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Integrated Soft Computing Based Methodologies for Modelling and Optimisation

  • Sudipto Chaki
  • Sujit Ghosal
Chapter
Part of the SpringerBriefs in Applied Sciences and Technology book series (BRIEFSAPPLSCIENCES)

Abstract

The chapter has explained working of two integrated soft computing based models comprising of Artificial Neural Networks (ANN), Genetic Algorithms (GA) and Simulated Annealing (SA) namely, ANN-GA and ANN-SA for modelling and optimisation of Laser Assisted Oxygen Cutting (LASOX) process. Three different ANN training algorithms such as back propagation neural networks (BPNN) with Levenberg Marquardt (LM) algorithm, BPNN with Bayesian Regularisation (BR) and Radial Basis Function Networks (RBFN) have been employed for training and subsequent prediction. The ANN producing best prediction performance is used for prediction of objective function value. That process is thereby eliminating the need for closed form objective functions to produce required optimisation accuracy. The Chapter detailed the working of ANN, GA, SA and integrated models in step by step for easy understanding of the readers. The process has been explained in a generalised manner so that it can be applied for modelling and optimisation of any manufacturing process.

Keywords

Artificial neural networks(ANN) Back propagation neural networks Levenberg marquardt algorithm Bayesian regularisation Radial basis function networks Genetic algorithms (GA) Simulated annealing (SA) Integrated ANN-GA Integrated ANN-SA 

References

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Copyright information

© The Author(s), under exclusive license to Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Sudipto Chaki
    • 1
  • Sujit Ghosal
    • 2
  1. 1.Department of Automobile EngineeringMCKV Institute of EngineeringHowrahIndia
  2. 2.Department of Mechanical EngineeringNetaji Subhas Engineering CollegeKolkataIndia

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