径向基函数网络模型在区域生态承载能力综合评价中的应用

径向基函数神经网络以其逼近能力、分类能力和学习速度等方面的优势,正受到越来越多学者的关注.本文尝试用MATLAB径向基函数网络工具进行区域生态承载能力的综合评价,给出了MATLAB6.5环境下径向基函数神经网络的结构、设计、仿真和图形结果的输出方法.作为实例,对盐城滨海湿地区域的生态承载能力进行了综合评价.结果表明,MATLAB径向基函数网络评价方法简单有效,既具有较强的分类功能,又具有较好的排序功能,评价结果可靠、适用性强,具有广阔的应用前景.     

四川省地级市生态环境质量评价模型

在建立四川省生态质量评价指标体系的基础上,将径向基函数（RBF）神经网络模型用于四川省18个地级市生态质量评价和区划,实现了评价结果的可视化与直观化。评价结果与四川省生态质量实际状况相对比,表明了RBF网络的正确性和实用性,为实现四川省生态质量的保护提供了一条新途径。     

基于BP神经网络模型的城市土地集约利用中观评价研究

中观层次的城市土地集约利用评价是以城市功能区为研究对象,通过建立各功能区的评价单元,对城市土地的投入产出效益进行定量分析研究的过程。从土地利用、土地投入、土地产出三个方面构建评价指标体系,借助BP神经网络模型从中观层次对淮安市清河区城市土地进行集约利用评价。结果显示,清河区土地集约利用水平总体较高,仍需加强土地的投入产出效益。研究表明,BP神经网络模型是一种较客观的评价方法,中观评价则能更详细地了解城市内部各个区域的土地利用情况,为政府决策提供更好的依据。     

基于BP神经网络的天线识别

本文比较研究了BP神经网络中的几种常用算法,针对这些不同算法下的BP神经网络进行训练,并得出了各自网络的性能。在此基础上,针对经典BP算法和LM算法进行对比研究,找到LM算法的改进之处。此外,在实际的应用中表明,不仅不同的BP算法对网络的运算速度、泛化能力等有较大的影响,而且BP神经网络对隐含层神经元数目也很敏感。我们希望在BP神经网络的基础上,构建一种合适的天线模型,来应用于天线的分类识别,这将具有很大的现实意义。     

模糊神经网络模型在拟建项目环境质量评价中的应用

由于评价因子与环境质量标准之间的模糊关系,导致传统的评价模型结果的不确定性。本文将模糊理论和神经网络模型相结合,利用模糊理论的隶属度反映各因子的质量相对状态,从而更精确地识别模糊性。通过模糊神经网络模型对环境质量标准样本的学习和测试,得出评价等级,并与模糊综合评价方法进行对比,实验结果表明,模糊神经网络模型用于环境质量评价是可行的,且比模糊综合评价方法更为客观、合理。     

小城镇规划中的可持续发展能力评价研究——以临沂市崔家峪镇为例

介绍了小城镇规划可持续发展能力评价的程序和距离函数模型，制订了崔家峪镇可持续发展评价指标体系，并运用距离函数模型对崔家峪镇可持续发展进行定量评价。结果表明，该镇可持续发展能力较强、但要加强环境建设。针对研究结果，提出了今后的发展对策。     

城镇建设用地需求预测与配置研究

针对我国现行城镇建设用地预测方法存在的问题，对城镇建设用地预测方法进行了探索性研究；通过构建科学的建设用地需求预测方法，为土地利用规划提供科学依据。在C—D生产函数的基础上，提出了土地、资本与产出GDP的要素关系模型、恩格尔系数与人均建设用地面积关系模型和时间序列的ARIMA建设用地模型。对成都市城镇建设用地总量进行了实证分析，对4种预测方法的结果进行了定量评价，并据此提出成都市建设用地的配置方案。     

基于遗传神经网络模型的大气环境质量评价方法

设计了用遗传算法训练神经网络权重的新方法，实验结果显示了遗传算法快速学习网络权重和全局搜索的能力，有效地解决了BP算法的局部收敛问题。误差反向传播的遗传——神经网络(GA—BP)模型用于大气环境质量综合评价，具有简便、准确、客观和适应性强等优点。     

基于BP-Adaboost强预测器的瓦斯涌出量预测

为了提高传统BP神经网络瓦斯涌出量预测模型精度,避免BP网络容易陷入局部极值、收敛速度慢等问题,将BP神经网络和Adaboost算法相结合,提出了一种BP-Adaboost强预测器模型.将该模型用于实际瓦斯涌出量预测,并进行了40次仿真实验.结果表明:该模型预测精度高于传统的BP神经网络,且收敛速度快,具有较强的鲁棒性,预测精度能满足实际工程需要,为瓦斯涌出量预测提供了一种新的途径.     

基于多目标决策的室内空气质量评价模型

在室内空气质量各单项评价指标的基础上，应用运筹学的决策理论，建立室内空气质量评价模型。并通过实例应用此模型对室内空气品质进行了评价研究，取得了满意的结果。与其他评价方法相比，该评价模型结构严谨、计算简便、结果合理、分辨率高。为室内空气质量评价提供了一种简单而适用的评价方法。     

Predicting Carbon Monoxide Concentrations in the Air of Pardis City,Iran, Using an Artificial Neural Network

To date, several methods have been proposed to explain the complex process of air pollution prediction. One of these methods uses neural networks. Artificial neural networks (ANN) are a branch of artificial intelligence, and because of their nonlinear mathematical structures and ability to provide acceptable forecasts, they have gained popularity among researchers. The goal of our study as documented in this article was to compare the abilities of two different ANNs, the multilayer perceptron (MLP) and radial basis function (RBF) neural networks, to predict carbon monoxide (CO) concentrations in the air of Pardis City, Iran. For the study, we used data collected hourly on temperature, wind speed, and humidity as inputs to train the networks. The MLP neural network had two hidden layers that contained 13 neurons in the first layer and 25 neurons in the second layer and reached a mean bias error (MBE) of 0.06. The coefficient of determination (R²), index of agreement (IA), and the Nash–Scutcliffe efficiency (E) between the observed and predicted data using the MLP neural network were 0.96, 0.9057, and 0.957, respectively. The RBF neural network with a hidden layer containing 130 neurons reached an MBE of 0.04. The R², IA, and E between the observed and predicted data using the RBF neural network were 0.981, 0.954, and 0.979, respectively. The results provided by the RBF neural network had greater acceptable accuracy than was the case with the MLP neural network. Finally, the results of a sensitivity analysis using the MLP neural network indicated that temperature is the primary factor in the prediction of CO concentrations and that wind speed and humidity are factors of second and third importance when forecasting CO levels.     

基于ANN的环境质量评价

人工神经网络通过神经元之间的相互作用来完成整个网络的信息处理，具有自学习和自适应等一系列优点，因而用它来评价环境质量是可行的。本文针对环境质量评价问题，建立了基于神经网络的评价系统，给出了应用实例。     

Is Learning without teaching possible? The productive tension between network governance and reflexivity

ABSTRACT

Theories of reflexive governance are closely linked with the claim that more traditional modes of coordination have been replaced by networked structures, allowing reflexivity to emerge and reflexive learning to function as a steering mechanism in rapidly changing policy contexts. This paper explores this connection between reflexivity, governance, learning and networks in societal transitions, focusing particularly on the claim that networks will deliver reflexive learning. Using network theories from both policy networks and network governance and a case study of the Canadian agricultural biotechnology (agbiotech) policy network, it suggests that the kind of learning produced in networks will be a function of network structure. In particular, higher order reflexive learning will be compromised by the inevitability of the political struggle for nodality or central place in networks and the ensuing distribution of opportunities for bridging and bonding activities. Networks such as the Canadian agbiotech policy network that may promote learning but not necessarily reflexive learning are increasingly disadvantaged in contemporary policy settings.     

基于模糊神经网络的煤炭企业循环经济评价模型

针对煤炭企业循环经济水平具有模糊性的特点,利用模糊神经网络具有模糊化和良好泛化的能力,在探讨煤炭企业发展循环经济影响因素的基础上,从经济发展、资源利用、节能减排、环境状况、循环特征五个方面建立了煤炭企业循环经济评价指标体系,构建基于模糊神经网络的煤炭企业循环经济评价模型,并按照模糊神经网络结构的建立、输入数据的模糊化、输出数据的反模糊化、BP神经网络训练进行模型求解和运算,算例验证了该模型具有较好的学习能力.利用该模型评价煤炭企业循环经济具有有效性和准确性.     

PREDICTION OF TWO-YEAR PEAK STREAM-DISCHARGES USING NEURAL NETWORKS1

ABSTRACT: The cascade correlation neural network was used to predict the two-year peak discharge (Q₂) for major regional river basins of the continental United States (US). Watersheds ranged in size by four orders of magnitude. Results of the neural network predictions ranged from correlations of 0.73 for 104 test data in the Souris-Red Rainy river basin to 0.95 for 141 test data in California. These results are improvements over previous multilinear regressions involving more variables that showed correlations ranging from 0.26 to 0.94. Results are presented for neural networks trained and tested on drainage area, average annual precipitation, and mean basin elevation. A neural network trained on regional scale data in the Texas Gulf was comparable to previous estimates of Q₂ by regression. Our research shows Q₂ was difficult to predict for the Souris-Red Rainy, Missouri, and Rio Grande river basins compared to the rest of the US, and acceptable predictions could be made using only mean basin elevation and drainage areas of watersheds.     

前馈神经网络在环境评价和预测中的应用进展

本文针对水环境中复杂的不确定性及非线性关系,在水环境不确定性分析的基础上,详细阐述了以BP网络和RBF网络为代表的前馈神经网络法的基本原理,分析了两种方法的优点。同时,本文对两种方法在水环境影响评价工作中的应用现状进行总结,分析了两种方法的研究发展趋势。     

神经网络在空气污染预报中的应用研究

空气污染预报是一项复杂的系统工程，是当今环境科学研究的热点，国内外已有将神经网络法应用于大气污染预报的研究。本论文以PM2．5为例，采用伦敦市PM2．5的小时平均浓度数据，使用传统的BP神经网络建立预报模型，定量预测伦敦市PM2.5的小时平均浓度，探讨了大气污染预报网络的建模过程中，扩大样本集、去除样本集数据噪声和在输入向量中加入气象变量等因素对建模所产生的影响。最后得出结论，适当的选择样本集、气象变量，有利于提高所建立网络模型的预测精度。     

MODELING TRANSIENT pH DEPRESSIONS IN COASTAL STREAMS OF BRITISH COLUMBIA USING NEURAL NETWORKS1

ABSTRACT: Transient events in water chemistry in small coastal watersheds, particularly pH depressions, are largely driven by inputs of precipitation. While the response of each watershed depends upon both the nature of the precipitation event and the season of the year, how the response changes over time can provide insight into landscape changes. Neural network models for an urban watershed and a rural‐suburban watershed were developed in an attempt to detect changes in system response resulting from changes in the landscape. Separate models for describing pH depressions for wet season and dry season conditions were developed for a seven year period at each watershed. The neural network models allowed separation of the effects of precipitation variations and changes in watershed response. The ability to detect trends in pH depression magnitudes was improved by analyzing neural network residuals rather than the raw data. Examination of sensitivity plots of the models indicated how the neural networks were affected by different inputs. There were large differences in effects between seasons in the rural‐suburban watershed whereas effects in the urban watershed were consistent between seasons. During the study period, the urban watershed showed no change in pH depression response, while the rural‐suburban watershed showed a significant increase in the magnitude of pH depressions, likely the result of increased urbanization.