Ensemble Interval Network-based Modelling of the Glutamic Acid Fermentation Process
Shouping Guan, Zhouying Cui · 2019
This paper presents a new architecture for an ensemble interval random vector functional-link network (EIRVFLN) with the learning algorithm, which enables the conventional ensemble neural network (ENN) to have the ability to process uncertain data. As an application case study, the EIRVFLN is used to model the glutamic acid fermentation process under the condition of bounded-error data, and the test results indicate that the accuracy of the model meets the manufacturing requirements.