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Author (up) Allende-Cid, H.; Canessa, E.; Quezada, A.; Allende, H. pdf  url
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  Title An Improved Fuzzy Rule-Based Automated Trading Agent Type Journal Article
  Year 2011 Publication Studies In Informatics And Control Abbreviated Journal Stud. Inform. Control  
  Volume 20 Issue 2 Pages 135-142  
  Keywords Automated Trading Agents; Fuzzy Rule-based Agents  
  Abstract In this paper an improved Fuzzy Rule-Based Trading Agent is presented. The proposal consists in adding machine-learning-based methods to improve the overall performance of an automated agent that trades in futures markets. The modified Fuzzy Rule-Based Trading Agent has to decide whether to buy or sell goods, based on the spot and futures time series, gaining a profit from the price speculation. The proposal consists first in changing the membership functions of the fuzzy inference model (Gaussian and Sigmoidal, instead of triangular and trapezoidal). Then using the NFAR (Neuro-Fuzzy Autoregressive) model the relevant lags of the time series are detected, and finally a fuzzy inference system (Self-Organizing Neuro-Fuzzy Inference System) is implemented to aid the decision making process of the agent. Experimental results demonstrate that with the addition of these techniques, the improved agent considerably outperforms the original one.  
  Address [Allende-Cid, H; Allende, H] Univ Tecn Federico Santa Maria, Dept Informat, Valparaiso 2390123, Chile, Email: vector@inf.utfsm.cl  
  Corporate Author Thesis  
  Publisher Natl Inst R&D Informatics-Ici Place of Publication Editor  
  Language English Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN 1220-1766 ISBN Medium  
  Area Expedition Conference  
  Notes WOS:000292015600006 Approved no  
  Call Number UAI @ eduardo.moreno @ Serial 157  
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