路径约束下基于人工智能的智慧航空旅游动态寻径方法

为了提高智慧航空旅游动态寻优控制能力,提出一种基于人工智能的路径约束下智慧航空旅游动态寻径方法。采用多目标进化方法进行智慧航空旅游动态寻径的路径约束控制,结合粒子群方法进行智慧航空旅游动态寻径的路径优化选择,采用多目标Pareto映射方法进行智慧航空旅游的路径规划设计,结合信息素导引方法进行智慧航空旅游动态寻径的自适应控制,构建智慧航空旅游动态寻径的蚁群滤波模型,根据蚁群路径约束寻优方法构建智慧航空旅游动态寻径的人工智能算法,实现智慧航空旅游动态寻径的人工智能控制和自适应寻优。仿真结果表明,采用该方法进行智慧航空旅游动态寻径的自适应性能较好,路径优化控制能力较强。     

SLF混沌神经网络及其应用

针对混沌神经网络的单调激励函数,引入Legendre函数和Sigmoid函数组合作为非单调激励函数,构造了一种新的暂态混沌神经元模型（SLF模型）,并给出了此混沌神经元的倒分岔图和最大Lyapunov指数时间演化图,利用该模型构建了一种暂态混沌神经网络,通过对非线性函数优化和TSP问题的求解验证了该模型的有效性。     

Chaotic time series method combined with particle swarm optimization and trend adjustment for electricity demand forecasting

Electricity demand forecasting plays an important role in electric power systems planning. In this paper, nonlinear time series modeling technique is applied to analyze electricity demand. Firstly, the phase space, which describes the evolution of the behavior of a nonlinear system, is reconstructed using the delay embedding theorem. Secondly, the largest Lyapunov exponent forecasting method (LLEF) is employed to make a prediction of the chaotic time series. In order to overcome the limitation of LLEF, a weighted largest Lyapunov exponent forecasting method (WLLEF) is proposed to improve the prediction accuracy. The particle swarm optimization algorithm (PSO) is used to determine the optimal weight parameters of WLLEF. The trend adjustment technique is used to take into account the seasonal effects in the data set for improving the forecasting precision of WLLEF. A simulation is performed using a data set that was collected from the grid of New South Wales, Australia during May 14–18, 2007. The results show that chaotic characteristics obviously exist in electricity demand series and the proposed prediction model can effectively predict the electricity demand. The mean absolute relative error of the new prediction model is 2.48%, which is lower than the forecasting errors of existing methods.     

带扰动的混沌神经网络的研究

为了研究混沌神经网络的抗扰动能力,在Chen’s混沌神经网络中引入了三角函数扰动项,研究了带扰动的Chen’s混沌神经元模型,给出了该混沌神经元的倒分岔图和Lyapunov指数图,分析了其动力学特性。基于该混沌神经元模型,构造了带扰动的Chen’s混沌神经网络,并将其应用于函数优化和旅行商问题（TSP）。仿真结果表明：Chen’s混沌神经网络有一定的抗扰动能力。     

基于反步法的自适应滑模大机动飞行控制

针对飞机大机动飞行时模型非线性和参数不确定性的特点,提出一种非线性反步自适应滑模变结构控制方法．基于反步法的思想适当选取Lyapunov函数,回馈递推得到自适应滑模控制律,并利用一种自适应参数策略的混沌粒子群算法优化控制器的固定参数改善系统性能,同时利用矩阵SDU分解克服高频控制增益矩阵估计时可能存在的奇异问题．仿真结果验证了算法的有效性．     

Shannon小波混沌神经网络及其TSP(城市旅行商)问题的求解

混沌神经网络已经被证明是解决组合优化问题的有效工具.针对混沌神经网络的单调的激励函数,通过引入Shannon小波和Sigmoid函数加和组成的非单调激励函数,提出了一种新型的暂态混沌神经元模型.给出了该混沌神经元的倒分岔图和最大Lyapunov指数时间演化图,分析了其动力学特性.基于该模型,构造了一种暂态混沌神经网络,并将其应用于函数优化和组合优化问题.通过经典的10城市TSP验证了该暂态混沌神经网络的有效性.     

一个新三维混沌电路设计及其同步控制

在Bao系统的基础上构建一个新的三维混沌系统。通过理论分析与数值仿真,研究该混沌系统的动力学特性,如系统耗散性和平衡点稳定性、Lyapunov指数谱和分岔图、Poincare截面和0-1测试运动轨迹等,结果表明该系统具有丰富的混沌动力学特性。通过谱熵复杂度和C0复杂度,分析不同参数下系统的复杂度大小,从而找到复杂度最高的参数取值范围。最后,采用非线性反馈同步法与线性反馈控制方法实现混沌系统同步,根据系统的最大Lyapunov指数确定线性同步控制的参数范围,并利用Multisim软件进行电路仿真,且仿真结果与数值分析完全一致,从而验证了新三维混沌电路同步控制的可行性,为混沌系统应用于同步保密通信领域提供了实验基础。     

Traffic chaos and its prediction based on a nonlinear car-following model

This paper discusses the dynamic behavior and its predictions for a simulated traffic flow based on the nonlinear response of a vehicle to the leading car's movement in a single lane.Traffic chaos is a promising field,and chaos theory has been applied to identify and predict its chaotic movement.A simulated traffic flow is generated using a car-following model(GM model),and the distance between two cars is investigated for its dynamic properties.A positive Lyapunov exponent confirms the existence of chaotic behavior in the GM model.A new algorithm using a RBF NN (radial basis function neural network) is proposed to predict this traffic chaos.The experiment shows that the chaotic degree and predictable degree are determined by the first Lyapunov exponent.The algorithm proposed in this paper can be generalized to recognize and predict the chaos of short-time traffic flow series.     

Traffm chaos and its prediction based on a nonlinear car-following model

This paper discusses the dynamic behavior and its predictions for a simulated traffic flow based on the nonlinear response of a vehicle to the leading car＇s movement in a single lane. Traffic chaos is a promising field, and chaos theory has been applied to identify and predict its chaotic movement. A simulated traffic flow is generated using a car-following model（ GM model）, and the distance between two cars is investigated for its dynamic properties. A positive Lyapunov exponent confirms the existence of chaotic behavior in the GM model. A new algorithm using a RBF NN （radial basis function neural network） is proposed to predict this traffic chaos. The experiment shows that the chaotic degree and predictable degree are determined by the first Lyapunov exponent. The algorithm proposed in this paper can be generalized to recognize and predict the chaos of short-time traffic flow series     

A novel intelligent particle swarm optimization algorithm for solving cell formation problem

The formation of manufacturing cells forms the backbone of designing a cellular manufacturing system. In this paper, we present a novel intelligent particle swarm optimization algorithm for the cell formation problem. The proposed solution method benefits from the advantages of particle swarm optimization algorithm (PSO) and self-organization map neural networks by combining artificial individual intelligence and swarm intelligence. Numerical examples demonstrate that the proposed intelligent particle swarm optimization algorithm significantly outperforms PSO and yields better solutions than the best solutions existed in the literature of cell formation. The application of the proposed approach is examined in a case problem where real data is utilized for cell reconfiguration of an actual company involved in agricultural manufacturing sector.

     

强迫布鲁塞尔振子动力学行为和全局指数同步的数值仿真

用Matlab软件数值模拟了系统分歧和混沌等的动力学行为发生的全过程,基于最大Lyapunov指数谱、分岔图、庞加莱截面以及功率谱和返回映射等仿真结果揭示了此系统混沌行为的普适特征.采用线性反馈同步控制方法实现该系统的全局指数同步.用Lyapunov第二方法从理论上证明了该同步方法的有效性.同时对同步系统进行仿真,验证了方法的有效性.     

采用LS-SVM计算时间序列的Lyapunov指数谱

为了计算未知系统的Lyapunov指数谱,首先,对一维观测数据序列进行相空间重构,然后,利用最小二乘支持向量机（LS-SVM）逼近重构系统的动力学方程,再通过雅克比矩阵计算Lyapunov指数谱。采用提出的方法计算Henon映射的Lyapunov指数谱,可以得到精确的计算结果且需要的序列步长小于1 000。计算了实测不同状态的交通流时间序列的Lyapunov指数谱。结果表明：在拥挤状态下,有多个Lyapunov指数大于零,说明系统是超混沌的;在同步状态下,有一个或多个Lyapunov指数大于零,说明系统是混沌的或超混沌的;在堵塞状态下,Lyapunov指数全小于零,说明系统不是混沌的。     

实测海杂波光电图像混沌特性研究

基于随机信号模型的光电图像海杂波抑制是目前海杂波背景下目标检测常用方法,检测性能不甚稳定。在分析混沌动力系统相空间重构的基础上,通过提取实际海杂波光电图像序列的关联维数和最大Lyapunov指数,检验海杂波是否具有混沌性。实验结果表明:海杂波光电图像序列具有有限的关联维数和正的最大Lyapunov指数,验证了海杂波光电图像序列的混沌特性。海杂波具有的混沌特性使得可采用短时间预测法抑制海杂波,为海杂波光电弱目标检测提供新的解决思路。     

Effect of noise on chaotic behavior in Roessler-type nonlinear system

The effects of noise on chaotic behaviors of a nonlinear dynamic model were described from a point of view of the system analysis and the previous studies associated with chaos and noise were reviewed as well. The quasi-white noise was used as the observation noise as well as the system noise to clarify the deterioration of the chaotic patterns of the Roessler model. The effects of the noise intensity on the chaotic signal were observed through the deformation of the attractors, increase of the correlation dimension, and change of the maximum Lyapunov exponent. It has been found that the deterioration of the chaotic patterns is more pronounced in the case of the observation noise than the system noise for the Roessler model. As an example of noisy time series data, the laser speckles time series data was employed and discussed from the point of view of the necessity of noise reduction and possible chaos extraction. © 1997 John Wiley & Sons, Inc.     

基于改进混沌粒子群优化算法的聚丙烯生产调优

通过算法混合提出了一种改进混沌粒子群优化算法。将混沌搜索融入到粒子群优化算法中,建立了早熟收敛判断和处理机制,显著提高了优化算法的局部搜索效率和全局搜索性能。将改进混沌粒子群优化算法应用于聚丙烯生产调优中,首先建立了聚丙烯最优牌号切换模型,然后采用改进混沌粒子群优化算法求解该最优牌号切换模型。优化结果:表明,与常规混沌粒子群优化算法相比,改进混沌粒子群优化算法具有更佳的优化效率和全局性能。     

粒子置换的双种群综合学习PSO算法

针对粒子群算法(PSO)种群多样性低和易于陷入局部最优等问题,提出一种粒子置换的双种群综合学习PSO算法(PP-CLPSO)。根据PSO算法的收敛特性和Logistic映射的混沌思想,设计并行进化的PSO种群和混沌化种群,结合粒子编号机制,形成双种群系统中粒子的同号结构和同位结构,其中粒子的惯性权重根据适应度值自适应调节;当搜索过程陷入局部最优时,PSO种群同位结构下适应度值较差的粒子,根据与混沌化种群间的同号结构执行粒子置换操作,实现了双种群系统资源的合理调度,增加了种群的多样性;进而综合双向搜索的同位粒子学习策略和线性递减搜索步长的局部学习策略,进行全局探勘和局部搜索,提高了算法的求解精度。实验选取9个基准测试函数,同时与4个改进的粒子群算法和4个群智能算法进行对比验证,实验结果表明,PP-CLPSO算法在求解精度和收敛速度等方面具备较好的综合性能。     

Nonlinear analysis and control of the uncertain micro-electro-mechanical system by using a fuzzy sliding mode control design

This study analyzes the chaotic behavior of a micromechanical resonator with electrostatic forces on both sides and investigates the control of chaos. A phase portrait, maximum Lyapunov exponent and bifurcation diagram are used to find the chaotic dynamics of this micro-electro-mechanical system (MEMS). To suppress chaotic motion, a robust fuzzy sliding mode controller (FSMC) is designed to turn the chaotic motion into a periodic motion even when the MEMS has system uncertainties.     

基于多次重构映射的相干混沌数字通信系统*

利用相空间多次重构的混沌映射,为相干方式的混沌数字通信问题,提供一种解决方案。新映射产生的混沌码序列性能更加优异,自相关性明显改善,最大Lyapunov指数由0.697增加到5.545,复杂度也明显提高,仿真证实,利用新的混沌映射,系统抗多址性能提高;基于新映射混沌码良好的相关性能和抗多址性能,设计了一种滑膜相关峰检测同步法,完成离散混沌系统同步,实现相干方式混沌数字通信。     

Dynamics of an ecological model with impulsive control strategy and distributed time delay

In this paper, using the theories and methods of ecology and ordinary differential equations, an ecological model with an impulsive control strategy and a distributed time delay is defined. Using the theory of the impulsive equation, small-amplitude perturbations, and comparative techniques, a condition is identified which guarantees the global asymptotic stability of the prey-(x) and predator-(y) eradication periodic solution. It is proved that the system is permanent. Furthermore, the influences of impulsive perturbations on the inherent oscillation are studied numerically, an oscillation which exhibits rich dynamics including period-halving bifurcation, chaotic narrow or wide windows, and chaotic crises. Computation of the largest Lyapunov exponent confirms the chaotic dynamic behavior of the model. All these results may be useful for study of the dynamic complexity of ecosystems.     

Chaotic oscillations of a shallow cylindrical shell with a concentrated mass under periodic excitation

This paper presents effects of a concentrated mass on chaotic oscillations of a shallow cylindrical shell under gravity and periodic acceleration. The rectangular shell is simply supported and is elastically in-plane constrained. Assuming mode functions, the Donnell equation with inertia force is reduced to non-linear coupled differential equations by the Galerkin method. The chaotic response is calculated numerically and is examined by the maximum Lyapunov exponent. Dominant chaotic responses are generated within restricted frequency regions of sub-harmonic resonance of 1/2 order. As the concentrated mass increases, the chaotic response is shifted to the lower frequency region. The increment of the concentrated mass decreases the maximum Lyapunov exponent.