The business model of intelligent manufacturing with Internet of Things and machine learning
Tongtong Geng, Yueping Du
Abstract
To establish a business model of intelligent manufacturing, the sequence Generative Adversarial Network (SeqGAN) was used to optimise the Back Propagation (BP) neural network algorithm improved by multi-objective Genetic Algorithm to propose the sequence Generative Adversarial Network-Genetic Algorithm Back Propagation Algorithm (SeqGAN-GABP). Meanwhile, the Elman algorithm was optimised by the SeqGAN model to propose the SeqGAN-Elman algorithm. The algorithms were constructed and trained and were applied to the Internet of Things platforms. The results showed that the SeqGAN-GABP algorithm outperforms the SeqGAN-Elman algorithm in terms of minimal error, fitting accuracy, training time and internal memory usage.
Topics & Concepts
Genetic algorithmComputer scienceArtificial neural networkArtificial intelligenceSequence (biology)BackpropagationGenerative grammarMachine learningAdversarial systemAlgorithmThe InternetData miningWorld Wide WebGeneticsBiologyDigital Transformation in IndustryIndustrial Vision Systems and Defect DetectionIndustrial Technology and Control Systems