Analysis On Industrial Internet Of Things Using Deep Neural Multi-Layer Perceptron Based Model-Based Engineering
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Abstract
In this paper, an analysis is presented using a Deep Neural Multi-Layer Perceptron based Model-based Engineering (DNMLP-MBE) that implements the industrial workflow in cloud-based IIoT. The integrated cloud-based IIoT combines cloud features with open connectivity with IoT. In this research, the validation stages consumes high energy for tracking the reference signal and it requires maximum voltage for the pump. In order to improve the tracking of reference signal with reduced energy and minimum voltage to pump, we use ML algorithm namely Artificial Neural Network (DNMLP-MBE) to optimize the operation in the workflow. The simulation is conducted to verify the benefits associated with Cloud-IIoT integration with MBE.
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