数优化中表现出良好性能,但在静不稳定工作点优化时容易限入局部最优,因此引入遗传算法(GA)的杂交思想对标准PSO算法进行了改进,以扩大解空间的范围.仿真结果表明:改进后的PSO优化算法具有更强的全局搜索能力,获得的参数能够满足给定的性能指标,并且可以节省大量的设计时间,具有很高的工程应用价值.     

粗糙集中连续属性离散化的一种新方法

提出一种新的间接离散化方法——超曲面法。首先给出了超曲面的定义，证明了超曲面为一个高阶的多项式，且给出了该多项式的总项数；同时证明了超曲面数与最大决策规则数之间的关系。然后提出了应用支持向量机来求取最优超曲面的方法。最后以空军低消耗器材的储存策略为例，说明了超曲面的求解过程。实例仿真结果表明，用支持向量机来求解超曲面，不仅方法简单，而且较容易寻得最优解。结果还表明，文中提出的超曲面间接离散方法能较好地区分决策表中的决策类别，从而获得更为简捷的决策规则。     

基于改进微粒群算法的无人机姿态控制参数智能整定(英文)

提出了基于改进微粒群算法的无人机姿态控制器参数智能整定方法.标准微粒群算法在搜索后期由于群体缺乏多样性而容易出现收敛停滞现象,为此提出了一种改进的微粒群算法.标准微粒群算法中的微粒速度是根据惯性运动、群体历史最优位置和自身历史最优位置来调节的.改进微粒群算法中的微粒除了保持惯性运动外,仅向当前群体中任意更优个体的状态学习,而且惯性权重系数是随机数.改进方案减少了算法不确定参数,简化了微粒学习机制,且增强了群体多样性.本文构建了无人机姿态控制系统,将改进微粒群算法用于四个控制参数的寻优整定.仿真结果表明,改进微粒群算法比一般微粒群算法具有更强的全局搜索能力,故获得更优的无人机姿态控制参数.