Lambda-Max neuro-fuzzy system / Nurul Adzlyana Mohd. Saadon

Accurate predictive modelling is highly essential and ANFIS has successfully been used as forecasting tool in various fields. ANFIS is made up of a multilayer feedforward network that comprises of two important elements in soft computing namely the neural network learning algorithm and fuzzy reas...

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Main Author:	Mohd. Saadon, Nurul Adzlyana
Format:	Thesis
Language:	English
Published:	2013
Subjects:	Neural networks (Computer science)
Online Access:	http://ir.uitm.edu.my/id/eprint/15503/ http://ir.uitm.edu.my/id/eprint/15503/1/TM_NURUL%20ADZLYANA%20MOHD.%20SAADON%20CS%2013_5.PDF

id	uitm-15503
recordtype	eprints
spelling	uitm-155032016-11-18T03:11:21Z http://ir.uitm.edu.my/id/eprint/15503/ Lambda-Max neuro-fuzzy system / Nurul Adzlyana Mohd. Saadon Mohd. Saadon, Nurul Adzlyana Neural networks (Computer science) Accurate predictive modelling is highly essential and ANFIS has successfully been used as forecasting tool in various fields. ANFIS is made up of a multilayer feedforward network that comprises of two important elements in soft computing namely the neural network learning algorithm and fuzzy reasoning which provides smoothness in data processing. However, the weight determined by the first three layers of ANFIS causes inconsistencies in coefficient signs with underlying monotonic relations thus making it impossible to represent known monotonic relations. Hence the objective of this research is to find an alternative method among the AHP techniques of determining the weights to be supplied to the back-propagation layers of ANFIS. Lambda-Max technique has been identified to be the most suitable weight determination technique due to its simple calculation and precision of weights obtained. The newly developed Lambda-Max ANFIS is then used to predict the physical properties of degradable plastics using real life data obtained from the laboratory of the Malaysian Palm Oil Board (MPOB). Bootstrapping resampling technique was applied to the data and consistency index measurement was carried out to ensure the suitability of the data prior to the model development. The system is capable to identify the most suitable input predictor sets based on the values of Root Mean Square Error (RMSE), R and R . The prediction ability of the Lambda-Max ANFIS is compared to the prediction accuracy of the conventional ANFIS. Both the Lambda-Max and conventional ANFIS were found to exhibit significantly similar high prediction accuracies. Predicted output of Lambda-Max ANFIS was also compared to the output of MPOB laboratories. The results show that Lambda-Max gives highly similar prediction output with the actual laboratory output. On top of that, Lambda-Max outputs are highly consistent for any given input combination. Hence, the developed Lambda-Max ANFIS can be used for forecasting purposes with high prediction accuracy and the system can be used as an alternative to laboratory prediction on the physical properties of degradable plastics. Hence it will save time and cost. 2013 Thesis NonPeerReviewed text en http://ir.uitm.edu.my/id/eprint/15503/1/TM_NURUL%20ADZLYANA%20MOHD.%20SAADON%20CS%2013_5.PDF Mohd. Saadon, Nurul Adzlyana (2013) Lambda-Max neuro-fuzzy system / Nurul Adzlyana Mohd. Saadon. Masters thesis, Universiti Teknologi MARA.
repository_type	Digital Repository
institution_category	Local University
institution	Universiti Teknologi MARA
building	UiTM Institutional Repository
collection	Online Access
language	English
topic	Neural networks (Computer science)
spellingShingle	Neural networks (Computer science) Mohd. Saadon, Nurul Adzlyana Lambda-Max neuro-fuzzy system / Nurul Adzlyana Mohd. Saadon
description	Accurate predictive modelling is highly essential and ANFIS has successfully been used as forecasting tool in various fields. ANFIS is made up of a multilayer feedforward network that comprises of two important elements in soft computing namely the neural network learning algorithm and fuzzy reasoning which provides smoothness in data processing. However, the weight determined by the first three layers of ANFIS causes inconsistencies in coefficient signs with underlying monotonic relations thus making it impossible to represent known monotonic relations. Hence the objective of this research is to find an alternative method among the AHP techniques of determining the weights to be supplied to the back-propagation layers of ANFIS. Lambda-Max technique has been identified to be the most suitable weight determination technique due to its simple calculation and precision of weights obtained. The newly developed Lambda-Max ANFIS is then used to predict the physical properties of degradable plastics using real life data obtained from the laboratory of the Malaysian Palm Oil Board (MPOB). Bootstrapping resampling technique was applied to the data and consistency index measurement was carried out to ensure the suitability of the data prior to the model development. The system is capable to identify the most suitable input predictor sets based on the values of Root Mean Square Error (RMSE), R and R . The prediction ability of the Lambda-Max ANFIS is compared to the prediction accuracy of the conventional ANFIS. Both the Lambda-Max and conventional ANFIS were found to exhibit significantly similar high prediction accuracies. Predicted output of Lambda-Max ANFIS was also compared to the output of MPOB laboratories. The results show that Lambda-Max gives highly similar prediction output with the actual laboratory output. On top of that, Lambda-Max outputs are highly consistent for any given input combination. Hence, the developed Lambda-Max ANFIS can be used for forecasting purposes with high prediction accuracy and the system can be used as an alternative to laboratory prediction on the physical properties of degradable plastics. Hence it will save time and cost.
format	Thesis
author	Mohd. Saadon, Nurul Adzlyana
author_facet	Mohd. Saadon, Nurul Adzlyana
author_sort	Mohd. Saadon, Nurul Adzlyana
title	Lambda-Max neuro-fuzzy system / Nurul Adzlyana Mohd. Saadon
title_short	Lambda-Max neuro-fuzzy system / Nurul Adzlyana Mohd. Saadon
title_full	Lambda-Max neuro-fuzzy system / Nurul Adzlyana Mohd. Saadon
title_fullStr	Lambda-Max neuro-fuzzy system / Nurul Adzlyana Mohd. Saadon
title_full_unstemmed	Lambda-Max neuro-fuzzy system / Nurul Adzlyana Mohd. Saadon
title_sort	lambda-max neuro-fuzzy system / nurul adzlyana mohd. saadon
publishDate	2013
url	http://ir.uitm.edu.my/id/eprint/15503/ http://ir.uitm.edu.my/id/eprint/15503/1/TM_NURUL%20ADZLYANA%20MOHD.%20SAADON%20CS%2013_5.PDF
first_indexed	2023-09-18T22:54:00Z
last_indexed	2023-09-18T22:54:00Z
_version_	1777417731727949824

Lambda-Max neuro-fuzzy system / Nurul Adzlyana Mohd. Saadon

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