Layered feedforward neural network is relevant to empirical physical formula construction: A theoretical analysis and some simulation results

Physics Letters, Section A: General, Atomic and Solid State Physics

Volumn 345, Issue 1-3, 2005, Pages 69-87

  Yildiz, Nihat ^a  

^a CUMHURIYET UNIVERSITY   (Turkey)

Author keywords

Data analysis; Empirical physical formula; Experimental physics; Feedforward neural network; Method of sieves; Non linearity; Non parametric estimation

Indexed keywords

DATA REDUCTION; MULTILAYER NEURAL NETWORKS; NETWORK LAYERS; PARAMETER ESTIMATION; SIEVES;

EMPIRICAL PHYSICAL FORMULA; EXPERIMENTAL PHYSICS; NON-LINEARITY; NON-PARAMETRIC ESTIMATIONS; NONPARAMETRIC METHODS; NUMBER OF HIDDEN NEURONS; PHYSICAL EQUATIONS; PHYSICAL PHENOMENA;

FEEDFORWARD NEURAL NETWORKS;

EID: 24644471947     PISSN: 03759601     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.physleta.2005.06.116     Document Type: Article

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