PENERAPAN PARTIAL LEAST SQUARES REGRESSION PADA MODEL FUZZY NEURAL NETWORK
| Main Authors: | , Havid Risyanto, , Prof. Drs. Subanar, Ph.D. |
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| Format: | Thesis NonPeerReviewed |
| Terbitan: |
[Yogyakarta] : Universitas Gadjah Mada
, 2013
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| Subjects: | |
| Online Access: |
https://repository.ugm.ac.id/120579/ http://etd.ugm.ac.id/index.php?mod=penelitian_detail&sub=PenelitianDetail&act=view&typ=html&buku_id=60616 |
Daftar Isi:
- In modeling the relationship between the response variable and the predictor variables usually must meet strict assumptions such as the normality of the data, the amount of data that a lot, and no multicollinearity. Partial Least Squares Regression is a powerfull multivariate method and is one of the best approach in the creation of a model that does not have to meet the strict assumptions. Besides being able to solve the problem of prediction, Partial Least Squares Regression can also be used to assist the method of Fuzzy Neural Network to provide maximum results in performance prediction. In this thesis, the model calculations performed using Fuzzy Neural Network input variables (input) form the major components of Partial Least Squares Regression extract resulting from the predictor variables using NIPALS algorithm, so as to simplify the number of fuzzy rules used in the fuzzy inference system and can also optimize the performance prediction. But to know the performance of the best and most accurate predictions were compared between the two methods, which result Fuzzy Neural Network method gives results more accurate predictions that can be used as one of the best alternatives to make predictions.