IDEAS home Printed from https://ideas.repec.org/h/spr/sprchp/978-3-031-35668-1_4.html
   My bibliography  Save this book chapter

On Fusion of Soft and Hard Computing: Traditional (“Hard Computing”) Optimal Rescaling Techniques Simplify Fuzzy Control

In: Fuzzy Optimization, Decision-making and Operations Research

Author

Listed:
  • Hugh F. VanLandingham

    (Virginia Polytechnic Institute and State University, The Bradley Department of Electrical Engineering)

  • Vladik Kreinovich

    (University of Texas at El Paso, Department of Computer Science)

Abstract

One of the main objectives of fuzzy control is to translate expert rules –formulated in imprecise (“fuzzy”) words from natural language– into a precise control strategy. This translation is usually done in two steps. First, we apply a fuzzy control methodology to get a rough approximation of the expert’s control strategy, and then we tune the resulting fuzzy control system. The first step (getting a rough approximation) is well analyzed. Having an expert’s intuitive understanding enables us to use soft computing techniques to perform in this step. At this first step, we only use expert rules. Then, we test the resulting control on a real or simulated system and tune the resulting control based on the results of this testing. This second (tuning) step is much more difficult: we no longer have any expert understanding of which tuning is better, and therefore, soft computing techniques are not that helpful. In this chapter, we present a particular case of the tuning problem as a traditional optimization problem and solve it by using traditional (“hard computing”) techniques. In a practical industrial control example, we show that the resulting fusion of soft computing (for a rough approximation) and hard computing (for tuning) leads to high-quality control.

Suggested Citation

  • Hugh F. VanLandingham & Vladik Kreinovich, 2023. "On Fusion of Soft and Hard Computing: Traditional (“Hard Computing”) Optimal Rescaling Techniques Simplify Fuzzy Control," Springer Books, in: Chiranjibe Jana & Madhumangal Pal & Ghulam Muhiuddin & Peide Liu (ed.), Fuzzy Optimization, Decision-making and Operations Research, chapter 0, pages 73-86, Springer.
    • Handle: RePEc:spr:sprchp:978-3-031-35668-1_4
    DOI: 10.1007/978-3-031-35668-1_4
    as

    Download full text from publisher

    To our knowledge, this item is not available for download. To find whether it is available, there are three options:
    1. Check below whether another version of this item is available online.
    2. Check on the provider's web page whether it is in fact available.
    3. Perform a
    for a similarly titled item that would be available.

    More about this item

    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:spr:sprchp:978-3-031-35668-1_4. 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.

    We have no bibliographic references for this item. You can help adding them by using 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    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.