Polynomial-based radial basis function neural networks (P-RBF NNs) realized with the aid of particle swarm optimization

Fuzzy Sets and Systems

Volumn 163, Issue 1, 2011, Pages 54-77

  Oh, Sung Kwun ^a   Kim, Wook Dong ^a   Pedrycz, Witold ^b,c   Park, Byoung Jun ^d  

^a Suwon University   (South Korea)

^b UNIVERSITY OF ALBERTA   (Canada)

^c POLISH ACADEMY OF SCIENCES   (Poland)

^d Electronics and Telecommunications Research Institute (ETRI)   (South Korea)

Author keywords

Fuzzy clustering; Particle swarm optimization; Pattern classification; Polynomial neural networks; Radial basis function neural networks

Indexed keywords

CLUSTERING METHODS; DATA SETS; DESIGN PARAMETERS; FUNCTIONAL MODULES; FUZZIFICATIONS; FUZZY C-MEANS; FUZZY INFERENCE MECHANISM; HIDDEN LAYERS; INPUT-OUTPUT MAPPING; LEARNING RATES; MACHINE-LEARNING; MATRIX; MOMENTUM COEFFICIENT; NONLINEAR NATURE; PARTICLE SWARM; PATTERN CLASSIFICATION; POLYNOMIAL NEURAL NETWORKS; POLYNOMIAL WEIGHT; RADIAL BASIS FUNCTION NEURAL NETWORKS; RULE BASED; THREE PHASIS; TRAINING DATA;

ATTITUDE CONTROL; CLUSTER ANALYSIS; FUZZY CLUSTERING; FUZZY INFERENCE; FUZZY NEURAL NETWORKS; FUZZY RULES; FUZZY SYSTEMS; NETWORK ARCHITECTURE; PARTICLE SWARM OPTIMIZATION (PSO); POLYNOMIALS;

RADIAL BASIS FUNCTION NETWORKS;

EID: 78249255465     PISSN: 01650114     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.fss.2010.08.007     Document Type: Article

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