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首页> 外文期刊>International journal of system control and information processing >Simplified stochastic configuration network-based optimised soft measuring model by using evolutionary computing framework with its application to dioxin emission concentration estimation
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Simplified stochastic configuration network-based optimised soft measuring model by using evolutionary computing framework with its application to dioxin emission concentration estimation

机译：通过使用进化计算框架的应用在二恶英发射浓度估计中简化了随机配置网络的优化软测量模型

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摘要

With a supervisory mechanism to randomly assigning input weights and biases, the prediction performance of the soft measuring model of industrial process has been improved by stochastic configuration networks (SCNs). Although SCNs theoretically exhibit a universal approximation capability, the learning parameters generate considerable fluctuation for the evaluated performance. Hence, addressing the parameters by an intelligent optimisation method is necessary. Thus, this study investigates the parameter optimisation of soft measuring model based on simplified SCN (SSCN) by using the evolutionary computing (EC) framework. A searching strategy based on EC theory is used to optimise jointly the input features and learning parameters of the soft measuring model. Moreover, sensitivity and robust analysis of key learning parameters are performed. Experiments on benchmark datasets and dioxin emission datasets from municipal solid waste incineration with different sizes and dimensions are conducted to validate the proposed strategy.
机译：通过对随机分配输入权重和偏置的监督机制，随机配置网络（SCNS）改善了工业过程软测量模型的预测性能。虽然SCNS理论上表现出通用近似能力，但学习参数为评估的性能产生相当大的波动。因此，需要通过智能优化方法解决参数。因此，本研究通过使用进化计算（EC）框架来研究基于简化的SCN（SSCN）的软测量模型的参数优化。基于EC理论的搜索策略用于共同优化软测量模型的输入特征和学习参数。此外，执行关键学习参数的灵敏度和鲁棒分析。采用不同尺寸和尺寸的市政固体废物焚烧的基准数据集和二恶英排放数据集的实验，以验证提出的策略。

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