GDL

Journal from gdlhub / 2017-08-16 10:14:46
Oleh : Babita Majhi, Minakhi Rout, Vikas Baghel, King Saud University
Dibuat : 2014-09-16, dengan 1 file

Keyword : Multi-objective optimization Radial basis function (RBF) Fuzzy decision making
Url : http://www.sciencedirect.com/science/article/pii/S1319157813000979
Sumber pengambilan dokumen : web

This paper develops and assesses the performance of a hybrid prediction model using a radial basis function neural network and non-dominated sorting multiobjective genetic algorithm-II (NSGA-II) for various stock market forecasts. The proposed technique simultaneously optimizes two mutually conflicting objectives: the structure (the number of centers in the hidden layer) and the output mean square error (MSE) of the model. The best compromised non-dominated solution-based model was determined from the optimal Pareto front using fuzzy set theory. The performances of this model were evaluated in terms of four different measures using Standard and Poor 500 (S&P500) and Dow Jones Industrial Average (DJIA) stock data. The results of the simulation of the new model demonstrate a prediction performance superior to that of the conventional radial basis function (RBF)-based forecasting model in terms of the mean average percentage error (MAPE), directional accuracy (DA), Thelis’ U and average relative variance (ARV) values.

Beri Komentar ?#(0) | Bookmark

Properti	Nilai Properti
ID Publisher	gdlhub
Organisasi	King Saud University
Nama Kontak	Herti Yani, S.Kom
Alamat	Jln. Jenderal Sudirman
Kota	Jambi
Daerah	Jambi
Negara	Indonesia
Telepon	0741-35095
Fax	0741-35093
E-mail Administrator	elibrarystikom@gmail.com
E-mail CKO	elibrarystikom@gmail.com