A data-driven based decomposition–integration method for remanufacturing cost prediction of end-of-life products

Keyphrases

• Remanufacturing Cost 100%
• End-of-life Products 100%
• Cost Prediction 100%
• Cost Data 10%
• Effective Tool 10%
• Prediction Accuracy 10%
• Neural Network 10%
• Nonlinearity 10%
• Economic Perspective 10%
• Time Series Data 10%
• Computation Time 10%
• Benchmark Model 10%
• Excavator 10%
• Improved Local Mean Decomposition 10%
• Low-precision 10%
• PSO-BP Algorithm 10%
• Remanufacturability 10%
• Periodic Fluctuation 10%
• BP Neural Network 10%
• Result Prediction 10%
• Less Computation 10%
• Particle Swarm Optimization (PSO) Algorithm 10%

Engineering

• Product Life 100%
• Remanufacturing 100%
• Illustrates 10%
• Particle Swarm Optimization 10%
• Computation Time 10%
• Cost Data 10%
• Local Mean 10%
• Data Series 10%
• Data Sample 10%
• Sample Data 10%
• Excavators 10%
• Nonlinearity 10%

Chemical Engineering

• Neural Network 100%