Mamdani Fuzzy Logic Research Articles

Aplikasi Fuzzy Logic Mamdani Untuk Perkembangan Pertumbuhan Anak Berdasarkan BGM-KMS

Considering the number of severe malnutrision cases or Balita di Bawah Garis Merah (BGM) to the deaths of children is still often happen, due to the lack of information of children growth development, service standards, and incomplete medical records also clinic rules that aren't obeyed. Example case is focused on Kartu Menuju Sehat (KMS).Decision making system for this BGM-KSM based children growth development is very appropriate to be applied on problems handling which need solution from computer to process toddlers data with rapid and accurate calculation. So the decision making system which can be used is Fuzzy Logic Mamdani reasoning.Fuzzy Logic Mamdani is also known as max-min method which can search for fuzzy set from fuzzy rule composition, while the output is resulted from that fuzzy set's domain. To modify the output from fuzzy numbers to firm numbers or defuzzytion using Centroid (Composite Moment) method which is used to search for Centre of Area (COA). This research is conducted to get fuzzy rule value, and then to determine health state and get the solution of energy/calori needed based on age, body weight, and body height.

Taguchi-fuzzy inference system and grey relational analysis to optimise process with multiple performance characteristics in precision turning of Ti-6AL-4V

The objective of the work is to perform precision turning using a conventional lathe on Ti6Al4V under dry working conditions. In this paper, a new approach for the optimisation of the precision turning process with multiple performance characteristics based on the orthogonal array with the grey relational analysis has been studied. A grey relational grade obtained from the grey relational analysis is used to solve the precision turning process with the multiple performance characteristics. Fuzzy inference system (Mamdani fuzzy logic) to predict the dimensional accuracy in part produced by precision turning. The model uses all input and output variables in linguistic terms enabling it convenient for practitioners. The inference engine in Mamdani type FIS uses rules which are obtained with the help of design of experimental technique (DOE).

Maximum Power Point Tracker of PV using Fuzzy Controller

This paper introduces two techniques to control systems. We introduce two methods for achieving maximum power. Appropriate conditions for the stability of system are drawn depend on perturb & observer (P&O) and Mamdani fuzzy logic. We present which of these techniques can extract maximum power under high disturbance. The proposed controllers design methodology are finally proved through a photovoltaic to maximize the Photovoltaic (PV) system.

Handoff Decision Using a Kalman Filter and Fuzzy Logic in Heterogeneous Wireless Networks

Handoff decision is key to service continuity when a user roams about. We propose a Kalman filtering and fuzzy logic approach to reducing handoff initiations, so as to support seamless communication. Kalman filtering is used as a channel equalization technique in channel propagation model, while a Mamdani fuzzy logic is applied subsequently for handoff decision. Several metrics such as received signal strength (RSS), data rate, velocity of mobile terminal, and traffic load are considered as criteria to initiate handoff from WLAN to the cellular network. Simulation results show that the proposed method reduces handoff initiations effectively.

Dynamic fuzzy logic and reinforcement learning for adaptive energy efficient routing in mobile ad-hoc networks

In this paper, a dynamic fuzzy energy state based AODV (DFES-AODV) routing protocol for Mobile Ad-hoc NETworks (MANETs) is presented. In DFES-AODV route discovery phase, each node uses a Mamdani fuzzy logic system (FLS) to decide its Route REQuests (RREQs) forwarding probability. The FLS inputs are residual battery level and energy drain rate of mobile node. Unlike previous related-works, membership function of residual energy input is made dynamic. Also, a zero-order Takagi Sugeno FLS with the same inputs is used as a means of generalization for state-space in SARSA-AODV a reinforcement learning based energy-aware routing protocol. The simulation study confirms that using a dynamic fuzzy system ensures more energy efficiency in comparison to its static counterpart. Moreover, DFES-AODV exhibits similar performance to SARSA-AODV and its fuzzy extension FSARSA-AODV. Therefore, the use of dynamic fuzzy logic for adaptive routing in MANETs is recommended.

An Atanassov’s intuitionistic fuzzy reasoning model

The task of the standard Mamdani fuzzy logic controller is to find a crisp control action from the fuzzy rule-base and from a set of crisp inputs. In this paper we modify this controller in order to work with Atanassov’s intuitionistic fuzzy sets and to activate a set of rules having the same concl usion. Usually, the inference rules used in a fuzzy logic controller are given by a domain expert; in our system, these rules are automatically induced as fuzzy association rules starting from a training set. The fuzzy confidence value associated with each rule is used to obtain the fuzzy set inferred by our system.

Supervised Intelligence Committee Machine to Evaluate Field Performance of Photocatalytic Asphalt Pavement for Ambient Air Purification

The ability of a titanium dioxide (TiO2) photocatalytic nanoparticle to trap and to decompose organic and inorganic air pollutants makes it a promising technology as a pavement coating to mitigate the harmful effects of vehicle emissions. Statistical models and artificial intelligence (AI) models are two applicable methods to quantify photocatalytic efficiency. The objective of this study was to develop a model based on field-collected data to predict the nitrogen oxide (NOx) reduction. To achieve this objective, the supervised intelligent committee machine (SICM) method as a combinational black box model was used to predict NOx concentration at the pavement level before and after TiO2 application on the pavement surface. SICM predicts NOx concentration by a nonlinear combination of individual AI models through an artificial intelligent system. Three AI models—Mamdani fuzzy logic, artificial neural network, and neuro-fuzzy—were used to predict NOx concentration in the air as a function of traffic count and climatic conditions, including humidity, temperature, solar radiation, and wind speed before and after the application of TiO2. In addition, an intelligent committee machine model was developed by combining individual AI model output linearly through a set of weights. Results indicated that the SICM model could provide a better prediction of NOx concentration as an air pollutant in the complex and multidimensional air quality data analysis with less residual mean square error than that given by multivariate regression models.

A comparative assessment of prediction capabilities of modified analytical hierarchy process (M-AHP) and Mamdani fuzzy logic models using Netcad-GIS for forest fire susceptibility mapping

The main purpose of this study is to assess forest fire susceptibility maps (FFSMs) and their performances comparison using modified analytical hierarchy process (M-AHP) and Mamdani fuzzy logic (MFL) models in a geographic information system (GIS) environment. This study was carried out in the Minudasht Forests, Golestan Province, Iran, and was conducted in three main stages such as spatial data construction, forest fire modelling using M-AHP and MFL, and validation of constructed models using receiver operating characteristic (ROC) curve. At first, seven conditioning factors, such as altitude, slope aspect, slope angle, annual temperature, wind effect, land use, and normalized different vegetation index, were extracted from the spatial database. In the next step, FFSMs were prepared using M-AHP and MFL modules in a Netcad-GIS Architect environment. Finally, the ROC curves and area under the curves (AUCs) were estimated for validation purposes. The results showed that the AUCs for MFL and M-AHP are 88.20% and 77.72%, respectively. The results obtained in this study also showed that the MFL model performed better than the M-AHP model. These FFSMs can be applied for land use planning, management, and prevention of future fire hazards.

Force control of a tri-layer conducting polymer actuator using optimized fuzzy logic control

Conducting polymers actuators (CPAs) are potential candidates for replacing conventional actuators in various fields, such as robotics and biomedical engineering, due to their advantageous properties, which includes their low cost, light weight, low actuation voltage and biocompatibility. As these actuators are very suitable for use in micro-nano manipulation and in injection devices in which the magnitude of the force applied to the target is of crucial importance, the force generated by CPAs needs to be accurately controlled. In this paper, a fuzzy logic (FL) controller with a Mamdani inference system is designed to control the blocking force of a trilayer CPA with polypyrrole electrodes, which operates in air. The particle swarm optimization (PSO) method is employed to optimize the controller’s membership function parameters and therefore enhance the performance of the FL controller. An adaptive neuro-fuzzy inference system model, which can capture the nonlinear dynamics of the actuator, is utilized in the optimization process. The optimized Mamdani FL controller is then implemented on the CPA experimentally, and its performance is compared with a non-optimized fuzzy controller as well as with those obtained from a conventional PID controller. The results presented indicate that the blocking force at the tip of the CPA can be effectively controlled by the optimized FL controller, which shows excellent transient and steady state characteristics but increases the control voltage compared to the non-optimized fuzzy controllers.

Wastewater reuse in citrus: a fuzzy logic model for optimum evapotranspiration

We established a Mamdani fuzzy logic model to determine the effect of wastewater evapotranspiration on citrus cultivation. The model uses most influential variables of soil and leaves samples. Since the number of the input variables to the model is large, these variables were classified into five categories as follows: (i) soil macro-element concentration, (ii) soil essential elements concentration, (iii) soil electrochemical properties, (iv) leaves macro-elements concentration, and (v) leaves essential elements concentration. For simplification, the model was divided into seven submodels and one main model. The output of the main model considered the outputs of all submodels to the final decision. The model applied to a citrus orchard suggested the optimal wastewater irrigation rate at 125% of crop evapotranspiration.

Adaptive differential amplitude pulse‐position modulation technique for optical wireless communication channels based on fuzzy logic

Optical wireless communication has the potential for extremely high data rates of up to tens of Gb/s. In this study, a hybrid modulation technique named adaptive differential amplitude pulse-position modulation (DAPPM) is proposed to improve channel immunity by utilising optimised modulation to channel. The unit transmission rate, channel capacity, peak-to-average power ratio, bandwidth requirement and power requirement of the DAPPM were determined and compared with other modulation schemes such as on–off key, pulse-amplitude modulation, pulse-position modulation (PPM), differential PPM and multilevel digital pulse interval modulation. The simulation results show that DAPPM gives better bandwidth and power efficiency depending on the number of amplitude level (A) and the maximum length (L) of a symbol. In addition, the fuzzy logic module is developed to assist the adaptation process of DAPPM. Mamdani fuzzy logic method is used in which the decisions made by the system will be approaching to what would be decided by the user in the real world.

Supervised Intelligent Committee Machine Method for Hydraulic Conductivity Estimation

Hydraulic conductivity is the essential parameter for groundwater modeling and management. Yet estimation of hydraulic conductivity in a heterogeneous aquifer is expensive and time consuming. In this study; artificial intelligence (AI) models of Sugeno Fuzzy Logic (SFL), Mamdani Fuzzy Logic (MFL), Multilayer Perceptron Neural Network associated with Levenberg–Marquardt (ANN), and Neuro-Fuzzy (NF) were applied to estimate hydraulic conductivity using hydrogeological and geoelectrical survey data obtained from Tasuj Plain Aquifer, Northwest of Iran. The results revealed that SFL and NF produced acceptable performance while ANN and MFL had poor prediciton. A supervised intelligent committee machine (SICM), which combines the results of individual AI models using a supervised artificial neural network, was developed for better prediction of the hydraulic conductivity in Tasuj plain. The performance of SICM was also compared to those of the simple averaging and weighted averaging intelligent committee machine (ICM) methods. The SICM model produced reliable estimates of hydraulic conductivity in heterogeneous aquifers.

Computational Intelligence Techniques Applied to Coagulant Estimation Models in Water Purification Process

Four coagulant estimation models in water purification process are presented. For its developing computational intelligence techniques are used, which include neural networks, fuzzy logic (Sugeno and Mamdani type) and ANFIS structures. The methodology described is based on the operator's experience extraction from operational historical data (in neural network, fuzzy logic Sugeno type and ANFIS case), and the linguistic information given by an experimented plant operator (in fuzzy logic Mamdani type case). According to the reached results, by applying some of this models in a control system strategy, could be possible overcome some of the current limitations found in the most widely used techniques for coagulant dosing in water purification plants; jar tests and streaming current detector.

Fuzzy Inference System for Prediction During Precision Turning Of Ti-6al-4v

This research work focuses on precision turning of Ti6Al4V material to investigate the machinability of the material. Precision turning is a type of machining where, very low feed rate and depth of cut is being used to machine using a cutting insert with a lower nose radius. The cutting parameters considered for the experiments include the cutting speed, feed rate, depth of cut and nose radius. PVD coated carbide cutting inserts with different nose radius and constant rake and clearance angle are being considered for experimentation. The experimentation was designed based on Taguchi's L 27 orthogonal array. Three different levels of cutting parameters were being considered for the experimentation. The turning experiments were carried out on a conventional variable speed motor lathe under dry working conditions. Based upon the experimental values, Analysis of Variance (ANOVA) was conducted to understand the influence of various cutting parameters on cutting force, surface roughness and cutting tool temperatures during precision turning. There are a number of techniques available for predicting responses using input parameters and the present work uses Fuzzy Inference System (Mamdani Fuzzy logic) to predict the out put parameters.

An Assessment Model for the State of Organizational Readiness Inservice Oriented architecture Implementation Based on Fuzzy Logic

Growth changes in organizations and on the other hand strong need to utilization of information and communication technology possibly implied applying changes in organization in accordance with changes in functionality. What's more, information technology structure, hardware, software and information infrastructure have to be synchronized. One of the most important approaches leading to this goal are service oriented architecture.Service oriented architecture (SOA) is a style of information systems architecture supporting loose coupling of services for flexibility and interoperability of systems and is independent of the technology. The most important achievement of creating service oriented architecture is the need for increasing flexibility, speed and agility. Consequently, it changes in the structure of its organization. According to broad scope of enterprise architecture projects, it is necessary before implementation and the related major cost, organization would be notified of its readiness to get right decisions, develop appropriate strategies and adopt new approach.In this paper, we propose a model for evaluating organizational readiness in order to implement SOA with the use of Mamdani fuzzy logic. According to the results of this assessment method, we can decide more precisely whether an organization is ready to adopt SOA or not.By using this assessment model, organizations can realize their strengths and weaknesses identify their improvement areas and thus increase their readiness. Consequently this model is tested by means of a case study in Tehran municipality ICT organization.

Mamdani approach for 2% wave run-up on breakwaters

Safe use of densely populated coastal zones depends on the performance of coastal structures against severe wave attack and strong currents. Economic and safe design of the crest height of these structures require accurate estimation of 2% wave run-up value on breakwater slope. Although, in practice, regression-based approach is widely-used, it is demonstrated herein that a set of restrictive assumptions hinder its use. As an alternative, Mamdani Fuzzy Logic model suggested with no restrictive assumptions. Hence, the goal is to provide an alternative approach that is better than the regression-based approach at estimating 2% wave run-up level. In order to verify and compare the presented method's applicability, 261 irregular wave run-up data of Van der Meer and Stam are considered for impermeable, permeable and homogenous rubble mound structures. Finally, 13 unique prototype data of Van de Walle are employed to test the proposed model. It is demonstrated that Mamdani Fuzzy Logic model gives smaller error than the regression-based approach.

Interval Type-2 Fuzzy Logic Adaptive Modelling for Human Operators Undergoing Mental Stress

This paper presents a modelling design framework and results for human participants undergoing mental stress. A mental stressful scenario is achieved while participants perform operations in a simulated control process on an automation-enhanced Cabin Air Management System simulator. Recordings from a previous experiment using psychophysiological markers (Heart Rate Variability and Task Load Index) as inputs with an output performance marker (Time In Range) were used for offline modelling training and validation. Interval Type-2 Fuzzy Logic (IT2FL) architecture optimized with Genetic Algorithms and with an efficient type-reduction algorithm, was selected as the modelling technique. Results demonstrate the ability of IT2FL systems to cope with an additional level of uncertainty and to adapt to important input-output relation changes on validation of the model. These results surpass performance in validation of a type 1 Mamdani Fuzzy Logic system constructed and validated previously in a similar fashion. The constructed model provides an adequate framework that can be used for the prevention of operator breakdown.

Comparative Analysis of Adaptive NeuroFuzzy Control Techniques for Full Car Active Suspension System

Suspension system of a vehicle is used to minimize the effect of different road disturbances on ride comfort and to improve the vehicle control. The main objective of this work is to design an efficient active suspension control paradigm for full car model with 8 degrees of freedom using adaptive soft-computing techniques. The detailed mathematical model of the adaptive Mamdani fuzzy logic control and different adaptive neurofuzzy wavelet networks have been developed and successfully applied to a full car model. The parameters of the proposed controllers have been adjusted using online adaptation by minimizing the cost function. The robustness of the presented active controllers has been proved on the basis of different performance indices. Two road profiles have been used to check the performance of the proposed active suspension controllers as compared with passive, semi-active suspension, backstepping based fuzzy logic control, and linear quadratic regulated based suspension systems. The performance of the active suspension systems has been optimized in terms of displacement and acceleration of seat, heave, pitch, and roll.

Supervised committee machine with artificial intelligence for prediction of fluoride concentration

The study introduces a supervised committee machine with artificial intelligence (SCMAI) method to predict fluoride in ground water of Maku, Iran. Ground water is the main source of drinking water for the area. Management of fluoride anomaly needs better prediction of fluoride concentration. However, the complex hydrogeological characteristics cause difficulties to accurately predict fluoride concentration in basaltic formation, non-basaltic formation, and mixing zone. SCMAI predicts fluoride by a nonlinear combination of individual AI models through an artificial intelligent system. Factor analysis is used to identify effective fluoride-correlated hydrochemical parameters as input to AI models. Four AI models, Sugeno fuzzy logic, Mamdani fuzzy logic, artificial neural network (ANN), and neuro-fuzzy are employed to predict fluoride concentration. The results show that all of these models have similar fitting to the fluoride data in the Maku area, and do not predict well for samples in the mixing zone. The SCMAI employs an ANN model to re-predict the fluoride concentration based on the four AI model predictions. The result shows improvement to the CMAI method, a committee machine with the linear combination of AI model predictions. The results also show significant fitting improvement to individual AI models, especially for fluoride prediction in the mixing zone.

A knowledge-based Mamdani fuzzy logic prediction of the motion resistance coefficient in a soil bin facility for clay loam soil

A rule-based Mamdani max–min fuzzy expert system for prediction of coefficient of motion resistance (CMR) is presented. Owing to nonlinear characteristic of soil–wheel interactions, application of fuzzy rule-based models for determination of CMR is instrumental. We were encouraged to apply fuzzy logic approach for the modeling by use of the experience of induced CMR as affected by tire inflation pressure, velocity, and wheel load to be practically applicable with wide range of unknown nonlinear systems. Employment of fuzzy if–then true rules makes it possible to handle ill-defined and nonlinear arrangements and gives higher privilege over conventional methods. Therefore, of aforementioned rules, 27 if–then true rules were incorporated to develop a sophisticated highly intelligent representation based on centroid method for defuzzification stage preceded by Mamdani max–min inference supposition. The model performance was evaluated on the basis of various statistical criteria and also was compared to a conventional model. Mean relative error lower than 10 %, good scattering around line (1:1), and high coefficient of determination (R 2 = 99 %) obtained by the fuzzy logics model are confirmed.