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Control law and pseudo neural networks synthesized by evolutionary symbolic regression technique

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dc.title Control law and pseudo neural networks synthesized by evolutionary symbolic regression technique en
dc.contributor.author Komínková Oplatková, Zuzana
dc.contributor.author Šenkeřík, Roman
dc.relation.ispartof Seminal Contributions to Modelling and Simulation: 30 Years of the European Council of Modelling and Simulation
dc.identifier.issn 2195-2817 Scopus Sources, Sherpa/RoMEO, JCR
dc.date.issued 2016
dc.citation.spage 91
dc.citation.epage 113
dc.type conferenceObject
dc.language.iso en
dc.publisher Springer International Publishing AG
dc.identifier.doi 10.1007/978-3-319-33786-9_9
dc.relation.uri https://link.springer.com/chapter/10.1007/978-3-319-33786-9_9
dc.subject Analytic programming en
dc.subject Differential evolution en
dc.subject Control law en
dc.subject Pseudo neural network en
dc.description.abstract This research deals with synthesis of final complex expressions by means of an evolutionary symbolic regression technique-analytic programming (AP)for novel approach to classification and system control. In the first case, classification technique-pseudo neural network is synthesized, i. e. relation between inputs and outputs created. The inspiration came from classical artificial neural networks where such a relation between inputs and outputs is based on the mathematical transfer functions and optimized numerical weights. AP will synthesize a whole expression at once. The latter case, the AP will create chaotic controller that secures the stabilization of stable state and high periodic orbit-oscillations between several values of discrete chaotic system. Both cases will produce a mathematical relation with several inputs, the latter case uses several historical values from the time series. For experimentation, Differential Evolution (DE) for the main procedure and also for meta-evolution version of analytic programming (AP) was used. en
utb.faculty Faculty of Applied Informatics
dc.identifier.uri http://hdl.handle.net/10563/1007409
utb.identifier.obdid 43876368
utb.identifier.wok 000389485000010
utb.source d-wok
dc.date.accessioned 2017-09-08T12:14:53Z
dc.date.available 2017-09-08T12:14:53Z
utb.contributor.internalauthor Komínková Oplatková, Zuzana
utb.contributor.internalauthor Šenkeřík, Roman
utb.fulltext.affiliation Zuzana Kominkova Oplatkova and Roman Senkerik Z.K. Oplatkova (✉) R. Senkerik Faculty of Applied Informatics, Tomas Bata University in Zlin, Nam T.G. Masaryka 5555, 760 01 Zlin, Czech Republic e-mail: oplatkova@fai.utb.cz R. Senkerik e-mail: senkerik@fai.utb.cz
utb.fulltext.dates -
utb.fulltext.references 1. Back T, Fogel DB, Michalewicz Z (1997) Handbook of evolutionary algorithms. Oxford University Press. ISBN: 0750303921 2. Deugo D, Ferguson D (2004) Evolution to the Xtreme: evolving evolutionary strategies using a meta-level approach. In: Proceedings of the 2004 IEEE congress on evolutionary computation. IEEE Press, Portland, Oregon, pp 31–38 3. Dioşan L, Oltean M (2009) Evolutionary design of evolutionary algorithms. Genet Program Evolvable Mach 10(3):263–306 4. Edmonds B (2001) Meta-genetic programming: co-evolving the operators of variation. Elektrik 9(1):13–29 5. Eiben AE, Michalewicz Z, Schoenauer M, Smith JE (2007) Parameter control in evolutionary algorithms. Springer, pp 19–46 6. Fausett LV (1993) Fundamentals of neural networks: architectures, algorithms and applications. Prentice Hall, ISBN: 9780133341867 7. Fekiac J, Zelinka I, Burguillo JC (2011) A review of methods for encoding neural network topologies in evolutionary computation. In: ECMS 2011, Krakow, Poland, ISBN: 978-0-9564944-3-6 8. Fisher RA (1936) The use of multiple measurements in taxonomic problems. Ann. Eugenics 7 (2):179–188. doi:10.1111/j.1469-1809.1936.tb02137.x 9. Gurney K (1997) An introduction to neural networks. CRC Press, ISBN: 1857285034 10. Hertz J, Kogh A, Palmer RG (1991) Introduction to the theory of neural computation. Addison-Wesley 11. Hilborn RC (2000) Chaos and nonlinear dynamics: an introduction for scientists and engineers. Oxford University Press, ISBN: 0-19-850723-2 12. Jones DF, Mirrazavi SK, Tamiz M (2002) Multi-objective meta-heuristics: an overview of the current state-of-the-art. Eur J Oper Res 137(1):1–9, ISSN: 0377-2217 13. Just W (1999) Principles of time delayed feedback control. In: Schuster HG (ed) Handbook of chaos control. Wiley-Vch, ISBN: 3-527-29436-8 14. Kalczynski PJ, Kamburowski J (2007) On the NEH heuristic for minimizing the makespan in permutation flow shops. Omega 35(1):53–60 15. Kordík P, Koutník J, Drchal J, Kovářík O, Čepek M, Šnorek M (2010) Meta-learning approach to neural network optimization. Neural Netw 23(4):568–582, ISSN: 0893-6080 16. Koza JR et al (1999) Genetic programming III; darwinian invention and problem solving. Morgan Kaufmann Publisher, ISBN: 1-55860-543-6 17. Koza JR (1998) Genetic programming. MIT Press, ISBN: 0-262-11189-6 18. Kwon OJ (1999) Targeting and stabilizing chaotic trajectories in the standard map. Phys Lett A 258:229–236 19. Lampinen J, Zelinka I (1999) New ideas in optimization—mechanical engineering design optimization by differential evolution, vol 1. McGraw-hill, London, 20p, ISBN: 007-709506-5 20. Machine learning repository with Iris data set http://archive.ics.uci.edu/ml/datasets/Iris 21. Murty KG (1983) Linear programming. Wiley, New York, ISBN: 0-471-09725-X 22. Murty KG (1988) Linear complementarity, linear and nonlinear programming, Sigma series in applied mathematics. Heldermann Verlag, Berlin, ISBN: 3-88538-403-5 23. O’Neill M, Ryan C (2003) Grammatical evolution. Evolutionary automatic programming in an arbitrary language. Kluwer Academic Publishers, ISBN: 1402074441 24. Oplatkova Z (2009) Metaevolution: synthesis of optimization algorithms by means of symbolic regression and evolutionary algorithms. Lambert Academic Publishing Saarbrücken, ISBN: 978-3-8383-1808-0 25. Oplatková Z, Zelinka I (2009) Investigation on evolutionary synthesis of movement commands, modelling and simulation in engineering, vol 2009, Article ID 845080, 12p. Hindawi Publishing Corporation, ISSN: 1687-559 26. Oplatkova Z, Senkerik R, Zelinka I, Holoska J (2010) Synthesis of control law for Chaotic Henon system—preliminary study, ECMS 2010, Kuala Lumpur, Malaysia, pp 277–282, ISBN: 978-0-9564944-0-5 27. Oplatkova Z, Senkerik R, Belaskova S, Zelinka I (2010) Synthesis of control rule for synthesized chaotic system by means of evolutionary techniques, Mendel 2010, Brno, Czech Republic, pp 91–98, ISBN: 978-80-214-4120-0 28. Ott E, Greboki C, Yorke JA (1990) Controlling chaos. Phys Rev Lett 64:1196–1199 29. Price K, Storn RM, Lampinen JA (2005) Differential evolution: a practical approach to global optimization, (Natural computing series), 1st edn. Springer 30. Price K, Storn R (2001) Differential evolution homepage. http://www.icsi.berkeley.edu/~storn/code.html, [Accessed 29/02/2012] 31. Pyragas K (1992) Continuous control of chaos by self-controlling feedback. Phys Lett A 170:421–428 32. Pyragas K (1995) Control of chaos via extended delay feedback. Phys Lett A 206:323–330 33. Senkerik R, Zelinka I, Navratil E (2006) Optimization of feedback control of chaos by evolutionary algorithms. In: Proceedings 1st IFAC conference on analysis and control of chaotic systems, Reims, France, pp 97–102 34. Senkerik R, Zelinka I, Davendra D, Oplatkova Z (2009) Utilization of SOMA and differential evolution for robust stabilization of chaotic logistic equation. Comput Math Appl 60(4):1026– 1037 35. Senkerik R, Oplatkova Z, Zelinka I, Davendra D, Jasek R (2010) Synthesis of feedback controller for chaotic systems by means of evolutionary techniques. In: Proceeding of fourth global conference on power control and optimization, Sarawak, Borneo (2010) 36. Smith J, Fogarty T (1997) Operator and parameter adaptation in genetic algorithms. Soft Comput 1(2):81–87 37. Voutsinas TG, Pappis CP (2010) A branch and bound algorithm for single machine scheduling with deteriorating values of jobs. Math Comput Model 52(1–2):55–61 38. Wasserman PD (1980) Neural computing: theory and practice. Coriolis Group, ISBN: 0442207433 39. Zelinka et al (2004) Analytical programming—a novel approach for evolutionary synthesis of symbolic structures, in Kita E.: evolutionary algorithms, InTech 2011, ISBN: 978-953-307-171-8 40. Varacha P, Zelinka I, Oplatkova Z (2006) Evolutionary synthesis of neural network, Mendel 2006—12th international conference on softcomputing, Brno, Czech Republic, 31 May–2 June 2006, pp 25–31, ISBN: 80-214-3195-4 41. Zelinka I,Oplatkova Z, Nolle L (2005) Boolean symmetry function synthesis by means of arbitrary evolutionary algorithms-comparative study. Int J Simul Syst Sci Technol 6(9):44–56, ISSN: 1473-8031 42. Zelinka I, Senkerik R, Navratil E (2009) Investigation on evolutionary optimization of chaos control. Chaos Solitons Fractals 40(1):111–129 43. Zelinka I, Guanrong Ch, Celikovsky S (2008) Chaos synthesis by means of evolutionary algorithms. Int J Bifurcat Chaos 18(4):911–942
utb.fulltext.sponsorship This work was supported by the Ministry of Education, Youth and Sports of the Czech Republic within the National Sustainability Programme project No. LO1303 (MSMT-7778/2014) and also by the European Regional Development Fund under the project CEBIA-Tech No. CZ.1.05/2.1.00/03.0089, further it was supported by Grant Agency of the Czech Republic—GACR 588P103/15/06700S.
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