Environmental Cost Control of Manufacturing Enterprises via Machine Learning under Data Warehouse

Environmental cost refers to the paid by enterprises reduce environmental pollution and resource depletion in production operation. To help costs, a manufacturing control algorithm based on machine learning is proposed. The probabilistic neural network used classify current level of different enterprises. Then, particle swarm optimization (PSO) improved build multi-objective backbone PSO for decision-making, which selection methods. experimental results show that there strong correlation between original data classification proposed network, reaches 96.1%. performance test has best performance, stability, shortest time needed find optimal solution set when initial number 140 iterations 60. Based comprehensive results, following conclusions are drawn. Enterprises should strengthen collaboration cooperation with customers, suppliers, waste-profiting enterprises, so as well costs. sum up, model provides some references adoption