An interpolative fuzzy inference using least square principle by means of β-function and high order polynomials

2006 ◽  
Author(s):  
F. Kiasi ◽  
C. Lucas ◽  
A. Fazl
Keyword(s):  
Fuzzy Inference ◽  
High Order ◽  
Least Square ◽  
Β Function

Studying Fitting Effect by ANFIS in the Electrodialysis Process

2011 ◽  
Vol 268-270 ◽  
pp. 336-339
Author(s):  
Guo Lin Jing ◽  
Wen Ting Du ◽  
Quan Zhou ◽  
Song Tao Li
Keyword(s):  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Back Propagation ◽  
Fuzzy Model ◽  
Least Square Method ◽  
Least Square ◽  
Back Propagation Algorithm ◽  
Inference System ◽  
Error Back Propagation ◽  
Error Back Propagation Algorithm

Fuzzy system is known to predict model in the electrodialysis process. This paper aimed to study fitting effect by ANFIS in a laboratory scale ED cell. Separation percent of NaCl solution is mainly as a function of concentration, temperature, flow rate and voltage. Besides, ANFIS(Adaptive Neuro-Fuzzy Inference System) based on Sugeno fuzzy model, its structure was similar to neural network and could generate fuzzy rules automatically, using the error back propagation algorithm and least square method to adjust the parameters of fuzzy inference system. We obtained fitted values of separation percent by ANFIS. Separation percent from experiments compared with the fitted values of separation percent. The result is shown that the correlation coefficient is 0.988. Therefore, it is verified as a good performance in the electrodialysis process.

scholarly journals Adaptive technique for smoothing telemetric measurements with the use of fuzzy inference

2016 ◽  
pp. 88-96
Author(s):  
N. A. Pervushina ◽  
D. E. Donovskiy
Keyword(s):  
Fuzzy Inference ◽  
Piecewise Linear ◽  
Least Square Method ◽  
Point Of View ◽  
Least Square ◽  
Piecewise Linear Approximation ◽  
Adaptation Mechanism ◽  
Inference System ◽  
Window Width ◽  
Data Location

The study focuses on the technique that allows smoothing the results of telemetric measurements by sliding piecewise-linear approximation. We calculated the model coefficients at smoothing, as well as coefficient dispersion, using the least square method (LSM). Moreover, we developed the algorithm for adapting the sliding window width depending on nature of the initial data location. We implemented the adaptation mechanism by means of a fuzzy logic inference system, which is of particular interest from the point of view of novelty of the approach and practical application in processing the results of telemetric measurements.

scholarly journals Time Series Prediction of Electricity Demand Using Adaptive Neuro-Fuzzy Inference Systems

2020 ◽  
Vol 2020 ◽  
pp. 1-14 ◽  
Author(s):  
Amevi Acakpovi ◽  
Alfred Tettey Ternor ◽  
Nana Yaw Asabere ◽  
Patrick Adjei ◽  
Abdul-Shakud Iddrisu
Keyword(s):  
Urban Areas ◽  
Fuzzy Inference ◽  
Predictive Accuracy ◽  
Resource Planning ◽  
Electricity Demand ◽  
Least Square ◽  
Quality Data ◽  
Support Vector ◽  
Inference System ◽  
Neuro Fuzzy

This paper is concerned with the reliable prediction of electricity demands using the Adaptive Neuro-Fuzzy Inference System (ANFIS). The need for electricity demand prediction is fundamental and vital for power resource planning and monitoring. A dataset of electricity demands covering the period of 2003 to 2018 was collected from the Electricity Distribution Company of Ghana, covering three urban areas namely Mallam, Achimota, and Ga East, all in Ghana. The dataset was divided into two parts: one part covering a period of 0 to 500 hours was used for training of the ANFIS algorithm while the second part was used for validation. Three scenarios were considered for the simulation exercise that was done with the MATLAB software. Scenario one considered four inputs sampled data, scenario two considered an additional input making it 5, and scenario 3 was similar to scenario 1 with the exception of the number of membership functions that increased from 2 to 3. The performance of the ANFIS algorithm was assessed by comparing its predictions with other three forecast models namely Support Vector Regression (SVR), Least Square Support Vector Machine (LS-SVM), and Auto-Regressive Integrated Moving Average (ARIMA). Findings revealed that the ANFIS algorithm can perform the prediction accurately, the ANFIS algorithm converges faster with an increase in the data used for training, and increasing the membership function resulted in overfitting of data which adversely affected the RMSE values. Comparison of the ANFIS results to other previously used methods of predicting electricity demands including SVR, LS-SVM, and ARIMA revealed that there is merit to the potentials of the ANFIS algorithm for improved predictive accuracy while relying on a quality data for training and reliable setting of tuning parameters.

scholarly journals Machine Learning Approaches for Accurate Prediction of Relative Humidity based on Temperature and Wet-Bulb Depression

2020 ◽  
Author(s):  
Mahdi Ghadiri ◽  
Azam Marjani ◽  
Samira Mohammadinia ◽  
Manouchehr Shokri
Keyword(s):  
Relative Humidity ◽  
Statistical Learning ◽  
Air Conditioning ◽  
Fuzzy Inference ◽  
Least Square ◽  
Support Vector ◽  
Cooling Towers ◽  
Learning Approaches ◽  
Inference System ◽  
Data Points

The main parameters for calculation of relative humidity are the wet-bulb depression and dry bulb temperature. In this work, easy-to-used predictive tools based on statistical learning concepts, i.e., the Adaptive Network-Based Fuzzy Inference System (ANFIS) and Least Square Support Vector Machine (LSSVM) are developed for calculating relative humidity in terms of wet bulb depression and dry bulb temperature. To evaluate the aforementioned models, some statistical analyses have been done between the actual and estimated data points. Results obtained from the present models showed their capabilities to calculate relative humidity for divers values of dry bulb temperatures and also wet-bulb depression. The obtained values of MSE and MRE were 0.132 and 0.931, 0.193 and 1.291 for the LSSVM and ANFIS approaches respectively. These developed tools are user-friend and can be of massive value for scientists especially, those dealing with air conditioning and wet cooling towers systems to have a noble check of the relative humidity in terms of wet bulb depression and dry bulb temperatures.

scholarly journals On the Prediction of Biogas Production from Vegetables, Fruits, and Food Wastes by ANFIS- and LSSVM-Based Models

2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Yong Yang ◽  
Shuaishuai Zheng ◽  
Zhilu Ai ◽  
Mohammad Mahdi Molla Jafari
Keyword(s):  
Mean Squared Error ◽  
Fuzzy Inference ◽  
Biogas Production ◽  
Input Parameter ◽  
Machine Learning Algorithms ◽  
Least Square ◽  
Support Vector ◽  
Robust Algorithms ◽  
Inference System ◽  
Proposed Model

This study is aimed at modeling biodigestion systems as a function of the most influencing parameters to generate two robust algorithms on the basis of the machine learning algorithms, including adaptive network-based fuzzy inference system (ANFIS) and least square support vector machine (LSSVM). The models are assessed utilizing multiple statistical analyses for the actual values and model outcomes. Results from the suggested models indicate their great capability of predicting biogas production from vegetable food, fruits, and wastes for a variety of ranges of input parameters. The values that are calculated for the mean relative error (MRE %) and mean squared error (MSE) were 29.318 and 0.0039 for ANFIS, and 2.951 and 0.0001 for LSSVM which shows that the latter model has a better ability to predict the target data. Finally, in order to have additional certainty, two analyses of outlier identification and sensitivity were performed on the input parameter data that proved the proposed model in this paper has higher reliability in assessing output values compared with the previous model.

scholarly journals Prediction of Pullout Behavior of Belled Piles through Various Machine Learning Modelling Techniques

Sensors ◽  
2019 ◽  
Vol 19 (17) ◽  
pp. 3678 ◽  
Author(s):  
Dieu Tien Bui ◽  
Hossein Moayedi ◽  
Mu’azu Mohammed Abdullahi ◽  
Ahmad Safuan A Rashid ◽  
Hoang Nguyen
Keyword(s):  
Neural Network ◽  
Fuzzy Inference ◽  
Learning Algorithm ◽  
Back Propagation ◽  
Least Square ◽  
General Regression Neural Network ◽  
Training Dataset ◽  
Coefficient Of Determination ◽  
Inference System ◽  
Testing Dataset

The main goal of this study is to estimate the pullout forces by developing various modelling technique like feedforward neural network (FFNN), radial basis functions neural networks (RBNN), general regression neural network (GRNN) and adaptive neuro-fuzzy inference system (ANFIS). A hybrid learning algorithm, including a back-propagation and least square estimation, is utilized to train ANFIS in MATLAB (software). Accordingly, 432 samples have been applied, through which 300 samples have been considered as training dataset with 132 ones for testing dataset. All results have been analyzed by ANFIS, in which the reliability has been confirmed through the comparing of the results. Consequently, regarding FFNN, RBNN, GRNN, and ANFIS, statistical indexes of coefficient of determination (R2), variance account for (VAF) and root mean square error (RMSE) in the values of (0.957, 0.968, 0.939, 0.902, 0.998), (95.677, 96.814, 93.884, 90.131, 97.442) and (2.176, 1.608, 3.001, 4.39, 0.058) have been achieved for training datasets and the values of (0.951, 0.913, 0.729, 0.685 and 0.995), (95.04, 91.13, 72.745, 66.228, 96.247) and (2.433, 4.032, 8.005, 10.188 and 1.252) are for testing datasets indicating a satisfied reliability of ANFIS in estimating of pullout behavior of belled piles.

scholarly journals Ranking of hybrid wavelet-AI models by TOPSIS method for estimation of daily flow discharge

2020 ◽  
Vol 20 (8) ◽  
pp. 3156-3171
Author(s):  
Hiwa Farajpanah ◽  
Morteza Lotfirad ◽  
Arash Adib ◽  
Hassan Esmaeili-Gisavandani ◽  
Özgur Kisi ◽  
...  
Keyword(s):  
Fuzzy Inference ◽  
Gene Expression Programming ◽  
Least Square ◽  
Support Vector ◽  
Discrete Wavelet ◽  
Topsis Method ◽  
M5 Model Tree ◽  
Inference System ◽  
Flow Discharge ◽  
Discharge Temperature

Abstract This research uses the multi-layer perceptron–artificial neural network (MLP-ANN), radial basis function–ANN (RBF-ANN), least square support vector machine (LSSVM), adaptive neuro-fuzzy inference system (ANFIS), M5 model tree (M5T), gene expression programming (GEP), genetic programming (GP) and Bayesian network (BN) with five types of mother wavelet functions (MWFs: coif4, db10, dmey, fk6 and sym7) and selects the best model by the TOPSIS method. The case study is the Navrood watershed in the north of Iran and the considered parameters are daily flow discharge, temperature and precipitation during 1991 to 2018. The derived results show that the best method is the hybrid of the M5T model with sym7 wavelet function. The MWFs were decomposed by discrete wavelet transform (DWT). The combination of AI models and MWFs improves the correlation coefficient of MLP, RBF, LSSVM, ANFIS, GP, GEP, M5T and BN by 8.05%, 4.6%, 8.14%, 8.14%, 22.97%, 7.5%, 5.75% and 10% respectively.

SIMPLIFIED FUZZY INFERENCE RULE-BASED GENETICALLY OPTIMIZED HYBRID FUZZY NEURAL NETWORKS

2008 ◽  
Vol 16 (02) ◽  
pp. 245-274 ◽  
Author(s):  
BYOUNG-JUN PARK ◽  
WITOLD PEDRYCZ ◽  
SUNG-KWUN OH
Keyword(s):  
Neural Networks ◽  
Fuzzy Inference ◽  
Inference Rule ◽  
Least Square Method ◽  
Fuzzy Neural Networks ◽  
Least Square ◽  
Rule Based ◽  
Fuzzy Partition ◽  
Fuzzy Neural ◽  
Input Variables

In this study, we introduce an advanced architecture of genetically optimized Hybrid Fuzzy Neural Networks (gHFNN) and develop a comprehensive design methodology supporting their construction. A series of numeric experiments is included to illustrate the performance of the networks. The construction of gHFNN exploits fundamental technologies of Computational Intelligence (CI), namely fuzzy sets, neural networks, and genetic algorithms (GAs). The architecture of the gHFNNs results from a synergistic usage of the genetic optimization-driven hybrid system generated by combining Fuzzy Neural Networks (FNN) with Polynomial Neural Networks (PNN). In this tandem, a FNN supports the formation of the condition part of the rule-based structure of the gHFNN. The conclusion part of the gHFNN is designed using PNNs. We distinguish between two types of the simplified fuzzy inference rule-based FNN structures showing how this taxonomy depends upon the type of a fuzzy partition of input variables. As to the conclusion part of the gHFNN, the development of the PNN dwells on two general optimization mechanisms: the structural optimization is realized via GAs whereas in case of the parametric optimization we proceed with a standard least square method-based learning. To evaluate the performance of the gHFNN, we experimented with three representative numerical examples. A comparative analysis demonstrates that the proposed gHFNN come with higher accuracy as well as superb predictive capabilities when compared with other neurofuzzy models.

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