一种基于最小资源分配网络的模拟器及其在工业甲醇氧化制甲醛中的应用

S. Deepa, S. Venkatesan
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引用次数: 1

摘要

本文介绍了一种模拟甲醇氧化制甲醛反应速率的方法。这里使用径向基函数网络对过程进行建模。为了选择最优数量的隐藏神经元,我们使用最小资源分配网络算法。它根据输入数据的新颖性来招募隐藏神经元。训练数据来自文献中可用的模型。用文献数据对网络进行训练,得到的模型能很好地预测甲醛生成的反应速率。
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A Novel Simulator Using Minimal Resource Allocation Network and Its Application in Industrial Methanol Oxidation to Formaldehyde
A simulator in order to calculate the rate of the reaction in the methanol oxidation to formaldehyde process is presented in this paper. Here the Radial Basis Function Network is used to model the process. To choose an optimum number of hidden neuron we use an algorithm called Minimal Resource allocation Network. It recruits hidden neuron based on the novelty of the input data. The training data were obtained from a model available in literature. The network is trained with the literature data and the resulted model gives a good prediction of rate of reaction of formaldehyde formation.
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