• Fri frakt över 249 kr
  • •
  • Snabba leveranser
  • •
  • Billiga böcker
Kundservice

Du är på sajten för privatpersoner.

Företag, bibliotek eller offentlig verksamhet?

Du handlar på classic.bokus.com, där alla dina funktioner finns intakta.
Till classic.bokus.com
Bokus logotyp. Gå till startsidan.
  • Erbjudanden
  • Nyheter
  • Student
  • Topplistor
  • Barn & ungdom
  • Bokus Play
  • E-böcker
  • Pocketböcker
  • Spel & pussel

10% rabatt på allt med kod NYSTART10 →

Sidfot

Mina sidor

    Hjälp

    • Kundservice
    • Vanliga frågor och svar
    • Frakt och leverans
    • Retur vid ångerrätt
    • Reklamera vara
    • Betalning
    • Köpvillkor
    • Allmänna villkor
    • Information om webbplatsens tillgänglighet

    Om Bokus

    • Om oss
    • Pressrum
    • För studenter
    • För företag
    • För bibliotek och offentlig verksamhet
    • För leverantörer
    • Hållbarhet

    Populärt

    • Aktuella erbjudanden
    • Presentkort
    • Studentlitteratur
    • Nya böcker
    • Topplistor
    • Signerade böcker
    • Engelska böcker

    Inspiration

    • Boktips
    • BookTok
    • Populära bokserier
    • Barnbokskaraktärer
    • Populära författare
    Logotyp för Bokus
    Följ oss på Facebook (extern länk)Följ oss på Instagram (extern länk)Följ oss på YouTube (extern länk)Följ oss på TikTok (extern länk)
    bokus @ CookiesAnpassa cookiesIntegritetspolicyKöpvillkor
    Till Citymail hemsida (extern länk)Till Budbee hemsida (extern länk)Till Postnord hemsida (extern länk)Till Schenker hemsida (extern länk)Till Early Bird hemsida (extern länk)Till Walleys hemsida (extern länk)
    1. Naturvetenskap och teknik
    2. Teknik och industri
    3. Elektronik och kommunikationer

    Fractional Order Darwinian Particle Swarm Optimization

    Applications and Evaluation of an Evolutionary Algorithm

    AvMicael Couceiro,Pedram Ghamisi

    Häftad, Engelska, 2015

    Del i serien SpringerBriefs in Applied Sciences and Technology

    546 kr

    Beställningsvara. Skickas inom 10-15 vardagar. Fri frakt över 249 kr.

    Beskrivning

    This book examines the bottom-up applicability of swarm intelligence to solving multiple problems, such as curve fitting, image segmentation, and swarm robotics. It compares the capabilities of some of the better-known bio-inspired optimization approaches, especially Particle Swarm Optimization (PSO), Darwinian Particle Swarm Optimization (DPSO) and the recently proposed Fractional Order Darwinian Particle Swarm Optimization (FODPSO), and comprehensively discusses their advantages and disadvantages. Further, it demonstrates the superiority and key advantages of using the FODPSO algorithm, such as its ability to provide an improved convergence towards a solution, while avoiding sub-optimality. This book offers a valuable resource for researchers in the fields of robotics, sports science, pattern recognition and machine learning, as well as for students of electrical engineering and computer science.

    Produktinformation

    • Utgivningsdatum:2015-06-26
    • Mått:155 x 235 x 6 mm
    • Vikt:149 g
    • Format:Häftad
    • Språk:Engelska
    • Serie:SpringerBriefs in Applied Sciences and Technology
    • Antal sidor:75
    • Upplaga:2016
    • Förlag:Springer International Publishing AG
    • ISBN:9783319196343

    Utforska kategorier

    • Elektronik och kommunikationer inom Naturvetenskap och teknik
    • Referensverk och tvärvetenskap inom Samhälle och politik
    • Artificiell intelligens inom Data och IT

    Mer om författaren

    Dr. Micael Couceiro obtained his BSc, Teaching Licensure and Master degrees on Electrical Engineering (Automation and Communications), at the Coimbra School of Engineering (ISEC), Coimbra Polytechnic Institute (IPC). He obtained his PhD degree on Electrical and Computer Engineering (Automation and Robotics) at the Faculty of Sciences and Technology of University of Coimbra (FCTUC), under a PhD Grant from the Portuguese Foundation for Science and Technology. Over the past 6 years, he has been conducting scientific research on several areas, namely robotics, computer vision, sports engineering, and others, all at the Institute of Systems and Robotics (ISR-FCTUC) and RoboCorp (IPC). This resulted in more than 25 scientific articles in international impact factor journals and more than 50 scientific articles at international conferences. Besides being currently a researcher at ISR, he has been invited for lecturing, tutoring and organization of events (e.g., professional courses, national and international conferences, among others), both in the public and private domains. He is co-founder and currently the CEO of Ingeniarius. Pedram Ghamisi graduated with a B.Sc. degree in Civil (Survey) Engineering from the Tehran South Campus of Azad University. Then, he obtained the M.Sc. degree with first Class Honours in Remote Sensing at K.N.Toosi University of Technology in 2012. He received the Best Researcher Award for M.Sc. students in K.N.Toosi University of Technology in the academic year 2010-2011. At the 2013 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Melbourne, July 2013, Mr. Ghamisi was awarded the IEEE Mikio Takagi Prize, for winning the Student Paper Competition at the conference between almost 70 people. He obtained his PhD degree on Electrical and Computer Engineering at the University of Iceland in 2015. His research interests are image processing, soft computing, pattern recognition and remote sensing with the current focus on spectral and spatial techniques for hyperspectral image classification and the integration of LiDAR and hyperspectral data for land cover assessment, and he has published extensively in those fields. He serves as a reviewer for a number of journals including IEEE Trans. on Image Processing, IEEE Trans. on Geoscience and Remote Sensing, IEEE JSTARS, IEEE GRSL and Pattern Recognition Letters.

    Innehållsförteckning

    • Particle Swarm Optimization (PSO).- Fractional Order Darwinian PSO (FODPSO).- Case Study I: Curve Fitting.- Case Study II: Image Segmentation.- Case Study III: Swarm Robotics.- Conclusions.