Type-2 Fuzzy Inference Research Articles

Shadowed Type-2 Fuzzy Sets in Dynamic Parameter Adaption in Cuckoo Search and Flower Pollination Algorithms for Optimal Design of Fuzzy Fault-Tolerant Controllers

In recent, various metaheuristic algorithms have shown significant results in control engineering problems; moreover, fuzzy sets (FSs) and theories were frequently used for dynamic parameter adaption in metaheuristic algorithms. The primary reason for this is that fuzzy inference system (FISs) can be designed using human knowledge, allowing for intelligent dynamic adaptations of metaheuristic parameters. To accomplish these tasks, we proposed shadowed type-2 fuzzy inference systems (ST2FISs) for two metaheuristic algorithms, namely cuckoo search (CS) and flower pollination (FP). Furthermore, with the advent of shadowed type-2 fuzzy logic, the abilities of uncertainty handling offer an appealing improved performance for dynamic parameter adaptation in metaheuristic methods; moreover, the use of ST2FISs has been shown in recent works to provide better results than type-1 fuzzy inference systems (T1FISs). As a result, ST2FISs are proposed for adjusting the Lèvy flight (P) and switching probability (P′) parameters in the original cuckoo search (CS) and flower pollination (FP) algorithms, respectively. Our approach investigated trapezoidal types of membership functions (MFs), such as ST2FSs. The proposed method was used to optimize the precursors and implications of a two-tank non-interacting conical frustum tank level (TTNCFTL) process using an interval type-2 fuzzy controller (IT2FLC). To ensure that the implementation is efficient compared with the original CS and FP algorithms, simulation results were obtained without and then with uncertainty in the main actuator (CV1) and system component (leak) at the bottom of frustum tank two of the TTNCFLT process. In addition, the statistical z-test and non-parametric Friedman test are performed to analyze and deliver the findings for the best metaheuristic algorithm. The reported findings highlight the benefits of employing this approach over traditional general type-2 fuzzy inference systems since we get superior performance in the majority of cases while using minimal computational resources.

Navigation of a Differential Wheeled Robot Based on a Type-2 Fuzzy Inference Tree

This paper presents a type-2 fuzzy inference tree designed for a differential wheeled mobile robot that navigates in indoor environments. The proposal consists of a controller designed for obstacle avoidance, a controller for path recovery and goal reaching, and a third controller for the real-time selection of behaviors. The system takes as inputs the information provided for a 2D laser range scanner, i.e., the distance of nearby objects to the robot, as well as the robot position in space, calculated from mechanical odometry. The real performance is evaluated through metrics such as clearance, path smoothness, path length, travel time and success rate. The experimental results allow us to demonstrate an appropriate performance of our proposal for the navigation task, with a higher efficiency than the reference methods taken from the state of the art.

Type-1 and type-2 fuzzy logic-based space vector modulation for two-level inverter fed induction motor

Two-level inverter control with type-1 and type-2 fuzzy logic-based space vector pulse-width modulation (PWM) method for induction motor drive (IMD) is presented in this paper. A new sampling time independent strategy with type-1 and type-2 fuzzy based methods are used in generating three phase duty ratios which are directly obtained without mathematical equations. The conventional method of space vector modulation (SVM) produces the duty ratios for the inverter which are sampling time dependent. However, in type-1 and type-2 fuzzy based space vector PWM algorithms, the three phases duty ratios generated are sampling time independent and with a new integrated dead-time insertion in SVM itself, can be implemented practically for any switching frequency. As the rule-base for Mamdani non-singleton interval type-1 and type-2 fuzzy inference systems are designed manually, with the expert knowledge of conventional space vector PWM, the duty ratios of the inverter are generated such that the performance of the IMD is improved. The simulation studies for aforementioned cases is performed in matrix laboratory (MATLAB) and experimental validation for the proposed space vector PWM algorithms is validated using real-time sacraetheologiae magister (STM) hardware with STM32F429I Cortex M4 processor for a 1 hp IMD.

Type-2 fuzzy logic applications designed for active parameter adaptation in metaheuristic algorithm for fuzzy fault-tolerant controller

PurposeIn recent times, fuzzy logic is gaining more and more attention, and this is because of the capability of understanding the functioning of the system as per human knowledge-based system. The main contribution of the work is dynamically adapting the important parameters throughout the execution of the flower pollination algorithm (FPA) using concepts of fuzzy logic. By adapting the main parameters of the metaheuristics, the performance and accuracy of the metaheuristic have been improving in a varied range of applications.Design/methodology/approachThe fuzzy logic-based parameter adaptation in the FPA is proposed. In addition, type-2 fuzzy logic is used to design fuzzy inference system for dynamic parameter adaptation in metaheuristics, which can help in eliminating uncertainty and hence offers an attractive improvement in dynamic parameter adaption in metaheuristic method, and, in reality, the effectiveness of the interval type-2 fuzzy inference system (IT2 FIS) has shown to provide improved results as matched to type-1 fuzzy inference system (T1 FIS) in some latest work.FindingsOne case study is considered for testing the proposed approach in a fault tolerant control problem without faults and with partial loss of effectiveness of main actuator fault with abrupt and incipient nature. For comparison between the type-1 fuzzy FPA and interval type-2 fuzzy FPA is presented using statistical analysis which validates the advantages of the interval type-2 fuzzy FPA. The statistical Z-test is presented for comparison of efficiency between two fuzzy variants of the FPA optimization method.Originality/valueThe main contribution of the work is a dynamical adaptation of the important parameters throughout the execution of the flower pollination optimization algorithm using concepts of type-2 fuzzy logic. By adapting the main parameters of the metaheuristics, the performance and accuracy of the metaheuristic have been improving in a varied range of applications.

Trapezoidal type-2 fuzzy inference system with tensor unfolding structure learning method

In the paper, trapezoidal type-2 fuzzy inference system (TT2FIS) is introduced. To construct the type-2 fuzzy inference system, trapezoidal type-2 fuzzy sets are adopted to construct the antecedent part of the fuzzy rules, the centers of the fuzzy sets are roughly clustered by an evolving autonomous data partitioning algorithm. To further eliminate the effect of the centers that are generated by dataset itself, and it can be easily impacted by the unbalance data, the data clouds that are generated by autonomous data partitioning algorithm are filtered by Hammersley sequence, and symmetrical trapezoidal type-2 fuzzy sets are generated by the generated cluster centers and problem-free standard deviation generating method. To the consequent part, generalized type-2 TSK consequent is formulated by tensor, which is obtained via three constituent parts of the trapezoidal type-2 fuzzy sets (lower membership function, upper membership function and type-reduction set of data samples), the parameters of consequent part are the iterative results of a matrix equation that is unfolded from the tensor. Finally, simulation results are carried out to verify the effectiveness of the proposed trapezoidal type-2 fuzzy inference system. Simulation results show that the generalization of the TT2FIS is better than the coincide type-2 fuzzy inference system or adaptive type-1 fuzzy inference system.

Online Interval Type-2 Fuzzy Extreme Learning Machine applied to 3D path following for Remotely Operated Underwater Vehicles

In marine missions that involve 3D path following tasks, the overall goal of Underwater Vehicles (UVs) is the successful completion of a path previously specified by the operator. This implies that the path must be followed by the UV as closely as possible and arrive at a location for collection by a vessel. In this paper, an Online Interval Type-2 Fuzzy Extreme Learning Machine (OIT2-FELM) is suggested to achieve a robust following behaviour along a predefined 3D path using a Remotely Operated Underwater Vehicle (ROV). The proposed machine is a fast sequential learning scheme to the training of a more generalised model of TSK Interval Type-2 Fuzzy Inference Systems (TSK IT2 FISs) equivalent to Single Layer Feedforward Neural Networks (SLFNs). Learning new input data in the OIT2-FELM can be done one-by-one or chunk-by-chunk with a fixed or varying size. The OIT2-FELM is implemented in a hierarchical navigation strategy (HNS) as the main guidance mechanism to infer local control motions and to provide the ROV with the necessary autonomy to complete a predefined 3D path. For local path-planning, the OIT2-FELM performs signal classification for obstacle avoidance and target detection based on data collected by an on-board scan sonar. To evaluate the performance of the proposed OIT2-FELM, two different experiments are suggested. First, a number of benchmark problems in the field of non-linear system identification, regression and classification problems are used. Secondly, a number of experiments to the completion of a predefined 3D path using an ROV is implemented. Compared to other fuzzy strategies, the OIT2-FELM offered two significant capabilities. On the one hand, the OIT2-FELM provides a better treatment of uncertainty and noisy signals in underwater environments while improving the ROV’s performance. Secondly, online learning in OIT2-FELM allows continuous knowledge discovery from survey data to infer the surroundings of the ROV. Experiment results to the completion of 3D paths show the effectiveness of the proposed approach to handle uncertainty and produce reasonable classification predictions (∼90.5% accuracy in testing data).

A bee colony optimization (BCO) and type-2 fuzzy approach to measuring the impact of speed perception on motor vehicle crash involvement

The major challenge of this paper is to examine how various forms of speed perception affect motor vehicle crash (MVC) involvement. To model this relationship, we use a type-2 fuzzy inference system (T2FIS). Another general challenge is to improve the performance of seven created T2FISs in a sense of compliance with the empirical data. This is achieved by a proposal of an algorithm based on the bee colony optimization (BCO) metaheuristic. The main novelty of this algorithm is the way how the testing points are selected in a type-2 fuzzy environment, which influences the execution efficiency. Data collection was carried out in twelve experiments. A total of 178 young drivers assessed the speed level from four positions; three of them relate to the speed perception of other vehicles on the road, while the remaining one represents the assessment of their own speed. At each position, three speed levels were assessed: 30, 50, and 70 km/h. As a result of the implemented methodology, a relationship between the various forms of speed perception and participation in MVCs can be quantified. The BCO-based algorithm achieved an average improvement of 21.17% in the performance of the initial T2FIS structures. The final results indicate that the drivers whose speed perception of the vehicle they are looking at from the rear side, as well as of the own vehicle, is poor have an elevated risk toward participation in MVCs compared to other forms of speed perception. This can be useful in various educational and recruitment procedures.

Interval type-2 fractional fuzzy inference systems: Towards an evolution in fuzzy inference systems

Following type-1 fractional fuzzy inference systems presented recently as the new generation of fuzzy inference systems, interval type-2 fractional fuzzy inference systems (IT2FFISs) as a leap further ahead in the evolution of fuzzy inference systems (FISs) are introduced in this article. The IT2FFISs, which are outlined in this article, add to the armamentarium of FISs some particular concepts such as interval type-2 fractional membership functions, type-2 fractional translation rule, type-2 fracture index, the concept of switching, the entanglement, the degeneracy concept, and so forth. An IT2FFIS exploits not only the tolerance for the uncertainty in the interpretation of the meaning of a word, but also the relevance between the quality and quantity levels of the given information to infer an answer to an inference query. The IT2FFISs make an increase in machine intelligence quotient possible by an increase in the range of FISs order rather than their type. Moreover, the synergy of the concepts coming with various modes of IT2FFISs such as the aggressive mode opens a gate to a space of fuzzy systems outputs which used to be indiscoverable. Furthermore, it is demonstrated that as the type-2 fracture index approaches zero, the space of IT2FFISs outputs contracts and eventually it coincides the space of IT2FISs output when the fracture index is equal to zero. It is also proved that, provided a particular order of the IT2FFIS is taken into account, independent of the problem in question, a typical IT2FIS never leads to results which are more satisfactory than those obtained by the IT2FFIS corresponding to the typical IT2FIS.

A variable selection method for a hierarchical interval type-2 TSK fuzzy inference system

In this paper, we propose a method to judge the degree of the relationship closeness between system input variables and theoretical output through independence test, and apply this method to construct a hierarchical interval type-2 Takagi-Sugeno-Kang (TSK) fuzzy inference system. Interval type-2 TSK fuzzy inference systems have been applied in many fields. An interval type-2 fuzzy inference system, which is a special case of generalized type-2 fuzzy inference systems, can reduce the computational complexity of type-reduction and save computational cost. An hierarchical fuzzy system can further reduce the complexity of systems. However, it is found that, with the increase in data dimensions, the computational complexity and error of the system will increase. The system accuracy of a hierarchical fuzzy system is also found related to the order of its input variables. Variable selection can select meaningful input variables to decrease input space dimensions, and sort the input variables in some way. This paper proposes a variable selection and sorting method based on independence test. Firstly, input variables data and theoretical output data are classified and contingency tables are established. Chi-square statistics of the contingency tables are calculated for independence test, and input variables that are not independent of theoretical output are selected. Then, the total inertia of the contingency tables is calculated to represent the degree of the relationship closeness between input variables and theoretical output. According to the total inertia, the input variables are sorted and entered the system in order. The proposed method and the model built by the method are tested by dealing with regression problems. By comparing the proposed method on several types of systems, the hierarchical interval type-2 TSK inference system shows a better performance. Then the model is compared with other advanced fuzzy models and non-fuzzy models. Experiments show that its performance is better than the rest.

Novel metaheuristic-based type-2 fuzzy inference system for predicting the compressive strength of recycled aggregate concrete

The use of recycled concrete aggregate (RCA) in the production of new concrete can provide several environmental benefits and reduce the pressure on natural resources. Due to the differences in the properties of RCA and natural aggregate, the properties of recycled aggregate concrete (RAC) (i.e., concrete containing RCA) are different from those of normal concrete. Compressive strength (CS) of the RAC, which is a key parameter in many design codes, can be determined either through expensive and time-taking laboratory-based procedures or using empirical relationships. In this study, two types of hybridized machine learning algorithms (i.e., type-1 fuzzy inference system (T1FIS) and interval type-2fuzzy inference system (IT2FIS)) were used to develop predictive models for the CS of RAC. Moreover, arithmetic optimization algorithm (AOA), as a novel optimization algorithm, was employed to optimize the parameters of FIS models. To develop the CS predictive models, a dataset containing information on 1868 data records was used. The results indicate that the IT2FIS model outperformed the T1FIS model. A comparison of the results of this study with those reported in previous studies also confirms the high accuracy of the IT2FIS model. The findings indicate that concrete age, natural fine aggregate to total natural aggregate ratio, and superplasticizer to binder ratio have positive impacts on the CS, while the remaining input variables have negative influences on the CS. Regarding the intensities of the variables, concrete age, total coarse aggregate to cement ratio, and water to binder ratio are in the first to third orders, respectively.

Emotion Recognition Based on Type-2 Recurrent Wavelet Fuzzy Brain Emotion Learning Network Model

Emotion recognition plays a crucial role in human-robot emotional interaction applications, and the brain emotional learning model is one of several emotion recognition methods, but the learning rules of original brain emotional learning model play poor adaptation and do not work very well. In fact, existing facial emotion recognition methods do not have high accuracy and are not sufficiently practical in real-time applications. In order to solve this problem, this paper introduces an optimal model, which merges interval type-2 recurrent wavelet fuzzy system and brain emotional learning network for emotion recognition. The proposed model takes advantage of type-2 recurrent wavelet fuzzy theory and brain emotional neural network. There are no rules initially, and then the structure and parameters of model are tuning online simultaneously by the gradient approach and Lyapunov function. The system input data streams are directly imported into the neural network through a type-2 recurrent wavelet fuzzy inference system; then, the results are subsequently piped into sensory and emotional channels which jointly produce the final outputs of the network. The proposed model could reduce the uncertainty in terms of vagueness by using type-2 recurrent wavelet fuzzy theory and removing noise samples. Finally, the superior performance of the proposed method is demonstrated by its comparison with some emotion recognition methods on five emotion databases.

Optimal design of type-2 fuzzy systems for diabetes classification based on genetic algorithms

Diabetes has become a global health problem, where a proper diagnosis is vital for the life quality of patients. In this article, a genetic algorithm is put forward for designing type-2 fuzzy inference systems to perform Diabetes Classification. We aim at finding parameter values of Type-2 Trapezoidal membership functions and the type of model (Mamdani or Sugeno) with this optimization. To verify the effectiveness of the proposed approach, the PIMA Indian Diabetes dataset is used, and results are compared with type-1 fuzzy systems. Five attributes are used considered as the inputs of the fuzzy inference systems to obtain a Diabetes diagnosis. The instances are divided into design and testing sets, where the design set allows the genetic algorithm to minimize the error of classification, and finally, the real behavior of the fuzzy inference system is validated with the testing set.

A New Approach for Dynamic Stochastic Fractal Search with Fuzzy Logic for Parameter Adaptation

Stochastic fractal search (SFS) is a novel method inspired by the process of stochastic growth in nature and the use of the fractal mathematical concept. Considering the chaotic stochastic diffusion property, an improved dynamic stochastic fractal search (DSFS) optimization algorithm is presented. The DSFS algorithm was tested with benchmark functions, such as the multimodal, hybrid, and composite functions, to evaluate the performance of the algorithm with dynamic parameter adaptation with type-1 and type-2 fuzzy inference models. The main contribution of the article is the utilization of fuzzy logic in the adaptation of the diffusion parameter in a dynamic fashion. This parameter is in charge of creating new fractal particles, and the diversity and iteration are the input information used in the fuzzy system to control the values of diffusion.

Load frequency control for multi-area power systems: A new type-2 fuzzy approach based on Levenberg–Marquardt algorithm

In this study, a new fuzzy approach is proposed for load frequency control (LFC) of a multi-area power system. The main control system is constructed by use of interval type-2 fuzzy inference systems (IT2FIS) and fractional-order calculus. In designing the controller, there is no need for the system dynamics, therefore the system Jacobian is obtained by a multilayer perceptron neural network (MLP-NN). Uncertainties are modeled by IT2FIS, and for training fuzzy parameters, Levenberg–Marquardt algorithm (LMA) is used, which is faster and more robust than gradient descent algorithm (GDA). The system stability is studied by Matignon’s stability method under time-varying disturbances. A comparison between the proposed controller with type-1 fuzzy controller on the New England 39-bus test system is also carried out. The simulations demonstrate the superiority of the designed controller.

Classification with Fuzzification Optimization Combining Fuzzy Information Systems and Type-2 Fuzzy Inference

In this research, we introduce a classification procedure based on rule induction and fuzzy reasoning. The classifier generalizes attribute information to handle uncertainty, which often occurs in real data. To induce fuzzy rules, we define the corresponding fuzzy information system. A transformation of the derived rules into interval type-2 fuzzy rules is provided as well. The fuzzification applied is optimized with respect to the footprint of uncertainty of the corresponding type-2 fuzzy sets. The classification process is related to a Mamdani type fuzzy inference. The method proposed was evaluated by the F-score measure on benchmark data.

Application of Interval Type-1 Fuzzy Inference System to analyze the quality of memorization Qur’an

In this modern world, the use of artificial intelligence needed. One of the intelligence methods raised in this study is to use the Interval Type-1-Fuzzy Inference System (IT-1-FIS). In Muslim schools (Madrasah Ibtidaiyah) where there is subject matter memorizing the Qur’an. The fluency determines the quality of memorization of the Qur’an in pronouncing letters (Input-1), Tajweed Rules (Input-2), and reading fluency (Input-3). These three points become input for forecasting Memorization Quality Of The Qur’an. IT-1-FIS requires primary inputs and rules to obtain the output of forecasting Memorization Quality Of The Qur’an. By using IT-1-FIS, it can predict that students will get the quality of memorization based on the trace value obtained on each point. So from the beginning the teacher will know the quality of a student’s memorization in the future. The estimated condition in memorization of the Qur’an can be known faster, and the teacher will be able to increase the points that are less to get the desired quality of memorization of the Qur’an by the habits practiced by students. These students’ patterns are the basic rules of input in the IT-1-FIS process.

Data-Driven Control Based on the Interval Type-2 Intuition Fuzzy Brain Emotional Learning Network for the Multiple Degree-of-Freedom Rehabilitation Robot

A novel interval type-2 intuition fuzzy brain emotional learning network model (IT2IFBELC) which depends only on the input and output data is proposed for the rehabilitation robot, which is different from model-based control algorithms that require exact dynamic model knowledge of the rehabilitation robot. The proposed model takes advantage of the type-2 intuition fuzzy theory and brain emotional neural network, and this is no rule initially; then, the structure and parameters of IT2IFBELC are tuned online simultaneously by the gradient approach and Lyapunov function. The system input data streams are directly imported into the neural network through an interval type-2 intuition fuzzy inference system (IT2IFIS), and then the results are subsequently piped into sensory and emotional channels which jointly produce the final outputs of the network. That is, the whole controller is composed of three parts, including the ideal sliding mode controller, the interval type-2 intuition fuzzy brain emotional learning network controller, and a powerful robust compensation controller, and then one Lyapunov function is designed to guarantee the rapid convergence of the control systems. For further illustrating the superiority of this model, several models are studied here for comparison, and the results show that the interval type-2 intuition fuzzy brain emotional learning network model can obtain better satisfactory control performance and be suitable to deal with the influence of the uncertainty of the rehabilitation robot.

Optimization using the firefly algorithm of ensemble neural networks with type-2 fuzzy integration for COVID-19 time series prediction.

In this paper, the latest global COVID-19 pandemic prediction is addressed. Each country worldwide has faced this pandemic differently, reflected in its statistical number of confirmed and death cases. Predicting the number of confirmed and death cases could allow us to know the future number of cases and provide each country with the necessary information to make decisions based on the predictions. Recent works are focused only on confirmed COVID-19 cases or a specific country. In this work, the firefly algorithm designs an ensemble neural network architecture for each one of 26 countries. In this work, we propose the firefly algorithm for ensemble neural network optimization applied to COVID-19 time series prediction with type-2 fuzzy logic in a weighted average integration method. The proposed method finds the number of artificial neural networks needed to form an ensemble neural network and their architecture using a type-2 fuzzy inference system to combine the responses of individual artificial neural networks to perform a final prediction. The advantages of the type-2 fuzzy weighted average integration (FWA) method over the conventional average method and type-1 fuzzy weighted average integration are shown.

Comparison of Type-2 Fuzzy Inference Method and Deep Neural Networks for Mass Detection from Breast Ultrasonography Images

In this study, mass detection from breast ultrasonography images was realized using deep neural networks. Dataset is a collection of publicly available ultrasonography images which were classified by their biopsy results. A total of 153 breast ultrasonography images that contain 89 malign and 64 benign tumours were used. Image augmentation and deep neural network software was developed using Python 3,5 environment on Visual Studio Community 2017 IDE. A hybrid method including Keras ImageDataGenerator Class and image preprocessing techniques was introduced. Twenty images from both classes were randomly split from the dataset for testing after the network was designed. The network had a success rate of 100% at an epoch value of 70. The result of this study was compared with the result of another study that implemented type-2 fuzzy inference system with a success rate of 99,34%. 
 As a conclusion, it can be expressed that the deep neural networks are more successful than fuzzy inference systems in tumour detection from breast ultrasonography images. Therefore, it can be more convenient to use deep neural network technology in computer aided detection systems for mass detection from breast ultrasonography images.

Assessing the agility of hospitals in disaster management: application of interval type-2 fuzzy Flowsort inference system

Disaster management is one of the most important issues in service organizations, especially in healthcare sector. Due to the occurrences of natural or human-made incidents, requirements of assistance and rescue increase gradually. Hospitals have the vital role in addressing these requirements and therefore should enhance their skill to provide a punctual and appropriate response to these events. An important prerequisite for this level of responsiveness is agility. This study aims to introduce and implement a new approach in order to estimate the agility level of hospitals in disaster management. Founded on the four phases of disaster management, a hospital agility framework is established which showcases the relations between hospital agility factors and phases of disaster management. A new Flowsort-based approach is also introduced through the integration of the conventional Flowsort method with interval type-2 fuzzy sets. This approach is applied for a case study consisting of 30 hospitals seeking to improve their agility in disasters. We use upper and lower control limits (UCL and LCL) to define the categories of Flowsort method. The results show that 40% of the hospitals position in between the $$ \pm \,2\delta $$ and $$ \pm \,3\delta $$ limits, i.e., the best and the worst categories. Results also approve the ability of the proposed method in evaluation of hospitals based on their agility factors and represent a geographical observation on the hospitals. Some indications about required actions for hospitals of each category in order to increase or maintain their agility level in disaster management are also provided.