PENERAPAN ALGORITMA OPTIMASI CHAOS PADA RIDGE POLYNOMIAL NEURAL NETWORK UNTUK PREDIKSI JUMLAH PENGANGGURAN (Studi kasus : Kalimantan Barat)
Ridge polynomial neural network (RPNN) originally proposed by Shin and Ghosh, constructed from an increase in the number of order pi-sigma neurons (PSN). RPNN maintain fast learning, a strong mapping of a single layer higher order neural network (HONN) and avoid many of the weight due to the increas...
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| Main Authors: | , |
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| Format: | Theses and Dissertations NonPeerReviewed |
| Published: |
[Yogyakarta] : Universitas Gadjah Mada
2012
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| Subjects: | |
| Online Access: | https://repository.ugm.ac.id/99097/ http://etd.ugm.ac.id/index.php?mod=penelitian_detail&sub=PenelitianDetail&act=view&typ=html&buku_id=55254 |
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| Summary: | Ridge polynomial neural network (RPNN) originally proposed by Shin and
Ghosh, constructed from an increase in the number of order pi-sigma neurons
(PSN). RPNN maintain fast learning, a strong mapping of a single layer higher
order neural network (HONN) and avoid many of the weight due to the increased
number of inputs. Chaos optimization algorithm is used to utilize the logistic
equation is sensitive to initial conditions, so the chaotic movement can go in any
situation in a certain scale in order, ergodic and maintain the diversity of
solutions.
Chaos optimization algorithm applied to RPNN and used for prediction of
unemployment in West Kalimantan. Network training process using ridge
polynomial neural network, while the search for initial values of weights and bias
network using chaos optimization algorithm. Structure used consists of 6 neurons
input layer and 1 neuron output layer. Data obtained from the Central Bureau of
Statistics.
The results of this study indicate that the proposed algorithm can be used for
prediction. |
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