Compensatory of Adaptive Neural Fuzzy Inference System

2021 ◽  
Author(s):  
Rabah Mellah ◽  
Hocine Khati ◽  
Hand Talem ◽  
Said Guermah
Keyword(s):  
Fuzzy Logic Controller ◽  
Fuzzy Inference ◽  
Fuzzy Controller ◽  
Learning Algorithm ◽  
Traditional Approach ◽  
Membership Functions ◽  
Inference System ◽  
Analysis Of Stability ◽  
Velocity Tracking ◽  
Neural Fuzzy

The traditional approach to fuzzy design is based on knowledge acquired by expert operators formulated into rules. However, operators may not be able to translate their knowledge and experience into a fuzzy logic controller. In addition, most adaptive fuzzy controllers present difficulties in determining appropriate fuzzy rules and appropriate membership functions. This chapter presents adaptive neural-fuzzy controller equipped with compensatory fuzzy control in order to adjust membership functions, and as well to optimize the adaptive reasoning by using a compensatory learning algorithm. An analysis of stability and transparency based on a passivity framework is carried out. The resulting controllers are implemented on a two degree of freedom robotic system. The simulation results obtained show a fairly high accuracy in terms of position and velocity tracking, what highlights the effectiveness of the proposed controllers.

scholarly journals Basalt Tectonic Discrimination Using Combined Machine Learning Approach

Minerals ◽  
2019 ◽  
Vol 9 (6) ◽  
pp. 376 ◽  
Author(s):  
Qiubing Ren ◽  
Mingchao Li ◽  
Shuai Han ◽  
Ye Zhang ◽  
Qi Zhang ◽  
...  
Keyword(s):  
Machine Learning ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Learning Algorithm ◽  
Volcanic Rocks ◽  
Classification Performance ◽  
Generation Process ◽  
Inference System ◽  
New Research ◽  
Neural Fuzzy

Geochemical discrimination of basaltic magmatism from different tectonic settings remains an essential part of recognizing the magma generation process within the Earth’s mantle. Discriminating among mid-ocean ridge basalt (MORB), ocean island basalt (OIB) and island arc basalt (IAB) is that matters to geologists because they are the three most concerned basalts. Being a supplement to conventional discrimination diagrams, we attempt to utilize the machine learning algorithm (MLA) for basalt tectonic discrimination. A combined MLA termed swarm optimized neural fuzzy inference system (SONFIS) was presented based on neural fuzzy inference system and particle swarm optimization. Two geochemical datasets of basalts from GEOROC and PetDB served as to test the classification performance of SONFIS. Several typical discrimination diagrams and well-established MLAs were also used for performance comparisons with SONFIS. Results indicated that the classification accuracy of SONFIS for MORB, OIB and IAB in both datasets could reach over 90%, superior to other methods. It also turns out that MLAs had certain advantages in making full use of geochemical characteristics and dealing with datasets containing missing data. Therefore, MLAs provide new research tools other than discrimination diagrams for geologists, and the MLA-based technique is worth extending to tectonic discrimination of other volcanic rocks.

scholarly journals Tuning of a TS Fuzzy Output Regulator Using the Steepest Descent Approach and ANFIS

2013 ◽  
Vol 2013 ◽  
pp. 1-14 ◽  
Author(s):  
Ricardo Tapia-Herrera ◽  
Jesús Alberto Meda-Campaña ◽  
Samuel Alcántara-Montes ◽  
Tonatiuh Hernández-Cortés ◽  
Lizbeth Salgado-Conrado
Keyword(s):  
Fuzzy Inference ◽  
Fuzzy Controller ◽  
Descent Method ◽  
Output Regulation ◽  
Steepest Descent ◽  
Steepest Descent Method ◽  
Membership Functions ◽  
Inference System ◽  
Fuzzy Membership Functions ◽  
Takagi Sugeno

The exact output regulation problem for Takagi-Sugeno (TS) fuzzy models, designed from linear local subsystems, may have a solution if input matrices are the same for every local linear subsystem. Unfortunately, such a condition is difficult to accomplish in general. Therefore, in this work, an adaptive network-based fuzzy inference system (ANFIS) is integrated into the fuzzy controller in order to obtain the optimal fuzzy membership functions yielding adequate combination of the local regulators such that the output regulation error in steady-state is reduced, avoiding in this way the aforementioned condition. In comparison with the steepest descent method employed for tuning fuzzy controllers, ANFIS approximates the mappings between local regulators with membership functions which are not necessary known functions as Gaussian bell (gbell), sigmoidal, and triangular membership functions. Due to the structure of the fuzzy controller, Levenberg-Marquardt method is employed during the training of ANFIS.

Design of a Hybrid Adaptive Neuro Fuzzy Inference System (ANFIS) Controller for Position and Angle Control of Inverted Pendulum (IP) Systems

Fuzzy Systems ◽  
2017 ◽  
pp. 308-320
Author(s):  
Ashwani Kharola
Keyword(s):  
Fuzzy Logic ◽  
Inverted Pendulum ◽  
Fuzzy Logic Controller ◽  
Fuzzy Inference ◽  
Fuzzy Controller ◽  
Data Sets ◽  
Inference System ◽  
Simulink Model ◽  
Neuro Fuzzy ◽  
Proposed Model

This paper illustrates a comparison study of Fuzzy and ANFIS Controller for Inverted Pendulum systems. IP belongs to a class of highly non-linear, unstable and multi-variable systems which act as a testing bed for many complex systems. Initially, a Matlab-Simulink model of IP system was proposed. Secondly, a Fuzzy logic controller was designed using Mamdani inference system for control of proposed model. The data sets from fuzzy controller was used for development of a Hybrid Sugeno ANFIS controller. The results shows that ANFIS controller provides better results in terms of Performance parameters including Settling time(sec), maximum overshoot(degree) and steady state error.

scholarly journals ANFIS modeling and sensitivity analysis for estimating solar still productivity using measured operational and meteorological parameters

2017 ◽  
Vol 18 (4) ◽  
pp. 1437-1448 ◽  
Author(s):  
Ahmed F. Mashaly ◽  
A. A. Alazba
Keyword(s):  
Sensitivity Analysis ◽  
Fuzzy Inference ◽  
Learning Algorithm ◽  
Membership Functions ◽  
Solar Still ◽  
Inference System ◽  
New Approach ◽  
Neuro Fuzzy ◽  
Input Parameters ◽  
Statistical Criteria

Abstract This study investigates a potential application of the adaptive neuro-fuzzy inference system (ANFIS) as a relatively new approach for predicting solar still productivity (SSP). Five variables, relative humidity (RH), solar radiation (SR), feed flow rate (MF), and total dissolved solids of feed (TDSF) and brine (TDSB), were used as input parameters. The data were collected from an experimental solar still system used to desalinate seawater in an arid climate. The data were distributed randomly into training, testing, and validation datasets. A hybrid learning algorithm and eight different membership functions were applied to generate the ANFIS models. Several statistical criteria were used to assess the model performances. The ANFIS model with a generalized bell membership function provided the best prediction accuracy compared with models with other membership functions. The coefficient of correlation values for this model were 0.999, 0.959, and 0.832 for training, testing, and validation datasets, respectively. Sensitivity analysis (SA) was used to show the effectiveness of the considered input parameters for predicting SSP. The SA results indicated that SSP is the most influential parameter on SSP. Generally, the findings indicate the robustness of the ANFIS approach for estimating SSP.

scholarly journals An Optimized Fuzzy Logic Control Model Based on a Strategy for the Learning of Membership Functions in an Indoor Environment

Electronics ◽  
2019 ◽  
Vol 8 (2) ◽  
pp. 132 ◽  
Author(s):  
Muhammad Fayaz ◽  
Israr Ullah ◽  
DoHyeun Kim
Keyword(s):  
Fuzzy Logic ◽  
Fuzzy Logic Control ◽  
Fuzzy Logic Controller ◽  
Environmental Temperature ◽  
Fuzzy Inference ◽  
Control Method ◽  
Learning Algorithm ◽  
Membership Functions ◽  
Inference Method ◽  
Logic Control

The Mamdani fuzzy inference method is one of the most important fuzzy logic control (FLC) techniques and has several applications in different fields. Despite its applications, the Mamdani fuzzy inference method has some core issues which still require solutions. The most critical issue is the selection of accurate shape and boundaries of membership functions (MFs) in the universe of discourse. In this work, we introduced a methodology called learning to control (LtC) to resolve the problem. The proposed methodology consisted of two main modules, namely, a control algorithm (CA) module and a learning algorithm (LA) module. In the CA module, the Mamdani FLC method has been used, whereas, in the LA module, we have used the artificial neural network (ANN) algorithm. Inputs into the ANN were the error difference between environmental temperature and the required temperature. The output of the ANN was the MF set to the FLC. Inputs into the fuzzy logic controller (FLC) were the error difference between environmental temperature and required temperature (D), and the output was the required power for the fan actuator. The purpose of the ANN was to tune the MFs of the FLC to improve its efficiency. The proposed learning-to-control method along with the conventional fuzzy logic controller method was applied to the data to evaluate the model’s performance. The results indicate that the proposed model’s performance is far better than that of conventional fuzzy logic techniques.

scholarly journals ANFIS optimized semi-active fuzzy logic controller for magnetorheological dampers

2016 ◽  
Vol 6 (1) ◽  
Author(s):  
Manuel Braz César ◽  
Rui Carneiro Barros
Keyword(s):  
Fuzzy Logic ◽  
Control System ◽  
Degrees Of Freedom ◽  
Fuzzy Logic Controller ◽  
Fuzzy Inference ◽  
Fuzzy Controller ◽  
Magnetorheological Dampers ◽  
Inference System ◽  
Neuro Fuzzy ◽  
Three Degrees Of Freedom

Abstract In this paper, we report on the development of a neuro-fuzzy controller for magnetorheological dampers using an Adaptive Neuro-Fuzzy Inference System or ANFIS. Fuzzy logic based controllers are capable to deal with non-linear or uncertain systems, which make them particularly well suited for civil engineering applications. The main objective is to develop a semi-active control system with a MR damper to reduce the response of a three degrees-of-freedom (DOFs) building structure. The control system is designed using ANFIS to optimize the fuzzy inference rule of a simple fuzzy logic controller. The results show that the proposed semi-active neuro-fuzzy based controller is effective in reducing the response of structural system.

scholarly journals Adaptive Neuro-Fuzzy Control Approach for a Single Inverted Pendulum System

2018 ◽  
Vol 8 (5) ◽  
pp. 3657 ◽  
Author(s):  
Mohammed A. A. Al-Mekhlafi ◽  
Herman Wahid ◽  
Azian Abd Aziz
Keyword(s):  
Fuzzy Inference System ◽  
Inverted Pendulum ◽  
Fuzzy Logic Controller ◽  
Fuzzy Inference ◽  
Fuzzy Controller ◽  
Inference System ◽  
Pendulum System ◽  
Control Approach ◽  
Inverted Pendulum System ◽  
Neuro Fuzzy

The inverted pendulum is an under-actuated and nonlinear system, which is also unstable. It is a single-input double-output system, where only one output is directly actuated. This paper investigates a single intelligent control system using an adaptive neuro-fuzzy inference system (ANFIS) to stabilize the inverted pendulum system while tracking the desired position. The non-linear inverted pendulum system was modelled and built using MATLAB Simulink. An adaptive neuro-fuzzy logic controller was implemented and its performance was compared with a Sugeno-fuzzy inference system in both simulation and real experiment. The ANFIS controller could reach its desired new destination in 1.5 s and could stabilize the entire system in 2.2 s in the simulation, while in the experiment it took 1.7 s to reach stability. Results from the simulation and experiment showed that ANFIS had better performance compared to the Sugeno-fuzzy controller as it provided faster and smoother response and much less steady-state error.

scholarly journals PATTERN OF TRADING PARTNER SELECTION IN DEPUTIZATION SYSTEMS BASED ON ADAPTIVE NEURO- FUZZY INFERENCE SYSTEM

2021 ◽  
Vol 9 (1) ◽  
pp. 17-27
Author(s):  
Seyed Sina Sharifi ◽  
Alireza Pooya ◽  
Mostafa Kazemi ◽  
Azar Kaffashpoor
Keyword(s):  
Fuzzy System ◽  
Fuzzy Inference ◽  
Insurance Company ◽  
Membership Functions ◽  
Business Partner ◽  
Inference System ◽  
Statistical Population ◽  
Mean Error ◽  
The Mean ◽  
Neural Fuzzy

Purpose: The purpose of this study is to develop a model for selecting a business partner in agency systems based on the method of the adaptive neural-fuzzy system. Methodology: The present research is applied in terms of purpose and descriptive in terms of the research method. The statistical population of the study, based on the subject of the research, the objectives of the research, and the spatial scope of the research, includes 98 agencies of Parsian Insurance Company in East Azarbaijan Province. According to the available statistics, the number of agencies of Parsian Insurance Company in East Azarbaijan Province is 98; Given that designed systems require more samples to arrive at the right answer. Therefore, the sample size will be done using the all-count sampling method. A questionnaire was used to collect the data of the input variables and the sales amount of different types of insurance policies was used for the output part. An adaptive neurophysiological system (ANFIS) has been used to analyze the data. Also, to evaluate the performance of each of the designed systems, the characteristics of the mean error squares and the root mean of the mean error squares were used. Main Findings: The research findings show that the best model designed to select a business partner in agency systems is a system with foot membership functions, some repetitions of 30, and two membership functions at each input. Application of Study: The results of this study can be used in agency systems to select business partners. Novelty/Originality:  The novelty of this study is developing a model for selecting a business partner in agency systems based on the method of the adaptive neural-fuzzy system.

Design and Implementation of Genetic Fuzzy Controller for Split Air-Conditioner Control Based on Fanger’s PMV Index

2019 ◽  
Vol 27 (04) ◽  
pp. 1950036 ◽  
Author(s):  
Chandrakant Balkrishna Patil ◽  
R. R. Mudholkar
Keyword(s):  
Fuzzy Logic Controller ◽  
Fuzzy Inference ◽  
Fuzzy Controller ◽  
Thermal Environment ◽  
Vital Role ◽  
Activity Level ◽  
Air Conditioner ◽  
Clothing Insulation ◽  
Inference System ◽  
Design And Implementation

This paper reports the design and implementation of genetically optimized fuzzy logic controller (GAFLC) for split air-conditioner based on the principle of Fanger’s Predicted Mean Vote (PMV) index. The proposed control strategy is aimed at improving the indoor thermal environment (ITE) at houses, offices, libraries, hotels, etc. because it plays a vital role in determining the health, physical and mental productivity of the occupants. The GAFLC has been implemented in MATLAB Simulink for computer simulation and also on hardware platform using the commercially available 8-bit ATmega-328 microcontroller through embedded C-coding for real practice. One part of the designed control algorithm examines the values of activity level, clothing insulation, air velocity, and relative humidity and decides the comfort temperature value to be set such that the PMV and PPD indices get satisfied. The other part generates a control signal to the air-conditioner compressor to maintain that temperature. From the simulation results it is seen that the generated comfort temperature values are in the range of 24.4∘– 26.55∘C for various combinations of environmental and personal parameters, which are well above the general temperature set value of 20∘C. This indicates the scope for reducing energy consumption to a greater extent. Also the PMV index lies in the range of [Formula: see text]0.23 to +[Formula: see text]0.36 with untuned fuzzy inference system (FIS), and in the range of [Formula: see text]0.32 to +[Formula: see text]0.14 with genetic algorithm (GA)-tuned FIS, which are acceptable comfort levels that human physiology can endure with more satisfaction. The experimental results show that GAFLC has generated a comfort temperature value for specified input parameters and also maintained the room temperature at that value to keep the thermal ambience more satisfactorily.

Design of a Hybrid Adaptive Neuro Fuzzy Inference System (ANFIS) Controller for Position and Angle control of Inverted Pendulum (IP) Systems

2016 ◽  
Vol 5 (1) ◽  
pp. 27-42 ◽  
Author(s):  
Ashwani Kharola
Keyword(s):  
Inverted Pendulum ◽  
Fuzzy Logic Controller ◽  
Fuzzy Inference ◽  
Fuzzy Controller ◽  
Data Sets ◽  
Comparison Study ◽  
Inference System ◽  
Simulink Model ◽  
Neuro Fuzzy ◽  
Proposed Model

This paper illustrates a comparison study of Fuzzy and ANFIS Controller for Inverted Pendulum systems. IP belongs to a class of highly non-linear, unstable and multi-variable systems which act as a testing bed for many complex systems. Initially, a Matlab-Simulink model of IP system was proposed. Secondly, a Fuzzy logic controller was designed using Mamdani inference system for control of proposed model. The data sets from fuzzy controller was used for development of a Hybrid Sugeno ANFIS controller. The results shows that ANFIS controller provides better results in terms of Performance parameters including Settling time(sec), maximum overshoot(degree) and steady state error.

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