Mamdani Fuzzy System Research Articles

Pseudo-spectral method for controlling the drug dosage in cancer.

A mixed chemotherapy-immunotherapy treatment protocol is developed for cancer treatment. Chemotherapy pushes the trajectory of the system towards the desired equilibrium point, and then immunotherapy alters the dynamics of the system by affecting the parameters of the system. A co-existing cancerous equilibrium point is considered as the desired equilibrium point instead of the tumour-free equilibrium. Chemotherapy protocol is derived using the pseudo-spectral (PS) controller due to its high convergence rate and simple implementation structure. Thus, one of the contributions of this study is simplifying the design procedure and reducing the controller computational load in comparison with Lyapunov-based controllers. In this method, an infinite-horizon optimal control problem is proposed for a non-linear cancer model. Then, the infinite-horizon optimal control of cancer is transformed into a non-linear programming problem. The efficient Legendre PS scheme is suggested to solve the proposed problem. Then, the dynamics of the system is modified by immunotherapy is another contribution. To restrict the upper limit of the chemo-drug dose based on the age of the patients, a Mamdani fuzzy system is designed, which is not present yet. Simulation results on four different dynamics cases how the efficiency of the proposed treatment strategy.

Implementation of Fuzzy Mamdani Method for Traffic Lights Smart City in Rangkasbitung, Lebak Regency, Banten Province (Case Study of the Traffic Light T-junction, Cibadak, By Pas Sukarno Hatta Street)

Traffic jams are a common sight that can be seen in almost all major cities in Indonesia. One of them is in Rangkasbitung City, Lebak Regency. This happens because the number of vehicles continues to increase. The traffic light control system implemented in Indonesia is a static preset time because the time of each phase is predetermined. This type of control system is still not effective in overcoming traffic congestion, especially at certain peak traffic jams. By using the Mamdani fuzzy logic system, it is possible to implement the human mindset into a system. Some rules can be set out in the fuzzy logic controller. The purpose of this study is to design a traffic light control system using fuzzy inference that regulates traffic based on its density. The data used are observations made at the research site. The conclusion of this study is to explain that the fuzzy mamdani method can solve existing problems in traffic congestion in Rangkasbitung City, Lebak Regency, Banten Province

Application of Machine Learning in Evaluation of the Static Young’s Modulus for Sandstone Formations

Prediction of the mechanical characteristics of the reservoir formations, such as static Young’s modulus (Estatic), is very important for the evaluation of the wellbore stability and development of the earth geomechanical model. Estatic considerably varies with the change in the lithology. Therefore, a robust model for Estatic prediction is needed. In this study, the predictability of Estatic for sandstone formation using four machine learning models was evaluated. The design parameters of the machine learning models were optimized to improve their predictability. The machine learning models were trained to estimate Estatic based on bulk formation density, compressional transit time, and shear transit time. The machine learning models were trained and tested using 592 well log data points and their corresponding core-derived Estatic values collected from one sandstone formation in well-A and then validated on 38 data points collected from a sandstone formation in well-B. Among the machine learning models developed in this work, Mamdani fuzzy interference system was the highly accurate model to predict Estatic for the validation data with an average absolute percentage error of only 1.56% and R of 0.999. The developed static Young’s modulus prediction models could help the new generation to characterize the formation rock with less cost and safe operation.

A new insight into implementing Mamdani fuzzy inference system for dynamic process modeling: Application on flash separator fuzzy dynamic modeling

In this work, a novel approach to model the dynamic behavior of the flash separation process (as a main building block of non-reacting stage-wise operations) based on Mamdani Fuzzy Inference Systems is proposed. This model surmounts the need to solve various types of mathematical equations governing the system and does not require thermodynamic properties which are either not available or computationally demanding. Hence it can be easily used in dynamic simulation of multi-phase flow in distributed systems. In the proposed approach the overall model is broken into several simple sub-models based on intuitive analysis of an expert. Moreover, a new fuzzy concept, named “Linguistic Composition Variable” is introduced to represent components mole fractions of each phase as a fuzzy variable. Accordingly, large number of rules which is the main shortcoming of the Mamdani Fuzzy system is significantly reduced.The performance of the proposed dynamic model is evaluated through comparing its results against their corresponding counterparts for a flash separator of crude oil. Overall MAPE (Mean Absolute Percentage Error) values of 7.17% for the gas molar fractions, 3.06% for liquid molar fractions, 10.16% for the temperature, 0.63% for the pressure and 16.44% for the liquid level of the separator have been achieved showing that the proposed fuzzy model can effectively capture the general trends of process data of the dynamic process.

Application of decision trees and fuzzy inference system for quality classification and modeling of black and green tea based on visual features

A fuzzified decision tree (DT)-based computer vision system was developed in this study for classification of common Iranian teas. Images of different tea categories, including five green tea and five black tea classes were captured using a CCD camera. In total, 83 image-based information (including 18 colour, 13 texture, and 52 wavelet-based features) were extracted from the images and introduced into DT classifier to distinguish between different tea categories. Reduced Error Pruning (REP) and J48 DTs were applied as classifier and the overall accuracy of both evaluated DTs was equal to 94.79% for differentiating all tea categories. However, these DTs had almost complex structures which made them unsuitable for developing a Mamdani fuzzy system, based on their structure. So, in order to simplify the structures of the DTs to be more suitable for developing fuzzy sets, the DTs were examined for categorizing black and green teas separately. The overall accuracy of J48 DT was 92.917% and 95.000% for classification of black and green teas, respectively and for REP tree the accuracy was equal to 90.83% and 97.08%, respectively. The REP tree structure was used to set the membership functions and rules of the fuzzy system because of the simpler structure of REP tree, even though J48 and REP trees had almost equal performances. It was concluded that the DT-based fuzzy logic system can be used effectively to classify different quality categories of tea, based on image extracted features.

New mixed-coding PSO algorithm for a self-adaptive and automatic learning of Mamdani fuzzy rules

Thanks to its algorithmic performances, PSO algorithm becomes a popular tune tool for fuzzy systems in literature. However, it still encounters many complications, especially when dealing with Mamdani fuzzy system type because of its nature. The Mamdani fuzzy system is known as a linguistic model where the semantic meaning of the fuzzy rules is an intrinsic characteristic that must be retained during the learning process, while seeking for high accuracy. Therefore, to tune the Mamdani fuzzy system, it is very crucial to well represent each rule in a way that preserves this characteristic firstly, and to look for a search mechanism to optimize them throughout this topic secondly. In this paper, we introduce a new and promising approach to optimize the Mamdani fuzzy systems without a need of any prior knowledge. To the best of our knowledge, this approach is the first to optimize simultaneously the membership functions, the scaling factor parameters and the fuzzy rule conclusions with a mixed-coding PSO algorithm by combining a special monitoring function and a self-adaptive threshold. The proposed approach is validated by a comparative study with other design strategies taken from Box–Jenkinsgas furnace system literature and two theoretical examples in addition to a real-time control of the inverted pendulum Feedback 33-200. The obtained results proved the potential and the effectiveness of the proposed approach.

Evaluation of the Total Organic Carbon (TOC) Using Different Artificial Intelligence Techniques

Total organic carbon (TOC) is an essential parameter used in unconventional shale resources evaluation. Current methods that are used for TOC estimation are based, either on conducting time-consuming laboratory experiments, or on using empirical correlations developed for specific formations. In this study, four artificial intelligence (AI) models were developed to estimate the TOC using conventional well logs of deep resistivity, gamma-ray, sonic transit time, and bulk density. These models were developed based on the Takagi-Sugeno-Kang fuzzy interference system (TSK-FIS), Mamdani fuzzy interference system (M-FIS), functional neural network (FNN), and support vector machine (SVM). Over 800 data points of the conventional well logs and core data collected from Barnett shale were used to train and test the AI models. The optimized AI models were validated using unseen data from Devonian shale. The developed AI models showed accurate predictability of TOC in both Barnett and Devonian shale. FNN model overperformed others in estimating TOC for the validation data with average absolute percentage error (AAPE) and correlation coefficient (R) of 12.02%, and 0.879, respectively, followed by M-FIS and SVM, while TSK-FIS model showed the lowest predictability of TOC, with AAPE of 15.62% and R of 0.832. All AI models overperformed Wang models, which have recently developed to evaluate the TOC for Devonian formation.

Analytic expression of Mamdani fuzzy system constructed by fuzzy similarity degree and its output algorithm

Analytic expression of Mamdani fuzzy system constructed by fuzzy similarity degree and its output algorithm

Development of information intelligent system for the organization and management of sea cargo transportation in arctic conditions - setting of Mamdani fuzzy logic system

Development of information intelligent system for the organization and management of sea cargo transportation in arctic conditions - setting of Mamdani fuzzy logic system

Intelligent Stock Portfolio Management Using a Long-Term Fuzzy System

ABSTRACTThe complexity of financial markets is driving researchers to multiply their efforts in order to improve their forecasting methods. This paper inoculates an old trading strategy with fuzzy subjective elements. The aim is to investigate whether the careful synthesis of a few long-term technical indicators, which have a different predictive philosophy, with an appropriately designed stock trading Mamdani fuzzy system, can produce satisfactory returns. More specifically, its purpose is to investigate whether the combination of moving averages, directional movement technical indicators and a fuzzified trading strategy can surpass the performance of buy and hold strategy (B&H). The proposed model has been tested in various (bull and bear) market environments for a period of more than 15 years, using the general index of ASE (Athens Stock Exchange). After taking into consideration transaction costs, it is found that the proposed model can produce better results (higher earnings) than the B&H strategy.

The Development of a Fuzzy Logic System in a Stochastic Environment with Normal Distribution Variables for Cash Flow Deficit Detection in Corporate Loan Policy

This paper develops a Mamdani fuzzy logic system (FLS) that has stochastic fuzzy input variables designed to identify cash-flow deficits in bank lending policies. These deficits do not cover the available cash-flow (CFA) resulting from the company's operating activity. Thus, due to these deficits, solutions must be identified to avoid companies' financial difficulties. The novelty of this paper lies in its using stochastic fuzzy variables, or those categories of variables that are defined by fuzzy sets, characterized by normally distributed density functions specific to random variables, and characterized by fuzzy membership functions. The variation intervals of the stochastic fuzzy variables allow identification of the probabilistic risk situations to which the company is exposed during the crediting period using the Mamdani-type fuzzy logic system. The mechanism of implementing the fuzzy logic system is based on two stages. The first is based on the determination of the cash-flow requirements resulting from loan reimbursement and interest rates. This stage has the role of determining the need for financial resources to cover the liabilities. The second stage is based on the identification of the stochastic fuzzy variables which have a role in influencing the cash flow deficits and the probability values estimation of these variables taking into account probability calculations. Based on these probabilistic values, using the Mamdani fuzzy logic system, estimations are computed for the available cash-flow (the output variable). The estimated values for CFA are then used to detect probability risk situations in which the company will not have enough resources to cover its liabilities to financial creditors. All the FLS calculations refer to future time periods. Testing and simulating the fuzzy controller confirms its functionality.

Fuzzy Control Systems: Past, Present and Future

More than 40 years after fuzzy logic control appeared as an effective tool to deal with complex processes, the research on fuzzy control systems has constantly evolved. Mamdani fuzzy control was originally introduced as a modelfree control approach based on expert?s experience and knowledge. Due to the lack of a systematic framework to study Mamdani fuzzy systems, we have witnessed growing interest in fuzzy model-based approaches with Takagi-Sugeno fuzzy systems and singleton-type fuzzy systems (also called piecewise multiaffine systems) over the past decades. This paper reviews the key features of the three above types of fuzzy systems. Through these features, we point out the historical rationale for each type of fuzzy systems and its current research mainstreams. However, the focus is put on fuzzy model-based approaches developed via Lyapunov stability theorem and linear matrix inequality (LMI) formulations. Finally, our personal viewpoint on the perspectives and challenges of the future fuzzy control research is discussed.

EEFR-R: extracting effective fuzzy rules for regression problems, through the cooperation of association rule mining concepts and evolutionary algorithms

Fuzzy rule-based systems, due to their simplicity and comprehensibility, are widely used to solve regression problems. Fuzzy rules can be generated by learning from data examples. However, this strategy may result in high numbers of rules that most of them are redundant and/or weak, and they affect the systems’ interpretability. Hence, in this paper, a new rule learning method, EEFR-R, is proposed to extract the effective fuzzy rules from regression data samples. This method is formed through the cooperation of association rule mining concepts and evolutionary algorithms in the three stages. Indeed, the components of a Mamdani fuzzy rule-based system are generated during the first two stages, and then, they will be refined through some modifications in the last stage. In EEFR-R, fuzzy rules are extracted from numerical data using the idea of Wang and Mendel’s method and utilizing the concepts of Support and Confidence; furthermore, a new rule pruning method is presented to refine these rules. By employing this method, non-effective rules can be pruned in three different modes as the preferences of a decision maker. The proposed model and its stages were validated using 19 real-world regression datasets. The experimental results and the conducted statistical tests confirmed the effectiveness of EEFR-R in terms of complexity and accuracy and in comparison with the three state-of-the-art regression solutions.

A New Geospatial Model Integrating a Fuzzy Rule-Based System in a GIS Platform to Partition a Complex Urban System in Homogeneous Urban Contexts

Here, we present a new unsupervised method aimed at obtaining a partition of a complex urban system in homogenous urban areas, called urban contexts. Our model integrates spatial analysis processes and a fuzzy rule-based system applied to manage the knowledge of domain experts; it is implemented using a GIS platform. The area of study is initially partitioned in microzones, homogeneous portions of the urban system, which are the atomic reference elements for the census data. With the contribution of domain experts, we identify the physical, morphological, environmental, and socio-economic indicators needed to identify synthetic characteristics of urban contexts and create the fuzzy rule set necessary for determining the type of urban context. We implement the set of spatial analysis processes required to calculate the indicators for the microzones and apply a Mamdani fuzzy rule system to classify the microzones. Finally, the partition of the area of study in urban contexts is obtained by dissolving continuous microzones belonging to the same type of urban context. Tests are performed on the Municipality of Pozzuoli (Naples, Italy); the reliability of the out model is measured by comparing the results with the ones obtained through a detailed analysis.

A Method Based on Extended Fuzzy Transforms to Approximate Fuzzy Numbers in Mamdani Fuzzy Rule-Based System

We propose a new Mamdani fuzzy rule-based system in which the fuzzy sets in the antecedents and consequents are assigned in a discrete set of points and approximated by using the extended inverse fuzzy transforms, whose components are calculated by verifying that the dataset is sufficiently dense with respect to the uniform fuzzy partition. We test our system in the problem of spatial analysis consisting in the evaluation of the livability of residential housings in all the municipalities of the district of Naples (Italy). Comparisons are done with the results obtained by using trapezoidal fuzzy numbers in the fuzzy rules.

Soft computing model for analysing the effect of friction stir processing parameters on the intergranular corrosion susceptibility of aluminium alloy AA5083

Abstract Aluminium alloy AA5083 is prone to intergranular corrosion in marine environments. In an attempt to reduce the intergranular corrosion, AA5083 was subjected to friction stir processing (FSP). The FSP experimental trials were conducted as per face-centered central composite design with three levels of variation in FSP process parameters viz. tool rotation speed (TRS), tool traverse speed (TTS) and tool shoulder diameter (SD). Intergranular corrosion susceptibility of the processed specimens was assessed by performing nitric acid mass loss test. The mass loss of the specimens was correlated with the intergranular corrosion susceptibility as per the standard ASTM G67-13. The experimental results indicate that FSP had significantly reduced the intergranular corrosion susceptibility of the AA5083 alloy. Soft computing techniques namely Artificial Neural Network, Mamdani Fuzzy system, and Sugeno Fuzzy system were used to predict the intergranular corrosion (IGC) susceptibility (mass loss) of the friction stir processed specimens. Among the developed models, Sugeno fuzzy system displayed minimum percentage error in prediction. So Sugeno fuzzy system was used to analyze the effect of friction stir processing process parameters on the IGC of the processed specimens. The results suggest that stir processing of AA5083 at a TRS of 1300 rpm, TTS of 60 mm/min and SD of 21 mm would make the alloy least susceptible to intergranular corrosion.

Real-time application of a fuzzy adaptive control to one level in a three-tank system

In this experimental work, a fuzzy combined control method has been described and applied to test the ability of the artificial intelligence technique in controlling nonlinear systems in real-time applications. A direct feedback linearization ideal control law is approximated with a Takagi–Sugeno fuzzy inference system. The adaptation law of the Takagi–Sugeno controller parameters is computed based on a fuzzy approximation term of the control error. This approximation is computed using a Mamdani fuzzy system. The experiment is applied on one level in a three-tank system and a test of the controller ability against perturbations is considered. The obtained results were compared to those leaded by a classical proportional–integral controller. The basic idea of this work is the use of the control error (between the actual control signal and the perfect control signal) instead of the tracking error (between the output of the one level in a three-tank system and the reference signal). As our approach is model free, that is, we do not use the mathematical model of the three-tank system, in the application part of this work, the output of the constructed Takagi–Sugeno fuzzy controller is injected to the real three-tank system.

A Fast and Accurate Rule-Base Generation Method for Mamdani Fuzzy Systems

The problem of learning fuzzy rule bases is analyzed from the perspective of finding a favorable balance between the accuracy of the system, the speed required to learn the rules, and, finally, the interpretability of the rule bases obtained. Therefore, we introduce a complete design procedure to learn and then optimize the system rule base, called the precise and fast fuzzy modeling approach. Under this paradigm, fuzzy rules are generated from numerical data using a parameterizable greedy-based learning method called selection-reduction , whose accuracy–speed efficiency is confirmed through empirical results and comparisons with reference methods. Qualitative justification for this method is provided based on the coaction between fuzzy logic and the intrinsic properties of greedy algorithms. To complete the precise and fast fuzzy modeling strategy, we finally present a rule-base optimization technique driven by a novel rule redundancy index, which takes into account the concepts of the distance between rules and the influence of a rule over the dataset. Experimental results show that the proposed index can be used to obtain compact rule bases, which remain very accurate, thus increasing system interpretability.

COMPARING MAMDANI AND SUGENO HIERARCHICAL FUZZY SYSTEMS FOR ENVIRONMENTAL IMPACT ASSESSMENT: A PIPELINE PROJECT CASE STUDY

Several studies have been conducted in various fields comparing Mamdani and Sugeno fuzzy inference systems (FISs). This study contributes to the literature by comparing the performance of Mamdani and Sugeno hierarchical fuzzy systems (HFSs) in terms of their technical performance within the context of environmental impact assessment (EIA), a multi-criteria decision analysis (MCDA) method, with the aim of illuminating practical issues that need to be taken into account during application. The context is a non-commercial, research-oriented EIA of a pipeline project in southern Iran. Mamdani and Sugeno HFSs were developed with data collected for and expertise gained via a previous formal EIA of the same project. The two developed HFSs were of binary structure, reducing complexity while also facilitating sensitivity analysis. A sensitivity analysis was carried out with the full range of possible HFS input values. Excluding the final FISs, the FIS outputs did not differ significantly between the two examined HFSs. However, the behavior of the Sugeno HFS was found to be more linear than that of its Mamdani counterpart with a higher degree of sensitivity to input value changes. This study indicates that the Mamdami HFS is unreliable in some areas due to fluctuations in the output surface, which necessitates smoothing before it can be applied.

Mamdani Fuzzy Systems for Modelling and Simulation: A Critical Assessment

Fuzzy logic presents many potential applications for modelling and simulation. In particular, this paper analyses one of the most popular fuzzy logic techniques: Mamdani systems. Mamdani systems can look particularly appealing because they are designed to incorporate expert knowledge in the form of IF-THEN rules expressed in natural language. While this is an attractive feature for modelling and simulating social and other complex systems, its actual application presents important caveats. This paper studies the potential use of Mamdani systems to explore the logical consequences of a model based on IF-THEN rules via simulation. We show that in the best-case scenario a Mamdani system provides a function that complies with its generating set of IF-THEN rules, which is a different exercise from that of finding the relation or consequences implied by those rules. In general, the logical consequences of a set of rules cannot be captured by a single function. Furthermore, the consequences of an IF-THEN rule in a Mamdani system can be very different from the consequences of that same rule in a system governed by the most basic principles of logical deductive inference. Thus, care must be taken when applying this tool to study “the consequences†of a set of hypothesis. Previous analyses have typically focused on particular steps of the Mamdani process, while here we present a holistic assessment of this technique for (deductive) simulation purposes.