Network-based Type-2 fuzzy system with water flow like algorithm for system identification and signal processing

Che Ting Kuo, Ching Hung Lee*

*Corresponding author for this work

Research output: Contribution to journalArticle

10 Scopus citations

Abstract

This paper introduces a network-based interval type-2 fuzzy inference system (NT2FIS) with a dynamic solution agent algorithm water flow like algorithm (WFA), for nonlinear system identification and blind source separation (BSS) problem. The NT2FIS consists of interval type-2 asymmetric fuzzy membership functions and TSK-type consequent parts to enhance the performance. The proposed scheme is optimized by a new heuristic learning algorithm, WFA, with dynamic solution agents. The proposed WFA is inspired by the natural behavior of water flow. Splitting, moving, merging, evaporation, and precipitation have all been introduced for optimization. Some modifications, including new moving strategies, such as the application of tabu searching and gradient-descent techniques, are proposed to enhance the performance of the WFA in training the NT2FIS systems. Simulation and comparison results for nonlinear system identification and blind signal separation are presented to illustrate the performance and effectiveness of the proposed approach.

Original languageEnglish
Pages (from-to)21-34
Number of pages14
JournalSmart Science
Volume3
Issue number1
DOIs
StatePublished - 2015

Keywords

  • Blind source separation
  • Interval type-2 fuzzy inference systems
  • Neural network
  • System identification
  • Water flow like algorithm

Fingerprint Dive into the research topics of 'Network-based Type-2 fuzzy system with water flow like algorithm for system identification and signal processing'. Together they form a unique fingerprint.

Cite this