Piecewise Linear Model Tree: A Modified Combination of two Learning Algorithms for Neuro-Fuzzy Models

2006 ◽  
Author(s):  
Atiye Jamab ◽  
Babak Araabi
Keyword(s):  
Linear Model ◽  
Piecewise Linear ◽  
Learning Algorithms ◽  
Fuzzy Models ◽  
Piecewise Linear Model ◽  
Model Tree ◽  
Neuro Fuzzy

scholarly journals PiLiMoT: A Modified Combination of LoLiMoT and PLN Learning Algorithms for Local Linear Neurofuzzy Modeling

2011 ◽  
Vol 2011 ◽  
pp. 1-9 ◽  
Author(s):  
Atiye Sarabi-Jamab ◽  
Babak N. Araabi
Keyword(s):  
Linear Model ◽  
Learning Algorithm ◽  
Piecewise Linear ◽  
Learning Algorithms ◽  
Nonlinear Function ◽  
Training Data ◽  
Linear Network ◽  
Data Set ◽  
Model Tree ◽  
Local Linear

Locally linear model tree (LoLiMoT) and piecewise linear network (PLN) learning algorithms are two approaches in local linear neurofuzzy modeling. While both methods belong to the class of growing tree learning algorithms, they use different logics. PLN learning relies on training data, it needs rich training data set and no division test, so it is much faster than LoLiMoT, but it may create adjacent neurons that may lead to singularity in regression matrix. On the other hand, LoLiMoT almost always leads to acceptable output error, but it often needs more rules. In this paper, to exploit the complimentary performance of both algorithms piecewise linear model tree (PiLiMoT) learning algorithm is introduced. In essence, PiLiMoT is a combination of LoLiMoT and PLN learning. The initially proposed algorithm is improved by adding the ability to merge previously divided local linear models, and utilizing a simulated annealing stochastic decision process to select a local model for splitting. Comparing to LoLiMoT and PLN learning, our proposed improved learning algorithm shows the ability to construct models with less number of rules at comparable modeling errors. Algorithms are compared through a case study of nonlinear function approximation. Obtained results demonstrate the advantages of combined modified method.

scholarly journals Piecewise linear model of self-organized hierarchy formation

2020 ◽  
Vol 102 (3) ◽  
Author(s):  
Tomoshige Miyaguchi ◽  
Takamasa Miki ◽  
Ryota Hamada
Keyword(s):  
Linear Model ◽  
Piecewise Linear ◽  
Self Organized ◽  
Piecewise Linear Model ◽  
Hierarchy Formation

scholarly journals Effect of Mean Stress on the Fatigue Life Prediction of Notched Fiber-Reinforced 2060 Al-Li Alloy Laminates under Spectrum Loading

2018 ◽  
Vol 2018 ◽  
pp. 1-16
Author(s):  
Weiying Meng ◽  
Liyang Xie ◽  
Yu Zhang ◽  
Yawen Wang ◽  
Xiaofang Sun ◽  
...  
Keyword(s):  
Fatigue Life ◽  
Linear Model ◽  
Life Prediction ◽  
Piecewise Linear ◽  
Forecast Accuracy ◽  
Cyclic Stress ◽  
Fatigue Life Prediction ◽  
Fiber Reinforced ◽  
Piecewise Linear Model ◽  
Spectrum Loading

This paper presents a study on the fatigue life prediction of notched fiber-reinforced 2060 Al-Li alloy laminates under spectrum loading by applying the constant life diagram. Firstly, a review on the state of the art of constant life diagram models for the life prediction of composite materials is given, which highlights the effect on the forecast accuracy. Then, the fatigue life of notched fiber-reinforced Al-Li alloy laminates (2/1 laminates and 3/2 laminates) is tested under cyclic stress, which has different stress cycle characteristics (constant amplitude loading and Mini-Twist spectrum loading). The introduced models are successfully realized based on the available experimental data of examined laminates. In the case of Mini-Twist spectrum loading, the effect of the constant life diagram on the life prediction accuracy of examined laminates is studied based on the rainflow-counting method and Miner damage criteria. The results show that the simple Goodman model and piecewise linear model have certain advantages compared to other complex models for the life prediction of notched fiber metal laminates with different structures under Mini-Twist loading. From the engineering perspective, the S-N curve prediction based on the piecewise linear model is most applicable and accurate among all the models.

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