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.

A Fuzzy Inference System for Identifying Tissue Elasticity Using Ultrasound

2003 ◽  
Vol 7 (1) ◽  
pp. 31-39 ◽  
Author(s):  
Tadashi Kimura ◽  
◽  
Kouki Nagamune ◽  
Syoji Kobashi ◽  
Katsuya Kondo ◽  
...  
Keyword(s):  
Fuzzy Inference ◽  
Fuzzy Rule ◽  
Measurement Range ◽  
Identification System ◽  
Membership Functions ◽  
Tissue Elasticity ◽  
Inference System ◽  
Fuzzy Membership Functions ◽  
Rule Based Approach ◽  
Two Stages

This paper proposes a fuzzy rule-based approach for identifying tissue elasticity using ultrasound. The purpose of this paper identifies automatically tissue elasticity. Information of tissue elasticity helps us to diagnose several diseases. Elastography was able to estimate tissue elasticity. However, this measurement range is limited due to the need of pressure. To avoid this limitation, this paper proposes the identification system without pressure. This inference system consists of two stages. In the first stage, fuzzy membership functions are constructed by known data of elasticity. The second stage identifies elasticity of unknown data by using the membership functions. We used five different phantoms (total 5×10 = 50) of elasticity as known data and applied this system into nine different phantoms (total 9×10 = 90) of elasticity as unknown data. As a result, the correlation coefficient between actual value and identified value was 0.789 and the error of means was 0.646. This system thus acquired smaller error ratio than that of the statistical method.

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.

A Fuzzy Rule Extraction Method for ANFIS Using CFCM and Fuzzy Equalization

2000 ◽  
Vol 4 (5) ◽  
pp. 355-361 ◽  
Author(s):  
Myung-Geun Chun ◽  
◽  
Keun-Chang Kwak ◽  
Jeong-Woong Ryu ◽  
Witold Pedrycz ◽  
...  
Keyword(s):  
Fuzzy Inference ◽  
Fuzzy Rule ◽  
Fuzzy Data ◽  
Rule Generation ◽  
Membership Functions ◽  
Fe Method ◽  
Inference System ◽  
Fuzzy Membership Functions ◽  
Satisfactory Performance ◽  
The Given

In this paper, an efficient fuzzy rule generation scheme for Adaptive Network-based Fuzzy Inference System (ANFIS) using the conditional fuzzy c-means (CFCM) and fuzzy equalization (FE) methods is proposed. Here, the CFCM is adopted to render clusters, which can represent the homogeneous properties of the given input and output fuzzy data. And also the FE method is used to automatically construct the fuzzy membership functions for ANFIS. From this, we can systematically obtain a small size of fuzzy rules that shows satisfactory performance for the given problems. We applied the proposed method to the truck-backing control and Box-Jenkins modeling problems and obtained a better result than previous work.

scholarly journals Study of Hybrid Neurofuzzy Inference System for Forecasting Flood Event Vulnerability in Indonesia

2019 ◽  
Vol 2019 ◽  
pp. 1-13 ◽  
Author(s):  
Sri Supatmi ◽  
Rongtao Hou ◽  
Irfan Dwiguna Sumitra
Keyword(s):  
Fuzzy Inference ◽  
Flood Event ◽  
Membership Functions ◽  
Fuzzy Inference Systems ◽  
Flood Vulnerability ◽  
Inference System ◽  
Forecasting Accuracy ◽  
Proposed Model ◽  
Inference Systems ◽  
Takagi Sugeno

An experimental investigation was conducted to explore the fundamental difference among the Mamdani fuzzy inference system (FIS), Takagi–Sugeno FIS, and the proposed flood forecasting model, known as hybrid neurofuzzy inference system (HN-FIS). The study aims finding which approach gives the best performance for forecasting flood vulnerability. Due to the importance of forecasting flood event vulnerability, the Mamdani FIS, Sugeno FIS, and proposed models are compared using trapezoidal-type membership functions (MFs). The fuzzy inference systems and proposed model were used to predict the data time series from 2008 to 2012 for 31 subdistricts in Bandung, West Java Province, Indonesia. Our research results showed that the proposed model has a flood vulnerability forecasting accuracy of more than 96% with the lowest errors compared to the existing models.

scholarly journals A Heating Controller Designing Based on Living Space Heating Dynamic’s Model Approach in a Smart Building

Energies ◽  
2021 ◽  
Vol 14 (4) ◽  
pp. 998
Author(s):  
Roozbeh Sadeghian Broujeny ◽  
Kurosh Madani ◽  
Abdennasser Chebira ◽  
Veronique Amarger ◽  
Laurent Hurtard
Keyword(s):  
Fuzzy Inference ◽  
Fuzzy Controller ◽  
Control Strategies ◽  
Time Series Prediction ◽  
Adaptive Controller ◽  
Inference System ◽  
Diverse Range ◽  
Smart Building ◽  
Living Space ◽  
Soft Computing Techniques

Most already advanced developed heating control systems remain either in a prototype state (because of their relatively complex implementation requirements) or require very specific technologies not implementable in most existing buildings. On the other hand, the above-mentioned analysis has also pointed out that most smart building energy management systems deploy quite very basic heating control strategies limited to quite simplistic predesigned use-case scenarios. In the present paper, we propose a heating control strategy taking advantage of the overall identification of the living space by taking advantage of the consideration of the living space users’ presence as additional thermal sources. To handle this, an adaptive controller for the operation of heating transmitters on the basis of soft computing techniques by taking into account the diverse range of occupants in the heating chain is introduced. The strategy of the controller is constructed on a basis of the modeling heating dynamics of living spaces by considering occupants as an additional heating source. The proposed approach for modeling the heating dynamics of living spaces is on the basis of time series prediction by a multilayer perceptron neural network, and the controlling strategy regarding the heating controller takes advantage of a Fuzzy Inference System with the Takagi-Sugeno model. The proposed approach has been implemented for facing the dynamic heating conduct of a real five-floor building’s living spaces located at Senart Campus of University Paris-Est Créteil, taking into account the occupants of spaces in the control chain. The obtained results assessing the efficiency and adaptive functionality of the investigated fuzzy controller designed model-based approach are reported and discussed.

scholarly journals An Adaptive Neuro-Fuzzy Control of Pneumatic Mechanical Ventilator

Actuators ◽  
2021 ◽  
Vol 10 (3) ◽  
pp. 51
Author(s):  
Jozef Živčák ◽  
Michal Kelemen ◽  
Ivan Virgala ◽  
Peter Marcinko ◽  
Peter Tuleja ◽  
...  
Keyword(s):  
Control System ◽  
Control Algorithm ◽  
Fuzzy Inference ◽  
Fuzzy Controller ◽  
Mechanical Design ◽  
Mechanical Ventilator ◽  
Mechanical Ventilators ◽  
Inference System ◽  
Control Approach ◽  
Neuro Fuzzy

COVID-19 was first identified in December 2019 in Wuhan, China. It mainly affects the respiratory system and can lead to the death of the patient. The motivation for this study was the current pandemic situation and general deficiency of emergency mechanical ventilators. The paper presents the development of a mechanical ventilator and its control algorithm. The main feature of the developed mechanical ventilator is AmbuBag compressed by a pneumatic actuator. The control algorithm is based on an adaptive neuro-fuzzy inference system (ANFIS), which integrates both neural networks and fuzzy logic principles. Mechanical design and hardware design are presented in the paper. Subsequently, there is a description of the process of data collecting and training of the fuzzy controller. The paper also presents a simulation model for verification of the designed control approach. The experimental results provide the verification of the designed control system. The novelty of the paper is, on the one hand, an implementation of the ANFIS controller for AmbuBag pressure control, with a description of training process. On other hand, the paper presents a novel design of a mechanical ventilator, with a detailed description of the hardware and control system. The last contribution of the paper lies in the mathematical and experimental description of AmbuBag for ventilation purposes.

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