Artificial neural networks (ANNs), inspired by the structure of the human brain and the way in which the brain functions, are central to understanding the domain of artificial intelligence, machine learning, and pattern recognition. Artificial neurons, also known as perceptrons, form the foundation of ANNs. They process input signals and apply weights and activation functions according to the requirements. These neurons are organized into the following layers: input, hidden, and output. Data are received by the input layers. The processing and output are done by the hidden layers and the output layers, respectively. Commercial implementations of ANNs find excellent use in quality control, predictive maintenance, and anomaly detection. Vast datasets are analyzed to identify patterns. This, in turn, helps to make real‐time decisions so that efficiency is increased and costs are reduced. The complexity of data in various industrial applications is efficiently managed by ANNs. For example, manufacturing defects can be identified early. This helps to reduce product flaws as well as predict equipment failures. Consequently, preemptive maintenance can be carried out to avoid costly downtimes. Their ability to adapt, learn, and handle complex data generated from industrial processes boosts productivity and profitability.


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    Titel :

    Artificial Neural Network for Industrial Applications


    Beteiligte:
    Mondal, Arindam (Herausgeber:in) / Ganguli, Souvik (Herausgeber:in) / Saha, Biswajit (Autor:in) / Thakur, Gour Sundar Mitra (Autor:in)


    Erscheinungsdatum :

    24.06.2025


    Format / Umfang :

    19 pages




    Medientyp :

    Aufsatz/Kapitel (Buch)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




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