Design of information granule-oriented RBF neural networks and its application to power supply for high-field magnet

Engineering Applications of Artificial Intelligence

Volumn 24, Issue 3, 2011, Pages 543-554

  Park, H S ^a   Chung, Y D ^a   Oh, S K ^a   Pedrycz, W ^b,c,d   Kim, H K ^a  

^a Suwon University   (South Korea)

^b UNIVERSITY OF ALBERTA   (Canada)

^c UNIVERSITY OF NOTTINGHAM   (United Kingdom)

^d SYSTEMS RESEARCH INSTITUTE   (Poland)

Author keywords

Fuzzy C Means (FCM) clustering method; Genetic algorithm; Information granule oriented radial basis function (RBF) neural networks; Information granules; K Means clustering; Power supply for high field magnet (PSHFM)

Indexed keywords

ACCURATE PREDICTION; ACTIVATION FUNCTIONS; CLUSTERING METHODS; COMPREHENSIVE DESIGNS; DATA SETS; FUZZIFICATIONS; FUZZY C MEANS CLUSTERING; FUZZY C-MEANS; GAUSSIAN FUNCTIONS; GENERALIZATION CAPABILITY; HIDDEN LAYERS; HIGH COSTS; HIGH FIELD MAGNETS; IDENTIFICATION PROCESS; INFORMATION GRANULATION; INFORMATION GRANULES; K-MEANS; K-MEANS CLUSTERING; LEARNING MECHANISM; MODELING POWER; NONLINEAR CHARACTERISTICS; PERFORMANCE INDICES; POWER SUPPLY; POWER SUPPLY FOR HIGH-FIELD MAGNET (PSHFM); RADIAL BASIS FUNCTION NEURAL NETWORKS; RADIAL BASIS FUNCTIONS; RBF NEURAL NETWORK; RECEPTIVE FIELDS; SMALL SIZE; WEIGHT FACTOR; WEIGHTED OBJECTIVE FUNCTION;

CLUSTER ANALYSIS; CLUSTERING ALGORITHMS; ELECTRIC POWER SUPPLIES TO APPARATUS; FUZZY CLUSTERING; FUZZY NEURAL NETWORKS; FUZZY SYSTEMS; GENETIC ALGORITHMS; GRANULATION; IMAGE SEGMENTATION; MAGNETS; OPTIMIZATION;

RADIAL BASIS FUNCTION NETWORKS;

EID: 79951949969     PISSN: 09521976     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.engappai.2010.11.001     Document Type: Article

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