Abstract
This study has proposed and empirically tested two Adaptive Neuro-Fuzzy Inference System (ANFIS) models for the first time for predicting Australia‘s domestic low cost carriers‘ demand, as measured by enplaned passengers (PAX Model) and revenue passenger kilometres performed (RPKs Model). In the ANFIS, both the learning capabilities of an artificial neural network (ANN) and the reasoning capabilities of fuzzy logic are combined to provide enhanced prediction capabilities, as compared to using a single methodology. Sugeno fuzzy rules were used in the ANFIS structure and the Gaussian membership function and linear membership functions were also developed. The hybrid learning algorithm and the subtractive clustering partition method were used to generate the optimum ANFIS models. Data was normalized in order to increase the model‘s training performance. The results found that the mean absolute percentage error (MAPE) for the overall data set of the PAX and RPKs models was 1.52% and 1.17%, respectively. The highest R2-value for the PAX model was 0.9949 and 0.9953 for the RPKs model, demonstrating that the models have high predictive capabilities.
Highlights
Forecasting is the process of making projections about future performance based on the existing historic data
Sugeno fuzzy rules were used in the Adaptive Neuro-Fuzzy Inference System (ANFIS) structure and the Gaussian membership function and linear membership functions were developed
The results found that the mean absolute percentage error (MAPE) for the overall data set of low cost carriers (LCCs) enplaned passengers (PAX) and revenue passenger kilometres performed (RPKs) models were 1.52% and 1.17%, respectively
Summary
Forecasting is the process of making projections about future performance based on the existing historic data. The Adaptive Neuro-Fuzzy Inference System (ANFIS), first introduced by Jang (1993), is a hybrid method comprising both fuzzy inference systems and the artificial neural network (ANN) (Fang 2012; Liu et al 2008) This system combines the benefits of both approaches; wherein the former brings prior knowledge into a set of constraints to obtain the optimal solution, the latter is good at capturing various patterns (Jang et al 1997; Xiao et al 2014; Yetilmezsoy et al 2011). The ANFIS is considered a more powerful approach than the simple fuzzy logic algorithm and artificial neural networks, as this technique provides a method whereby fuzzy modelling learns about the data set; in order to compute the membership function parameters which best allow the associated fuzzy inference system to track the given input/output data (Al-Ghandoor et al 2012: 130). To overcome the problematic conditions of ANNs and fuzzy systems, a new system combining both ANNs and the fuzzy system, called the adaptive-network-based fuzzy inference system (ANFIS) was proposed by Jang (1993)
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