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 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.