scholarly journals ADAPTIVE NEURO-FUZZY COMPUTING TECHNIQUE FOR PRECIPITATION ESTIMATION

2016 ◽  
Vol 14 (2) ◽  
pp. 209 ◽  
Author(s):  
Dalibor Petković ◽  
Milan Gocić ◽  
Shahaboddin Shamshirband
Keyword(s):  
Neural Network ◽  
Fuzzy Inference ◽  
Monthly Precipitation ◽  
Mathematical Functions ◽  
Inference System ◽  
Computing Technique ◽  
Neuro Fuzzy ◽  
Precipitation Estimation ◽  
Artificial Neural Network Ann ◽  
Fuzzy Computing

The paper investigates the accuracy of an adaptive neuro-fuzzy computing technique in precipitation estimation. The monthly precipitation data from 29 synoptic stations in Serbia during 1946-2012 are used as case studies. Even though a number of mathematical functions have been proposed for modeling the precipitation estimation, these models still suffer from the disadvantages such as their being very demanding in terms of calculation time. Artificial neural network (ANN) can be used as an alternative to the analytical approach since it offers advantages such as no required knowledge of internal system parameters, compact solution for multi-variable problems and fast calculation. Due to its being a crucial problem, this paper presents a process constructed so as to simulate precipitation with an adaptive neuro-fuzzy inference (ANFIS) method. ANFIS is a specific type of the ANN family and shows very good learning and prediction capabilities, which makes it an efficient tool for dealing with encountered uncertainties in any system such as precipitation. Neural network in ANFIS adjusts parameters of membership function in the fuzzy logic of the fuzzy inference system (FIS). This intelligent algorithm is implemented using Matlab/Simulink and the performances are investigated.  The simulation results presented in this paper show the effectiveness of the developed method.

scholarly journals Global Image Thresholding Adaptive Neuro-Fuzzy Inference System Trained with Fuzzy Inclusion and Entropy Measures

Symmetry ◽  
2019 ◽  
Vol 11 (2) ◽  
pp. 286 ◽  
Author(s):  
Athanasios Bogiatzis ◽  
Basil Papadopoulos
Keyword(s):  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Initial Step ◽  
Local Area ◽  
Mathematical Functions ◽  
Inference System ◽  
Neuro Fuzzy ◽  
Entropy Measures ◽  
Complex Procedure ◽  
Artificial Neural Network Ann

Thresholding algorithms segment an image into two parts (foreground and background) by producing a binary version of our initial input. It is a complex procedure (due to the distinctive characteristics of each image) which often constitutes the initial step of other image processing or computer vision applications. Global techniques calculate a single threshold for the whole image while local techniques calculate a different threshold for each pixel based on specific attributes of its local area. In some of our previous work, we introduced some specific fuzzy inclusion and entropy measures which we efficiently managed to use on both global and local thresholding. The general method which we presented was an open and adaptable procedure, it was free of sensitivity or bias parameters and it involved image classification, mathematical functions, a fuzzy symmetrical triangular number and some criteria of choosing between two possible thresholds. Here, we continue this research and try to avoid all these by automatically connecting our measures with the wanted threshold using some Artificial Neural Network (ANN). Using an ANN in image segmentation is not uncommon especially in the domain of medical images. However, our proposition involves the use of an Adaptive Neuro-Fuzzy Inference System (ANFIS) which means that all we need is a proper database. It is a simple and immediate method which could provide researchers with an alternative approach to the thresholding problem considering that they probably have at their disposal some appropriate and specialized data.

scholarly journals Specific Wear Rate Modeling of Polytetraflouroethylene Composites Via Artificial Neural Network (ANN) and Adaptive Neuro Fuzzy Inference System (ANFIS) Tools

2021 ◽  
Author(s):  
Musa Alhaji Ibrahim ◽  
Yusuf Şahin ◽  
Auwal Ibrahim ◽  
Auwalu Yusuf Gidado ◽  
Mukhtar Nuhu Yahya
Keyword(s):  
Neural Network ◽  
Wear Rate ◽  
Fuzzy Inference ◽  
Nonlinear Behavior ◽  
Specific Wear Rate ◽  
Inference System ◽  
Modeling And Prediction ◽  
Neuro Fuzzy ◽  
Artificial Neural Network Ann ◽  
Ptfe Composites

Lately, artificial neural network (ANN) and adaptive neuro-fuzzy inference system (ANFIS) models have been recognized as potential and good tools for mathematical modeling of complex and nonlinear behavior of specific wear rate (SWR) of composite materials. In this study, modeling and prediction of specific wear rate of polytetraflouroethylene (PTFE) composites using FFNN and ANFIS models were examined. The performances of the models were compared with conventional multilinear regression (MLR) model. To establish the proper choice of input variables, a sensitivity analysis was performed to determine the most influential parameter on the SWR. The modeling and prediction performance results showed that FFNN and ANFIS models outperformed that of the MLR model by 45.36% and 45.80%, respectively. The sensitivity analysis findings revealed that the volume fraction of reinforcement and density of the composites and sliding distance were the most and more influential parameters, respectively. The goodness of fit of the ANN and ANFIS models was further checked using t-test at 5% level of significance and the results proved that ANN and ANFIS models are powerful and efficient tools in dealing with complex and nonlinear behavior of SWR of the PTFE composites.

Genetic Algorithmised Neuro Fuzzy System for Forecasting the Online Journal Visitors

2021 ◽  
pp. 181-189
Author(s):  
Wayan Firdaus Mahmudy ◽  
Aji Prasetya Wibawa ◽  
Nadia Roosmalita Sari ◽  
H. Haviluddin ◽  
P. Purnawansyah
Keyword(s):  
Neural Network ◽  
Fuzzy System ◽  
Fuzzy Inference ◽  
Hybrid Approach ◽  
Online Journal ◽  
Inference System ◽  
The Neural Network ◽  
Neuro Fuzzy ◽  
Artificial Neural Network Ann ◽  
Network Weight

Artificial Neural Network (ANN) is recognized as one of effective forecasting engines for various business fields. This approach fits well with non-linear data. In fact, it is a black box system with random weighting, which is hard to train. One way to improve its performance is by hybridizing ANN with other methods. In this paper, a hybrid approach, Genetic Algorithm-Neural Fuzzy System (GA-NFS) is proposed to predict the number of unique visitors of an online journal website. The neural network weight is precisely determined using GA. Afterwards, the best weight has been used for testing data and processed using Sugeno Fuzzy Inference System (FIS) for time-series forecasting. Based on experiment, GA-NFS have been produced accuracy with 0.989 of root mean square error (RMSE) that is lower than the RMSE of a common NFS (2,004). This may indicate that the GA based weighting is able to improve the NFS performance on forecasting the number of journal unique visitors.

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