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首页> 外文会议>European symposium of the working party on computer aided process engineering >Modelling of Air Pollution in an Environmental System by use of Nonlinear Independent Component analysis
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Modelling of Air Pollution in an Environmental System by use of Nonlinear Independent Component analysis

机译：非线性独立分量分析利用非线性独立分量分析建模环境体系中的空气污染

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In this paper an empirical, non-linear state space model of a metropolitan environmental system is constructed by use of singular spectrum analysis and non-linear independent component analysis. Environmental systems are complex, high-dimensional and non-linear. Conventional modelling techniques demand expensive fundamental models, as well as costly supercomputers to effectively simulate and predict these systems. On the other hand, numerical methods such as empirical state space parameterisation and multiple-layer perceptron neural networks promise simpler models that can be accommodated on affordable desktop computers. The state space model presented in this paper is constructed by embedding and separation of the individual observations of the polluting agents. The observations are regarded as a nonlinear mixture of the underlying process state variables and are classified as deterministic by using a surrogate data technique. It is shown that non-linear separation enhances the ability of the non-linear model to predict the dependent observations, especially in the presence of unknown levels of dynamic and measurement noise. No pre-assumptions are made on the statistical distributions of the original state variables or the noise content. Instead, these distributions are estimated as mixtures of Gaussian distributions. An ensemble learning technique is implemented in the parameter estimation algorithm for the separation model. The results show a reduction in complexity in the attractor and satisfactory one step ahead predictions.
机译：本文通过使用奇异谱分析和非线性独立分量分析来构建大都市环境系统的经验，非线性状态空间模型。环境系统复杂，高维和非线性。传统的建模技术需要昂贵的基本模型，以及昂贵的超级计算机，以有效地模拟和预测这些系统。另一方面，数值方法如经验状态空间参数和多层的Perceptron神经网络，这是可以在经济实惠的台式计算机上提供更简单的模型。本文呈现的状态空间模型是通过嵌入和分离污染剂的个体观察来构建。观察结果被认为是下面的过程状态变量的非线性混合物，并且通过使用代理数据技术被分类为确定性。结果表明，非线性分离增强了非线性模型预测依赖观察的能力，特别是在存在未知的动态和测量噪声水平。没有对原始状态变量的统计分布或噪声内容进行预先假设。相反，这些分布估计为高斯分布的混合物。在分离模型的参数估计算法中实现了集合学习技术。结果表明，吸引子的复杂性降低，并令人满意的一步前预测。

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