scholarly journals Artificial Intelligence-Based Controller for DC-DC Flyback Converter

2019 ◽  
Vol 9 (23) ◽  
pp. 5108
Author(s):  
Muhammad Arslan Shahid ◽  
Ghulam Abbas ◽  
Mohammad Rashid Hussain ◽  
Muhammad Usman Asad ◽  
Umar Farooq ◽  
...  
Keyword(s):  
Fuzzy Inference ◽  
Input Voltage ◽  
Operating Conditions ◽  
Model Parameters ◽  
Learning Capability ◽  
Inference System ◽  
Flyback Converter ◽  
Response Characteristics ◽  
Voltage Controller ◽  
Neuro Fuzzy

This paper presents an intelligent voltage controller designed on the basis of an adaptive neuro-fuzzy inference system (ANFIS) for a flyback converter (FC) working in continuous conduction mode (CCM). The union of fuzzy logic (FL) and adaptive neural networks (ANN) makes ANFIS more robust against model parameters’ uncertainties and perturbations in input voltage or load current. ANFIS inherits the advantages of structured knowledge representation from FL and learning capability from NN. Comparative analysis showed that the ANFIS controller offers not only the superior transient response characteristics, but also excellent steady-state characteristics compared to those of the FL controller (FLC) and proportional–integral–derivative (PID) controllers, thus validating its superiority over these traditional controllers. For this purpose, MATLAB/Simulink environment-based simulation results are presented for validation of the proposed converter compensated system under all operating conditions.

scholarly journals Lifetime Prediction for a Cell-on-Board (COB) Light Source Based on the Adaptive Neuro-Fuzzy Inference System (ANFIS)

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
İsmail Kıyak ◽  
Gökhan Gökmen ◽  
Gökhan Koçyiğit
Keyword(s):  
Fuzzy Inference ◽  
Prediction Method ◽  
Operating Conditions ◽  
Lifetime Prediction ◽  
Statistical Characteristics ◽  
Ambient Conditions ◽  
Inference System ◽  
Initial Task ◽  
Neuro Fuzzy ◽  
A Cell

Predicting the lifetime of a LED lighting system is important for the implementation of design specifications and comparative analysis of the financial competition of various illuminating systems. Most lifetime information published by LED manufacturers and standardization organizations is limited to certain temperature and current values. However, as a result of different working and ambient conditions throughout the whole operating period, significant differences in lifetimes can be observed. In this article, an advanced method of lifetime prediction is proposed considering the initial task areas and the statistical characteristics of the study values obtained in the accelerated fragmentation test. This study proposes a new method to predict the lifetime of COB LED using an artificial intelligence approach and LM-80 data. Accordingly, a database with 6000 hours of LM-80 data was created using the Neuro-Fuzzy (ANFIS) algorithm, and a highly accurate lifetime prediction method was developed. This method reveals an approximate similarity of 99.8506% with the benchmark lifetime. The proposed methodology may provide a useful guideline to lifetime predictions of LED-related products which can also be adapted to different operating conditions in a shorter time compared to conventional methods. At the same time, this method can be used in the life prediction of nanosensors and can be produced with the 3D technique.

ANFIS Modeling of Dynamic Load Balancing in LTE

2013 ◽  
pp. 343-360
Author(s):  
Matthew K. Luka ◽  
Aderemi A. Atayero
Keyword(s):  
Load Balancing ◽  
User Satisfaction ◽  
Theoretical Basis ◽  
Fuzzy Inference ◽  
Cost Effective ◽  
Computationally Efficient ◽  
Network Resources ◽  
Learning Capability ◽  
Inference System ◽  
Neuro Fuzzy

Modelling of ill-defined or unpredictable systems can be very challenging. Most models have relied on conventional mathematical models which does not adequately track some of the multifaceted challenges of such a system. Load balancing, which is a self-optimization operation of Self-Organizing Networks (SON), aims at ensuring an equitable distribution of users in the network. This translates into better user satisfaction and a more efficient use of network resources. Several methods for load balancing have been proposed. While some of them have a very buoyant theoretical basis, they are not practical. Furthermore, most of the techniques proposed the use of an iterative algorithm, which in itself is not computationally efficient as it does not take the unpredictable fluctuation of network load into consideration. This chapter proposes the use of soft computing, precisely Adaptive Neuro-Fuzzy Inference System (ANFIS) model, for dynamic QoS aware load balancing in 3GPP LTE. The use of ANFIS offers learning capability of neural network and knowledge representation of fuzzy logic for a load balancing solution that is cost effective and closer to human intuition. Three key load parameters (number of satisfied user in the network, virtual load of the serving eNodeB, and the overall state of the target eNodeB) are used to adjust the hysteresis value for load balancing.

Recurrent ANFIS-Coordinated Controller Design for Multimachine Power System with FACTS Devices

2016 ◽  
Vol 26 (02) ◽  
pp. 1750034 ◽  
Author(s):  
J. Sangeetha ◽  
P. Renuga
Keyword(s):  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Controller Design ◽  
Test System ◽  
Operating Conditions ◽  
Superior Performance ◽  
Control Technique ◽  
Inference System ◽  
Neuro Fuzzy ◽  
Oscillation Damping

This paper proposes the design of auxiliary-coordinated controller for static VAR compensator (SVC) and thyristor-controlled series capacitor (TCSC) devices by adaptive fuzzy optimized technique for oscillation damping in multimachine power systems. The performance of the coordinated control of SVC and TCSC devices based on feedforward adaptive neuro fuzzy inference system (F-ANFIS) is compared with that of the adaptive neuro fuzzy inference system (ANFIS) structure based on recurrent adaptive neuro fuzzy inference system (R-ANFIS) network architecture. The objective of the coordinated controller design is to tune the parameters of SVC and TCSC fuzzy lead lag compensator simultaneously to minimize the deviation of rotor angle and rotor speed of the generators. The performance of the system is enhanced by optimally tuning the membership functions of fuzzy lead lag controller parameter of the flexible AC transmission system (FACTS) by R-ANFIS controller. The training data for F-ANFIS and R-ANFIS are generated by conventional linear control technique under various operating conditions. The offline trained controller tunes the parameter of lead lag controller in online. The oscillation damping ability of the system is analyzed for three-machine test system by calculating the standard deviation and cost function. The superior performance of R-ANFIS controller is compared with various particle swarm optimization-based feedforward ANFIS controllers available in literature.

Dynamic Behaviour and Crack Detection of a Multi Cracked Rotating Shaft using Adaptive Neuro-Fuzzy-Inference System

Fuzzy Systems ◽  
2017 ◽  
pp. 1540-1551
Author(s):  
Rajeev Ranjan
Keyword(s):  
Finite Element ◽  
Fuzzy Inference System ◽  
Crack Detection ◽  
Fuzzy Inference ◽  
Crack Depth ◽  
Rotating Shaft ◽  
Inference System ◽  
Response Characteristics ◽  
Neuro Fuzzy ◽  
Cracked Structures

The presence of crack changes the physical characteristics of a structure which in turn alter its dynamic response characteristics. So it is important to understand dynamics of cracked structures. Crack depth and location are the main parameters influencing the vibration characteristics of the rotating shaft. In the present study, a technique based on the measurement of change of natural frequencies has been employed to detect the multiple cracks in rotating shaft. The model of shaft was generated using Finite Element Method. In Finite Element Analysis, the natural frequency of the shaft was calculated by modal analysis using the software ANSYS. The Numerical data were obtained from FEA, then used to train through Adaptive Neuro-Fuzzy-Inference System. Then simulations were carried out to test the performance and accuracy of the trained networks. The simulation results show that the proposed ANFIS estimate the locations and depth of cracks precisely.

scholarly journals Comparison of Damping Performance of Conventional And Neuro–Fuzzy Based Power System Stabilizers Applied in Multi–Machine Power Systems

2013 ◽  
Vol 64 (6) ◽  
pp. 366-370 ◽  
Author(s):  
Duraiswamy Murali ◽  
Marimuthu Rajaram
Keyword(s):  
Power Systems ◽  
Power System ◽  
Fuzzy Inference ◽  
Low Frequency ◽  
Operating Conditions ◽  
Power System Stabilizers ◽  
Inference System ◽  
Neuro Fuzzy ◽  
The Time Domain ◽  
Low Frequency Power

Abstract The objective of this paper is to investigate the power system damping enhancement via power system stabilizers (PSSs). However, the conventional power system stabilizers (CPSSs) have certain drawbacks. There are many techniques proposed in the literature for damping improvement of low frequency power system oscillations. In this paper, adaptive neuro-fuzzy inference system (ANFIS) technology has been proposed to coordinate the CPSSs in a multi-machine power system. The time-domain simulations are carried out in Matlab/Simulink environment to validate the effectiveness of the proposed control scheme under different operating conditions.

scholarly journals Rainfall-runoff modelling using adaptive neuro-fuzzy inference system

2020 ◽  
Vol 17 (2) ◽  
pp. 1117
Author(s):  
Nurul Najihah Che Razali ◽  
Ngahzaifa Ab. Ghani ◽  
Syifak Izhar Hisham ◽  
Shahreen Kasim ◽  
Nuryono Satya Widodo ◽  
...  
Keyword(s):  
Time Series Data ◽  
Fuzzy Inference ◽  
Water Discharge ◽  
Series Data ◽  
Model Parameters ◽  
Rainfall Runoff ◽  
Inference System ◽  
Runoff Forecasting ◽  
Neuro Fuzzy ◽  
Pros And Cons

<span lang="EN-GB">This paper discusses the working mechanism of ANFIS, the flow of research, the implementation and evaluation of ANFIS models, and discusses the pros and cons of each option of input parameters applied, in order to solve the problem of rainfall-runoff forecasting. The rainfall-runoff modelling considers time-series data of rainfall amount (in mm) and water discharge amount (in m<sup>3</sup>/s). For model parameters, the models apply three triangle membership functions for each input. Meanwhile, the accuracy of the data is measured using the Root Mean Square Error (RMSE). Models with good performance in training have low values of RMSE. Hence, the 4-input model data is the best model to measure prediction accurately with the value of RMSE as 22.157. It is proven that ANFIS has the potential to be used for flood forecasting generally, or rainfall-runoff modelling specifically.</span>

Application of Adaptive Neuro-Fuzzy Inference System Techniques to Predict Water Activity in Proton Exchange Membrane Fuel Cell

2018 ◽  
Vol 15 (4) ◽  
Author(s):  
Khaled Mammar ◽  
Slimane Laribi
Keyword(s):  
Fuel Cell ◽  
Water Activity ◽  
Fuzzy Inference System ◽  
Proton Exchange Membrane ◽  
Fuzzy Inference ◽  
Proton Exchange ◽  
Operating Conditions ◽  
Inference System ◽  
Neuro Fuzzy ◽  
Exchange Membrane

This work defines and implements a technique to predict water activity in proton exchange membrane fuel cell. This technique is based on the electrochemical impedance spectroscopy (EIS) as sensor and adaptive neuro-fuzzy inference system (ANFIS) as estimator. For this purpose, a proton exchange membrane fuel cell (PEMFC) model has been proposed to study the performances of the fuel cell for different operating conditions where the simulation model for water activity behavior is in the proposed structure. The technique based on ANFIS predicts the PEM fuel cell relative humidity (RH) from the EIS. For creation of ANFIS training and checking database, a new method based on factorial design of experimental is used. To check the proposed technique, the ANFIS estimator will be compared with the output humidity relative observation.

Adaptive Neuro Fuzzy Control Design of a Solar Power Plant’s Complete Oil Cycle

2009 ◽  
Author(s):  
P. Shahmaleki ◽  
M. Amiri ◽  
M. Mahzoon
Keyword(s):  
Power Plant ◽  
Solar Power ◽  
Fuzzy Inference ◽  
Operating Conditions ◽  
Solar Power Plant ◽  
Inference System ◽  
System A ◽  
Neuro Fuzzy ◽  
Plant Simulation ◽  
Safe Operating

To enhance the performance and achieve a controlled condition with an optimized system a more precise modeling for power plant dynamics is needed. In this paper, a complete oil cycle of Shiraz solar power plant is modeled and controlled. Also, adaptive network-based fuzzy inference system (ANFIS) was employed to control collectors’ field. Furthermore, fuzzy switching control is utilized In order to prevent chattering phenomena of this multi-loop plant. Simulation results of the oil cycle solar power plant and the controller system show that the applied controller system can manage the oil cycle in different situations within safe operating conditions and with better performances.

A genetic algorithm approach for optimization of machinery noise calculations

2019 ◽  
Vol 50 (4) ◽  
pp. 112-123 ◽  
Author(s):  
DS Rao ◽  
DP Tripathy
Keyword(s):  
Genetic Algorithm ◽  
Fuzzy Inference System ◽  
Statistical Models ◽  
Fuzzy Inference ◽  
Operating Conditions ◽  
Computational Time ◽  
Support Vector ◽  
Inference System ◽  
Neuro Fuzzy ◽  
Artificial Neural

Noise produced by various noise sources in the mines is considered as a serious environmental problem. Exposure to such noise levels is considered as hazardous to workers working in such conditions. Noise assessment was exercised in a highly mechanized opencast bauxite mine, located in eastern India according to the Director General of Mines Safety Technical Circular No. 18 of 1975 and No.5 of 1990. There are numerous approaches in the literature on machinery noise prediction based on statistical models, soft computing techniques such as fuzzy inference system, artificial neural networks, support vector machines, adaptive neuro fuzzy inference system and other classification methods. The main drawback of statistical models, fuzzy inference system, artificial neural network, support vector machine and adaptive neuro fuzzy inference system is lack of interpretation for human and optimization issues. An attempt has been made to examine the applicability of a genetic algorithm, to take the advantage of genetic structures to find an optimal sound pressure level of the machinery noise taking into consideration the distance, directivity index, sound power level and other attenuation parameters under several noisy operating conditions according to ISO 9613-2:1996, ISO 6395:2008 and other related standards. Genetic algorithm used in this article has several advantages: it can be applied for low, high and dynamical system and uses a simple procedure to determine the order and the parameters with high accuracy. Genetic algorithm model is trained and tested in MATLAB to find the optimum parameters. Experimental results show that genetic algorithm is able to converge and find the optimum values faster along with acceptable computational time. By comparing the predicted values with the measured values, it proves the effectiveness of the proposed model as a useful and efficient method for machinery noise optimization problems.

ADAPTIVE NEURO-FUZZY INFERENCE SYSTEM FOR PREDICTING ALPHA BAND POWER OF EEG DURING MUSLIM PRAYER (SALAT)

2016 ◽  
Vol 28 (06) ◽  
pp. 1650043
Author(s):  
Hazem Doufesh ◽  
Fatimah Ibrahim ◽  
Noor Azina Ismail ◽  
Wan Azman Wan Ahmad
Keyword(s):  
Heart Rate ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Model Parameters ◽  
Alpha Power ◽  
Nonlinear Prediction ◽  
Eeg Signals ◽  
Inference System ◽  
Neuro Fuzzy ◽  
Prediction Problems

The features of electroencephalographic (EEG) signals include important information about the function of the brain. One of the most common EEG signal features is alpha wave, which is indicative of relaxation or mental inactivity. Until now, the analysis and the feature extraction procedures of these signals have not been well developed. This study presents a new approach based on an adaptive neuro-fuzzy inference system (ANFIS) for extracting and predicting the alpha power band of EEG signals during Muslim prayer (Salat). Proposed models can acquire information related to the alpha power variations during Salat from other physiological parameters such as heart rate variability (HRV) components, heart rate (HR), and respiration rate (RSP). The models were developed by systematically optimizing the initial ANFIS model parameters. Receiver operating characteristic (ROC) curves were performed to evaluate the performance of the optimized ANFIS models. Overall prediction accuracy of the proposed models were achieved of 94.39%, 92.89%, 93.62%, and 94.31% for the alpha power of electrodes positions at O1, O2, P3, and P4, respectively. These models demonstrated many advantages, including efficiency, accuracy, and simplicity. Thus, ANFIS could be considered as a suitable tool for dealing with complex and nonlinear prediction problems.

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