基于GA-ANN改进的空气质量预测模型

基于人工神经网络的空气质量预测模型优于传统的逐步回归模型,但由于性能差异不明显而较少在空气质量预报中应用. 设计了将遗传算法和神经网络算法相结合的基于GA_-ANN的空气质量预测模型,并利用天津市2003—2007年气象和污染物监测资料对该模型进行验证. 对2007年全年的ρ(SO₂),ρ(NO₂)和ρ(PM₁₀₎进行预测,预测值与实测值的相关系数分别为0.899 6,0.828 3和0.600 0. 与一般的人工神经网络预测模型相比较,GA_-ANN模型将空气质量等级预报的准确率从77.57%提高到79.67%. GA_-ANN模型可结合其他方法进行日常空气质量预报.      

应用遗传算法优化氢气/空气燃烧反应系数

研究目的是优化氢气/空气简化反应机理的系数,使得简化机理在一定的误差范围内,能代替详细机理.通过计算氢气/空气详细反应机理得到的组分浓度作为遗传算法的适应度目标,对简化机理进行优化.结果显示,优化后的简化机理不仅在在计算组分浓度,而且在层流预混火焰速度和组分时空分布方面,更加接近详细机理所能达到的计算结果,比优化之前的简化反应机理计算结果更加准确.     

基于人工神经网络和遗传算法研究锰过氧化酶脱色甲基橙

基于锰过氧化物酶(MnP)氧化脱色偶氮类染料的原理,实验研究MnP对甲基橙的脱色工艺,采用人工神经网络(ANN)和遗传算法(GA)建立脱色模型并优化工艺。建立的ANN模型的误差、相关系数、均方根误差和绝对平均偏差分别为0.0009、0.9971、1.21和6.82,模型有效且能够用于预测和工艺优化。采用GA对ANN模型进行数值寻优,得到的最佳工艺条件为酶液量0.6 mL,Mn2+浓度4 mmol/L,H2O2浓度0.49 mmol/L。该条件下脱色率达到(90.74±0.59)%。ANN耦合GA有效地建立了锰过氧化物酶脱色甲基橙的模型,并优化了工艺参数,为甲基橙脱色的研究提供一定参考。     

遗传算法对QSAR研究中变量选择的应用

将遗传算法引入定量结构活性关系（ＱＳＡＲ）研究中,对变量进行造反选择,可同时建立几种比较好的ＱＳＡＲ模型,并以交互验证的决定系数作为适应函数,以保证模型质量的可靠性。将其分别应用于氯代酚和单取代苯系列化合物,均得到较好的结果。同时,在这些应用中也反映了遗传算法在变量选择中存在的局限性,限不能保证选择的所有变量在模型中都有显著贡献。     

A novel objective function for optimal DG allocation in distribution systems using meta-heuristic algorithms

Meta heuristic algorithms have been introduced as a powerful method to solve the nonlinear optimization problems. These algorithms have been employed in many complex engineering problems due to their high capability in finding the solutions and reaching the optimal results within a short period of time. Optimization of distributed generation units in distribution systems, which have profoundly impacted on the system losses and voltage profile, is one of these nonlinear problems. In this study, a novel objective function was proposed for optimization procedure by meta-heuristic algorithms. The related objective function consists of the total cost of distributed generation units, cost of the purchased natural gas, cost of distribution system power losses, and penalty for greenhouse gas emissions. The electrical, cooling, and heating loads were considered in this study. In the distribution system, the waste and fuel cell were used to supply the required heating and cooling loads. The meta-heuristic algorithms including Particle Swarm Optimization (PSO), Genetic Algorithm (GA), and Imperialist Competitive Algorithm (ICA) were employed to find the optimal location and size of distributed generation units in a distribution system. A detailed performance analysis was done on 13 bus radial distribution system. The performances of three algorithms were compared with each other and results showed that the PSO was the fastest; and had the best solution and optimum results. Furthermore, the PSO reached the optimum solution in a fewer number of iterations than the GA and ICA algorithms.     

基于人工免疫的柔性EMI滤波器自动进化设计研究

针对电力电子系统的EMI滤波器设计中的问题,开展基于神经网络和遗传算法的柔性滤波器电路自动进化设计研究,综合多项EMI滤波器设计目标进行自动优化设计,并在实际应用中实现了柔性滤波器这一目标。     

Determining factors that influence the dispersal of a pelagic species: A comparison between artificial neural networks and evolutionary algorithms

Because of increasing transport and trade there is a growing threat of marine invasive species being introduced into regions where they do not presently occur. So that the impacts of such species can be mitigated, it is important to predict how individuals, particularly passive dispersers are transported and dispersed in the ocean as well as in coastal regions so that new incursions of potential invasive species are rapidly detected and origins identified. Such predictions also support strategic monitoring, containment and/or eradication programs. To determine factors influencing a passive disperser, around coastal New Zealand, data from the genus Physalia (Cnidaria: Siphonophora) were used. Oceanographic data on wave height and wind direction and records of occurrences of Physalia on swimming beaches throughout the summer season were used to create models using artificial neural networks (ANNs) and Na?ve Bayesian Classifier (NBC). First, however, redundant and irrelevant data were removed using feature selection of a subset of variables. Two methods for feature selection were compared, one based on the multilayer perceptron and another based on an evolutionary algorithm. The models indicated that New Zealand appears to have two independent systems driven by currents and oceanographic variables that are responsible for the redistribution of Physalia from north of New Zealand and from the Tasman Sea to their subsequent presence in coastal waters. One system is centred in the east coast of northern New Zealand and the other involves a dynamic system that encompasses four other regions on both coasts of the country. Interestingly, the models confirm, molecular data obtained from Physalia in a previous study that identified a similar distribution of systems around New Zealand coastal waters. Additionally, this study demonstrates that the modelling methods used could generate valid hypotheses from noisy and complicated data in a system about which there is little previous knowledge.     

7075铝合金板自然环境太阳辐射热效应及影响因素分析

目的 研究典型金属材料在自然太阳辐射环境下的热效应,并分析试验件属性及环境参数等不同因素对材料热效应的影响规律。方法 通过设计自然环境下太阳辐射热效应试验方案,分析涂层颜色、粗糙度等对7075铝合金材料太阳辐射热效应的影响,建立一种太阳辐射环境下稳态温度的计算算法。结果 获取了在9月前后20 d内的外场辐照度及10组试验件的温度测量数据,得到了涂层颜色、粗糙度等对材料热效应的影响规律。此外,建立的简化算法和试验数据误差在5%以内,具有精度高、形式简单等优势,可以应用于给定辐照强度下典型材料热效应的预测。结论 针对特定试验件,辐照度和风速是影响其热效应的主要因素。当辐照强度在较高的范围内变化时,试验件温升基本和辐照度成正比。随着自然环境风速的增加,试验件温度的下降幅度减小。     

边坡稳定性评价的自适应模拟退火聚类分析法

本文针对常规聚类法对初始聚类中心 的敏感性以及聚类结果与样本输入次序有关等问题,提出了基于模拟退火算法的边坡稳定性评价的自适应聚类分析方法。此方法对三峡库区36个边坡的研究结果表明，它是一个具有全局最优解和有较高计算效率的聚类法,其结果明显优于常规聚类法。     

Integrated Protected Area Selection in Australian Biogeographic Regions

/ This paper reports on the development of abiogeographic-based protected area selection procedure (PASP) that addresseslimitations of current reserve selection methods such as inflexibility,restrictive criteria structures, and disregard of social criteria. We arguethat selection criteria are legitimately identified through the politicalprocess and can therefore have both social and biological content. PASPprovides a systematic, flexible and efficient procedure for identifying anetwork of protected areas that satisfies a given set of selection criteria.PASP has 11 steps divided into three main stages: compilation, selection, andvalidation. A selection algorithm is used into which criteria specific to thebiogeographic region under study can be incorporated. The procedure is ableto accommodate both nominal and ranking criteria and gives the analyst theopportunity to specify thresholds and targets for each criterion. Otherfeatures of PASP include the minimization of data collection effort,consideration of reserve design principles, and inclusion of a networkevaluation as a check to ensure that politically determined objectives havebeen met.KEY WORDS: Land use; Protected area networks; Selection algorithms