• DocumentCode
    2142434
  • Title

    A Faster Approach for Design of Optimum Gain L-Band Pyramidal Horn Using Adaptive Neuro Fuzzy Inference System (ANFIS)

  • Author

    Modi, A.Y. ; Mehta, Jigar ; Pisharody, Nilima

  • Author_Institution
    Dept. of Electron. & Commun. Eng., Nirma Univ. Ahmedabad, Ahmedabad, India
  • fYear
    2013
  • fDate
    27-29 Sept. 2013
  • Firstpage
    37
  • Lastpage
    40
  • Abstract
    In this paper, optimization of optimum gain pyramidal horn Antenna for L-band has been done using Adaptive neuro fuzzy inference systems (ANFIS). The ANFIS is trained in such a way that for any desired gain and frequency (in the L-band), it can generate design parameters of pyramidal horn antenna with a great amount of accuracy. The errors in the desired gain are less than 1%. The numerical procedure followed here is that of synthesis i.e. the gain and resonant frequency are taken as input parameters while length, width and height of the antenna are considered as outputs. The advantage of the following paper lies in the fact that the geometry of a desired antenna (pyramidal) can be judged if only the optimum gain and the frequency of operation are provided. Without the trained system, finding this out would take substantial time.
  • Keywords
    fuzzy neural nets; fuzzy reasoning; geometry; horn antennas; optimisation; ANFIS training; adaptive neuro fuzzy inference system; antenna geometry; design parameters; numerical procedure; optimum gain L-band pyramidal horn design; optimum gain pyramidal horn antenna optimization; resonant frequency; Adaptive systems; Firing; Fuzzy logic; Gain; Horn antennas; L-band; Mathematical model; Adaptive neuro fuzzy inference system; full wave solver; pyramidal horn antenna;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Computational Intelligence and Communication Networks (CICN), 2013 5th International Conference on
  • Conference_Location
    Mathura
  • Type

    conf

  • DOI
    10.1109/CICN.2013.17
  • Filename
    6657949