基于人工蜂群算法与BP神经网络的水质评价模型

针对BP网络水质评价模型的不足,引入人工蜂群(ABC)算法,将求解BP神经网络各层权值、阀值的过程转化为蜜蜂寻找最佳蜜源的过程,提出了一种新的结合人工蜂群算法的BP网络水质评价方法(ABC-BP)。并以2000—2006年渭河监测断面的10组实测数据作为测试样本对其水质进行了评价,实验结果表明该方法得到的水质评价结果准确,并具有很强的稳定性和鲁棒性。     

基于蛙跳算法优化的生态环境质量评价的韦伯-费希纳指数公式

为了建立简便、实用的生态环境质量评价模型,在构建生态环境评价指标体系的基础上,设定各指标的参照值和规范变换式,使规范变换后的不同指标同级标准的规范值差异不大。进而提出了一个对多项指标的规范值都适用的生态环境质量评价的韦伯-费希纳(W-F)指数公式。采用混合蛙跳算法对公式的参数进行优化,得出优化后对多项指标皆适用的生态环境质量评价的W-F指数公式。运用该公式对巢湖流域的生态环境质量进行评价,其结果与该流域生态环境质量实际情况基本相符,表明模型有一定的实用性。     

Energy consumption estimation model for dual-motor electric vehicles based on multiple linear regression

ABSTRACT

The drive range of electric vehicle (EV) is one of the major limitations that impedes its universalism. A great deal of research has been devoted to drive range improvement of EV, an accurate and efficiency energy consumption estimation plays a crucial role in these researches. However, the majority of EV’s energy consumption estimation models are based on single motor EV, these models are not suitable for dual-motor EVs, which are composed of more complex transmission mechanisms and multiple operating modes. Thus, an energy consumption estimation model for dual-motor EV is proposed to estimate battery power. This article focuses on studying the operating modes and system efficiency in each operating mode. The limitation of working area of each mode ensures the vehicle dynamic performance, then PSO algorithm is adopted to optimize the torque (speed) distribution between two motors to improve the system efficiency in the coupled driving mode. Finally, the energy consumption estimation model is established by multiple linear regression (MLR). The result shows that the proposed model has a high precision in energy consumption estimation of dual-motor EV.     

改进的BP算法在黄河下游枯季径流预测中的应用

本在黄河下游地区应用多层前向人工神经网络理论,通过改进BP算法,建立下游枯季径流预测的BP神经网络模型,使用花园口-利津水站26年的完整序列测流资料训练和检验网络并用于预测,结果表明,通过本次研究建立的BP网络模型是合理的,可靠的,它较好地反映了黄河下游的枯季径流规律,可为今后黄河流域水资源的统一调度,管理,尤其是预防黄河下游再次出现断流提供科学依据。     

遗传投影寻踪回归在城市环境质量评价中的应用

引入投影降维的思想,将遗传投影寻踪与回归分析技术运用到城市环境质量评价中。将此技术与神经网络方法进行实例比较,投影寻踪回归方法不但可以合理地作出环境质量的综合评价,而且消除了神经网络方法中类别判断不够精确的影响。     

煤炭产业国际竞争力的遗传—投影寻踪评价

根据煤炭产业发展的实际状况,提出了煤炭产业竞争力评价的指标体系。采用探索性数据分析—投影寻踪法,结合遗传算法,建立了遗传—投影寻踪综合评价模型。以全球11个主要产煤国为对象进行了实证研究,验证了该方法的科学性和实践的可行性,提出提升我国煤炭产业竞争力的对策建议。     

Suitable error evaluation criteria selection in the wind energy assessment via the K-means clustering algorithm

In this paper, wind energy potential of four locations in Xinjiang region is assessed. The Weibull distribution as well as the Logistic and the Lognormal distributions are applied to describe the distributions of the wind speed at different heights. In determining the parameters in the Weibull distribution, four intelligent parameter optimization approaches including the differential evolutionary, the particle swarm optimization, and two other approaches derived from these two algorithms and combined advantages of these two approaches are employed. Then the optimal distribution is chosen through the Chi-square error (CSE), the Kolmogorov–Smirnov test error (KSE), and the root mean square error (RMSE) criteria. However, it is found that the variation range of some criteria is quite large, thus these criteria are analyzed and evaluated both from the anomalous values and by the K-means clustering method. Anomaly observation results have shown that the CSE is the first one should be considered to be eliminated from the consequent optimal distribution function selection. This idea is further confirmed by the K-means clustering algorithm, by which the CSE is clustered into a different group with KSE and RMSE. Therefore, only the reserved two error evaluation criteria are utilized to evaluate the wind power potential.     

Optimizing mixture properties of biodiesel production using genetic algorithm-based evolutionary support vector machine

Nowadays, biodiesel is used as one of the alternative renewable energy due to the increasing energy demand. However, optimum production of biodiesel still requires a huge number of expensive and time-consuming laboratory tests. To address the problem, this research develops a novel Genetic Algorithm-based Evolutionary Support Vector Machine (GA-ESIM). The GA-ESIM is an Artificial Intelligence (AI)-based tool that combines K-means Chaotic Genetic Algorithm (KCGA) and Evolutionary Support Vector Machine Inference Model (ESIM). The ESIM is utilized as a supervised learning technique to establish a highly accurate prediction model between the input--output of biodiesel mixture properties; and the KCGA is used to perform the simulation to obtain the optimum mixture properties based on the prediction model. A real biodiesel experimental data is provided to validate the GA-ESIM performance. Our simulation results demonstrate that the GA-ESIM establishes a prediction model with better accuracy than other AI-based tool and thus obtains the mixture properties with the biodiesel yield of 99.9%, higher than the best experimental data record, 97.4%.     

模拟退火算法在大气环境质量综合评价中的应用

为了建立一种对多项指标都能普遍使用的大气环境质量综合评价模式,利用模拟退火算法对幂函数加和型指数评价中参数进行优化,并用优化后的综合评价指数公式对大气环境进行综合评价。该公式应用于大量实例的分析评价结果与其他多种评价方法评价结果比较表明:该公式不受污染物种类和数目多少的限制,计算简便,具有可行性、实用性和通用性。