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Inclined planes system optimisation (IPO) and its applications in data mining and system identification

Inclined planes system optimisation (IPO) and its applications in data mining and system identification

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In this chapter, a swarm intelligence algorithm [called inclined planes system optimisation (IPO)] is described, and its applications in different aspects of data mining and system modelling are investigated. IPO is a new swarm intelligence technique that is inspired by the dynamic of sliding motions along frictionless inclined planes. In fact, a swarm of agents (balls) cooperate with each other and move towards better positions in the search space by employing Newton's second law and equations of motion on inclined planes. After introducing IPO, its applications on data clustering, decision function estimation, circuit design and image processing (which are three branches of data mining) are described. Also, its performance on infinite-impulseresponse model identification as an instance of system identification is investigated. IPO shows comparable results to other optimisation methods in different applications. A lot of control parameters in IPO give freedom to designers but sometimes make it need a lot of try and errors for finding optimal parameters which lead to complexity. A modified version of IPO (MIPO) can solve this problem. While in MIPO, the parameters are more independent from user, it shows better, more reliable results than normal IPO. Another concern is optimising multi-objective problems, which can be done using multi-objective IPO. MIPO shows its robustness in designing LC_VCO circuits.

Chapter Contents:

  • Abstract
  • 17.1 Introduction
  • 17.2 Inclined planes system optimisation algorithm
  • 17.2.1 Principles of IPO
  • 17.2.2 Exploration and exploitation in IPO
  • 17.2.3 Performance on benchmark functions
  • 17.3 Application of IPO in machine learning and data mining
  • 17.3.1 Clustering
  • 17.3.2 Classification
  • 17.4 Application of IPO in system identification and system modelling
  • 17.4.1 Infinite-impulse-response (IIR) model identification
  • 17.4.1.1 Modified version of IPO
  • 17.4.1.2 IIR model identification using MIPO
  • 17.4.1.3 Discussion on results
  • 17.4.1.4 Conclusion
  • 17.4.2 Application of IPO in optimisation of CMOS LC_VCOs
  • 17.4.2.1 LC_VCO structure
  • 17.4.2.2 Optimisation of CMOS LC_VCOs
  • 17.4.2.3 Discussion on results
  • 17.5 Conclusion
  • References

Inspec keywords: voltage-controlled oscillators; data mining; optimisation; particle swarm optimisation; swarm intelligence

Other keywords: normal IPO; frictionless inclined planes; infinite-impulseresponse model identification; system identification; system modelling; data clustering; multiobjective IPO; swarm intelligence algorithm; inclined plane system optimisation; swarm intelligence technique

Subjects: Data handling techniques; Optimisation techniques; Knowledge engineering techniques

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