TY - JOUR
T1 - A Tabu-Harmony Search-Based Approach to Fuzzy Linear Regression
AU - Mashinchi, M. Hadi
AU - Orgun, Mehmet A.
AU - Mashinchi, Mashaallah
AU - Pedrycz, Witold
PY - 2011/6
Y1 - 2011/6
N2 - We propose an unconstrained global continuous optimization method based on tabu search and harmony search to support the design of fuzzy linear regression (FLR) models. Tabu and harmony search strategies are used for diversification and intensification of FLR, respectively. The proposed approach offers the flexibility to use any kind of an objective function based on clients requirements or requests and the nature of the dataset and then attains its minimum error. Moreover, we elaborate on the error produced by this method and compare it with the errors resulting from the other known estimation methods. To study the performance of the method, three categories of datasets are considered: Numeric inputssymmetric fuzzy outputs, symmetric fuzzy inputssymmetric fuzzy outputs, and numeric inputsasymmetric fuzzy outputs. Through a series of experiments, we demonstrate that in terms of the produced error with different model-fitting measurements, the proposed method outperforms or is Pareto-equivalent to the existing methods reported in the literature.
AB - We propose an unconstrained global continuous optimization method based on tabu search and harmony search to support the design of fuzzy linear regression (FLR) models. Tabu and harmony search strategies are used for diversification and intensification of FLR, respectively. The proposed approach offers the flexibility to use any kind of an objective function based on clients requirements or requests and the nature of the dataset and then attains its minimum error. Moreover, we elaborate on the error produced by this method and compare it with the errors resulting from the other known estimation methods. To study the performance of the method, three categories of datasets are considered: Numeric inputssymmetric fuzzy outputs, symmetric fuzzy inputssymmetric fuzzy outputs, and numeric inputsasymmetric fuzzy outputs. Through a series of experiments, we demonstrate that in terms of the produced error with different model-fitting measurements, the proposed method outperforms or is Pareto-equivalent to the existing methods reported in the literature.
UR - http://www.scopus.com/inward/record.url?scp=79958004907&partnerID=8YFLogxK
U2 - 10.1109/TFUZZ.2011.2106791
DO - 10.1109/TFUZZ.2011.2106791
M3 - Article
AN - SCOPUS:79958004907
SN - 1063-6706
VL - 19
SP - 432
EP - 448
JO - IEEE Transactions on Fuzzy Systems
JF - IEEE Transactions on Fuzzy Systems
IS - 3
M1 - 5688449
ER -