# Performance Comparison of ANFIS Models by Input Space Partitioning Methods
## 1 Introduction
[PERSON] [27] proposed an ANFIS model using the Parzen window membership function to predict the defects of the distillation column used in the fossil fuel process. [PERSON] [28] proposed a model combining structural equation modeling (SEM) and ANFIS to predict factors contributing to the success and development of the hotel industry in Malaysia. [PERSON] [29] proposed a hybrid wavelet-ANFIS model that combines wavelets and ANFIS to predict important temperatures for agricultural production. [PERSON] [30] proposed an ANFIS model using a radial basis function (RBF) to predict the density of biodiesel. [PERSON] [31] proposed a randomized ANFIS that randomly selects the weights for earthquake prediction via ground motion parameters. [PERSON] [32] proposed a PSO (particle swarm optimization) -based ANFIS to predict the thermal conductivity of carbon dioxide. In addition, performance evaluations of ANFIS models based on an input partitioning method such as the grid partitioning method and scatter partitioning method have been carried out. [PERSON] [33] compared the subtractive clustering (SC) method and the grid partitioning method to confirm the prediction performance according to the input space division method of the ANFIS model. [PERSON] [34] compared the predictive performance of ANFIS models with input space partitioning methods to predict biomass heating prices. The input space division method used was a SC method and a fuzzy C-means (FCM) clustering method. [PERSON] [35] compared the predictive performance of ANFIS models with input space partitioning methods to predict thermal errors in CNC (computerized numerical control) machine tools. The input space division method used is the FCM clustering method and the grid partitioning method. [PERSON] [3] predicted and compared the performance of biodiesel fuel cetane numbers using three ANFIS models using grid partitioning, subtractive clustering, and FCM clustering. [PERSON] [36] predicted and compared the performance of the Tehran stock market using two ANFIS models using subtractive clustering and FCM clustering. [PERSON] [37] predicted and compared the performance of the Taiwanese stock market using two ANFIS models using subtractive clustering and FCM clustering. [PERSON] [38] predicted website quality and compared performance using two ANFIS models using subtractive clustering and FCM clustering. [PERSON] [39] estimated the condensation heat transfer coefficient and pressure drop using three ANFIS models using grid partitioning, subtractive clustering, and FCM clustering and compared their performance. The existing comparative studies of the above ANFIS models focus on influential input variable selection and the input space division method. Among them, the input space division methods generally used in comparison are the SC and FCM clustering methods of the gird partitioning and scatter partitioning methods. In this paper, we compare and analyze the prediction performance of four ANFIS models by adding an ANFIS model based on the context-based fuzzy C-means (CFCM) clustering method, which is a new input space partitioning method. An ANFIS1 is an ANFIS model that uses the grid partitioning method, and ANFIS2 is an ANFIS model using the SC method, which is a scatter partitioning method. An ANFIS3 is an ANFIS model using the FCM clustering method, and ANFIS4 is an ANFIS model using the CFCM clustering method. To verify the predictive performance of the four ANFIS models, experiments were conducted using a combined cycle power plant (CCPP) database and an Auto-MPG database. The CCPP database was collected from within the plant from 2006 to 2011 where the plant was set to operate at maximum load, and was used to predict the electrical energy output over time with average temperature, ambient pressure, relative humidity, and discharge vacuum time. The Auto-MPG database was used to predict the vehicle fuel consumption based on the number of cylinders, displacement, horsepower, weight, acceleration, and model year. The composition of this paper is as follows.