Complex Neural Fuzzy Prediction Using Multi-Swarm
Continuous Ant Colony Optimization
Volume 1 - Issue 3
Chunshien Li1* and Wei-Chu Weng2
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- 1Department of Information Management, Nation Central University, Taiwan
- 2Department of Systems Engineering and Naval Architecture, National Taiwan Ocean University, Taiwan
*Corresponding author:
Chunshien Li, Department of Information Management, Nation Central University No. 300, Zhongli Taoyuan
City 32001, Taiwan
Received: June 14, 2019; Published: July 02, 2019
DOI:
10.32474/CTCSA.2018.01.000115
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Abstract
Prediction of time series is one of major research subjects in data science. This paper proposes a novel approach to the problem
of multiple-target prediction. The proposed approach is mainly composed of three parts: the complex neuro-fuzzy system (CNFS)
built by using complex fuzzy sets, the two-stage feature selection method for multiple targets, and the hybrid machine learning
method that uses the multi-swarm continuous ant colony optimization (MCACO) and the recursive least squares estimation
(RLSE). The CNFS predictive model is responsible for prediction after training. During the training of the model, the parameters are
updated by the MCACO method and the RLSE method where the two methods work cooperatively to become one machine learning
procedure. For the predictive model, complex fuzzy sets (CFSs) are with complex-valued membership degrees within the unit disk
of the complex plane, useful to the non-linear mapping ability of the CNFS model for multiple target prediction. This CFS property
is contrast to real-valued membership degrees in the unit interval [0,1] of traditional fuzzy sets. The two-stage feature selection
applies to select significant features to be the inputs to the model for multiple target prediction. Experiments using real world data
sets obtained from stock markets for the prediction of multiple targets have been conducted. With the results and performance
comparison, the proposed approach has shown outstanding performance over other compared methods.
Keywords: Feature selection for multiple targets; Complex fuzzy set; Continuous ant colony optimization; Multiple swarms;
Multiple target prediction
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