# Performance Comparison of ANFIS Models by Input Space Partitioning Methods
## 1 Introduction  
In the real world, there are active research studies on prediction in various fields such as weather, energy, communication, control, architecture, and pattern recognition. Among them, the adaptive neuro fuzzy inference system (ANFIS) model is applied to various fields as a system that combines artificial neural networks with adaptive and learning ability and fuzzy reasoning similar to the human thinking ability. Studies that apply ANFIS models to various fields in the real world are underway. [PERSON] [1] applied ANFIS to predict the thermal contact conductance between the exhaust valve and the seat. [PERSON] [2] applied ANFIS to predict the convective dryer energy consumption used to produce agricultural products. [PERSON] [3] applied ANFIS to predict cetane numbers of biodiesel fuel using a desirability function. [PERSON] [4] applied ANFIS and linear regression (LR) to predict the depth of the insulation of sheathed file integrated bridges. [PERSON] [5] applied ANFIS, artificial neural networks (ANNs), and logistic methods to predict biogas production from spent mushroom compost (SMC). [PERSON] [6] applied ANFIS to predict the compressive strength and elasticity of heterogeneous sedimentary rocks. [PERSON] [7] applied ANFIS to predict SnO\({}_{2}\) performance and ZrO\({}_{2}\) and CaCO\({}_{3}\) nanoparticle solution transport and self-compression characteristics. [PERSON] [8] applied ANFIS to predict the ratio of diesel and gas in an oil reservoir. [PERSON] [9] applied ANFIS to predict the convection of internal CuO-water nanofluids through the thermal cycling of a circular cylinder. [PERSON] [10] applied ANFIS to predict farmyard, multipass, and water content for clay soil compaction. [PERSON] [11] applied ANFIS to predict the characteristics of biodiesel fuel owing to fatty acid composition. [PERSON] [12] applied ANFIS, LR, and ANN to predict body mass index. [PERSON] [13] applied ANFIS to predict the composite index and fit index for a physical habitat simulation of freshwater fish. [PERSON] [14] applied ANFIS to predict the compressive strength of self-compacting concrete. [PERSON] [15] applied ANFIS to predict the critical heat flux (CHF) to determine the economic efficiency and safety of water-cooled reactors. [PERSON] [16] applied ANFIS to predict the performance of a laser cutter in intelligent manufacturing mode. [PERSON] [17] applied ANFIS to predict the debris created by the long contraction of a channel. [PERSON] [18] applied ANFIS, ANN, and multiple nonlinear regression (MNLR) to predict the water flow in the Karuvannur River Basin. [PERSON] [19] applied ANFIS and support vector machine (SVM) to predict the performance and emissions of SI (spark ignition) engines using gasoline-ethanol blended fuels. In addition to research that applies the ANFIS model to various fields, research is being actively conducted to propose a new ANFIS model in combination with other models and methods. [PERSON] [20] proposed a bubble column hydrodynamic prediction model combining computational fluid dynamics (CFD) and ANFIS. [PERSON] [21] proposed an ensemble-ANFIS prediction model to predict multiscalar standard precipitation indices. [PERSON] [22] proposed a fast ensemble empirical mode decomposition (FEEMD)-ANFIS and FEEMD-MLP (Multilayer Perceptron), which is a hybrid model for wind speed prediction. [PERSON] [23] proposed an ensemble ARIMA (auto regressive moving average) -ANFIS hybrid model for predicting energy consumption. [PERSON] [24] proposed a hybrid model combining wavelets and ANFIS for groundwater flow simulation prediction of the Miandarbanol plain. [PERSON] [25] proposed a hybrid model combining the firefly algorithm and ANFIS to predict the monthly flow rate of a streamflow. [PERSON] [26] proposed an optimization model that combines particle swarm optimization (PSO) and ANFIS to predict the destruction that occurs during underground excavation.