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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| Format: | Thesis |
| Language: | English |
| Published: |
2013
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| 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 |
| Summary: | 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. |
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