Reconstruction of chirped fiber Bragg grating parameters and phase spectrum using two thermally modulated intensity spectra and a genetic algorithm

This study presents a synthesis method for reconstructing the multiple parameters such as grating position, length, period profile, refractive index modulation, and phase response of a chirped fiber Bragg grating (FBG) and its phase spectrum using two thermally modulated reflection intensity spectra and a genetic algorithm. In the proposed approach, the FBG parameters are determined using the genetic algorithm and are then used to reconstruct the phase response. The advantages of the proposed method include its simplicity, low cost, and nondestructive nature. Furthermore, the proposed method does not require a prior knowledge of the sign of the chirp of the grating period distribution of FBGs.     

基于一种新的遗传算法的天线方向图综合技术

给出了一种基于实数编码遗传算法的天线方向图综合方法.在遗传算法模型中采用了向下、向上外推和非一致杂交算子等交叉技术,并结合内插、交换等多种技术形成综合交叉方式.该算法克服了已有算法早熟、对初始群体依赖性等缺陷的同时,较大幅度地提高了算法的收敛速度和可靠性;通过在目标函数中加入零陷方差项克服了现有算法零陷不均衡的缺陷.计算机仿真结果表明,与现有算法相比,该方法用于天线方向图综合具有收敛速度快、零陷均衡、可靠等优势.     

The design synthesis of periodical microstrip structures using adaptive genetic algorithm and a multiresolution MoM analysis

A multiresolution MoM analysis for the design of periodical microstrip structures is presented. An adaptive genetic algorithm (AGA) is introduced for the synthesis of planar structure designs that can have nearly any desired frequency response, and to optimize the choice of trial functions used on the MoM method. Results for a bandstop structure are presented to show the effectiveness of the proposed method.     

Reconstruction of piecewise chaotic dynamic using a genetic algorithm multiple model approach

Reconstruction of chaotic dynamics from its time-series measurement is an important problem for many engineering applications. In this paper, we propose using a novel multiple model (MM) predictor based on a genetic algorithm (GA) to reconstruct piecewise chaotic dynamics. The motivation relies on the observation that conventional single model is usually unable to reconstruct the piecewise dynamics properly because a piecewise map is nonsmooth. In our approach, multiple radial basis function (RBF) neural predictors are used to model the piecewise dynamic in different partition intervals. Switching between different intervals could be estimated by a nonlinear gate model. In particular, a GA is employed here to train the MM and to obtain the optimal RBF parameters. Compared to conventional chaos dynamic reconstruction techniques, the proposed GA-MM method is shown to greatly improve the reconstruction performance for piecewise chaotic dynamics. The superiority is further verified by applying the GA-MM method to model the real-life radar sea-clutter signal obtained from Nova Scotia (NS), Canada, and to predict the electric power pool price time series from Alberta (AB), Canada. Both kinds of real data show that the GA-MM is effective in building a dynamical model. The proposed GA-MM method is also applied to the channel equalization problem of chaos communication systems. Based on the minimum nonlinear prediction error equalization method, it is shown that the GA-MM method has a satisfactory equalization performance even under strong channel effects.     

Statistical analysis of the main parameters involved in the designof a genetic algorithm

Most genetic algorithm (GA) users adjust the main parameters of the design of a GA (crossover and mutation probability, population size, number of generations, crossover, mutation, and selection operators) manually. Nevertheless, when GA applications are being developed it is very important to know which parameters have the greatest influence on the behavior and performance of a GA. The purpose of this study was to analyze the dynamics of GAs when confronted with modifications to the principal parameters that define them, taking into account the two main characteristics of GAs; their capacity for exploration and exploitation. Therefore, the dynamics of GAs have been analyzed from two viewpoints. The first is to study the best solution found by the system, i.e., to observe its capacity to obtain a local or global optimum. The second viewpoint is the diversity within the population of GAs; to examine this, the average fitness was calculated. The relevancy and relative importance of the parameters involved in GA design are investigated by using a powerful statistical tool, the analysis of the variance (ANOVA)     

Design and implementation of a fast algorithm for modulated lappedtransform

A new fast algorithm for the computation of the modulated lapped transform (MLT) is proposed and its efficient implementation using pipelining techniques and complex programmable logic device (CPLD) is presented. The new algorithm computes a length-M MLT via the length-M/2 fast Fourier transform (FFT). Computational overhead due to data shuffling in pre-processing and post-processing is offset in hardware realisation. Hence the overall throughput of the MLT computation for real-time applications is significantly improved. The pipelined CPLD architecture and circuitry are described in detail. Computational complexity of the proposed algorithm is analysed, and throughput improvement is verified by experimental results     

双交换遗传算法及其在非周期天线阵方向图综合中的应用

提出了一种双交换遗传出法,它拓展了传统遗传算法的基本思想,通过引入双交换繁殖操作和参数化染色体,摆脱了传统遗传算法收敛对初始群体的依赖性,避免了传统遗传算法繁琐的编解码过程,因而,它比传统遗传算法更有效和更实用,为了说明双交换遗传算法的有效性和实用性,将双交换遗传算法应用于非周期圆形天线阵扫描方向图综合,应用表明双交换遗传算法可以成为一种通用的阵列天线辐射方向图综合工具。     

基于空间映射及遗传算法的车载天线优化配置

车载天线间干扰十分严重,不能采用普通的电磁兼容方法进行干扰抑制,只能通过合理的天线布局优化来降低干扰.工程中多采用简单的数学模型结合测试的方法来进行车载天线布局设计,效率低又不够精确.基于矩量法的车载天线耦合的数学模型,并在此基础上结合实际装车的特点提出了车载天线布局优化的目标函数,详细讨论了如何利用空间映射算法和遗传算法实现车载天线布局的优化配置,既兼顾精度和效率,又为实际工程提供了理论上的依据.经过实际测试验证,这种方法是行之有效的.     

Identification of nonminimum phase FIR systems via fourth-order cumulants and genetic algorithm

In this paper, the problem of estimating the parameters of an FIR system from only the fourth-order cumulants of the noisy system output is considered. The FIR system is driven by a symmetric, independent, and identically distributed (i.i.d) non-Gaussian sequence. We propose a new formula called Weighted Overdetermined C(q, k) (WOC(q, k)) by extending the conventional C(q, k) formula. The optimal selection of the weights in WOC(q, k) is performed by using the Genetic Algorithm (GA) optimization method which minimizes a nonlinear error function based on the fourth-order cumulants alone. Simulations are provided to reveal the effectiveness and the superiority of this novel technique over the C(q, k) and other existing techniques.     

Simultaneous strain and temperature distribution sensing using two fiber Bragg grating pairs and a genetic algorithm

This study uses two pairs of FBGs to perform simultaneous strain and temperature field measurements. The first pair of FBGs, comprising one uniform FBG and one chirped FBG, measures the strain and temperature fields, while the second pair of FBGs, also comprising one uniform FBG and one chirped FBG, measures the temperature field only. A genetic algorithm is applied to reconstruct the arbitrary strain and temperature distribution profiles simultaneously from the measured reflection intensity spectra of the four FBGs. Various examples of nonmonotonic variations in the strain and temperature fields are considered for illustration purposes. It is found that the proposed scheme successfully reconstructs the applied strain and temperature fields.     

A genetic algorithm for the maximum likelihood estimation of the parameters of sinusoids in a noisy environment

An adaptive generic algorithm was developed to solve the optimization problem of the maximum likelihood estimation of the sum of sinusoids in a noisy environment. The algorithm is based on genetic concepts and is extended, with modifications, to this problem. Simulation results were performed to see the effect of different parameters such as permutation and crossover probabilities. The effects of the signal-to-noise ratio (SNR) were also studied. It was found that the key factor for accuracy is the probabilities of permutation and crossover. Thus, we developed an adaptive method to estimate these probabilities, on line, to reduce the error. This was accomplished by considering them as unknown parameters to be estimated with the signal parameters. The mean square error of the frequency estimates was compared favorably to the Cramér-Rao lower bound. Several simulations are shown for SNR values ranging between –7 dB and 20 dB.     

湍流尺度和相干参数对传输光强的影响

文章利用交叉谱密度函数得到了部分相干光的光强表达式,利用该表达式分析了斜程大气传输中湍流内外尺度、光源相干参数、天顶角对接收光强的影响.结果表明:斜程大气传输过程中,光源相干参数ζs越大,接收光强受湍流内外尺度变化的影响越小.在传输距离小于4 km时,湍流内外尺度变化对接收光强的影响可以忽略不计.相干参数对小天顶角时的...     

遗传模拟退火算法在玻璃和晶体色散方程参量反演中的应用

根据已测K9玻璃和晶体(ZnS,MgF2,Calcite)的实验数据,将遗传模拟退火算法应用于修正的Sellimeier方程的参数反演中,建立了上述材料的色散方程。同时比较了遗传模拟退火算法和遗传算法(包括标准遗传算法和多种群遗传算法)在迭代搜索性能方面的差异。结果表明:遗传模拟退火算法的优化效果最优并且性能最稳定。同时,将通过遗传模拟退火算法所得K9玻璃和晶体在某一光谱区域的色散方程应用于其他光谱区域中,发现色散方程的拟合值与实验值符合较好,这表明通过该方法所得色散方程具有较好的外推性。因此,通过遗传模拟退火算法进行色散方程的参量反演方法可以用于其他材料色散方程的拟合。     

基于改进粒子群算法的天线方向图综合技术

针对基本粒子群算法的早熟收敛、易收敛于局部极值的特点，提出一种改进的粒子群优化算法，采用对全局最佳微扰和惯性权重跳变阈值的设置改善了算法的优化速度和收敛精度。经过对一系列测试函数的计算，证明该方法具有良好的优化效果。最后，给出了该方法应用于阵列天线方向图综合中的模型和仿真实例。     

基于改进粒子群算法的天线方面图综合技术

针对基本粒子群算法的早熟收敛、易收敛于局部极值的特点,提出一种改进的粒子群优化算法,采用对全局最佳微扰和惯性权重跳变阈值的设置改善了算法的优化速度和收敛精度.经过对一系列测试函数的计算,证明该方法具有良好的优化效果.最后,给出了该方法应用于阵列天线方向图综合中的模型和仿真实例.     

Cost analysis of a two unit priority standby system with imperfect switch and arbitrary distributions

This paper deals with cost analysis of a single server two-unit (one priority and the other ordinary) cold standby system with two modes—normal and total failure. A switch is used to operate the standby unit (ordinary) and it works successfully with known probability p( = 1 ? q). Priority unit gets preference both for operation and repair. Failure and repair time distributions are arbitrary. System fails when switch or both the units fail totally. The system is observed at suitable regenerative epochs in order to obtain reliability characteristics of interest to system designers and operations managers. Explicit results for the exponential time distributions have been obtained in particular cases.     

Design and analysis of planar monopole antennas using a genetic algorithm approach

The application of genetic algorithm (GA) optimization to the design and analysis of planar monopole antennas is presented. GA is first used to optimize the impedance matching bandwidth of two particular planar element shapes, the bow-tie (BT) and reverse bow-tie (RBT). The results of this study indicate that the RBT can achieve a significantly wider bandwidth with a much smaller size than the traditional BT. In a follow-on study, GA is used to generate arbitrarily shaped planar monopole designs, which exhibit improved broadband performance and/or reduced size compared with the RBT. The designs generated by the GA demonstrate a better tradeoff between matching bandwidth and electrical size compared with planar monopole designs previously characterized in the literature. Analysis of results from simulation and measurement are presented, which provide insight into the operation of these antennas as well as the key parameters that lead to improved performance. Finally, a performance bound is generated to relate the bandwidth limitation of planar monopoles to size.     

实数编码遗传和单纯形混合算法在膜系优化设计中的应用

在传统遗传算法(GA)基础上,通过引入标准偏差函数构造了新的适应度函数,同时提出了一种自动降温的方法来控制退火选择策略中的温度.将这种改进的实数编码遗传算法(FGA)和单纯型算法(SA)有机结合起来,形成了新的膜系优化算法-实数编码遗传和单纯形混合算法,并编制了优化程序.实例表明该算法优化性能优越,既具有强大全局搜索能力,又能很好地实现局部搜索功能.用该算法实现了中心波长534 nm,带宽35 nm的可见光波段凹陷滤波器和高性能中性分束镜.     

Grooming of dynamic traffic in WDM star and tree networks using a genetic algorithm

The advances in WDM technology lead to the great interest in traffic grooming problems. As traffic often changes from time to time, the problem of grooming dynamic traffic is of great practical value. In this article, we discuss the dynamic grooming of traffic in star and tree networks. A genetic algorithm (GA) based approach is proposed to support arbitrary dynamic traffic patterns, which minimizes the number of ADMs and wavelengths. To evaluate the algorithm, tighter bounds are derived. Computer simulation results show that our algorithm is efficient in reducing both the number of ADMs and wavelengths in tree and star networks.     

Retrieval of dry-snow parameters from microwave radiometric data using a dense-medium model and genetic algorithms

A numerical technique based on genetic algorithms (GAs) is used to invert the equations of an electromagnetic model based on dense-medium radiative transfer theory (DMRT) to retrieve snow depth, mean grain size, and fractional volume from microwave brightness temperatures. In order to study the sensitivity of the GA to its parameters, the technique is initially tested on simulated microwave data with and without adding a random noise. A configuration of GA parameters is selected and used for the retrieval of snow parameters from both ground-based observations and brightness temperatures recorded by the Advanced Microwave Scanning Radiometer-EOS (AMSR-E). Retrieved snow parameters are then compared with those measured on ground. Although more investigation is required, results suggest that the proposed technique is able to retrieve snow parameters with good accuracy.