Chaos detection and parameter identification in fractional-order chaotic systems with delay

The paper first applies the 0–1 test for chaos to detecting chaos exhibited by fractional-order delayed systems. The results of the test reveal that there exists chaos in some fractional-order delayed systems with specific parameter values, which coincides with previous reports based on the phase portrait. In addition, it is very important to identify exactly the unknown specific parameters of fractional-order chaotic delayed systems in chaos control and synchronization. Thus, a method for parameter identification of fractional-order chaotic delayed systems based on particle swarm optimization (PSO) is presented. By treating the orders as parameters, the parameters and orders are identified through minimizing an objective function. PSO can efficiently find the optimal feasible solution of the objective function. Finally, numerical simulations on fractional-order chaotic logistic delayed system and fractional-order chaotic Chen delayed system show that the proposed method has effective performance of parameter identification.     

改进PSO算法在结构作动器位置优化中的应用

针对空间结构振动主动控制中的作动器位置优化问题, 提出了一种改进的粒子群(PSO) 优化方法, 以系统总能量为性能指标进行优化; 应用改进PSO方法对算例结构进行了计算, 并与其他算法的优化结果进行了对比; 结果表明: 几种优化方法计算结果相符; 且 PSO优化算法能更有效快速地解决复杂优化问题, 从而有效地进行结构的振动控制.     

A novel APSO-aided maximum likelihood identification method for Hammerstein systems

Identification of Hammerstein nonlinear models has received much attention due to its ability to describe a wide variety of nonlinear systems. In this paper the maximum likelihood estimator which was originally derived for linear systems is extended to work for Hammerstein nonlinear systems in colored-noise environment. The maximum likelihood estimate is known to be statistically efficient, but can lead to complex nonlinear multidimensional optimization problem; traditional methods solve this problem at the computational cost of evaluating second derivatives. To overcome these shortcomings, a particle swarm optimization (PSO) aided maximum likelihood identification algorithm (Maximum Likelihood-Particle Swarm Optimization, ML-PSO) is first proposed to integrate PSO’s simplicity in implementation and computation, and its ability to quickly converge to a reasonably good solution. Furthermore, a novel adaptive strategy using the evolution state estimation technique is proposed to improve PSO’s performance (maximum likelihood-adaptive particle swarm optimization, ML-APSO). A simulation example shows that ML-APSO method outperforms ML-PSO and traditional recursive least square method in various noise conditions, and thus proves the effectiveness of the proposed identification scheme.     

求解非线性方程组的混沌粒子群算法及应用

针对非线性方程组的求解在工程上具有广泛的实际意义,经典的数值算法如牛顿法存在其收敛性依赖于初值而实际计算中初值难确定的问题,提出以混沌粒子群算法求解非线性方程。它通过将混沌搜索机制有机地引入粒子群算法,使每个粒子从混沌搜索机制与粒子群算法搜索机制中获得适当的搜索方向,以混沌变量的遍历性增强粒子的搜索性能与更全面地应用目标函数的信息,并反映到逐代更新的个体极值和群体极值中,可更有效地调整粒子的移向并最终获得最优解。测试结果表明这一尝试的有效性。最后将所提的方法用于建立复合材料结构的疲劳寿命与应力、温度、湿度的关系模型。     

粒子群优化算法在传递对准中的应用

给出了一种基于粒子群优化算法的捷联惯导传递对准算法。简单分析了传递对准任务要求和主子惯导惯性器件输出之间的关系,将传递对准问题作为参数优化问题进行求解,给出了基于粒子群优化算法进行传递对准的数学模型。定义了传递对准的优化目标函数,介绍了粒子群优化算法及其应用于传递对准的具体算法设置。用粒子群优化算法求解目标函数的最小值,可获得主子惯导之间的失准角,进行一次校正即可完成传递对准过程。通过计算机仿真对算法进行了验证分析,在仿真条件下（陀螺精度为0.1°/h）,能达到方位0.1°的精度。与其他对准算法一样,算法受载体机动条件的影响较大,一般需要姿态机动来提高陀螺的信噪比。     

Particle swarm optimization-based algorithm of a symplectic method for robotic dynamics and control

Multibody system dynamics provides a strong tool for the estimation of dynamic performances and the optimization of multisystem robot design. It can be described with differential algebraic equations(DAEs). In this paper, a particle swarm optimization(PSO) method is introduced to solve and control a symplectic multibody system for the first time. It is first combined with the symplectic method to solve problems in uncontrolled and controlled robotic arm systems. It is shown that the results conserve the energy and keep the constraints of the chaotic motion, which demonstrates the efficiency, accuracy, and time-saving ability of the method. To make the system move along the pre-planned path, which is a functional extremum problem, a double-PSO-based instantaneous optimal control is introduced. Examples are performed to test the effectiveness of the double-PSO-based instantaneous optimal control. The results show that the method has high accuracy, a fast convergence speed, and a wide range of applications.All the above verify the immense potential applications of the PSO method in multibody system dynamics.     

Thermal-economic multi-objective optimization of shell and tube heat exchanger using particle swarm optimization (PSO)

Many studies are performed by researchers about shell and tube heat exchanger (STHE) but the multi-objective particle swarm optimization (PSO) technique has never been used in such studies. This paper presents application of thermal-economic multi-objective optimization of STHE using PSO. For optimal design of a STHE, it was first thermally modeled using e-number of transfer units method while Bell–Delaware procedure was applied to estimate its shell side heat transfer coefficient and pressure drop. Multi objective PSO (MOPSO) method was applied to obtain the maximum effectiveness (heat recovery) and the minimum total cost as two objective functions. The results of optimal designs were a set of multiple optimum solutions, called ‘Pareto optimal solutions’. In order to show the accuracy of the algorithm, a comparison is made with the non-dominated sorting genetic algorithm (NSGA-II) and MOPSO which are developed for the same problem.     

基于多轮升维策略与改进量子行为粒子群优化算法的热导率函数估计方法

将改进的量子行为粒子群优化算法应用于材料热导率函数估计问题中,并提出了一种多轮升维策略对算法的搜索过程进行优化,形成了一种鲁棒性强且高效的反演方法。通过数值实验测试了该方法在测量误差以及系统误差下的表现,并对不同粒子群优化算法的性能进行了比较研究。结果表明,采用的反演方法能够在较大的搜索范围与反演维度下稳定收敛,对测量误差的敏感度较低;提出的多轮升维策略能够使各类粒子群优化算法在热导率函数估计问题中的搜索效率得到提升。     

A new design method for adaptive IIR system identification using hybrid particle swarm optimization and gravitational search algorithm

Design of adaptive infinite impulse response (IIR) filter is the process of utilizing adaptive algorithm to iteratively determine the filter parameters to obtain an optimal model for the unknown plant based on minimizing the error cost function. However, the error cost surface of IIR filter is generally nonlinear, non-differentiable and multimodal. Hence, an efficient global optimization technique is required to minimize the error cost objective. A novel hybrid particle swarm optimization and gravitational search algorithm (HPSO–GSA) is proposed in this paper for IIR filter design. The proposed HPSO–GSA updates particle positions through obeying the influence of gravity acceleration in GSA and receiving direction of cognitive memory and social sharing information from PSO by means of coevolutionary strategy. The effect of key parameters on the performance of the proposed algorithm is firstly studied, and the proper parameters in HPSO–GSA are established using five benchmark plants along with the same-order model. The simulation studies have been performed for the performance comparison of eight algorithms such as PSO, GSA, QPSO, DPSO, FO-DPSO, GAPSO, PSOGSA and the proposed HPSO–GSA for unknown IIR system identification with the same-order and reduced-order filters. Simulation results show that the proposed algorithm has advantages over PSO, GSA and other PSO-based variants in terms of the convergence speed and the MSE levels.     

An optimal type II fuzzy sliding mode control design for a class of nonlinear systems

This study deals with the problem of controlling a class of uncertain nonlinear systems in the presence of external disturbances. To achieve this goal, a new Optimal Type-2 Fuzzy Sliding Mode Controller (OT2FSMC) is introduced. In the proposed controller, a novel heuristic algorithm, namely particle swarm optimization with random inertia weight (RNW–PSO), is employed. To achieve an optimal performance, the parameters of the proposed controller as well as the input and output membership functions are optimized simultaneously by RNW–PSO. The globally asymptotic stability of the closed-loop system is mathematically proved. Finally, this method of control is applied to the inverted pendulum system as a case study. Simulation results show the system performance is desirable.     

基于修正PSO-UKF的SINS/GPS组合导航滤波算法

针对噪声时变特性引起滤波精度下降的问题,提出了一种基于修正粒子群技术( PSO)的自适应UKF算法.为了克服传统粒子群算法过早收敛,容易陷入局部最优的问题,基于粒子的适应值方差提出了一种惯性权值实时修正算法,有效改善了传统PSO算法.在使用新息序列对观测噪声进行实时跟踪的同时,通过构造合理的适应度函数将修正PSO算法和...     

A MODIFIED INTERPOLATION APPROACH FOR TOPOLOGY OPTIMIZATION

In view of the fact that the follow-up search for an optimal topology is affected by deleting a large number of high-relative-density elements. When the typical density interpolation approach, namely, solid isotropic microstructures with penalization(SIMP), is employed in the continuum structural topology optimization, a new density interpolation approach based on the logistic function is proposed in this paper. This method can weaken low-relative-density elements while enhancing high-relative-density elements by polarization, and then rationally realize polarization of the intermediate density elements. It can reduce the number of gray-scale elements as much as possible to get the optimal topology with distinct boundaries in conjunction with the sensitivity filtering method based on particle swarm optimization(PSO). Several typical numerical examples are given to demonstrate this method.     

基于微粒群算法的区间模型非概率可靠性指标的计算

把工程实际中的不确定参数考虑为区间变量,研究基于微粒群算法的区间模型非概率可靠性指标的计算。利用非概率可靠性指标只存在于过标准化区间变量张成的空间顶点和原点的直线与功能函数的交点,建立基于微粒群算法的优化模型,并对目标函数进行改进,使其更利于优化计算。一系列数值算例和与以前方法的比较证明了该方法计算简便,结论较为精确,具有一定的可行性。     

Parameter identification for cracks in elastic plate structures based on remote strain fields

A method for the detection of cracks in plate structures is presented. In contrast to most of the common monitoring concepts taking advantage of the reflection of elastic waves at crack faces, the presented approach is based on the strain measured at different locations on the surface of the structure. This allows both the identification of crack position parameters, such as length, location and angles with respect to a reference coordinate system and the calculation of stress intensity factors (SIF). The solution of the direct problem is performed on the basis of the BFM (body force method). The inverse problem is solved applying the particle swarm optimization (PSO) algorithm. The BFM is based on the principle of linear superposition which allows the calculation of the strain field in a cracked body. The strain at an arbitrary point in the structure is replaced by the strain provided by body force doublets in the uncracked structure. The doublets as well as external loads are parameters which have to be determined solving the inverse problem by minimizing a fitness function, which is defined by a square sum of residuals between measured strain distributions and computed ones for an assumed crack. The PSO algorithm applied to the fitness function operates on the basis of a swarm of candidate solutions. Once knowing loading and crack parameters, the SIF can be determined.     

基于加速度信号的柔性板的挠性参数辨识简

以柔性板为对象,开展了结构挠性参数辨识技术的研究. 给出了一种基于加速度信号输出的特征系统实现算法的计算格式,基于粒子群优化算法给出了加速度传感器在柔性板上的优化位置. 仿真结果显示,粒子群方法能够有效地确定出传感器在板上的优化位置,特征系统实现算法能够有效地辨识出结构的挠性参数.     

Stability properties of a two-dimensional system involving one Caputo derivative and applications to the investigation of a fractional-order Morris–Lecar neuronal model

Adaptive infinite impulse response filters have received much attention due to its utilization in a wide range of real-world applications. The design of the IIR filters poses a typically nonlinear, non-differentiable and multimodal problem in the estimation of the coefficient parameters. The aim of the current study is the application of a novel hybrid optimization technique based on the combination of cellular particle swarm optimization and differential evolution called CPSO–DE for the optimal parameter estimation of IIR filters. DE is used as the evolution rule of the cellular part in CPSO to improve the performance of the original CPSO. Benchmark IIR systems commonly used in the specialized literature have been selected for tuning the parameters and demonstrating the effectiveness of the CPSO–DE method. The proposed CPSO–DE method is experimentally compared with two new design methods: the tissue-like membrane system (TMS), the hybrid particle swarm optimization and gravitational search algorithm (HPSO–GSA), the original CPSO-outer and CPSO-inner, and classical implementations of PSO, GSA and DE. Computational results and comparison of CPSO–DE with the other evolutionary and hybrid methods show satisfactory results. The hybridization of CPSO and DE demonstrates powerful estimation ability. In particular, to our knowledge, this hybridization has not yet been investigated for the IIR system identification.     

基于加速度信号的柔性板的挠性参数辨识

以柔性板为对象,开展了结构挠性参数辨识技术的研究. 给出了一种基于加速度信号输出的特征系统实现算法的计算格式,基于粒子群优化算法给出了加速度传感器在柔性板上的优化位置. 仿真结果显示,粒子群方法能够有效地确定出传感器在板上的优化位置,特征系统实现算法能够有效地辨识出结构的挠性参数.      

An effective method for the reduction of the device utilization amount in experimental realization of a fractional-order system

This study focuses on the experimental realization of the fractional-order FitzHugh–Nagumo (FHN) neuron model. Firstly, a second-order approximation function is included to the FHN neuron model to satisfy the fractional-order definition. Since these approximation functions can meet the response of the ideal system only in a limited frequency band, the identification of their center frequency is very critical. Thus, the center frequency ‘ω_c’ of this second-order approximation functions is swept until getting the spiking responses of this neuron model for the first time in this study. After the center frequency is determined, this approximation function is transferred into the ‘z’ domain by employing the Tustin discretization operator. This achieved discrete defined and fractional-order FHN neuron model becomes suitable for implementation on the digital platforms. To verify the proficiency of the proposed sweeping process experimentally, the fractional-order FHN model in ‘z’ domain is implemented on the FPGA platform. After these applications, the order of the approximation function is reduced to one. Once this followed frequency sweeping process is repeated for the first-order approximation, the fractional-order FHN neuron model, which is built by this least-order approximation function, is also implemented with the FPGA. Therefore, the reductions of the device utilization amounts by using this least-order approximation function and the importance of the specific frequency identification process are seen clearly.

     

Retrieval of aerosol size distribution using improved quantum-behaved particle swarm optimization on spectral extinction measurements

An improved quantum-behaved particle swarm optimization (IQPSO) algorithm is employed to determine aerosol size distribution (ASD). The direct problem is solved using the anomalous diffraction approximation and Lambert–Beer's Law. Compared with the standard particle swarm optimization algorithm, the stochastic particle size optimization algorithm and the original QPSO, our IQPSO has faster convergence speed and higher accuracy within a smaller number of generations. Optimization parameters for the IQPSO were also evaluated; we recommend using four measurement wavelengths and 50 particles. Size distributions of various aerosol types were estimated using the IQPSO under dependent and independent models. Finally, experimental ASDs at different locations in Harbin were recovered using the IQPSO. All our results confirm that the IQPSO algorithm is an effective and reliable technique for estimating ASD.     

Hybrid approach for dynamic model identification of an electro-hydraulic parallel platform

In this paper, a hybrid optimization algorithm is proposed to identify the dynamic parameters of a 6-DOF electro-hydraulic parallel platform. The dynamic model of a parallel platform with arbitrary geometry, inertia distribution and frictions is obtained based on a structured Boltzmann–Hamel–d’Alembert formulation, and then the estimation equations are explicitly expressed in terms of a linear form with respect to the identified inertial and the friction coefficients in accordance with a linear friction model. However, when nonlinear friction models are considered, the parameter identification of the electro-hydraulic parallel platform is considered as an optimization process with an objective function minimizing the errors between the measurement and identification, and then an effective combination of the particle swarm optimization (PSO) method and the local quasi-Newton method is proposed to solve the identification problem. Experimental identification processes are carried out for the identified parameters, and the identified models are compared by the predicted forces between the LS method and the optimization technique as well as between the linear and nonlinear friction models.