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General Aircraft Material Demand Forecast Based on Modified PSO Optimized BP Neural Network

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Abstract
 1. Introduction 
 2. Influencing Factors Analysis of General Aircraft Material Demand 
  2.1. Accumulated Flight Time  within Compute Cycles (P1) 
  2.2. Air Materiel Failure Rate (P2) 
  2.3. Mean Time between Failures of Air Materiel (P3)
  2.4. Technical Level of Maintenance Crew (P4)
  2.5. Environmental Factor (P5)
 3. BP Neural Network
  3.1 Basic Theory of BP Neural Network
  3.2. The Training of BPNN
 4. The Modified of PSO Algorithm
  4.1. The Basic PSO Algorithm
  4.2. The Modified PSO Algorithm
  4.3 MPSO Algorithm Flow
 5. Construction of MPSO-BP Neural Network Forecast Model
  5.1 Determination Structure of MPSO-BP Neural Network
  5.2 The Training and Forecast of MPSO-BP Neural Network
 6. Actual Example and Analysis
 7. Conclusion
 Acknowledgements 
 References

저자정보

  • Xia Chen School of Economics and Management, Shenyang Aerospace University, ShenYang, China, School of Automation, Shenyang Aerospace University, Shen Yang, China
  • Tuo Wang School of Economics and Management, Shenyang Aerospace University, ShenYang, China

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