Type-2 Fuzzy Inference System Research Articles

Data preprocessing strategy in constructing convolutional neural network classifier based on constrained particle swarm optimization with fuzzy penalty function

Convolutional neural networks (CNNs) have attracted increasing attention in recent years because of their powerful abilities to extract and represent spatial/temporal information. However, for general data, its features are assumed to have weak or no correlation, and directly applying CNN to classify such data could result in poor classification performance. To address this problem, a combined technique of original data representation method of fuzzy penalty function-based constrained particle swarm optimization (FCPSO) and CNN, so-called FCPSO-CNN is designed to effectively solve the classification problems for generic dataset and applied to recognize (classify) black plastic wastes in recycling problems. In more detail, CPSO is introduced to optimize feature reordering matrix under constraints and the construction of this matrix is driven by fitness function of CNN that quantifies classification performance. The Mamdani type fuzzy inference system (FIS) is employed to realize the fuzzy penalty function (FPF) which is utilized to realize the constrained problems of CPSO as well as alleviate the issues of the original penalty function method suffering from the lack of robustness. Experimental results demonstrate that FCPSO-CNN achieves the best classification accuracy on 13 out of 17 datasets; the statistical analysis also confirms the superiority of FCPSO-CNN. An interesting point is worth to mention that some feature reordering matrices in the infeasible space come with better classification accuracy. It has been found that the proposed method results in more accurate solution than one-dimensional CNN, random reordering feature-based CNN and some well-known classifiers (e.g., Naive Bayes, Multilayer perceptron, Support vector machine).

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.

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.

A comparative study of multiband mamdani fuzzy classification methods for west of Iraq satellite image

In our paper, performance of four fuzzy membership function generation methods was studied. These methods were studied in the context of implementing Mamdani fuzzy classification on a set of satellite images for western Iraqi territory. The first method generate triangulate membership functions using mean, minimum (min) and maximum (max) of histogram attribute values (AV), while peak and standard deviation (STD) of these AV were used in the second. On the other hand, in the third and fourth methods, Gaussian membership functions are generated using same mentioned values in the first and second method respectively. The goal was to generate a Mamdani type fuzzy inference system the membership function (MF) of each fuzzy set and implementing the AV of western Iraqi territory training data sets. A pixel-by-pixel comparison of each method with traditional maximum likelihood method (ML) was made on data sets comprising six bands of satellite imagery of the western Iraqi region taken by the Landsat-5 satellite. Simulation results of these performance comparisons singled out that the method using Gaussian MFs together with peak and STD of the AV as the best achiever with a similarity of 83.16 percent for band (3) of the studied area.

Intrusion detection based on fuzzy logic for wireless body area networks: review and proposition

Wireless body area networks (WBANs) are very helpful for monitoring the patient’s case, due to the medical sensors. However, this technology faces several problems such as loss communication, security issues and energy consumption. Our work focused on the security and specifically the intrusion detection system (IDS), which is one of the most effective techniques used to identify the presence of intrusions in a network. To make the IDS more efficient, the fuzzy logic (FL) is one of the well-known techniques that is known for its powerful mechanism used to differentiate network traffic levels. In this paper, we start to present an overview of IDS and FL functionality. Moreover, we give a survey of recent works dealing IDS based on FL in wireless sensor and classify them on different measures. Hence, our comparative study is very helpful for the researchers, to understand the use of FL in IDS and have clear vision for developing their own security solution. In the second part, we develop a novel IDS based on Mamdani type fuzzy inference system for detecting jamming attacks in WBAN. Our IDS was built in Matlab, also we are used Castalia platform and OMNET++ simulator to simulate different scenarios of WBAN.

An alternative approach to determination of Covid-19 personal risk index by using fuzzy logic.

COVID-19 disease is an outbreak that seriously affected the whole world, occurred in December 2019, and thus was declared a global epidemic by WHO (World Health Organization). To reduce the impact of the epidemic on humans, it is important to detect the symptoms of the disease in a timely and accurate manner. Recently, several new variants of COVID-19 have been identified in the United Kingdom (UK), South Africa, Brazil and India, and preliminary findings have been suggested that these mutations increase the transmissibility of the virus. Therefore, the aim of this study is to construct a support system based on fuzzy logic for experts to help detect of COVID-19 infection risk in a timely and accurate manner and to get a numerical output on symptoms of the virus from every person. The decision support system consists of three different sub and one main Mamdani type fuzzy inference systems (FIS). Subsystems are Common- Serious symptoms (First), Rare Symptoms (Second) and Personal Information (Third). The first FIS has five inputs, fever-time, cough-time, fatigue-time, shortness of breath and chest pain/dysfunction; the second FIS has four inputs, Loss of Taste/Smell, Body Aches, Conjuctivitis, and Nausea/Vomiting/Diarrhea; and the third FIS has three inputs, Age, Smoke, and Comorbidities. Then, we obtain personal risk index of individual by combining the outputs of these subsystems in a final FIS. The results can be used by health professionals and epidemiologists to make inferences about public health. Numerical output can also be useful for self-control of an individual.

Estimation of infection risk using symptoms of COVID-19: an approach based on fuzzy expert system

According to the published reports and studies, the symptoms of the disease caused by the COVID-19 virus have not yet been fully determined. It is a major stress on clinicians to make a correct and consistent decision about whether to apply the test or not, as many factors with extreme uncertainty need to be evaluated at once. In this study, it is aimed to provide assistance to the clinicians by processing the data using fuzzy logic based decision support system at the time of the decision-making process. In the designed fuzzy logic based decision support system, a fuzzy rule-base was created with linguistic information by interpreting the symptoms that are naturally uncertain by experts. With the help of the obtained fuzzy rule base, the input data of symptoms will be processed and the risk of a person being infected will be obtained as an output. As the results of the estimation module constructed with the existing parameters are examined, it is observed to be compatible with the data published before. In this context, a data set with 50 different patients were designed randomly to evaluate the system. For the analysis of the nonlinear mapping obtained with the Mamdani type fuzzy inference system, random test data is used and infection risk at rates varying between 12.5-83% was determined. The fuzzy logic based decision support system for COVID-19 can be accepted as applicable, flexible, and trustworthy for clinicians. It can be said that this system is not only suitable for COVID-19 but also applicable for future epidemics.

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.

A Mamdani Type Fuzzy Inference System to Calculate Employee Susceptibility to Phishing Attacks

It is a well known fact that the weakest link in a cyber secure system is the people who configure, manage or use it. Security breaches are persistently being attributed to human error. Social engineered based attacks are becoming more sophisticated to such an extent where they are becoming increasingly more difficult to detect. Companies implement strong security policies as well as provide specific training for employees to minimise phishing attacks, however these practices rely on the individual adhering to them. This paper explores fuzzy logic and in particular a Mamdani type fuzzy inference system to determine an employees susceptibility to phishing attacks. To negate and identify the susceptibility levels of employees to social engineering attacks a Fuzzy Inference System FIS was created through the use of fuzzy logic. The utilisation of fuzzy logic is a novel way in determining susceptibility due to its ability to resemble human reasoning in order to solve complex inputs, or its Interpretability and simplicity to be able to compute with words. This proposed fuzzy inference system is based on a number of criteria which focuses on attributes relating to the individual employee as well as a companies practices and procedures and through this an extensive rule base was designed. The proposed scoring mechanism is a first attempt towards a holistic solution. To accurately predict an employees susceptibility to phishing attacks will in any future system require a more robust and relatable set of human characteristics in relation to the employee and the employer.

Fuzzy based relaying scheme for high impedance faults in DG integrated distribution system

Most of the high impedance faults (HIF) remain un-detected by the conventional relays due to non-linear nature of the fault and low magnitude of current. In this work, a combination of discrete wavelet transforms (DWT) and fuzzy inference system (FIS) has been proposed for HIF detection and classification. Modified IEEE 13 node test feeder system has been to validate the proposed scheme. The proposed method uses current signals from one end that are pre-processed using discrete wavelet transform to obtain appropriate input features. The wavelet processed features are given to the FIS for fault detection and classification. Proposed method has been validated using both Mamdani and Sugeno type FIS. Different operating and fault conditions are tested to validate the proposed method such as varying DG parameters, noisy signals, HIFs, evolving faults, fault inception angle, fault resistance, fault location, and non-fault events (e.g. motor load switching, capacitor switching, DG switching, transformer energization, non-linear load switching). The accuracy in detecting and classifying the faults is 100% of all the tested fault cases. Results shows that the overall detection time required to detect the HIFs is minimum 4.25 cycles in most of the cases and maximum 8 cycles in few cases whereas for shunt faults is within 4.25 to 6 cycles only. Advantage of the proposed method is that it can detect adverse situation faults like evolving faults, in presence of noisy signals and remains intact against any switching events. The results of the proposed method are promising and the method is robust against various operating conditions.

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.

An evolving α‐dependent mobility model for a fleet of unmanned aerial vehicles in wireless sensor networks

SummaryIn this paper, we represent an innovative α‐based mobility model for a fleet of unmanned aerial vehicles (UAVs) using Sugeno type‐2 fuzzy inference system (T2FIS) in wireless sensor networks (WSNs). Specifically, the spotlight is on the deployment of suchlike energy level, neighbor count, and hop count to base station (BS) solution to critical applications, like monitoring applications for public security, border control enforcement, and so on. A type‐2 fuzzy logic (T2FL)‐dependent α mobility controller is introduced to support on the variation of a followship weighting metric α to select that close by UAV is the relevant to be pursued. Modification the proper values for the followship weight α is somewhat a demanding function. We have selected T2FL app for variation the values of the followship metric α choosing appropriate type‐2 fuzzy signifiers essentially energy level, neighbor count, and hop count to BS. T2FIS is utilized to pick the appropriate followship weight computation. Efficacy of the suggested framework is made by way of analysis of the statistics and multiple linear regressions (MLRs).

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.

Reliability Based Power Distribution Network Planning Using Fuzzy Logic

This paper presents a fuzzy system for reliability-based power distribution network planning. The proposed Mamdani type fuzzy inference system with subsequent application of the Bellman-Zadeh decision-making method is used to evaluate the reliability of the power line feeders as criteria for power system planning. Unplanned outages of system components, the Energy Not Supplied (ENS) and age of the power lines are used as input variables of the system and are fuzzified using triangular fuzzy functions. The proposed model was tested on a model of a realistic distribution network in order to prove its relevance and applicability. Results demonstrated that this model could make a contribution in this field as it can be used in practical planning situations for project priority ranking.