Novel continuous function prediction model using an improved Takagi-Sugeno fuzzy rule and its application based on chaotic time series
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- 作者：Feng Guo ; Lin Lin ; waiwaiyl@163.com" class="auth_mail" title="E-mail the corresponding author ; Chen Wang
- 关键词：CFPM ; continuous function prediction model ; CF ; continuous function ; TS ; Takagi&ndash ; Sugeno ; DTW ; dynamic time warping ; PNN ; process neural network ; PTS ; process TS ; PSO ; particle swarm optimization ; OLB-QPSO ; quantum-behaved PSO ; FGS ; function group sample ; EGT ; exhausted gas temperature ; EGTM ; EGT margin ; DEGT ; delta EGT ; MIN ; maximum iteration number ; CC ; convergence condition ; CR ; coefficient range ; PN ; particle number ; CIN ; current iteration number ; BPNN ; back propagation neural network ; p
- 刊名：Engineering Applications of Artificial Intelligence
- 出版年：2016
- 出版时间：October 2016
- 年：2016
- 卷：55
- 期：Complete
- 页码：155-164
- 全文大小：1314 K
文摘
A novel continuous function prediction model (CFPM) is proposed to resolve prediction problem whose input and output are both continuous functions (CFs). CFPM can simplify sample space reconstruction by using the coefficients of CFs, and use an improved Takagi–Sugeno (TS) fuzzy rule to predict output CF by optimizing the tendency of input CFs. The improved TS fuzzy rule handles each input CF as a consequent parameter and can obtain the nonlinear tendency. After learning process by using opinion-leader-based particle swarm optimization, output CF is determined. In the data prediction based on chaotic time series, CF can either be obtained directly or be fitted by discrete data points, thus the prediction range is enlarged because more discrete data points can be generated once output CF is determined.
Two experiments and three cases based on chaotic time series are performed to validate CFPM. The Mackey–Glass chaotic time series is used to prove CFPM validation, while the NN3 time series is used to evaluate CFPM performance. The cases on exhaust gas temperature (EGT), EGT margin and delta EGT are used to show that CFPM is valuable in health status prediction for a particular aircraft engine in the practical engineering field.