遗传算法进化设计BP神经网络气象预报建模研究

利用遗传算法进化设计神经网络的结构和连接权,并针对遗传算法局部调节能力比较弱的问题,采用从进化后的神经网络中用训练样本再次寻优的方法,建立神经网络气象预报模型,该方法克服了神经网络极易陷入局部解和遗传算法局部调节能力比较弱的问题,以广西的月降水量进行实例分析,计算结果表明该方法预报精度高、而且稳定。     

Nonlinear evaluations of unconditionally stable explicit algorithms

Two explicit integration algorithms with unconditional stability for linear elastic systems have been successfully developed for pseudodynamic testing.Their numerical properties in the solution of a linear elastic system have been well explored and their applications to the pseudodynamic testing of a nonlinear system have been shown to be feasible. However,their numerical properties in the solution of a nonlinear system are not apparent.Therefore,the performance of both algorithms for use in the solution...     

跨孔地震层析成像的级联方法

对厚度小于1/4波长的超薄波速干扰体进行高分辨率成像,作者曾采用走时反演和波场反演相结合的方法,体现了由低分辨率向高分辨率逐步逼近的思想.级联算法是这种思想的进一步发展,我们将具有不同分辨率的算法串联起来,以达到高分辨率成像的目的.本文介绍一种三级串联的算法,并进行了算法分析,数值计算的例子说明这种级联算法分辨率高、稳定性好,只需要地震资料而不要求其它先验信息,因此能较好地满足实际应用的要求.     

基于资源限制性人工免疫系统的多光谱遥感影像分类方法

提出了一种基于资源限制性人工免疫系统（resouree limited artificial immune systems, RLAIS ）的多光谱遥感影像分类方法。该方法采用RLAIS对遥感影像分类中选取的感兴趣样区进行样本训练，得到全局聚类中心，利用聚类中心对遥感影像进行分类。实验证明，该方法在分类精度上优于传统方法，其总精度和Kappa系数分别达到了91%和0．88，具有实用价值。     

Landslide susceptibility mapping using machine learning algorithms and comparison of their performance at Abha Basin,Asir Region,Saudi Arabia

The current study aimed at evaluating the capabilities of seven advanced machine learning techniques(MLTs),including,Support Vector Machine(SVM),Random Forest(RF),Multivariate Adaptive Regression Spline(MARS),Artificial Neural Network(ANN),Quadratic Discriminant Analysis(QDA),Linear Discriminant Analysis(LDA),and Naive Bayes(NB),for landslide susceptibility modeling and comparison of their performances.Coupling machine learning algorithms with spatial data types for landslide susceptibility mapping is a vitally important issue.This study was carried out using GIS and R open source software at Abha Basin,Asir Region,Saudi Arabia.First,a total of 243 landslide locations were identified at Abha Basin to prepare the landslide inventory map using different data sources.All the landslide areas were randomly separated into two groups with a ratio of 70%for training and 30%for validating purposes.Twelve landslide-variables were generated for landslide susceptibility modeling,which include altitude,lithology,distance to faults,normalized difference vegetation index(NDVI),landuse/landcover(LULC),distance to roads,slope angle,distance to streams,profile curvature,plan curvature,slope length(LS),and slope-aspect.The area under curve(AUC-ROC)approach has been applied to evaluate,validate,and compare the MLTs performance.The results indicated that AUC values for seven MLTs range from 89.0%for QDA to 95.1%for RF.Our findings showed that the RF(AUC=95.1%)and LDA(AUC=941.7%)have produced the best performances in comparison to other MLTs.The outcome of this study and the landslide susceptibility maps would be useful for environmental protection.     

A combined simulation–genetic algorithm optimization model for optimal rule curves of a reservoir: a case study of the Nam Oon Irrigation Project,Thailand

A combined simulation–genetic algorithm (GA) optimization model is developed to determine optimal reservoir operational rule curves of the Nam Oon Reservoir and Irrigation Project in Thailand. The GA and simulation models operate in parallel over time with interactions through their solution procedure. A GA is selected as an optimization model, instead of traditional techniques, owing to its powerful and robust performance and simplicity in combining with a simulation technique. A GA is different from conventional optimization techniques in the way that it uses objective function information and does not require its derivatives, whereas in real‐world optimization problems the search space may include discontinuities and may often include a number of sub‐optimum peaks. This may cause difficulties for calculus‐based and enumerative schemes, but not in a GA. The simulation model is run to determine the net system benefit associated with state and control variables. The combined simulation–GA model is applied to determine the optimal upper and lower rule curves on a monthly basis for the Nam Oon Reservoir, Thailand. The objective function is maximum net system benefit subject to given constraints for three scenarios of cultivated areas. The monthly release is calculated by the simulation model in accordance with the given release policy, which depends on water demand. The optimal upper and lower rule curves are compared with the results of the HEC‐3 model (Reservoir System Analysis for Conservation model) calculated by the Royal Irrigation Department, Thailand, and those obtained using the standard operating policy. It was found that the optimal rule curves yield the maximum benefit and minimum damages caused by floods and water shortages. The combined simulation–GA model shows an excellent performance in terms of its optimization results and efficient computation. Copyright © 2007 John Wiley & Sons, Ltd.     

Multireservoir system optimization in the Han River basin using multi‐objective genetic algorithms

In this study, NSGA‐II is applied to multireservoir system optimization. Here, a four‐dimensional multireservoir system in the Han River basin was formulated. Two objective functions and three cases having different constraint conditions are used to achieve nondominated solutions. NSGA‐II effectively determines these solutions without being subject to any user‐defined penalty function, as it is applied to a multireservoir system optimization having a number of constraints (here, 246), multi‐objectives, and infeasible initial solutions. Most research by multi‐objective genetic algorithms only reveals a trade‐off in the objective function space present, and thus the decision maker must reanalyse this trade‐off relationship in order to obtain information on the decision variable. Contrastingly, this study suggests a method for identifying the best solutions among the nondominated ones by analysing the relation between objective function values and decision variables. Our conclusions demonstrated that NSGA‐II performs well in multireservoir system optimization having multi‐objectives. Copyright © 2005 John Wiley & Sons, Ltd.     

Soil parameter identification using a genetic algorithm

This paper is dedicated to the identification of constitutive parameters of the Mohr–Coulomb constitutive model from in situ geotechnical measurements. A pressuremeter curve and the horizontal displacements of a sheet pile wall retaining an excavation are successively used as measurements. Two kinds of optimization algorithms are used to minimize the error function, the first one based on a gradient method and the second one based on a genetic algorithm. The efficiency of each algorithm related to the error function topology is discussed. Finally, it is shown that the use of a genetic algorithm to identify the soil parameters seems particularly suitable when the topology of the error function is complex. Copyright © 2007 John Wiley & Sons, Ltd.     

改进的遗传算法在GPS基线解算上的研究

遗传算法(GA)处理数值优化计算问题具有的简单通用、并行、稳健等特点,因此应用于高精度GPS定位的基线解算过程。针对双差模糊度的整数域和基线向量的实数域解的特性,进行了GA算法改进,包括实数编码的改进、遗传算子及其控制参数等算法设计,提出了基于非线性最小二乘准则的GPS相对定位同步解算基线向量和双差模糊度的优化搜索新方法,避免了分步解算模糊度中对浮点解的依赖性,首次实现了大范围、高精度、整数实数不同域上的同步求解,提高了GPS相对定位的稳定性,也体现了遗传算法的优越性。算例表明改进的实数编码遗传算法对同步解算GPS相对定位是可行有效的。     

可见光定位关键技术的研究与展望

室内定位由于在工业生产和日常生活中存在重要的应用价值而成为研究的热点,传统的Wi-Fi、蓝牙等无线定位技术由于电磁干扰和多径效应等原因很难实现高精度的室内定位.经过调制的白光LED在满足照明需求的同时能够传送位置信息,实现室内的高精度定位.首先介绍了可见光定位系统中常用的几种调制方式,对不同调制方式的特点做出比较,提出了一种适用于可见光定位的调制方式;然后,介绍并讨论了可见光定位系统的两种解调方法;之后介绍了几种可见光定位的算法,并对每一种算法的性能进行详细的分析;最后对于可见光定位中存在的问题进行了讨论与展望.