A comprehensive review for industrial applicability of artificial neural networks

By Magali Rezende Gouvêa Meireles, Paulo Eduardo Maciel de Almeida, Marcelo Simoes. IEEE transactions on industrial electronics (1982. Print), 2003

473 citations recorded by the source corpus.

This paper presents a comprehensive review of the industrial applications of artificial neural networks (ANNs), in the last 12 years. Common questions that arise to practitioners and control engineers while deciding how to use NNs for specific industrial tasks are answered. Workable issues regarding implementation details, training and performance evaluation of such algorithms are also discussed, based on a judiciously chronological organization of topologies and training methods effectively used in the past years. The most popular ANN topologies and training methods are listed and briefly discussed, as a reference to the application engineer. Finally, ANN industrial applications are grouped and tabulated by their main functions and what they actually performed on the referenced papers. The authors prepared this paper bearing in mind that an organized and normalized review would be suitable to help industrial managing and operational personnel decide which kind of ANN topology and training method would be adequate for their specific problems.

Subfields: Artificial Intelligence, Control and Systems Engineering

View Paper: https://www.semanticscholar.org/paper/8bec02ba5b4a34d8aec4e96791828cf2d71b875f

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Citations in the corpus

Advanced control in factory automation: a survey (2017, methodology): “From the viewpoint of industrial applications, ANN applications can be divided into four main categories (Magali, Meireles, and Simões 2003): modelling and identi ﬁ cation; optimisation and classi ﬁ cation; process control; pattern recognition.”
Sensor Failure Detection, Identification, and Accommodation Using Fully Connected Cascade Neural Network (2015, background): “The popular architecture for NN-based applications is the multilayer perceptron (MLP) NN [12]–[14].”
An Overview of Artificial Intelligence Applications for Power Electronics (2020, background): “Although these techniques are further discussed in [15], it emphasizes on illustrative examples while an in-depth analysis of AI algorithms is not provided.”
Enabling Health Monitoring Approach Based on Vibration Data for Accurate Prognostics (2015, background): “Although several learning schemes for ANNs exist [32], relatively, a new algorithm was proposed for a single-layer feedforward network (SLFN), i.”