Evolution strategies based coefficient of TSK fuzzy forecasting engine

IJAIN (International Journal of Advances in Intelligent Informatics)

Nadia Roosmalita Sari¹  Wayan Firdaus Mahmudy²  Aji Prasetya Wibawa³ 

[1] Institut Agama Islam Negeri (IAIN) Tulungagung;Universitas Brawijaya;Universitas Negeri Malang;

关键词: evolution strategies;    tsk fuzzy logic;    inflation rate;    forecasting;    mean square error;   

DOI  :  10.26555/ijain.v7i1.376

来源: DOAJ

【 摘 要 】

Forecasting is a method of predicting past and current data, most often by pattern analysis. A Fuzzy Takagi Sugeno Kang (TSK) study can predict Indonesia's inflation rate, yet with too high error. This study proposes an accuracy improvement based on Evolution Strategies (ES), a specific evolutionary algorithm with good performance optimization problems. ES algorithm used to determine the best coefficient values on consequent fuzzy rules. This research uses Bank Indonesia time-series data as in the previous study. ES algorithm uses the popSize test to determine the number of initial chromosomes to produce the best optimal solution for this problem. The increase of popSize creates better fitness value due to the ES's broader search area. The RMSE of ES-TSK is 0.637, which outperforms the baseline approach. This research generally shows that ES may reduce repetitive experiment events due to Fuzzy coefficients' manual setting. The algorithm complexity may cost to the computing time, yet with higher performance.

【 授权许可】

Unknown