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Applied Sciences
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Intelligent Control in Industrial Processes
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Intelligent Control in Industrial Processes

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: closed (31 July 2023) | Viewed by 1742

Special Issue Editor

Prof. Dr. Ayman A. Aly

E-Mail Website
Guest Editor
Department of Mechanical Engineering, College of Engineering, Taif University, Taif 21944, Saudi Arabia
Interests: intelligent control; robotics control; genetic algorithms; neural network; fuzzy systems; nonlinear control systems; fluid power system control; antilock brake system; thermoelectric system control

Special Issue Information

Dear Colleagues,

Intelligent control is multidisciplinary technology encompassing many research fields, such as artificial intelligence, cognitive science, fuzzy theory, network technology, communication technology, etc. It is seeing increasing use in industrial processes due to its contribution to the improvement of industrial efficiency and quality.

This Special Issue, entitled “Intelligent Control in Industrial Processes”, aims to publish the latest research on the applications of intelligent control technologies in industrial processes. Original research articles and reviews are welcome. Potential topics may include (but are not limited to):

  • Real-time detection and control;
  • Nonlinear control systems;
  • Robotics control and intelligent robotics;
  • Intelligent multi-agent systems;
  • Intelligent modeling and identification;
  • Intelligent optimization and learning;
  • Intelligent fault detection and diagnosis;
  • Sensing technology and system;
  • Fuzzy logic control;
  • Neuro-fuzzy control;
  • Internet of Things;
  • Industrial 4.0.

Prof. Dr. Ayman A. Aly
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Applied Sciences is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • computing and artificial intelligence
  • robotics and automation
  • nonlinear control
  • adaptive control
  • nonlinear control systems
  • dynamics system model and simulation
  • vibrations system
  • sensors and measurements

Published Papers (1 paper)

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Research

15 pages, 5485 KiB  
Article
Reactor Temperature Prediction Method Based on CPSO-RBF-BP Neural Network
by Xiaowei Tang, Bing Xu and Zichen Xu
Appl. Sci. 2023, 13(5), 3230; https://doi.org/10.3390/app13053230 - 02 Mar 2023
Cited by 6 | Viewed by 1262
Abstract
A neural network model based on a chaotic particle swarm optimization (CPSO) radial basis function-back propagation (RBF-BP) neural network was suggested to improve the accuracy of reactor temperature prediction. The training efficiency of the RBF-BP neural network is influenced to some degree by [...] Read more.
A neural network model based on a chaotic particle swarm optimization (CPSO) radial basis function-back propagation (RBF-BP) neural network was suggested to improve the accuracy of reactor temperature prediction. The training efficiency of the RBF-BP neural network is influenced to some degree by the large randomness of the initial weight and threshold. To address the impact of initial weight and threshold uncertainty on the training efficiency of the RBF-BP combined neural network, this paper proposes using a chaotic particle swarm optimization algorithm to correct the RBF-BP neural network’s initial weight and threshold, as well as to optimize the RBF-BP neural network to speed up the algorithm and improve prediction accuracy. The measured temperature of the reactor acquired by on-site enterprises was confirmed and compared to the predicted results of the BP, RBF-BP, and PSO-RBF-BP neural network models. Finally, Matlab simulation tests were performed, and the experimental data revealed that the CPSO-RBF-BP combined neural network model suggested in this paper had a root-mean-square error of 17.3%, an average absolute error of 11.4%, and a fitting value of 99.791%. Prediction accuracy and efficiency were superior to those of the BP, RBF-BP, and PSO-RBF-BP models. The suggested model’s validity and feasibility were established. The study findings may provide some reference values for the reactor’s temperature prediction. Full article
(This article belongs to the Special Issue Intelligent Control in Industrial Processes)
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