Teknik Tapisan Dalam Pemodelan Perceptron Multi Aras
This research is done to see the implication filtering technique on house price modeling performance in predicting based on pre-processing technique in learning capability in neural network using Multilayer Perceptron. This research also discuss the relationship between house price analysis and neu...
Saved in:
Main Author: | |
---|---|
Format: | Thesis |
Language: | eng eng |
Published: |
1999
|
Subjects: | |
Online Access: | https://etd.uum.edu.my/708/1/ROSHIDI_BIN_DIN_-_Teknik_Tapisan_Dalam_Pemodelan_Perceptron_Multi_Aras.pdf https://etd.uum.edu.my/708/2/1.ROSHIDI_BIN_DIN_-_Teknik_Tapisan_Dalam_Pemodelan_Perceptron_Multi_Aras.pdf |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
id |
my-uum-etd.708 |
---|---|
record_format |
uketd_dc |
institution |
Universiti Utara Malaysia |
collection |
UUM ETD |
language |
eng eng |
topic |
QA76 Computer software |
spellingShingle |
QA76 Computer software Roshidi, Din Teknik Tapisan Dalam Pemodelan Perceptron Multi Aras |
description |
This research is done to see the implication filtering technique on house price modeling performance in predicting based on pre-processing technique in learning capability in neural network using Multilayer Perceptron. This research also discuss the relationship between house
price analysis and neural network application in predicting terrace house price.
This research uses functional mathematics approach based on previous years house price index to predict the actual value of terrace house in the future. This approach is also used in predicting medium period by collecting actual terrace house price data in Kuala Lumpur. These data is
not only based on terrace house price index but also based on various aspect which involve direct or indirectly.
The obtained result is compared with actual price index the following year. Furthermore, the finding gained from the research model shows that is used will produce better prediction.
|
format |
Thesis |
qualification_name |
masters |
qualification_level |
Master's degree |
author |
Roshidi, Din |
author_facet |
Roshidi, Din |
author_sort |
Roshidi, Din |
title |
Teknik Tapisan Dalam Pemodelan Perceptron Multi Aras |
title_short |
Teknik Tapisan Dalam Pemodelan Perceptron Multi Aras |
title_full |
Teknik Tapisan Dalam Pemodelan Perceptron Multi Aras |
title_fullStr |
Teknik Tapisan Dalam Pemodelan Perceptron Multi Aras |
title_full_unstemmed |
Teknik Tapisan Dalam Pemodelan Perceptron Multi Aras |
title_sort |
teknik tapisan dalam pemodelan perceptron multi aras |
granting_institution |
Universiti Utara Malaysia |
granting_department |
Sekolah Siswazah |
publishDate |
1999 |
url |
https://etd.uum.edu.my/708/1/ROSHIDI_BIN_DIN_-_Teknik_Tapisan_Dalam_Pemodelan_Perceptron_Multi_Aras.pdf https://etd.uum.edu.my/708/2/1.ROSHIDI_BIN_DIN_-_Teknik_Tapisan_Dalam_Pemodelan_Perceptron_Multi_Aras.pdf |
_version_ |
1747826978909585408 |
spelling |
my-uum-etd.7082013-07-24T12:08:37Z Teknik Tapisan Dalam Pemodelan Perceptron Multi Aras 1999-05-16 Roshidi, Din Sekolah Siswazah Graduate School QA76 Computer software This research is done to see the implication filtering technique on house price modeling performance in predicting based on pre-processing technique in learning capability in neural network using Multilayer Perceptron. This research also discuss the relationship between house price analysis and neural network application in predicting terrace house price. This research uses functional mathematics approach based on previous years house price index to predict the actual value of terrace house in the future. This approach is also used in predicting medium period by collecting actual terrace house price data in Kuala Lumpur. These data is not only based on terrace house price index but also based on various aspect which involve direct or indirectly. The obtained result is compared with actual price index the following year. Furthermore, the finding gained from the research model shows that is used will produce better prediction. 1999-05 Thesis https://etd.uum.edu.my/708/ https://etd.uum.edu.my/708/1/ROSHIDI_BIN_DIN_-_Teknik_Tapisan_Dalam_Pemodelan_Perceptron_Multi_Aras.pdf application/pdf eng validuser https://etd.uum.edu.my/708/2/1.ROSHIDI_BIN_DIN_-_Teknik_Tapisan_Dalam_Pemodelan_Perceptron_Multi_Aras.pdf application/pdf eng public masters masters Universiti Utara Malaysia Adrian.E.,(1946),"The physical background of perceptron", Clarendon Press, Oxford. Antogetti Paolo, Veljko Milutinovic (1991), "Neural network (Concepts, application and implementation)", Prentice Hall. New Jersey. DARPA (1988), "Neural network study", AFCEA International Press. New York. Evans.J.T, J.B Gomm and D. Williams (1993), "A practical application of neural modelling and predictive control", Chapman & Hall. London. Gomm.J.B, G.F Page and D.Williams (1993), "Application of neural networks to modelling and control", Chapman & Hall. London. Halbert White. (1998) "Economic prediction using neural networks: The case of IBM daily stock returns", Proceeding of the IEEE International Conference on Neural Networks, pp. 11 11451-11450. Haykin Simon (1994), "Neural network (A comprehensive foundation)", MacMillan Publishing Company. New York. INSPEN (1996), "Malaysia house price index : A technical summary", Kementerian Kewangan Malaysia. Kuala Lumpur. Komo D., C. Chang and H. KO. (1994) "Neural network technology for stock market index prediction", Proceeding of Symposium on Speech, Image Processing and Neural Network, Hong Kong, April. Kuffler. Simon, J. Nicholls, A Martin (1984), "From neural to brain", Sinauer Publishers, Sunderland, Mass. 2nd edition. Ku Mahamud K.R, Abu Bakar and Nawawi (1999), "Multi Layer Perceptron Modelling in Housing Market", Malaysian Management Journal v3. Marcus D.Odom and Ramesh Sharda. (1990), "A neural network model for bankruptcy prediction", Proceeding of the IEEE International Conference on Neural Networks, pp. Ill 63-11] 68. San Diego, Jun. Parker, D.B (1987), "Optimal algorithms for adaptive networks:second order back propagation, second order direct propagation and second Hebbian learning. "IEEE I" International Conference on Neural Networks, Vol. 2. pp 593-600, San Diego. Postma.E.0 and P.T.W Hudson (1995), "Choosing and using a neural net". Springer. Razali Agus (1997), "Housing the nation : A definitive study", Cagamas Berhad, Kuala Lumpur . Skapura , D.M (1995), "Building neural networks", Addision Wesley. New York. Soumitra Dutta and Sashi Shekhar. (1998), "Bond rating: A nonconservative application of neural network, Proceeding of the IEEE International Conference on Neural Networks, pp. II 1443- 1450. Turban Efraim (1992), "Expert systems and applied artificial intelligent", MacMillan Publishing Company. New York. Wang, H and Ho, K.H (1995), "Artificial intelligent modelling of the private housing market in Singapore", Proceeding of the International Congress on Real Estate, Singapore, April. Youngohc Yoon and George Swales. (1991), "Predicting stock price performance : A neural network approach", Proceeding of the IEEE 2dth Annual Hawaii International Conference of Systems Sciences, pp. 156- 162, January. |