PiLiMoT: A Modified Combination of LoLiMoT and PLN Learning Algorithms for Local Linear Neurofuzzy Modeling
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 ric...
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| Format: | Article |
| Language: | English |
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Wiley
2011-01-01
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| Series: | Journal of Control Science and Engineering |
| Online Access: | http://dx.doi.org/10.1155/2011/121320 |
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| author | Atiye Sarabi-Jamab Babak N. Araabi |
| author_facet | Atiye Sarabi-Jamab Babak N. Araabi |
| author_sort | Atiye Sarabi-Jamab |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-969e31ff3334465fa21207461174e131 |
| institution | Kabale University |
| issn | 1687-5249 1687-5257 |
| language | English |
| publishDate | 2011-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Control Science and Engineering |
| spelling | doaj-art-969e31ff3334465fa21207461174e1312025-02-03T01:27:33ZengWileyJournal of Control Science and Engineering1687-52491687-52572011-01-01201110.1155/2011/121320121320PiLiMoT: A Modified Combination of LoLiMoT and PLN Learning Algorithms for Local Linear Neurofuzzy ModelingAtiye Sarabi-Jamab0Babak N. Araabi1Control and Intelligent Processing Center of Excellence, School of Electrical and Computer Engineering, University of Tehran, Tehran 1439957131, IranControl and Intelligent Processing Center of Excellence, School of Electrical and Computer Engineering, University of Tehran, Tehran 1439957131, IranLocally 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.http://dx.doi.org/10.1155/2011/121320 |
| spellingShingle | Atiye Sarabi-Jamab Babak N. Araabi PiLiMoT: A Modified Combination of LoLiMoT and PLN Learning Algorithms for Local Linear Neurofuzzy Modeling Journal of Control Science and Engineering |
| title | PiLiMoT: A Modified Combination of LoLiMoT and PLN Learning Algorithms for Local Linear Neurofuzzy Modeling |
| title_full | PiLiMoT: A Modified Combination of LoLiMoT and PLN Learning Algorithms for Local Linear Neurofuzzy Modeling |
| title_fullStr | PiLiMoT: A Modified Combination of LoLiMoT and PLN Learning Algorithms for Local Linear Neurofuzzy Modeling |
| title_full_unstemmed | PiLiMoT: A Modified Combination of LoLiMoT and PLN Learning Algorithms for Local Linear Neurofuzzy Modeling |
| title_short | PiLiMoT: A Modified Combination of LoLiMoT and PLN Learning Algorithms for Local Linear Neurofuzzy Modeling |
| title_sort | pilimot a modified combination of lolimot and pln learning algorithms for local linear neurofuzzy modeling |
| url | http://dx.doi.org/10.1155/2011/121320 |
| work_keys_str_mv | AT atiyesarabijamab pilimotamodifiedcombinationoflolimotandplnlearningalgorithmsforlocallinearneurofuzzymodeling AT babaknaraabi pilimotamodifiedcombinationoflolimotandplnlearningalgorithmsforlocallinearneurofuzzymodeling |