ANFIS networks design using hybrid genetic and SVD methods for modelling of the level variations of the Caspian sea

  • Authors:

  • A. Mehrdad;N. Nariman-zadeh;A. Jamali;A. Teymoorzadeh

  • Affiliations:

  • Department of Mechanical Engineering, Engineering Faculty, The University of Guilan, Rasht, Iran;Department of Mechanical Engineering, Engineering Faculty, The University of Guilan, Rasht, Iran;Department of Mechanical Engineering, Engineering Faculty, The University of Guilan, Rasht, Iran;The University of Mohaghegh-Ardebili, Ardebil, Iran

  • Venue:
  • AIKED'05 Proceedings of the 4th WSEAS International Conference on Artificial Intelligence, Knowledge Engineering Data Bases
  • Year:
  • 2005

Quantified Score

Hi-index 0.00

Visualization

Abstract

Genetic Algorithm (GA) and Singular Value Decomposition (SVD) are deployed for optimal design of both Gaussian membership functions of antecedents and vector of linear coefficients of consequents, respectively, of ANFIS (Adaptive Neuro-Fuzzy Inference Systems) networks which are used for modelling of level variations of the Caspian Sea. It is demonstrated that SVD can be effectively used to optimally find the vector of linear coefficients of conclusion parts in ANFIS models whilst their Gaussian membership functions in premise parts are determined by GA.