ANFIS Model for Time Series Prediction

2013 ◽  
Vol 385-386 ◽  
pp. 1411-1414 ◽  
Author(s):  
Xue Bo Jin ◽  
Jiang Feng Wang ◽  
Hui Yan Zhang ◽  
Li Hong Cao
Keyword(s):  
Time Series ◽  
Fuzzy Inference System ◽  
Prediction Error ◽  
Fuzzy Inference ◽  
Time Series Prediction ◽  
Prediction Performance ◽  
Membership Functions ◽  
Adaptive Network ◽  
Anfis Model ◽  
Inference System

This paper describes an architecture of ANFIS (adaptive network based fuzzy inference system), to the prediction of chaotic time series, where the goal is to minimize the prediction error. We consider the stock data as the time series. This paper focuses on how the stock data affect the prediction performance. In the experiments we changed the number of data as input of the ANFIS model, the type of membership functions and the desired goal error, thereby increasing the complexity of the training.

scholarly journals A Modified Adaptive Neuro-Fuzzy Inference System Using Multi-Verse Optimizer Algorithm for Oil Consumption Forecasting

Electronics ◽  
2019 ◽  
Vol 8 (10) ◽  
pp. 1071 ◽  
Author(s):  
Mohammed A. A. Al-qaness ◽  
Mohamed Abd Elaziz ◽  
Ahmed A. Ewees ◽  
Xiaohui Cui
Keyword(s):  
Time Series ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Time Series Prediction ◽  
Prediction Method ◽  
Oil Consumption ◽  
Anfis Model ◽  
Inference System ◽  
Neuro Fuzzy ◽  
Prediction Problems

Oil is the primary source of energy, therefore, oil consumption forecasting is essential for the necessary economic and social plans. This paper presents an alternative time series prediction method for oil consumption based on a modified Adaptive Neuro-Fuzzy Inference System (ANFIS) model using the Multi-verse Optimizer algorithm (MVO). MVO is applied to find the optimal parameters of the ANFIS. Then, the hybrid method, namely MVO-ANFIS, is employed to forecast oil consumption. To evaluate the performance of the MVO-ANFIS model, a dataset of two different countries was used and compared with several forecasting models. The evaluation results show the superiority of the MVO-ANFIS model over other models. Moreover, the proposed method constitutes an accurate tool that effectively improved the solution of time series prediction problems.

An Adaptive-Network-Based Fuzzy Inference System for Predicting Springback of U-Bending

2013 ◽  
Vol 284-287 ◽  
pp. 25-30 ◽  
Author(s):  
Bor Tsuen Lin ◽  
Kun Min Huang ◽  
Chun Chih Kuo
Keyword(s):  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Training Data ◽  
Adaptive Network ◽  
Anfis Model ◽  
Inference System ◽  
Bending Process ◽  
Punch Radius ◽  
Triangular Membership Function ◽  
Springback Angle

Springback will occur when the external force is removed after bending process in sheet metal forming. This paper proposed an adaptive-network-based fuzzy inference system (ANFIS) model for prediction the springback angle of the SPCC material after U-bending. Three parameters were selected as the main factors of affecting the springback after bending, including the die clearance, the punch radius, and the die radius. The training data were obtained from results of U-bending experiment. The training data with four different membership functions – triangular, trapezoidal, bell, and Gaussian functions –were employed in the ANFIS to construct a predictive model for the springback of the U-bending. After the comparison of the predicted value with the checking data, we found that the triangular membership function has the best accuracy, which make it the best function to predict the springback angle of sheet metals after U-bending.

scholarly journals Monthly Average Rainfall Forecasting Based On An Adaptive Neuro-Fuzzy Inference System in Upper Brahmani Basin, Odisha, India

2022 ◽  
Author(s):  
M.Uma Maheswar Rao ◽  
Kanhu Charan Patra ◽  
Suvendu Kumar Sasmal
Keyword(s):  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Membership Functions ◽  
Climate Data ◽  
Rainfall Forecasting ◽  
Anfis Model ◽  
Inference System ◽  
Neural Network Learning ◽  
Neuro Fuzzy ◽  
Learning Capabilities

Abstract Floods disrupt human activities, resulting in the loss of lives and property of a region. Excessive rainfall is one of the reasons for flooding, especially in the downstream areas of a catchment. Because of its complexity, understanding and forecasting rainfall is incredibly a challenge. This study investigates the use of an Adaptive Neuro-Fuzzy Inference System (ANFIS) in predicting rainfall using several surface weather parameters as predictors. An ANFIS model is developed for forecasting rainfall over the Upper Brahmani Basin by using 30 years of climate data. A hybrid model with six membership functions gives the best forecast for an area. The suggested method blends neural network learning capabilities with language representations of fuzzy systems that are transparent. The application of ANFIS is to the upper Brahmani river basin is tried for the first time. The ANFIS model with various input structures and membership functions has been built, trained, and tested to evaluate the capability of the model. Statistical performance indices are used to evaluate the performance. Using the developed model, forecast is done for year 2021 – 2030.

Application of Adaptive Network-Based Fuzzy Inference System (ANFIS) in Aerodynamics Prediction of Low-Reynolds-Number Flapping Motion

2010 ◽  
Author(s):  
M. R. Amiralaei ◽  
M. Partovibakhsh ◽  
H. Alighanbari
Keyword(s):  
Reynolds Number ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Low Reynolds Number ◽  
Adaptive Network ◽  
Anfis Model ◽  
Inference System ◽  
Flapping Motion ◽  
Low Reynolds ◽  
Unsteady Lift

The objective of the present study is to develop an Adaptive Network-based Fuzzy Inference System (ANFIS) model to predict the unsteady lift coefficients of an airfoil. The airfoil performs a flapping motion in Low-Reynolds-Number (LRN) flow regime. Computational Fluid Dynamics (CFD) simulations of the flow field are conducted and the corresponding unsteady lift coefficients are used as the input data to ANFIS. The results show that the ANFIS model is capable of predicting the lift coefficients with very good accuracy, which could be of great value in the preliminary design stages.

scholarly journals Optimization of Indoor Thermal Comfort Parameters with the Adaptive Network-Based Fuzzy Inference System and Particle Swarm Optimization Algorithm

2017 ◽  
Vol 2017 ◽  
pp. 1-13 ◽  
Author(s):  
Jing Li ◽  
Shao-Wu Yin ◽  
Guang-Si Shi ◽  
Li Wang
Keyword(s):  
Particle Swarm Optimization ◽  
Thermal Comfort ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Adaptive Network ◽  
Anfis Model ◽  
Swarm Optimization ◽  
Inference System

The goal of this study is to improve thermal comfort and indoor air quality with the adaptive network-based fuzzy inference system (ANFIS) model and improved particle swarm optimization (PSO) algorithm. A method to optimize air conditioning parameters and installation distance is proposed. The methodology is demonstrated through a prototype case, which corresponds to a typical laboratory in colleges and universities. A laboratory model is established, and simulated flow field information is obtained with the CFD software. Subsequently, the ANFIS model is employed instead of the CFD model to predict indoor flow parameters, and the CFD database is utilized to train ANN input-output “metamodels” for the subsequent optimization. With the improved PSO algorithm and the stratified sequence method, the objective functions are optimized. The functions comprise PMV, PPD, and mean age of air. The optimal installation distance is determined with the hemisphere model. Results show that most of the staff obtain a satisfactory degree of thermal comfort and that the proposed method can significantly reduce the cost of building an experimental device. The proposed methodology can be used to determine appropriate air supply parameters and air conditioner installation position for a pleasant and healthy indoor environment.

scholarly journals Forecasting Stock Market Volatility Using Hybrid of Adaptive Network of Fuzzy Inference System and Wavelet Functions

2021 ◽  
Vol 2021 ◽  
pp. 1-10 ◽  
Author(s):  
Abdullah H. Alenezy ◽  
Mohd Tahir Ismail ◽  
S. Al Wadi ◽  
Muhammad Tahir ◽  
Nawaf N. Hamadneh ◽  
...  
Keyword(s):  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Arima Model ◽  
Least Square ◽  
Percentage Error ◽  
Discrete Wavelet ◽  
Stock Market Volatility ◽  
Adaptive Network ◽  
Anfis Model ◽  
Inference System

This study aims to model and enhance the forecasting accuracy of Saudi Arabia stock exchange (Tadawul) data patterns using the daily stock price indices data with 2026 observations from October 2011 to December 2019. This study employs a nonlinear spectral model of maximum overlapping discrete wavelet transform (MODWT) with five mathematical functions, namely, Haar, Daubechies (Db), Least Square (LA-8), Best localization (BL14), and Coiflet (C6) in conjunction with adaptive network-based fuzzy inference system (ANFIS). We have selected oil price (Loil) and repo rate (Repo) as input values according to correlation, the Engle and Granger Causality test, and multiple regressions. The input variables in this study have been collected from Saudi Authority for Statistics and Saudi Central Bank. The output variable is obtained from Tadawul. The performance of the proposed model (MODWT-LA8-ANFIS) is evaluated in terms of mean error (ME), root mean square error (RMSE), and mean absolute percentage error (MAPE). Also, we have compared the MODWT-LA8-ANFIS model with traditional models, which are autoregressive integrated moving average (ARIMA) model and ANFIS model. The obtained results show that the performance of MODWT-LA8-ANFIS is better than that of the traditional models. Therefore, the proposed forecasting model is capable of decomposing in the stock markets.

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