Chaos-Based transmitted-reference ultra-wideband communications

Transmitted-reference (TR) ultra-wideband (UWB) communication systems have gained increasing popularity for the usage in the low data rate application, due to its non-coherent receiver structure. In conventional TR system, non-coherency at the receiver is achieved by sending reference pulses prior to the data-bearing pulses. Then, at the receiver side, reference pulses are used as template signals for correlation with data-bearing pulses. Therefore, the orthogonality between reference and data pulses is obtained in time division multiple access (TDMA) fashion. However, the implementation of a wideband delay line is very difficult in the current low power integrated circuits. In this paper, a TR method called Chaos-Based TR (CB-TR) is proposed. In the proposed method, chaotic sequences are used to separate the reference and data pulses. Such approach exploits the benefits of chaotic signals, such as non-periodicity, easy-to-generate, impulse-like autocorrelation value and low cross-correlation value. Furthermore, in order to decrease the influence of some negative properties of conventional chaotic maps, a modified chaotic generator (MCS) is proposed. Simulation results over the IEEE 802.15.4a channel model show comparable bit error rate performance to other TR methods.     

基于双态混沌PSO的机载激光通信视轴稳定控制

机载空间激光通信视轴稳定是激光通信链路建立的前提。在视轴稳定平台中应用自抗扰控制方法取得了良好的控制效果,但自抗扰控制需调整参数众多且缺乏规范的调整手段。针对自抗扰控制调参难的问题,本文提出了一种利用双态混沌粒子群算法优化自抗扰控制参数的方法。仿真结果表明,与PSO-PID控制方法相比,该方法具有更快的响应速度,更强的抗干扰能力和更好的鲁棒性。     

Parallel chaos optimization algorithm with migration and merging operation

Chaos optimization algorithm (COA) utilizes the chaotic maps to generate the pseudo-random sequences mapped as the decision variables for global optimization applications. A kind of parallel chaos optimization algorithm (PCOA) has been proposed in our former studies to improve COA. The salient feature of PCOA lies in its pseudo-parallel mechanism. However, all individuals in the PCOA search independently without utilizing the fitness and diversity information of the population. In view of the limitation of PCOA, a novel PCOA with migration and merging operation (denoted as MMO-PCOA) is proposed in this paper. Specifically, parallel individuals are randomly selected to be conducted migration and merging operation with the so far parallel solutions. Both migration and merging operation exchange information within population and produce new candidate individuals, which are different from those generated by stochastic chaotic sequences. Consequently, a good balance between exploration and exploitation can be achieved in the MMO-PCOA. The impacts of different one-dimensional maps and parallel numbers on the MMO-PCOA are also discussed. Benchmark functions and parameter identification problems are used to test the performance of the MMO-PCOA. Simulation results, compared with other optimization algorithms, show the superiority of the proposed MMO-PCOA algorithm.     

混沌理论对建筑与城市设计领域的启示

混沌理论给人类思维方式带来了深刻的变革。本文简要介绍了混沌理论的五个要点及其适用对象,针对每个要点探讨了对建筑与城市设计领域的启示,指出复杂性是城市的本质属性,要实现城市与建筑的可持续发展应该将它们视为有生命的存在。     

动态轧制过程下轧机辊系分岔与混沌特性

基于热连轧机的机械结构和工艺结构,考虑态轧制力对轧机振动的影响,建立了轧机辊系的非线性动态轧制振动模型。根据Lagrange原理,得到了轧机辊系的动力学方程,应用多尺度法求解了系统的分岔响应方程,采用奇异性理论分析得到了系统稳态响应的转迁集及其相应的分岔曲线拓扑结构,利用Melnikov函数法研究了系统发生分岔与混沌的参数临界条件。最后进行数值仿真,通过分岔混沌图、最大Lyapunov指数、相轨迹和Poincare截面验证了理论的有效性,这为抑制和控制轧机系统非线性振动提供了理论参考和借鉴。     

Capacitor placement of distribution systems using particle swarm optimization approaches

Capacitor placement plays an important role in distribution system planning and operation. In distribution systems of electrical energy, banks of capacitors are widely installed to compensate the reactive power, reduce the energy loss in system, voltage profile improvement, and feeder capacity release. The capacitor placement problem is a combinatorial optimization problem having an objective function composed of power losses and capacitor installation costs subject to bus voltage constraints. Recently, many approaches have been proposed to solve the capacitor placement problem as a mixed integer programming problem. This paper presents a new capacitor placement method which employs particle swarm optimization (PSO) approaches with operators based on Gaussian and Cauchy probability distribution functions and also in chaotic sequences for a given load pattern of distribution systems. The proposed approaches are demonstrated by two examples of application. Simulation results show that the proposed method can achieve an optimal solution as the exhaustive search can but with much less computational time.     

Chaos embedded krill herd algorithm for optimal VAR dispatch problem of power system

Chaotic krill herd algorithm (KHA) (CKHA) is proposed in this paper to solve the optimal VAR dispatch problem of power system considering either minimization of real power loss or that of absolute value of total voltage deviation or improvements of voltage profile as an objective while satisfying all the equality and the inequality constraints of the power system network. Detailed studies of different chaotic maps are illustrated. Among these, Logistic map is considered in the proposed technique to improve the performance of the basic KHA. The performance of the proposed CKHA is implemented, successfully, on standard IEEE 14- and IEEE 118-bus test power systems in which the control of bus voltages, tap position of transformers and reactive power sources are involved. The results offered by the proposed CKHA are compared to other evolutionary optimization based techniques surfaced in the recent state-of-the-art literature. Simulation results indicate that the proposed CKHA approach yields better optimization efficacy over some other recent popular techniques in terms of results offered, effectiveness, quality of solution and convergence speed.     

Chaotic swarming of particles: A new method for size optimization of truss structures

A new combination of swarm intelligence and chaos theory is presented for optimal design of truss structures. Here the tendency to form swarms appearing in many different organisms and chaos theory has been the source of inspiration, and the algorithm is called chaotic swarming of particles (CSP). This method is a kind of multi-phase optimization technique which employs chaos theory in two phases, in the first phase it controls the parameter values of the particle swarm optimization (CPVPSO) and the second phase is utilized for local search (CLSPSO). Some truss structures are optimized using the CSP algorithm, and the results are compared to those of the other meta-heuristic algorithms showing the effectiveness of the new method.     

Stochastic fracture mechanics using polynomial chaos

Crack propagation in metals has long been recognized as a stochastic process. As a consequence, crack propagation rates have been modeled as random variables or as random processes of the continuous. On the other hand, polynomial chaos is a known powerful tool to represent general second order random variables or processes. Hence, it is natural to use polynomial chaos to represent random crack propagation data: nevertheless, no such application has been found in the published literature. In the present article, the large replicate experimental results of Virkler et al. and Ghonem and Dore are used to illustrate how polynomial chaos can be used to obtain accurate representations of random crack propagation data. Hermite polynomials indexed in stationary Gaussian stochastic processes are used to represent the logarithm of crack propagation rates as a function of the logarithm of stress intensity factor ranges. As a result, crack propagation rates become log-normally distributed, as observed from experimental data. The Karhunen–Loève expansion is used to represent the Gaussian process in the polynomial chaos basis. The analytical polynomial chaos representations derived herein are shown to be very accurate, and can be employed in predicting the reliability of structural components subject to fatigue.     

基于混沌粒子群优化算法的NOMA功率分配研究

非正交多址接入(NOMA)技术使多个用户共享一个时频资源块,显著地提高了频谱利用率,已成为5G多址技术的研究热点。针对NOMA系统能量效率最大化问题,对非正交多址接入技术中功率分配进行了研究,提出了基于混沌粒子群优化算法的NOMA功率分配方案。建立了能量效率最大化的NOMA优化模型,利用混沌粒子群优化算法对系统功率进行分配,使NOMA系统的能量效率得到优化。仿真结果表明,当发射功率为36 dBm时,系统能量效率最大,且与传统粒子群算法相比所需迭代次数更少,获得的能量效率更优。