Sugeno Fuzzy Logic Research Articles

Hybrid fuzzy model to predict strength and optimum compositions of natural Alumina-Silica-based geopolymers

This study introduces the supervised committee fuzzy model as a hybrid fuzzy model to predict compressive strength (CS) of geopolymers prepared from alumina-silica products. For this purpose, more than 50 experimental data that evaluated the effect of Al2O3/SiO2, Na2O/Al2O3, Na2O/H2O and Na/[Na+K] on (CS) of geopolymers were collected from the literature. Then, three different Fuzzy Logic (FL) models (Sugeno fuzzy logic (SFL), Mamdani fuzzy logic (MFL), and Larsen fuzzy logic (LFL)) were adopted to overcome the inherent uncertainty of geochemical parameters and to predict CS. After validating the model, it was found that the SFL model is superior to MFL and LFL models, but each of the FL models has advantages to predict CS. Therefore, to achieve the optimal performance, the supervised committee fuzzy logic (SCFL) model was developed as a hybrid method to combine the benefits of individual FL models. The SCFL employs an artificial neural network (ANN) model to re-predict the CS of three FL model predictions. The results also show significant fitting improvement in comparison with individual FL models.

Capturing the stakeholders' managerial competency risks of mega infrastructure projects: a fuzzy logic modelling approach

This paper develops a fuzzy-logic model to aggregate the numerous managerial risks driven by the various project stakeholders at different project stages. The model accounts for the various perceptions of assessors involved in the risk evaluation process. The proposed model provides means for performing scenario analysis at an early project procurement stage to manage macro risks. Four managerial processes or competencies were considered as critical success factors for infrastructure projects. These are communication, coordination, knowledge sharing and decision-making. A Sugeno fuzzy logic model was calibrated using three input variables representing the importance of the various projects stakeholder groups, the criticalness level of the management process as perceived by the various groups at the various project stages, and the effectiveness level of the management competency. The model was validated using surveys of various stakeholder groups of a mega project.

Comparing Mamdani Sugeno fuzzy logic and RBF ANN network for PV fault detection

Abstract This work proposes a new fault detection algorithm for photovoltaic (PV) systems based on artificial neural networks (ANN) and fuzzy logic system interface. There are few instances of machine learning techniques deployed in fault detection algorithms in PV systems, therefore, the main focus of this paper is to create a system capable to detect possible faults in PV systems using radial basis function (RBF) ANN network and both Mamdani, Sugeno fuzzy logic systems interface. The obtained results indicate that the fault detection algorithm can detect and locate accurately different types of faults such as, faulty PV module, two faulty PV modules and partial shading conditions affecting the PV system. In order to achieve high rate of detection accuracy, four various ANN networks have been tested. The maximum detection accuracy is equal to 92.1%. Furthermore, both examined fuzzy logic systems show approximately the same output during the experiments. However, there are slightly difference in developing each type of the fuzzy systems such as the output membership functions and the rules applied for detecting the type of the fault occurring in the PV plant.

Perancangan Kendali Robot pada Smartphone Menggunakan Sensor Accelerometer Berbasis Metode Fuzzy Logic

Telecommunications and robotics technology is being developed to assist and facilitate the work of a human. In the field of telecommunications particularly smartphone has reached the planting of operating systems like android until planting sensors such as an accelerometer, gyro, proximity, etc. We would like to take advantage of the accelerometer sensor on a smartphone as robot control. We will compare the use of Sugeno Fuzzy Logic and Mamdani Fuzzy Logic to determine the best control method. The basic components of the robot are the Bluetooth module HC-05 as a medium of communication with the android, arduino as the control system and actuators such as DC motors drive the rear wheels to adjust the speed of the robot, and servo motor drives the front wheels to adjust the degree of turn robot. In robot’s movement test, 4 of 8 trials or approximately 50% stated better Sugeno Fuzzy Logic than Mamdani Fuzzy Logic in terms of linearity. In robot's controller response test, for Sugeno Fuzzy Logic method the average delay is 0.41 seconds, and for Mamdani Fuzzy Logic method the average delay is 10.80 seconds.

Autonomous path following by fuzzy adaptive curvature-based point selection algorithm for four-wheel-steering car-like mobile robot

Autonomous path following in mobile robots with nonholonomic constraints can be divided into two problems: first, selecting the tracking point, and then designing an appropriate controller to follow the selected point. When selecting tracking point, considering the kinematics of the robot as well as characteristics of the desired path is of considerable importance. For these purposes, a curvature-based point selection algorithm is first proposed for the car-like mobile robot with independent steering mechanisms. Each instant, the proposed algorithm finds a point which enables the robot to be tangent to the path at that specific point. Afterwards, in order to take into consideration characteristics of the path, a fuzzy adaptive curvature-based point selection algorithm is proposed. In this algorithm, in addition to the kinematic constraints, path characteristics are also considered in selecting the tracking point. This gives the robot the ability to show better performance when the path slope changes suddenly, resulting in less overshoot/undershoot around the desired path. The fuzzy adaptive curvature-based point selection algorithm is combined with a controller based on the Takagi–Sugeno fuzzy logic, such that the fuzzy adaptive curvature-based point selection algorithm selects the tracking point, while the Takagi–Sugeno fuzzy logic controller makes the robot follow the selected point. Finally, the fuzzy adaptive curvature-based point selection–Takagi–Sugeno fuzzy logic tracker is implemented on the robot, and the results are compared with a similar path-following algorithm. Obtained results show that for tracking a piecewise linear path, the steering activity and the following root mean square error decrease from 170.74° and 0.37 m for the conventional fuzzy controller to 63.37° and 0.09 m for the fuzzy adaptive curvature-based point selection–Takagi–Sugeno fuzzy logic controller, respectively.

TCSC based automatic generation control of deregulated power system using quasi-oppositional harmony search algorithm

In present aspect, automatic generation control (AGC) of deregulated power system with thyristor controlled series compensator (TCSC) device is investigated. The objective is to discuss bilateral power transaction issue with the TCSC effect. A deregulated two-area power system model having two thermal units in each control area is considered for this act. A quasi-oppositional harmony search (QOHS) algorithm is being applied for the constrained optimization problem. Three cases, commonly studied in deregulation, are discussed for the effectiveness of the proposed technique. Further, sensitivity analysis is studied by varying the test system parameters up to ±25% from their rated values. The obtained simulation plots are analytically discussed with the calculation of oscillatory modes, transient details and the studied performance indices. Sugeno fuzzy logic control technique is also investigated to the studied test system. The simulation results show that the proposed QOHS based TCSC controller is quite effective in deregulated environment.

PERANCANGAN SISTEM INFORMASI ANALISIS MEDIK MENGGUNAKAN LOGIKA FUZZY SUGENO BERBASIS DATA REKAM MEDIK PADA PENYAKIT HIPERTENSI

Number of patients with hypertension have increased from year to year. This is due to an unhealthy lifestyle, excessive stress and so forth. Patiens with hypertension are often unaware of the dangers that can be caused by disease. The medical diagnosis of hypertension is made when obtained blood pressure is obtained more than 140/90 mmHg. In this study, created a system of enforcement of hypertension using sugeno fuzzy logic. This application uses the parameters of age, body mass index, blod pressure(systole and diastole), family history (genetics), diabetes mellitus. From testing the fuzzy logic that has been done, can be obtained on the calculation of 60 patients who did not suffer from hypertension (normal) as much 3 people, who suffer from prehypertension as much 17 people, who suffer from hypertension grade 1 as much 25 people and who suffer from hypertension degrees 2 as much 15 people. With an average accuracy test rate 99.999989% for patients who did not suffer from hypertension, 99.999985% for patients who suffer from prehypertension, 100% who suffer from hypertension grade 1 and 99.999946% who suffer from hypertension degrees 2. With an average the level of error 0.00333% for patients who did not suffer from hypertension, 0.00471% for patients who suffer from prehypertension, 0% who suffer from hypertension grade 1 and 0.00733% who suffer from hypertension degrees 2. From the application made can provide clear, accurate information about hypertension disease.

Mapping vulnerability of multiple aquifers using multiple models and fuzzy logic to objectively derive model structures

Driven by contamination risks, mapping Vulnerability Indices (VI) of multiple aquifers (both unconfined and confined) is investigated by integrating the basic DRASTIC framework with multiple models overarched by Artificial Neural Networks (ANN). The DRASTIC framework is a proactive tool to assess VI values using the data from the hydrosphere, lithosphere and anthroposphere. However, a research case arises for the application of multiple models on the ground of poor determination coefficients between the VI values and non-point anthropogenic contaminants. The paper formulates SCFL models, which are derived from the multiple model philosophy of Supervised Committee (SC) machines and Fuzzy Logic (FL) and hence SCFL as their integration. The Fuzzy Logic-based (FL) models include: Sugeno Fuzzy Logic (SFL), Mamdani Fuzzy Logic (MFL), Larsen Fuzzy Logic (LFL) models. The basic DRASTIC framework uses prescribed rating and weighting values based on expert judgment but the four FL-based models (SFL, MFL, LFL and SCFL) derive their values as per internal strategy within these models. The paper reports that FL and multiple models improve considerably on the correlation between the modeled vulnerability indices and observed nitrate-N values and as such it provides evidence that the SCFL multiple models can be an alternative to the basic framework even for multiple aquifers. The study area with multiple aquifers is in Varzeqan plain, East Azerbaijan, northwest Iran.

Assessment of groundwater vulnerability using supervised committee to combine fuzzy logic models.

Vulnerability indices of an aquifer assessed by different fuzzy logic (FL) models often give rise to differing values with no theoretical or empirical basis to establish a validated baseline or to develop a comparison basis between the modeling results and baselines, if any. Therefore, this research presents a supervised committee fuzzy logic (SCFL) method, which uses artificial neural networks to overarch and combine a selection of FL models. The indices are expressed by the widely used DRASTIC framework, which include geological, hydrological, and hydrogeological parameters often subject to uncertainty. DRASTIC indices represent collectively intrinsic (or natural) vulnerability and give a sense of contaminants, such as nitrate-N, percolating to aquifers from the surface. The study area is an aquifer in Ardabil plain, the province of Ardabil, northwest Iran. Improvements on vulnerability indices are achieved by FL techniques, which comprise Sugeno fuzzy logic (SFL), Mamdani fuzzy logic (MFL), and Larsen fuzzy logic (LFL). As the correlation between estimated DRASTIC vulnerability index values and nitrate-N values is as low as 0.4, it is improved significantly by FL models (SFL, MFL, and LFL), which perform in similar ways but have differences. Their synergy is exploited by SCFL and uses the FL modeling results "conditioned" by nitrate-N values to raise their correlation to higher than 0.9.

IMPLEMENTASI LOGIKA FUZZY PADA PEMBUATAN KARAKTER MUSUH UNTUK GAME SINGLE FIGHTER BERPLATFORM ANDROID

This adventure game tells the story of an elephant who struggles to save his son from enemy prisoners. Elephants should be able to beat all three enemies, namely monkeys, wolves, and kingkong to be able to find his son. In the course of fighting the enemy will be no relief in the form of bombs and weapons are power ups in the form of apples to increase the number of lives player. The behavior of the enemy characters in this game are designed with the application of fuzzy logic Sugeno method. Each enemy by artificial intelligence has a health status (lives), ammo (the number of stones to attack the player) and distance (distance to the player during the attack). This game has a rule of implementation Sugeno Fuzzy Logic method for calculating the value of output behavior of the enemy in attacking player. The system design of the game is using the storyboard that contains a description of the story of each scene and any component material that will be used. Then the algorithm implementation using the Unity game development software. For game development, programming language used is C # of Unity. The results of this study are the Application Game "Single Fighter" that has succeeded in publishing in the Google Play Store are made of Unity game engine, side-scrolling genre game, and the android platform. From the application of fuzzy logic in this game also has gone well, where there are 41 types of enemy attackers%, 36% type of archer-distance and 87% of type giant enemy very aggressive in attacking player. Kata Kunci: Game, Logika Fuzzy, Sugeno, Unity, Android

A novel routing strategy for cognitive radio ad hoc network based on Sugeno fuzzy logic

A novel routing strategy for cognitive radio ad hoc network based on Sugeno fuzzy logic

A novel routing strategy for cognitive radio ad hoc network based on Sugeno fuzzy logic

In cognitive radio networks, a secondary user (SU) is dependent upon the spectrum, of varied frequency, temporarily vacated by primary users (PU) thereby making it quite a challenging task to design a protocol for finding a reliable and efficient routing path. To address the issue many routing schemes have been proposed based upon Mamdani type fuzzy inference system (FIS) to create a human intelligence analogy. The Mamdani type fuzzy inference mechanism is unable to simultaneously optimise multiple quality of service (QoS) parameters. This paper proposes a routing scheme based upon the Sugeno type fuzzy inference system (SFIS) that handles the continuously changing input parameters in quite effective manner and provides an effective performance with respect to multiple QoS parameters. The proposed routing scheme has been implemented using Matlab-9.0 and simulation results showed that the paths selected using this scheme are much more efficient than the paths selected by other routing schemes.

APLIKASI SISTEM PERINGATAN TABRAKAN PADA KAPAL BERBASIS DATA GPS MENGGUNAKAN LOGIKA FUZZY

Automatic Identification System (AIS) is an important equipment in ship for giving ship’s information to other ships or harbour. Unfortunately, there is still no ship collision warning system included in AIS. Hence, an application of ship collision early warning system based on Global Positioning System (GPS) data is proposed in this paper. Here, the zero-order sugeno fuzzy logic is used to process the ships speed and position data. The output of this warning system are recommended ships speed and heading direction to prevent the ship collision based on IMO (International Maritime Organization) regulation. The object used is a ship prototype equiped with GPS. The testing is held in four position of the ship prototype againts static object. The positions are -45 o , -25 o , 25 o dan 45 o . The testing results yield 100% accuracy to the IMO regulation of the head on situations case .

Modelling of the Interdependence Between Speed and Traffic Flow Density. A Neuro – Fuzzy Logic Approach

The speed-traffic flow density interdependence diagram has a number of variations, starting with the theoretical model, through various empirical models that were developed and models based on actual research done on traffic flow. The functional interdependence is obtained using the Sugeno fuzzy logic system, where representative values proposed in HCM 2010 have been adopted as parameters of output association functions. Subsequently the neural network is trained based on actual traffic flow data, which by adjusting the association function of the fuzzy logic system yields an output form of the basic traffic flow diagram. It was noticed that this hybrid expert system produces better output results by applying the “subtractive clustering“ method on data that are used for training a neural network. Finally, the model was tested on several input data groups, and the interdependence between speed and traffic flow density is shown in graphical form.

Model-based control using interval type-2 fuzzy logic systems

Type-2 fuzzy logic introduced new formalisms capable of overcoming the inherent uncertainties of approximating real-world processes by computational models. Yet, despite increasingly present in the nonlinear modeling literature, model-based control theory does not seem to be taking full advantage of the improvements that type-2 fuzzy logic provides. Therefore, the present work proposes the development of a new control methodology based on the generalized predictive control theory supported by interval type-2 Takagi---Sugeno fuzzy logic systems. The developed control system is based on locally linear approximations of the type-2 Takagi---Sugeno model and will be evaluated using a nonlinear process based on the yeast fermentation reaction. For comparison purposes, two additional generalized predictive control implementations based on a linear auto-regressive model with exogenous inputs and a type-1 Takagi---Sugeno fuzzy model will be used. The performance of the closed loop systems will be evaluated by subjecting the process to quick changes in the operation regime and to unmeasured external disturbances. The mean squared error, control effort and typical transient step response metrics (overshoot and settling time) will provide the benchmark criteria. The results achieved demonstrate that at the expense of a small increase in the computational effort, type-2 fuzzy logic systems improve the transient behavior of the closed loop system, presenting significant advantages when the controlled process is subject to unmodeled disturbances.

Comparative evaluation of artificial intelligence models for prediction of uniaxial compressive strength of travertine rocks, Case study: Azarshahr area, NW Iran

Uniaxial compressive strength (UCS) is one of the most widely used and important rock mechanical parameters in rock engineering. The main objective of the present study was to evaluate the ability of artificial intelligence models including multi-layer perceptron (MLP), Sugeno fuzzy logic (SFL), Mamdani fuzzy logic (MFL), adaptive neuro-fuzzy inference system (ANFIS) and support vector machine (SVM) to predict the UCS of travertine rocks in the Azarshahr area (NW Iran). To attempt this objective, 85 core samples of travertine rock were collected from the study area and the laboratory tests were performed to determine the P-wave velocity [Vp (km/s)], porosity (n %), Schmidt rebound hardness (Rn) and UCS of the rocks at the Rock Mechanics Laboratory in the Tarbiat Modares University. The data set including Vp (km/s), n % and Rn as the inputs and UCS as the output were divided into training (80 % of dataset) and testing (20 % of dataset) subsets to construct the models. The coefficient of determination (R2), root mean square error (RMSE) and mean absolute error (MAE) were used to evaluate the models performance. The models accuracy followed the order SVM > ANFIS > SFL > MLP > MFL. The SVM model with RBF kernel function yielded the highest R2 (0.9516), and the lowest RMSE (2.14 MPa) and MAE (1.351 MPa) in the testing step. Accuracy results indicated that SVM model predictions were better than MLP, SFL, MFL and ANFIS models for prediction of UCS of travertine rocks.

PERANCANGAN SISTEM PERAMALAN CUACA BERBASIS LOGIKA FUZZY

This study aims to Implement Sugeno Fuzzy Logic method for weather forecasts and testing the accuracy of this research. The problem definition covers only the Data-Based Design of Weather Forecasting Sugeno Fuzzy Logic method using Matlab so as not to come out of the issues raised, it is used as the input is temperature, humidity and wind. In determining the results of research is an important weather forecasting particularly determined membership functions and the rule is used. Fuzzy logic using Sugeno method is best used in forecasting for the accuracy above 60%. The more variables that are used as input it will produce better output (inaccurate).Keywords: Weather Forecasting, Fuzzy Logic

PID controller for automatic voltage regulator using teaching–learning based optimization technique

Abstract The present work presents teaching–learning based optimization (TLBO) algorithm as an optimization technique in the area of tuning of the classical controller installed in automatic voltage regulator (AVR). The proposed TLBO algorithm is applied with an aim to find out the optimum value of proportional integral derivative (PID) controller gains with first order low pass filter installed in the AVR. The voltage response of the AVR system, as obtained by using the proposed TLBO based PID controller with first order low pass filter, is compared to those offered by the other algorithms reported in the recent state-of-the-art literatures. The advantage of using this control strategy may be noted by providing good dynamic responses over a wide range of system parametric variations. For on-line, off-nominal operating conditions, fast acting Sugeno fuzzy logic technique is applied to obtain the on-line dynamic responses of the studied model. Furthermore, robustness analysis is also carried out to check the performance of the designed TLBO based PID controller. An analysis, based on voltage response profile, has been investigated with the variations of the model parameters. The simulation results show that the proposed TLBO based PID controller is a significant optimization tool in the subject area of the AVR system. The essence of the present work signifies that the proposed TLBO technique maybe, successfully, applied for the AVR of power system.

Automatic generation control of multi‐unit multi‐area deregulated power system using a novel quasi‐oppositional harmony search algorithm

This initiative work deals with the automatic generation control (AGC) of multi-unit multi-area power system in the prospective of deregulated power environment owing to load following issues. In present outlook, a deregulated four-area power system having eight generation companies and eight distribution companies is treated as a plant model. The concerned objective is to intensify the bilateral behaviour and investigate the system AGC performance. In the state of deregulation, the influence of a novel quasi-oppositional harmony search (QOHS) algorithm based Sugeno fuzzy-logic (SFL) technique is proposed as an AGC optimisation task by showing its consequences on bilateral policy/dynamics. In the entire work, the proposed QOHS based SFL technique is incorporated for the tuning of on-line, off-nominal controller gains. The salient advantage of this control strategy is to provide good dynamic responses over a wide range of system parametric variations. The observations are based on the three deregulated cases which, generally, takes place in deregulated power market. The presented simulation results show that the proposed QOHS based SFL is a promising optimisation technique in the field of deregulated AGC study.

A novel quasi-oppositional harmony search algorithm for AGC optimization of three-area multi-unit power system after deregulation

The present work addresses a decentralized, well tested three-area multi-unit power system for its automatic generation control (AGC) after deregulation which is characterized by price-based market operation. To match with the actual deregulated environment, as prevailing in the real one, the market structure is kept generic enough enabling to capture all possibilities occurring in real-time day-to-day power environment. In accordance to the modifications, as done in the investigated three-area power system model, the concerned objective is to intensify the deregulated AGC operation followed by load disturbances. At the present platform, three different classes of case study results are postulated for the studied test system. The first two illustrate the behavior of unilateral and bilateral based power contract transactions while the third one considers the contract violation case as it exists in present time. The contractual agreement, instituted by DISCO participation matrix, is initialized to address the power transaction contracts. In this work, a novel quasi-oppositional harmony search (QOHS) algorithm is explored and presented its significances in deregulated AGC operation. In the second phase of investigation, fast acting Sugeno fuzzy logic technique is explored for on-line, off-nominal operating conditions. For analysis purpose, both the qualitative and the quantitative aspects of the proposed QOHS are presented in reference to genetic algorithm (GA). Additionally, the sensitivity analysis is also performed to evaluate the performance of the proposed QOHS based controller. Simulation work reveals that the proposed QOHS may be, effectively, worked out to order to improve the deregulated AGC performance. It is also being observed that the proposed QOHS outperforms the GA in sense of deregulated AGC operation of power system.