IDEAS home Printed from https://ideas.repec.org/p/pra/mprapa/79846.html

Analyzing Semiconductor component's market sales data to create an Expert Fuzzy inference system

Author

Listed:
  • Saljooghi, Saeed
  • Safisamghabadib, Azamdokht

Abstract

The Policy for buying chips is determined from Original Equipment Manufacturer (OEM's) laptops and computers through similarity criteria and probability rules. This study aims to create expert system that is prediction of purchase behavior in Semiconductor market. similarity criteria and probability rules are extracted from Quarterly list OEM's Information Order in Semiconductor market. we analyze and extract rules form OEM purchase behavior data by Data collection and statistical methods of data mining and then convert them into Fuzzy sets. In addition to information received from market nature, we create an expert system for deduction. our analysis of similar products show that there is two major groups of OEM in purchasing similar products That restoration information is done for a period of one year by using probability rules and getting approximately 95% of average score quarterly.

Suggested Citation

  • Saljooghi, Saeed & Safisamghabadib, Azamdokht, 2016. "Analyzing Semiconductor component's market sales data to create an Expert Fuzzy inference system," MPRA Paper 79846, University Library of Munich, Germany.
    • Handle: RePEc:pra:mprapa:79846
    as

    Download full text from publisher

    File URL: https://mpra.ub.uni-muenchen.de/79846/1/MPRA_paper_79846.pdf
    File Function: original version
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Malte Schwoon, 2006. "Simulating the adoption of fuel cell vehicles," Journal of Evolutionary Economics, Springer, vol. 16(4), pages 435-472, October.
    2. Malte Schwoon, 2005. "Simulating the Adoption of Fuel Cell Vehicles," Working Papers FNU-59, Research unit Sustainability and Global Change, Hamburg University, revised Feb 2006.
    3. David L. Olson & Dursun Delen, 2008. "Advanced Data Mining Techniques," Springer Books, Springer, number 978-3-540-76917-0, March.
    4. Dawid, Herbert, 2006. "Agent-based Models of Innovation and Technological Change," Handbook of Computational Economics, in: Leigh Tesfatsion & Kenneth L. Judd (ed.), Handbook of Computational Economics, edition 1, volume 2, chapter 25, pages 1235-1272, Elsevier.
    5. S. David Wu & Berrin Aytac & Rosemary T. Berger & Chris A. Armbruster, 2006. "Managing Short Life-Cycle Technology Products for Agere Systems," Interfaces, INFORMS, vol. 36(3), pages 234-247, June.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Palmer, J. & Sorda, G. & Madlener, R., 2015. "Modeling the diffusion of residential photovoltaic systems in Italy: An agent-based simulation," Technological Forecasting and Social Change, Elsevier, vol. 99(C), pages 106-131.
    2. Kaufmann, Peter & Stagl, Sigrid & Franks, Daniel W., 2009. "Simulating the diffusion of organic farming practices in two New EU Member States," Ecological Economics, Elsevier, vol. 68(10), pages 2580-2593, August.
    3. Karolina Safarzyńska & Jeroen Bergh, 2010. "Evolutionary models in economics: a survey of methods and building blocks," Journal of Evolutionary Economics, Springer, vol. 20(3), pages 329-373, June.
    4. Elmar Kiesling & Markus Günther & Christian Stummer & Lea Wakolbinger, 2012. "Agent-based simulation of innovation diffusion: a review," Central European Journal of Operations Research, Springer;Slovak Society for Operations Research;Hungarian Operational Research Society;Czech Society for Operations Research;Österr. Gesellschaft für Operations Research (ÖGOR);Slovenian Society Informatika - Section for Operational Research;Croatian Operational Research Society, vol. 20(2), pages 183-230, June.
    5. Balint, T. & Lamperti, F. & Mandel, A. & Napoletano, M. & Roventini, A. & Sapio, A., 2017. "Complexity and the Economics of Climate Change: A Survey and a Look Forward," Ecological Economics, Elsevier, vol. 138(C), pages 252-265.
    6. Valeria Costantini & Francesco Crespi, 2013. "Public policies for a sustainable energy sector: regulation, diversity and fostering of innovation," Journal of Evolutionary Economics, Springer, vol. 23(2), pages 401-429, April.
    7. Petschnig, Martin & Heidenreich, Sven & Spieth, Patrick, 2014. "Innovative alternatives take action – Investigating determinants of alternative fuel vehicle adoption," Transportation Research Part A: Policy and Practice, Elsevier, vol. 61(C), pages 68-83.
    8. Blanco, Herib & Gómez Vilchez, Jonatan J. & Nijs, Wouter & Thiel, Christian & Faaij, André, 2019. "Soft-linking of a behavioral model for transport with energy system cost optimization applied to hydrogen in EU," Renewable and Sustainable Energy Reviews, Elsevier, vol. 115(C).
    9. repec:spo:wpmain:info:hdl:2441/1nlv566svi86iqtetenms15tc4 is not listed on IDEAS
    10. repec:spo:wpmain:info:hdl:2441/5qr7f0k4sk8rbq4do5u6v70rm0 is not listed on IDEAS
    11. Albert Faber & Koen Frenken, 2008. "Models in evolutionary economics and environmental policy: Towards an evolutionary environmental economics," Innovation Studies Utrecht (ISU) working paper series 08-15, Utrecht University, Department of Innovation Studies, revised Apr 2008.
    12. Robinson, Scott A. & Rai, Varun, 2015. "Determinants of spatio-temporal patterns of energy technology adoption: An agent-based modeling approach," Applied Energy, Elsevier, vol. 151(C), pages 273-284.
    13. Dumas, Marion & Rising, James & Urpelainen, Johannes, 2016. "Political competition and renewable energy transitions over long time horizons: A dynamic approach," Ecological Economics, Elsevier, vol. 124(C), pages 175-184.
    14. Guido Ala & Gabriella Di Filippo & Fabio Viola & Graziella Giglia & Antonino Imburgia & Pietro Romano & Vincenzo Castiglia & Filippo Pellitteri & Giuseppe Schettino & Rosario Miceli, 2020. "Different Scenarios of Electric Mobility: Current Situation and Possible Future Developments of Fuel Cell Vehicles in Italy," Sustainability, MDPI, vol. 12(2), pages 1-22, January.
    15. Jeroen Bergh, 2007. "Evolutionary thinking in environmental economics," Journal of Evolutionary Economics, Springer, vol. 17(5), pages 521-549, October.
    16. Martin Zsifkovits & Markus Günther, 2015. "Simulating resistances in innovation diffusion over multiple generations: an agent-based approach for fuel-cell vehicles," Central European Journal of Operations Research, Springer;Slovak Society for Operations Research;Hungarian Operational Research Society;Czech Society for Operations Research;Österr. Gesellschaft für Operations Research (ÖGOR);Slovenian Society Informatika - Section for Operational Research;Croatian Operational Research Society, vol. 23(2), pages 501-522, June.
    17. Hossein Sabzian & Mohammad Ali Shafia & Mehdi Ghazanfari & Ali Bonyadi Naeini, 2020. "Modeling the Adoption and Diffusion of Mobile Telecommunications Technologies in Iran: A Computational Approach Based on Agent-Based Modeling and Social Network Theory," Sustainability, MDPI, vol. 12(7), pages 1-36, April.
    18. Köhler, Jonathan & Whitmarsh, Lorraine & Nykvist, Björn & Schilperoord, Michel & Bergman, Noam & Haxeltine, Alex, 2009. "A transitions model for sustainable mobility," Ecological Economics, Elsevier, vol. 68(12), pages 2985-2995, October.
    19. Y. Li & C.J.M. Kool & P.J. Engelen, 2016. "Hydrogen-Fuel Infrastructure Investment with Endogenous Demand: A Real Options Approach," Working Papers 16-12, Utrecht School of Economics.
    20. Esteban Lopez-Arboleda & Alfonso T. Sarmiento & Laura M. Cardenas, 2019. "Systematic Review of Integrated Sustainable Transportation Models for Electric Passenger Vehicle Diffusion," Sustainability, MDPI, vol. 11(9), pages 1-19, April.
    21. Ye Li & Clemens Kool & Peter-Jan Engelen, 2020. "Analyzing the Business Case for Hydrogen-Fuel Infrastructure Investments with Endogenous Demand in The Netherlands: A Real Options Approach," Sustainability, MDPI, vol. 12(13), pages 1-22, July.
    22. Hanna Wang-Helmreich & Stefan Lochner, 2011. "Natural Gas in Road Transportation - A Low-emission Bridging Technology?," EWI Working Papers 2011-14, Energiewirtschaftliches Institut an der Universitaet zu Koeln (EWI).

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;

    JEL classification:

    • C0 - Mathematical and Quantitative Methods - - General
    • C6 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:pra:mprapa:79846. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Joachim Winter (email available below). General contact details of provider: https://edirc.repec.org/data/vfmunde.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.