A New ANFIS-based Hybrid Method in the Design and Fabrication of a High-performance Novel Microstrip Diplexer for Wireless Applications

2021 ◽  
pp. 2250050
Author(s):  
Salah I. Yahya ◽  
Abbas Rezaei ◽  
Rafaa I. Yahya
Keyword(s):  
High Performance ◽  
Fuzzy Inference ◽  
Back Propagation ◽  
Least Square ◽  
Anfis Model ◽  
Microstrip Resonator ◽  
Inference System ◽  
Wireless Applications ◽  
Anfis Method ◽  
Insertion Losses

In this work, we have used a novel adaptive neuro-fuzzy inference system (ANFIS) method to design and fabricate a high-performance microstrip diplexer. For developing the proposed ANFIS model, the hybrid learning method consisting of least square estimation and back-propagation (BP) techniques is utilized. To achieve a compact diplexer, a designing process written in MATLAB 7.4 software is introduced based on the proposed ANFIS model. The basic microstrip resonator used in this study is mathematically analyzed. The designed microstrip diplexer operates at 2.2[Formula: see text]GHz and 5.1[Formula: see text]GHz for wideband wireless applications. Compared to the previous works, it has the minimum insertion losses and the smallest area of 0.007 [Formula: see text] (72.2[Formula: see text]mm2). It has flat channels with very low group delays (GDs) and wide fractional bandwidths (FBWs). The GDs at its lower and upper channels are only 0.48[Formula: see text]ns and 0.76[Formula: see text]ns, respectively. Another advantage of this work is its suppressed harmonics up to 12.9[Formula: see text]GHz (5th harmonic). To design the proposed diplexer, an LC model of the presented resonator is introduced and analyzed. To verify the simulation results and the presented ANFIS method, we fabricated and measured the proposed diplexer. The results show that both simulations and measurements data are in good agreement, which give reliability to the proposed ANFIS method.

The Study of System Model Based on Fuzzy Inference and Neural Network

2012 ◽  
Vol 197 ◽  
pp. 547-552
Author(s):  
Ming Ming Gao ◽  
Liang Shan
Keyword(s):  
Neural Network ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Back Propagation ◽  
Least Square ◽  
Fuzzy Rules ◽  
System Model ◽  
Back Propagation Algorithm ◽  
Inference System ◽  
Anfis Method

For the characteristics of fuzziness, indeterminacy etc. in nonlinear systems, this paper, combining fuzzy inference system with neural network, Adaptive Neural Fuzzy Inference System model had been provided in the paper, ANFIS method is based on Sugeno fuzzy model and has a structure similar to neural network that tunes the parameters of the fuzzy inference system with back propagation algorithm and least - square method and can produce fuzzy rules automatically. This solutes extraction of fuzzy rules and learning of parameters of membership functions play an essential role in the design. This paper gives the simulation example of modeling a typical system with ANFIS method and good result is obtained.

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 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 Missing Samples Estimation of Synthetic ECG Signals by FCM-based Adaptive Neuro-Fuzzy Inference System (FCMANFIS)

2021 ◽  
Author(s):  
asghar dabiri ◽  
Nader Jafarnia Dabanloo ◽  
Fereidoon Nooshirvan Rahatabad ◽  
Keivan Maghooli
Keyword(s):  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Back Propagation ◽  
Synthetic Data ◽  
Least Square ◽  
Inference System ◽  
Ecg Signals ◽  
Neuro Fuzzy ◽  
Fuzzy C Means Clustering ◽  
System File

Abstract This paper presents estimation of missed samples recovery of Synthetic electrocardiography (ECG) signals by an ANFIS (Adaptive neuro-fuzzy inference system) method. After designing the ANFIS model using FCM (Fuzzy C Means) clustering method. In MATLAB’s standard library for ANFIS, only least-square-estimation and the back-propagation algorithms are used for tuning membership functions and generation of fis (fuzzy inference system) file, but at current work we have used FCM method that shows better result. Root mean square error (difference of the reference input and the generated data by ANFIS) for the three synthetic data cases are: a. Train data: RMSE = 1.7112e-5b. Test data: RMSE = 5.184e-3c. All data: RMSE = 2.2663e-3

Identification and Equalization of Nonlinear Channel Based on ANFIS

2012 ◽  
Vol 195-196 ◽  
pp. 240-244
Author(s):  
Qiang Qu ◽  
Xiao Li Wang ◽  
Xue Bo Chen
Keyword(s):  
High Speed ◽  
Fuzzy Inference ◽  
Learning Algorithm ◽  
Back Propagation ◽  
Least Square ◽  
Identification Accuracy ◽  
Bp Network ◽  
Inference System ◽  
Cluster Partition ◽  
Better Than

The demand of high-speed communication leads that the application for resource of channel exceeds the range of linear model. The channel should be described as a nonlinear model. So, the paper uses the adaptive neuron-fuzzy inference system (ANFIS) to identify and equalize the nonlinear channel of the high-speed communication system. Meanwhile, the subtract cluster is applied to identify the construction of the ANFIS and the hybrid learning algorithm based on the least square and back-propagation is used to train network. The simulation results show that the convergence rate and identification accuracy of the ANFIS are better than BP network and the efficiency of the ANFIS based on subtract cluster partition algorithm is higher than that of the ANFIS based on the grid partition algorithm.

Adaptive Neuro-Fuzzy Inference System for Predicting Compressive Strength of Fibres Self Compacting Concrete

2019 ◽  
Vol 892 ◽  
pp. 46-54
Author(s):  
L.V. Prasad Meesaraganda ◽  
Prasenjit Saha
Keyword(s):  
Compressive Strength ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Learning Algorithm ◽  
Back Propagation ◽  
Data Sets ◽  
Fine Aggregate ◽  
Self Compacting Concrete ◽  
Anfis Model ◽  
Inference System

This research focused on the applicability of Adaptive Network-Based Fuzzy Inference System (ANFIS) for predict the compressive strength of fibers self-compacting concrete. An ANFIS model combines the benefit of ANN and fuzzy logic. The data developed experimentally for fibers self-compacting concrete and the data sets of a total 99 concrete samples were used in this work. In this paper research is computational based for prediction of concrete compressive strength. A model was developed using ANFIS with five input nodes as w/p ratio, course aggregate, fine aggregate, fiber and superplastizers. In this model Feed-forward three-layer back-propagation neural networks with 10 hidden nodes were examined using learning algorithm. ANFIS model proposed analytically that gives more compatible results. Hence, the model is adopted to predict the strength of fibrous self-compacting concrete.

ADAPTIVE NEURO-FUZZY INFERENCE SYSTEM APPROACH FOR FAULT DETECTION AND DIAGNOSIS OF PNEUMATIC VALVE IN CEMENT INDUSTRY

2011 ◽  
Vol 10 (04) ◽  
pp. 399-423
Author(s):  
P. SUBBARAJ ◽  
B. KANNAPIRAN
Keyword(s):  
Fault Detection ◽  
Fuzzy Inference ◽  
Back Propagation ◽  
Cement Industry ◽  
Anfis Model ◽  
Inference System ◽  
Pneumatic Valve ◽  
Spray System ◽  
Neuro Fuzzy ◽  
Detection And Diagnosis

The detection and diagnosis of faults in technical systems are of great practical significance and paramount importance for the safe operation of the plant. The early detection of fault can help avoid system shutdown, breakdown and even catastrophe involving human fatalities and material damage. Since the operator cannot monitor all the variables simultaneously, an automated approach is needed for the real time monitoring and diagnosis of the system. This paper presents the design and development of adaptive neuro-fuzzy inference system (ANFIS) model based for the fault detection of pneumatic valve in cooler water spray system in cement industry. The fault detection model is developed by using two different approaches, namely, ANFIS and feed forward network with back propagation algorithm (BPN). The training and testing data required are developed for the ANFIS model and BPN model that were generated at different operating conditions by operating the pneumatic valve and by creating various faults in real time in a laboratory experimental model. The performance of the developed ANFIS model and back propagation were tested and also compared for a total of 19 faults in pneumatic valve used in cooler water spray system. Obtained results of the ANFIS performed better than BPN.

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.

scholarly journals PEMODELAN PRODUKSI BAWANG MERAH DI JAWA TENGAH DENGAN MENGGUNAKAN HYBRID AUTOREGRESSIVE INTEGRATED MOVING AVERAGE – ADAPTIVE NEURO FUZZY INFERENCE SYSTEM

2018 ◽  
Vol 7 (3) ◽  
pp. 281-292
Author(s):  
Inas Husna Diarsih ◽  
Tarno Tarno ◽  
Agus Rusgiyono
Keyword(s):  
Fuzzy Inference ◽  
Moving Average ◽  
Arima Model ◽  
Anfis Model ◽  
Inference System ◽  
Central Java ◽  
Neuro Fuzzy ◽  
National Production ◽  
Anfis Method ◽  
Method Model

Red onion is one of the strategic horticulture commodities in Indonesia considering its function as the main ingredients of the basic ingredients of Indonesian cuisine. In an effort to increase production to supply national necessary, Central Java as the main center of red onion production should be able to predict the production of several periods ahead to maintain the balance of national production. The purpose of this research is to get the best model to forecast the production of red onion in Central Java by ARIMA, ANFIS, and hybrid ARIMA-ANFIS method. Model accuracy is measured by the smallest RMSE and AIC values. The results show that the best model to modeling red onion production in Central Java is obtained by hybrid ARIMA-ANFIS model which is a combination between SARIMA ([2], 1, [12]) and residual ARIMA using ANFIS model with input et,1, et,2 on the grid partition technique, gbell membership function, and membership number of 2 that produce RMSE 12033 and AIC 21.6634. While ARIMA model yield RMSE 13301,24 and AIC 21,89807 with violation of assumption. And the ANFIS model produces RMSE 14832 and AIC 22,0777. This shows that ARIMA-ANFIS hybrid method is better than ARIMA and ANFIS.Keywords: production of red onion, ARIMA, ANFIS, hybrid ARIMA-ANFIS

scholarly journals Drug Stock Prediction at Balige HKBP Hospital Using Adaptive Neuro-Fuzzy Inference System

SinkrOn ◽  
2020 ◽  
Vol 5 (1) ◽  
pp. 26-34
Author(s):  
Arie Satia Dharma ◽  
Lily Andayani Tampubolon ◽  
Daniel Somanta Purba
Keyword(s):  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Back Propagation ◽  
Least Square ◽  
Financial Loss ◽  
Stock Prediction ◽  
Least Square Estimator ◽  
Inference System ◽  
Error Back Propagation ◽  
Neuro Fuzzy

Currently the purchases of drugs at Instalasi Farmasi RSU (IFRS) HKBP Balige are based on the examination of the amount of drugs usage. The purchases of drugs based on the examination of the amount of drugs usage cause frequent unplanned drugs purchases that must be hastened (cito) and purchases to other pharmacies. The purchases of cito and purchases to other pharmacies will inflict a financial loss to the patients, because when IFRS makes drugs purchases of cito or to other pharmacies, the cost of the drugs will be more expensive. Therefore, in this research, a prediction of drugs stock in IFRS HKBP Balige using Adaptive Neuro Fuzzy Inference System (ANFIS) will be carried out. ANFIS is a combination of Least Square Estimator (LSE) and Error Back Propagation (EBP) algorithms. ANFIS consists of forward pass and the backward pass learning. The sample data used to predict drugs stock in this research is data of drugs sales at the IFRS HKBP Balige from 2013 to 2015. From the results of drugs stock prediction research with ANFIS, obtained that number of errors of ANFIS model is 5.52%. Based on MAPE accuracy level evaluation, number of errors have an excellent rate so that it can be concluded that the predicted results of the drugs stock are good.

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