基于MFOA-SVR露天矿边坡变形量预测研究

为实现边坡危险性及时预警预报,以露天矿边坡变形量为研究对象,提出采用七项影响指标作为边坡位移变形量的响应参数,建立支持向量机回归预测模型(SVR)。引入修正的果蝇优化算法(MFOA)对模型参数进行优化,构建基于MFOA-SVR露天矿边坡变形量协同预测模型,并以实际监测数据进行模型仿真预测。结果表明:该模型平均绝对误差为0.9167mm,平均相对误差为4.2737%,较其他模型预测精度高,综合性能好,将其运用于露天矿边坡变形量预测研究具有较好的适用性和可靠性。     

基于自适应调整蚁群-RBF神经网络模型的中长期径流预测

径流预测历来是水利部门的一项重要工作,针对水库和河流中长期径流预测精度不高,提出了自适应调节人工蚁群算法(ARACS),对RBF神经网络参数进行优化,建立了自适应调节人工蚁群-RBF神经网络组合算法(ARACS-RBF)预测模型,综合考虑影响径流预变化因素,对安康水库进行中长期径流预测。对预测效果进行检验,结果证实该模型可真实地反映河川径流变化的总体趋势, 并为判断时间序列数据的非线性提供了一种新方法。与RBF神经网络模型、人工蚁群-RBF神经网络模型预测结果进行对比,结果表明,应用ARACS-RBF模型对中长期径流量进行预测,预测精度更高、效果更好。该方法克服了RBF神经网络和人工蚁群算法易陷于局部极值、搜索质量差和精度不高的缺点,改善了RBF神经网络的泛化能力,收敛速度快,输出稳定性好,提高了径流预测的精度,置信度为98%时的预测相对误差小于6.5%。可有效用于水库和河川中长期径流预测。     

Conservation decision-making in large state spaces

When looking for the best course of management decisions to efficiently conserve metapopulation systems, a classic approach in the ecology literature is to model the optimisation problem as a Markov decision process and find an optimal control policy using exact stochastic dynamic programming techniques. Stochastic dynamic programming is an iterative procedure that seeks to optimise a value function at each timestep by evaluating the benefits of each of the actions in each state of the system defined in the Markov decision process.Although stochastic dynamic programming methods provide an optimal solution to conservation management questions in a stochastic world, their applicability in metapopulation problems has always been limited by the so-called curse of dimensionality. The curse of dimensionality is the problem that adding new state variables inevitably results in much larger (often exponential) increases in the size of the state space, which can make solving superficially small problems impossible. The high computational requirements of stochastic dynamic programming methods mean that only simple metapopulation management problems can be analysed. In this paper we overcome the complexity burden of exact stochastic dynamic programming methods and present the benefits of an on-line sparse sampling algorithm proposed by Kearns, Mansour and Ng (2002). The algorithm is particularly attractive for problems with large state spaces as the running time is independent of the size of the state space of the problem. This appealing improvement is achieved at a cost: the solutions found are no longer guaranteed to be optimal.We apply the algorithm of Kearns et al. (2002) to a hypothetical fish metapopulation problem where the management objective is to maximise the number of occupied patches over the management time horizon. Our model has multiple management options to combat the threats of water abstraction and waterhole sedimentation. We compare the performance of the optimal solution to the results of the on-line sparse sampling algorithm for a simple 3-waterhole case. We find that three look-ahead steps minimises the error between the optimal solution and the approximation algorithm. This paper introduces a new algorithm to conservation management that provides a way to avoid the effects of the curse of dimensionality. The work has the potential to allow us to approximate solutions to much more complex metapopulation management problems in the future.     

水安全智能识别模型的建立及应用

通过人口迁移算法优化投影寻踪模型,提出了一种新的水安全智能识别模型。与遗传算法优化的投影模型相对比,人口迁移算法的自身优势有效地避免了网络早熟现象及寻找全局最优解的困扰。从水安全的评价结果来看,用人口迁移算法优化投影寻踪是可行的,并显示出优越性。人口迁移算法为求解投影寻踪模型的非线性约束提供了新的优化方法,并为水安全评价工作提供了新的智能识别模型。     

水质评价中模糊-集对分析法的改进

针对水环境的不确定性和模糊性,基于集对分析法和模糊理论相结合的评价模型已被广泛运用到水质评价当中。传统的评价模型在确定水质等级的复合算法中虽引入了权值的概念,但仍会出现指标浓度重复计算、信息丢失和指标权值的影响得不到体现等缺陷,具有局限性。针对这些缺陷,将基于加权后的内梅罗指数法思想应用于传统模糊-集对分析法中的复合算法上,考虑到加权内梅罗指数法兼顾极值的特性及计权性,通过取平均值和极值替代累加的方法来实现减小指标浓度重复计算的影响,同时加强复合算法中权值所占比重使得权值的作用得到充分体现。实验表明,改进的复合算法使得评价结果更为精确、客观。     

Optimization of thermal performance of the parabolic trough solar collector systems based on GA-BP neural network model

The aim of this paper is to optimize the thermal performance (system output energy, thermal efficiency, and heat loss of cavity absorber) of parabolic trough solar collector (PTC) systems in order to improve its thermal performance, based on the genetic algorithm-back propagation (GA-BP) neural network model. There are a number of undefined problems, fuzzy or incomplete information and a complex thermal performance of the PTC systems. Therefore, the thermal performance prediction of the PTC systems based on GA-BP neural network model was developed. Subsequently, the metrics performances have been adopted to comprehensively understand the algorithm and evaluate the prediction accuracy. Results revealed that the GA-BP neural network model can be successfully used to predict the complex nonlinear relationship between the input variables and thermal performance of the PTC systems. The cosine effect has a great influence on the thermal performance; thereby the geometrical structure of the PTC systems was optimized. It was found that the optimized geometrical structure was beneficial to improve the thermal performance of the PTC system. In conclusion, the GA-BP neural network model has higher prediction accuracy than the other algorithm and it can be feasible and reliable.     

Optimal water quality monitoring network design for river systems

Typical tasks of a river monitoring network design include the selection of the water quality parameters, selection of sampling and measurement methods for these parameters, identification of the locations of sampling stations and determination of the sampling frequencies. These primary design considerations may require a variety of objectives, constraints and solutions. In this study we focus on the optimal river water quality monitoring network design aspect of the overall monitoring program and propose a novel methodology for the analysis of this problem. In the proposed analysis, the locations of sampling sites are determined such that the contaminant detection time is minimized for the river network while achieving maximum reliability for the monitoring system performance. Altamaha river system in the State of Georgia, USA is chosen as an example to demonstrate the proposed methodology. The results show that the proposed model can be effectively used for the optimal design of monitoring networks in river systems.     

辽东湾MODIS生物光学算法研究(Ⅰ)算法适用性评估

辽东湾是典型的复杂二类水体,我们利用2002～2004年在辽东湾现场实测的叶绿素a数据(分光法、荧光法和HPLC法),以及水面之上法现场实测的离水辐射率和遥感反射率数据模拟的MODIS各相应波段值对MODIS海洋水色算法进行了评估.评估的MODIS生物光学算法有四个,即CZCS_pigm、chlor_MODIS、chlor_a_2和chlor_a_3算法.结果表明,两个一类水体生物光学算法(CZCS_pigm和chlor_MODIS)反演值与分光法和荧光法测试分析结果相比,算法均低估了叶绿素a浓度,与HPLC分析结果相比算法高估了叶绿素a浓度;两个二类水体算法(chlor_a_2和chlor_a_3)与所有叶绿素a分析方法相比,均高估了叶绿素a浓度.根据MODIS生物光学算法在辽东湾的具体表现,我们认为HPLC法分析的叶绿素a浓度更适合于作为海洋水色算法评估的现场检验依据.叶绿素a浓度的反演值和实测值之间较低的相关系数则显示MODIS两个一类水体算法所采用的波段比值不适合本海区.     

蚁群算法在生命线网络系统抗震拓扑优化中的应用

近年来,遗传算法和模拟退火算法已经应用于网络系统的抗震拓扑优化,蚁群算法也已经成功应用到多个组合优化问题中。首先论述了生命线网络系统抗震拓扑优化模型,然后介绍了网络抗震可靠度分析的最小路递推分解算法。通过对优化问题解形式的分析,采用二进制编码的蚁群算法对优化模型进行了求解。最后,结合实例分析,并与遗传算法和模拟退火算法的计算结果进行了对比。结果表明,蚁群算法可以作为一种新的工具进行网络系统的优化设计。     

基于DE-BPNN模型的含腐蚀缺陷管道失效压力预测

为提升含腐蚀缺陷管道失效压力预测精度,准确把控管道状态,建立基于DE-BPNN的含腐蚀缺陷管道失效压力预测模型,有效避免BPNN模型陷入局部最优问题,提升预测精度.基于61组管道爆破实验数据,分别用DE-BPNN与BPNN模型进行仿真计算.结果表明:DE-BPNN预测结果平均相对误差为3.26％,R2为0.98585,...