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.

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.

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.

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 Adaptive neuro fuzzy inference system for profiling of the atmosphere

2014 ◽  
Vol 7 (3) ◽  
pp. 2715-2736 ◽  
Author(s):  
K. Ramesh ◽  
A. P. Kesarkar ◽  
J. Bhate ◽  
M. Venkat Ratnam ◽  
A. Jayaraman
Keyword(s):  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Percentage Error ◽  
Computational Techniques ◽  
Temperature Profiles ◽  
Atmospheric Parameters ◽  
Wet Season ◽  
Anfis Model ◽  
Inference System ◽  
Neuro Fuzzy

Abstract. Retrieval of accurate profiles of temperature and water vapor is important for the study of atmospheric convection. However, it is challenging because of the uncertainties associated with direct measurement of atmospheric parameters during convection events using radiosonde and retrieval of remote-sensed observations from satellites. Recent developments in computational techniques motivated the use of adaptive techniques in the retrieval algorithms. In this work, we have used the Adaptive Neuro Fuzzy Inference System (ANFIS) to retrieve profiles of temperature and humidity over tropical station Gadanki (13.5° N, 79.2° E), India. The observations of brightness temperatures recorded by Radiometrics Multichannel Microwave Radiometer MP3000 for the period of June–September 2011 are used to model profiles of atmospheric parameters up to 10 km. The ultimate goal of this work is to use the ANFIS forecast model to retrieve atmospheric profiles accurately during the wet season of the Indian monsoon (JJAS) season and during heavy rainfall associated with tropical convections. The comparison analysis of the ANFIS model retrieval of temperature and relative humidity (RH) profiles with GPS-radiosonde observations and profiles retrieved using the Artificial Neural Network (ANN) algorithm indicates that errors in the ANFIS model are less even in the wet season, and retrievals using ANFIS are more reliable, making this technique the standard. The Pearson product movement correlation coefficient (r) between retrieved and observed profiles is more than 99% for temperature profiles for both techniques and therefore both techniques are successful in the retrieval of temperature profiles. However, in the case of RH the retrieval using ANFIS is found to be better. The comparison of mean absolute error (MAE), root mean square error (RMSE) and symmetric mean absolute percentage error (SMAPE) of retrieved temperature and RH profiles using ANN and ANFIS also indicates that profiles retrieved using ANFIS are significantly better compared to the ANN technique. The error analysis of profiles concludes that retrieved profiles using ANFIS techniques have improved the retrievals substantially; however, retrieval of RH by both techniques (ANN and ANFIS) has limited success.

AN ADAPTIVE-NETWORK-BASED FUZZY INFERENCE SYSTEM FOR PREDICTING SPRINGBACK OF U-BENDING

2013 ◽  
Vol 37 (3) ◽  
pp. 335-344 ◽  
Author(s):  
Bor-Tsuen Lin ◽  
Kun-Min Huang
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, the results show 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 Discrete Wavelet Transform with Adaptive Network-Based Fuzzy Inference System for Compression of Images

2019 ◽  
Vol 8 (9) ◽  
pp. 748-756
Keyword(s):  
Wavelet Transform ◽  
Discrete Wavelet Transform ◽  
Image Encryption ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Median Filter ◽  
Discrete Wavelet ◽  
Hybrid Technique ◽  
Adaptive Network ◽  
Inference System

Image Encryption has a significant role to play in different fields like information security.Images are encryptedfor various purposes. Compression refers to the process that is carried out once the encryption is completed. In this review work, a hybrid technique has been followed for image encryption and decryption. First, input images are sent for preprocessing employing the median filter with the aim of removing the noise that is regarded to be unnecessary. This elimination process aids in improving the quality of the particular image. So the denoised image can be divided into different segments with the goal of encrypting the various blocks of images. This way, the required and unwanted blocks can be found during this above mentioned process. Encryption technique would follow Hybrid Chaos along with Discrete Cosine Transform shortly known as DCT. The encrypted image is then compressed with the help of Discrete Wavelet Transform (DWT) With Adaptive Network-Based Fuzzy Inference System (ANFIS). The experimental results indicate that the newly introduced DWT-ANFIS based compression attains a better performance in comparison with the availablecompression approaches in terms of Compression Ratio (CR) and Peak-Signal-Noise-Ratio (PSNR)

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