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ANFIS identification model of an Advanced Process Control (APC) pilot plant

机译:先进过程控制(APC)试验工厂的ANFIS识别模型

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Fuzzy Inference System structured in form of adaptive networks is an intelligent technique being used for modeling not only linear systems but also for ill-conditioned systems. Adaptive Network Based Fuzzy Inference System (ANFIS) uses a hybrid computational algorithm for modeling systems. This paper discusses the system identification model developed for an Advanced Process Control (APC) pilot plant (continuous binary distillation column) located in APC laboratory of Universiti Teknologi PETRONAS, Malaysia, using ANFIS technique. Estimation and validation of the models was performed using the experimental data collected from the pilot plant. The developed model has been validated using the best fit criteria against the measured data of the pilot plant. The result shows that the Multi Input Single Output (MISO) ANFIS model developed is capable of modeling the non-linear APC plant by means of the input-output pairs obtained from the plant experiment.
机译:模糊推理系统以自适应网络的形式构成的是一种智能技术,用于建模不仅是线性系统,而且是用于不合形的系统。基于自适应网络的模糊推理系统(ANFIS)使用用于建模系统的混合计算算法。本文讨论了使用ANFIS技术的高级工艺控制(APC)试验工厂(连续二元蒸馏塔)开发的系统识别模型,使用ANFIS技术。使用从试验工厂收集的实验数据进行模型的估计和验证。已经使用最佳拟合标准对开发的模型进行了验证,以防止导频设备的测量数据。结果表明,开发的多输入单输出(MISO)ANFIS模型能够通过从工厂实验中获得的输入输出对来建模非线性APC设备。

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