Design Of Fuzzy System Research Articles

Fuzzy control system design for wheel slip prevention and tracking of desired speed profile in electric trains

AbstractThis study proposes an anti‐slip control system for electric trains based on the fuzzy logic theory, which prevents the wheels from slipping during the acceleration and simultaneously tracks the desired speed profile. To improve the control performance, the train longitudinal velocity and the slip ratio are estimated. By using a field‐oriented control (FOC), the angular speed of the traction motor is controlled. The fuzzy control system determines the desired angular speed of the traction motor as the reference input of FOC to obtain the desired slip ratio and track the desired speed profile. Simulation results show the effectiveness of the control system in various wheel–rail surface conditions based on the real parameters of ER24PC locomotive.

Entropy Fuzzy System Identification for the Heave Flight Dynamics of a Model-Scale Helicopter

This article studies nonlinear system identification of a small scale and flybar-free unmanned helicopter, the Trex450 chopper, built using commercial off-the-shelf components. We employ the real-time input-output data, obtained from human-controlled flight tests, operating the aircraft under severe ground effects during the vertical flight maneuvers. We highlight the efficacy of the entropy fuzzy system identification method with respect to the performance of several well-known nonlinear system identification techniques (i.e., a Takagi-Sugeno Fuzzy system, an adaptive neuro-fuzzy inference system (ANFIS), and a nonlinear autoregressive with exogenous (NARX) model) as our benchmarks. Our research confirms the benefits of the entropy fuzzy identification technique. Despite being nonlinear, the proposed fuzzy model is relatively simple, transparent, and highly accurate to represent the complex non-linear dynamic behaviors of our unmanned helicopter under severe ground effects. Another major advantage of the proposed system identification technique is its ability to avoid overfitting, an essential requirement in modeling. Overall, the fuzzy system is also capable of achieving a delicate balance between maximizing the accuracy while minimizing the complexity of the acquired model.

Optimal Design of Fuzzy Systems Using Differential Evolution and Harmony Search Algorithms with Dynamic Parameter Adaptation

This paper presents a study of two popular metaheuristics, namely differential evolution (DE) and harmony search (HS), including a proposal for the dynamic modification of parameters of each algorithm. The methods are applied to two cases, finding the optimal design of a fuzzy logic system (FLS) applied to the optimal design of a fuzzy controller and to the optimization of mathematical functions. A fuzzy logic controller (FLC) of the Takagi–Sugeno type is used to find the optimal design in the membership functions (MFs) for the stabilization problem of an autonomous mobile robot following a trajectory. A comparative study of the results for two modified metaheuristic algorithms is presented through analysis of results and statistical tests. Results show that, statistically speaking, optimal fuzzy harmony search (OFHS) is better in comparison to optimal fuzzy differential evaluation (OFDE) for the two presented study cases.

Design of an IoT-Based Fuzzy Approximation Prediction Model for Early Fire Detection to Aid Public Safety and Control in the Local Urban Markets

Fire monitoring in local urban markets within East Africa (EA) has been seriously neglected for a long time. This has culminated in a severe destruction of life and property worth millions. These rampant fires are attributed to electrical short circuits, fuel spillages, etc. Previous research proposes single smoke detectors. However, they are prone to false alarm rates and are inefficient. Also, satellite systems are expensive for developing countries. This paper presents a fuzzy model for early fire detection and control as symmetry’s core contribution to fuzzy systems design and application in computer and engineering sciences. We utilize a fuzzy logic technique to simulate the performance of the model using MATLAB, using six parameters: temperature, humidity, flame, CO, CO2 and O2 vis-à-vis the Estimated Fire Intensity Prediction (EFIP). Results show that, using fuzzy logic, a significant improvement in fire detection is observed with an overall accuracy rate of 95.83%. The paper further proposes an IoT-based fuzzy prediction model for early fire detection with a goal of minimizing extensive damage and promote intermediate fire suppression and control through true fire incidences. This solution provides for future public safety monitoring, and control of fire-related situations among the market community. Hence, fire safety monitoring is significant in providing future fire safety planning, control and management by putting in place appropriate fire safety laws, policies, bills and related fire safety practices or guidelines to be applied in public buildings, market centers and other public places.

A Unified Framework for Asymptotic Observer Design of Fuzzy Systems With Unmeasurable Premise Variables

This article develops a unified framework to design fuzzy-model-based observers of general nonlinear systems for both discrete-time and continuous-time cases. This observer problem is known as a challenging task due to the mismatch caused by the unmeasurable premise variables. To deal with this major challenge, we propose to rewrite the nonlinear system as a specific fuzzy model with two types of local nonlinearities: measurable and unmeasurable. Then, a differential mean value theorem for vector-valued functions is applied to local unmeasurable nonlinearities. This allows to represent the estimation error dynamics in a special polytopic form involving measurable membership functions and unknown but bounded time-varying parameters. Using Lyapunov-based arguments, design conditions in terms of linear matrix inequalities are derived to guarantee the asymptotic convergence of the estimation error. Three illustrative examples are given to demonstrate the interests of the new fuzzy observer framework in reducing: 1) the design conservatism and 2) the numerical complexity of the fuzzy observer structure for real-world applications.

A Review of Multi-Objective Evolutionary Based Fuzzy Classifiers

Background: Fuzzy systems are employed in several fields like data processing, regression, pattern recognition, classification and management as a result of their characteristic of handling uncertainty and explaining the feature of the advanced system while not involving a particular mathematical model. Fuzzy rule-based systems (FRBS) or fuzzy rule-based classifiers (mainly designed for classification purpose) are primarily the fuzzy systems that consist of a group of fuzzy logical rules and these FRBS are unit annexes of ancient rule-based systems, containing the "If-then" rules. During the design of any fuzzy systems, there are two main objectives, interpretability and accuracy, which are conflicting with each another, i.e., improvement in any of those two options causes the decrement in another. This condition is termed as Interpretability –Accuracy Trade-off. To handle this condition, Multi-Objective Evolutionary Algorithms (MOEA) are often applied within the design of fuzzy systems. This paper reviews the approaches to the problem of developing fuzzy systems victimization evolutionary process Multi-Objective Optimization (EMO) algorithms considering ‘Interpretability-Accuracy Trade-off, current research trends and improvement in the design of fuzzy classifier using MOEA in the future scope of authors. Methods: The state-of-the-art review has been conducted for various fuzzy classifier designs, and their optimization is reviewed in terms of multi-objective. Results: This article reviews the different Multi-Objective Optimization (EMO) algorithms in the context of Interpretability -Accuracy tradeoff during fuzzy classification. Conclusion: The evolutionary multi-objective algorithms are being deployed in the development of fuzzy systems. Improvement in the design using these algorithms include issues like higher spatiality, exponentially inhabited solution, I-A tradeoff, interpretability quantification, and describing the ability of the system of the fuzzy domain, etc. The focus of the authors in future is to find out the best evolutionary algorithm of multi-objective nature with efficiency and robustness, which will be applicable for developing the optimized fuzzy system with more accuracy and higher interpretability. More concentration will be on the creation of new metrics or parameters for the measurement of interpretability of fuzzy systems and new processes or methods of EMO for handling I-A tradeoff.

Fuzzy logic-based decision-making system design for safe forklift truck speed: cast cobblestone production application

This paper presents a model in which “fuzzy logic multi-criteria decision-making method” is suggested to determine real-time forklift speed to reduce occupational accidents caused by operators. The model developed in the study uses the variables: the weight and height of the load carried by the forklift, the number of products on its pallet, the places with high risk of accident, and the wet-dry condition of the ground. In order to evaluate the performance of the suggested model, a data set comprises 128 different conditions in cast cobblestone production. Determined forklift speeds were compared with the forklift speeds determined by fuzzy logic using statistically analyses. Results showed that fuzzy logic model has a high accuracy and low error. Fuzzy logic modeling has proved to be a good way to decide the real-time speed of the forklifts being used in production without compromising occupational safety. Friedman test and Wilcoxon test have been used to estimate the significance of fuzzy logic method. The fuzzy logic results showed that our method achieved better results compared to beginner operator.

Design and Implementation a New Energy Efficient Clustering Algorithm Using the Fuzzy Logic and Genetic Algorithm for Wireless Sensor Networks

Wireless Sensor Networks are consist of small battery powered devices with limited energy resources. Once deployed, the small sensor nodes are usually inaccessible to the user, and thus replacement of the energy source is not feasible. Hence, one of the most important issues that need to be enhanced in order to improve the life span of the network is energy efficiency. to overcome this demerit many research have been done. The clustering is the one of the representative approaches. In this paper, we introduce a dynamic clustering algorithm using Fuzzy Logic and genetic algorithm. In fact, using fuzzy system design and system optimization by genetic algorithm is presented approach to select the best cluster head in sensor networks. Using random data set has been addressed to evaluate of fuzzy-genetic system presented in this paper and finally, MSE rate or mean error of sending the messages using proposed fuzzy system in comparison with LEACH method is calculated in select the cluster head. The results of evaluations is representative of a reduction the MSE metric in proposed method in comparison with LEACH method for select the cluster head. Reduce of MSE directly is effective on energy consumption and lifetime of wireless sensor network and can cause the reduce energy consumption and increase network lifetime.

Multitasking Genetic Algorithm (MTGA) for Fuzzy System Optimization

Multi-task learning uses auxiliary data or knowledge from relevant tasks to facilitate the learning in a new task. Multi-task optimization applies multi-task learning to optimization to study how to effectively and efficiently tackle multiple optimization problems simultaneously. Evolutionary multi-tasking, or multi-factorial optimization, is an emerging subfield of multi-task optimization, which integrates evolutionary computation and multi-task learning. This paper proposes a novel and easy-to-implement multi-tasking genetic algorithm (MTGA), which copes well with significantly different optimization tasks by estimating and using the bias among them. Comparative studies with eight state-of-the-art single- and multi-task approaches in the literature on nine benchmarks demonstrated that on average the MTGA outperformed all of them, and had lower computational cost than six of them. Based on the MTGA, a simultaneous optimization strategy for fuzzy system design is also proposed. Experiments on simultaneous optimization of type-1 and interval type-2 fuzzy logic controllers for couple-tank water level control demonstrated that the MTGA can find better fuzzy logic controllers than other approaches.

Converter matlab fuzzy inference to arduino Csystem

This study aims to utilize the FIS matlab converter (fuzzy inference system) into the C arduino programming language with the aim of simplifying the fuzzy logic model that can be applied immediately with control hardware such as Arduino. In this study, an example of fuzzy logic is used with two sensor inputs, namely temperature and smoke sensors as inputs and two outputs in the form of buzzers and pump motors. The method used is sugeno in matlab. Then the formation of input variables modeled mapping scale is formed 0 - 100 and scale output 0 - 2. Converter. Fis fuzzy inference system can be applied to the design of fuzzy logic systems with the results of the .ino format conversion program template that can be set output results as arduino triger directly . This setting is enough to do with the reading of the g_fisOutput [] variable with IF-ELSE.

Fuzzy Observer-Based Dynamic Surface Control for Input-Saturated Nonlinear Systems and its Application to Missile Guidance

In this paper, a fuzzy observer-based tracking control scheme is proposed for a class of strict-feedback uncertain nonlinear system with input saturation. To facilitate the fuzzy logic system (FLS) design, the original uncertain nonlinear system is transformed into an output feedback system firstly. Then, an adaptive state observer is constructed. In order to efficiently tackle the input saturation, an auxiliary system (AS) is introduced. Using the estimated states and AS, the dynamic surface control (DSC) strategy is developed based on backstepping method. Under the DSC scheme, the tracking control problem is reduced to optimal regulation problem of an affine system. Subsequently, an adaptive dynamic programming (ADP) based optimal control scheme is proposed. The stability of the overall system is guaranteed by Lyapunov theory. Finally, guidance simulation is made to confirm the effectiveness of the developed control method.

Multipopulation Nature-Inspired Algorithm (MNIA) for the Designing of Interpretable Fuzzy Systems

The solutions proposed in this article are based on our experience with fuzzy systems (FSs), their interpretability, and population-based algorithms (PBAs). They provide a consistent approach to the design of interpretable FSs. First, PBAs can make a useful tool for selecting both parameters and the FS structure. In practice, such structure is usually chosen by trial and error. Second, in this article, we propose a new multipopulation PBA, which uses a variety of search formulas. This helps to eliminate the problem of incorrect PBA selection. Third, we propose interesting solutions for the interpretability of FSs with trapezoidal membership functions. These functions are well suited for modeling ranges of linguistic variables. We are particularly interested in providing them with original interpretability criteria, which are used by a PBA to design FSs. Furthermore, we offer an original way of setting up trapezoidal functions, which prevents them from overlapping with each other. Fourth, we believe that interpretability can be achieved through a capable extension of fuzzy rules. That is the reason why we used weights and dedicated operators for their processing. In this article, a different extension of the rules base is proposed. It involves adding relation operators (ROs) (e.g., “ $>$ ” and “ $>=$ ”), which can be used to model linguistic phrases, e.g. “electrical voltage less than or equal to high.” An additional task of the PBA is the automatic selection of an RO, which facilitates the extraction of knowledge from the data. The approach proposed in this article was tested using well-known classification benchmarks.

Novel Fuzzy System Identification: Comparative Study and Application for Data Forecasting

A novel fuzzy clustering technique for system identification based on the Takagi-Sugeno fuzzy inference is proposed. In this paper, the identification algorithm differs from others found in the literature, in the manner that the inference’s antecedents and consequents are built. Gaussian fuzzy sets with support on the cluster’s ordinal set of a determined α-level are unidimensional antecedents that have as a consequent an affine function, which recovers the attributes of the collected data. Two simulations examples are performed to compare to the new method: the comparisons are done with well-known algorithms in terms of the normalized root mean square goodness of the fit measure, and the computational speed in the identification process. For that purpose, Matlab System Identification toolbox is used in order to consolidate a unique source of comparison. The method has been tested in the prediction field, as part of a project supporting Brazilian local companies.

Design Methodology for the Implementation of Fuzzy Inference Systems Based on Boolean Relations

This paper proposes a methodology for the design of fuzzy inference systems based on Boolean relations. The approach using Boolean sets presents limited performance due to the abrupt transitions that occur during its functioning, therefore, fuzzy sets can be used aiming the improvement of the performance. In this approach, firstly, the design of a Boolean controller is performed, which is later extended into fuzzy under design guidelines proposed in this paper. The methodology uses Kleene algebra via truth tables for the fuzzy system design, allowing the simplification of the equations that implement the fuzzy system.

A new method of black-box fuzzy system identification optimized by genetic algorithm and its application to predict mixture thermal properties

PurposeThis paper aims to present a black-box fuzzy system identification method coupled with genetic algorithm optimization approach to predict the mixture thermal conductivity at dissimilar temperatures and nanoparticle concentrations, in the examined domains.Design/methodology/approachWO3 nanoparticles are dispersed in the deionized water to produce a homogeneous mixture at various nanoparticles mass fractions of 0.1, 0.5, 1.0 and 5.0 Wt.%.FindingsThe results depicted that the models not only have satisfactory precision, but also have acceptable accuracy in dealing with non-trained input values.Originality/valueThe transmission electron microscopy is applied to measure the mean diameters, shape and morphology of the dry nanoparticles. Moreover, the stability of nanoparticles inside the water is evaluated by using zeta potential and dynamic light scattering (DLS) tests. Then, the prepared nanofluid thermal conductivity is presented at different values of temperatures and concentrations.

A hybrid design of shadowed type-2 fuzzy inference systems applied in diagnosis problems

Computer aided systems have been frequently used in recent times and the evolution of artificial intelligence allows the inclusion of this kind of systems in most complex problems, for example in diagnosis problems. Some of the most powerful algorithms or methods that have been applied to diagnosis problems are: Artificial Neural Networks, Support Vector Machines, Decision Threes, Fuzzy Inference Systems and hybrids of these algorithms. The present paper explores the applications of a special approximation of the Type-2 Fuzzy Inference System, which is the Shadowed Type-2 Fuzzy Inference System, and the reason for using this approach (and not another one) is because it provides a good approximation to General Type-2 Fuzzy Inference Systems, but with a computational cost reduction. Shadowed Type-2 Fuzzy Inference Systems are inspired on the Shadows Sets that simplify the traditional Type-1 Membership Function using two optimal α-cuts, which in our case are applied in the secondary membership function of the General Type-2 Fuzzy Inference System (modeled by the α-planes representation) in order to model the system with only two α-planes. Experiments were realized with eleven benchmark datasets in order to evaluate the accuracy of the proposed system. The obtained results demonstrate the advantages of using this approach over the conventional General Type-2 Fuzzy Inference Systems, and this is because we obtain better performance in most of the cases and with less computational resources.

Heuristic design of fuzzy inference systems: A review of three decades of research

This paper provides an in-depth review of the optimal design of type-1 and type-2 fuzzy inference systems (FIS) using five well known computational frameworks: genetic-fuzzy systems (GFS), neuro-fuzzy systems (NFS), hierarchical fuzzy systems (HFS), evolving fuzzy systems (EFS), and multi-objective fuzzy systems (MFS), which is in view that some of them are linked to each other. The heuristic design of GFS uses evolutionary algorithms for optimizing both Mamdani-type and Takagi–Sugeno–Kang-type fuzzy systems. Whereas, the NFS combines the FIS with neural network learning systems to improve the approximation ability. An HFS combines two or more low-dimensional fuzzy logic units in a hierarchical design to overcome the curse of dimensionality. An EFS solves the data streaming issues by evolving the system incrementally, and an MFS solves the multi-objective trade-offs like the simultaneous maximization of both interpretability and accuracy. This paper ofers a synthesis of these dimensions and explores their potentials, challenges, and opportunities in FIS research. This review also examines the complex relations among these dimensions and the possibilities of combining one or more computational frameworks adding another dimension: deep fuzzy systems.

Global Research Performance on the Design and Applications of Type-2 Fuzzy Logic Systems: A Bibliometric Analysis

There has been a significant contribution to scientific literature in the design and applications of Type-2 fuzzy logic systems (T2FLS). The T2FLSs found applications in many aspects of our daily lives, such as engineering, pure science, medicine and social sciences. The online web of science was searched to identify the 100 most frequently cited papers published on the design and application of T2FLS from 1980 to 2016. The articles were analyzed based on authorship, source title, country of origin, institution, document type, web of science category, and year of publication. The correlation between the average citation per year (ACY) and the total citation (TC) was analyzed. It was found that there is a strong relationship between the ACY and TC (r = 0.91643, P<0.01), based on the papers consider in this research. The “Type -2 fuzzy sets made simple” authored by Mendel and John (2002), published in IEEE Transactions on Fuzzy Systems received the highest TC as well as the ACY. The future trend in this research domain was also analyzed. The present analysis may serve as a guide for selecting qualitative literature especially to the beginners in the field of T2FLS.

T–S Fuzzy Model Identification with Sparse Bayesian Techniques

This paper introduces a novel method for fuzzy modeling based on sparse Bayesian techniques. The sparse representation problems in the Takagi–Sugeno (T–S) fuzzy system identification are studied, which is to establish a T–S fuzzy system with a adaptive number of fuzzy rules and simultaneously have a minimal number of nonzero consequent parameters. The proposed method is called sparse Bayesian fuzzy inference systems (B-sparseFIS). There are two main procedures in the paper. Firstly, initial fuzzy rule of antecedent part is extracted automatically by an AP clustering method. By using the algorithm of adaptive block orthogonal matching pursuit, the number of rules is computed statistically then the main important fuzzy rules can be selected. In the algorithm, the redundant rules are eliminated for better model accuracy and generalization performance; secondly, an adaptive B-sparseFIS is exploited. The consequence of fuzzy system is identified and simplified with sparse Bayesian techniques such that many consequent parameters will approximate to zero. Four examples are provided to illustrate the effectiveness of the proposed algorithm. Furthermore, the performances of the algorithm are validated through the results of statistical analyses including parameter estimate error, MSE, NRMSE, etc.

Recommendations on designing practical interval type-2 fuzzy systems

Interval type-2 (IT2) fuzzy systems have become increasingly popular in the last 20 years. They have demonstrated superior performance in many applications. However, the operation of an IT2 fuzzy system is more complex than that of its type-1 counterpart. There are many questions to be answered in designing an IT2 fuzzy system: Should singleton or non-singleton fuzzifier be used? How many membership functions (MFs) should be used for each input? Should Gaussian or piecewise linear MFs be used? Should Mamdani or Takagi–Sugeno–Kang (TSK) inference be used? Should minimum or product t-norm be used? Should type-reduction be used or not? How to optimize the IT2 fuzzy system? These questions may look overwhelming and confusing to IT2 beginners. In this paper we recommend some representative starting choices for an IT2 fuzzy system design, which hopefully will make IT2 fuzzy systems more accessible to IT2 fuzzy system designers.