Deep Neural Multi-Layer Perceptron Integrated Hybrid Engineering for IIoT Resource Management

The combination of both Deep Neural Multi-Layer Perceptron (DNMLP) and Model-Based Engineering (MBE) creates a novel hybrid engineering methodology for the resource management in IIoT applications. The DNMLP-MBE framework builds upon cloud-based IIoT architectures to improve the productivity, energy, and automation of Dense Industrial domains. Thus, solving problems related to voltage optimization and possible tasks in pumping systems results in efficient and synchronized activity. The DNMLP-MBE algorithm can work out through multi-view modeling, each neuron imitating biological cells in order to process signals that are input and reach optimal output. Iterative testing for validation points to overall improvements on aspects like delayed computation time and costs to claims efficiency on other functions including milieu such as Model-in-the-Loop and Hardware-in-the-Loop. The proposed system also ensures a reasonable distribution of computational load and resources Implemented reference signal voltages exhibit enhanced performance relative to typical approaches. This promising paradigm shifts the resource management of IIoT to the next level by applying state-of-the-art deep learning into sound engineering processes in pursuit of better industrial automation, less breakdown time and cost optimization. The outcomes ensure the flexibility and the capability of DNMLP-MBE for contemporary industrial procedures.