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Lookup NU author(s): Dr Jie ZhangORCiD
This is the authors' accepted manuscript of a conference proceedings (inc. abstract) that has been published in its final definitive form by Elsevier, 2020.
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Echo state network (ESN) has emerged as an effective alternative to conventional recurrent neural networks due to its simple training process and good modelling ability for solving a variety of problems, especially time-series modelling tasks. To improve modelling capability and to decrease the reservoir topology complexity, a new attention mechanism based ESN optimised by covariance matrix adaption evolutionary strategy (CMA-ES) is proposed in this paper. CMA-ES is a stochastic and derivative-free algorithm for solving non-linear optimization problems. Attention mechanism is incorporated to guide ESN to focus on regions of interest relevant to the modelling task. The proposed optimised ESN with attention mechanism is used to model a fed-batch penicillin fermentation process and the results are better than those from the standard ESN and ESN with attention mechanism.
Author(s): Liu K, Zhang J
Editor(s): Pierucci S; Manenti F; Bozzano GL; Manca D
Publication type: Conference Proceedings (inc. Abstract)
Publication status: Published
Conference Name: 30th European Symposium on Computer Aided Chemical Engineering
Year of Conference: 2020
Online publication date: 25/09/2020
Acceptance date: 08/01/2020
Date deposited: 27/03/2020
Notes: Conference website: https://www.aidic.it/escape30/enter.php
August 31- September 2, 2020
The ESCAPE 30 European Symposium on Computer Aided Process Engineering, originally scheduled live on August 31-September 2 in Milano, is going to be held virtually in the same date.
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