Fuzzy Input Research Articles

ОПРЕДЕЛЕНИЕ НЕЧЕТКИХ УСЛОВИЙ И АНАЛИЗ СУЩЕСТВУЮЩИХ РЕШЕНИЙ В ЗАДАЧЕ ЭВАКУАЦИИ ПРИ ЧРЕЗВЫЧАЙНЫХ СИТУАЦИЯХ

Quantification in collective behavior and decision-making in fuzzy conditions is crucial toensure the health and safety of the population. The task of modeling and predicting behavior infuzzy conditions, as is known, has increased complexity due to a large number of factors fromwhich an NP-complete multi-criteria problem is formed. There is a difficulty in quantifying the impact of fuzzy factors using a mathematical model. In this regard, the paper proposes a stochasticmodel of human decision-making to describe the empirical behavior of subjects in an experimentsimulating an emergency scenario. The developed fuzzy model combines fuzzy logic into aconventional model of social behavior. Unlike existing models and applications, this approachuses fuzzy sets and membership functions to describe the evacuation process in an emergencysituation. The purpose of this work is to define fuzzy rules and analyze existing solutions. The scientificnovelty lies in the formation of a set of factors that form fuzzy rules for making dynamicdecisions. The problem statement in this paper is as follows: to form a set of factors affecting thebehavior of pedestrians, which are modeled as fuzzy input data. The practical value of the worklies in the creation of a new set of fuzzy rules that allows them to be used in the evacuation algorithmfor the effective solution of the task. The fundamental difference from the known approachesis in the application of a new set of fuzzy rules, which contains factors: perception, intention, attitude.To implement the proposed model, the process of social behavior during evacuation, independentvariables are determined. These variables include measurements related to social factors,in other words, the behavior of individual subjects and individual small groups, which are fundamentalat an early stage of evacuation.

Indoor illuminance selection procedure using fuzzy techniques

The current practice of selecting target illuminance based on the recommendations of the Illuminating Engineering Society has serious shortcomings. For example, the visual age of the observer has precise boundaries, the illuminance values are exact instead of an acceptable range, and the sudden increase of illuminance values with age is rather arbitrary. To come up with an exact output from an input that is, by its very nature, uncertain and imprecise is virtually impossible. The aim of this work is to develop an approach that overcomes the limitations of the current illuminance selection method. This work proposes an alternative method for illuminance selection based on Fuzzy techniques. Fuzzy techniques are approximations used to model phenomena or decisions for which mathematical precision is impractical. The vagueness and uncertainties of many lighting design variables suit Fuzzy logic modeling, whereby a crisp output can be obtained from a range of Fuzzy input variables. The new method involves visual age, task difficulty and task importance as input variables which lead to a target illuminance. The target illuminance is given the name “Fuzzy Illuminance” (FILL). A MATLAB digital application was developed and can be used by lighting designers. It is expected that the proposed technique and the digital application will not only resolve the limitations of the current illuminance selection procedure but will also simplify the process of the selection of illuminance design targets and on-site verification of such design targets.

Fuzzy Logic-Based Cluster Head Election-Led Energy Efficiency in History-Assisted Cognitive Radio Networks

The performance and the network lifetime of cooperative spectrum sensing (CSS) infrastructure-based cognitive radio (CR) networks are hugely affected by the energy consumption of the power-constrained CR nodes during spectrum sensing, followed by data transmission and reception. To overcome this issue and improve the network lifetime, clustering mechanisms with several nodes inside a single cluster can be employed. It is usually the cluster head (CH) in every cluster that is responsible for aggregating the data collected from individual CR nodes before it is being forwarded to the base station (BS). In this article, an energy-efficient fuzzy logic-based clustering (EEFC) algorithm is proposed, which uses a novel set of fuzzy input parameters to elect the most suitable node as CH. Unlike most of the other probabilistic as well as fuzzy logic-based clustering algorithms, EEFC increments the fuzzy input parameters from three to four to obtain improved solutions employing the Mamdani method for fuzzification and the Centroid method for defuzzification. It ensures that the best candidate is selected for the CH role by obtaining the crisp value from the fuzzy logic rule-based system. While compared to other well-known clustering algorithms such as low-energy adaptive clustering hierarchy (LEACH), CH election using fuzzy logic (CHEF), energy-aware unequal clustering using fuzzy logic (EAUCF), and fuzzy logic-based energy-efficient clustering hierarchy (FLECH), our proposed EEFC algorithm demonstrates significantly enhanced network lifetime where the time taken for first node dead (FND) in the network is improved. Moreover, EEFC is implemented in the existing history-assisted energy efficient infrastructure CR network to analyze and demonstrate the overall augmented energy efficiency of the system.

Dual Input Fuzzy Logic Controllers for Closed Loop Hemorrhagic Shock Resuscitation

Hemorrhage remains a leading cause of preventable death in emergency situations, including combat casualty care. This is partially due to the high cognitive burden that constantly adjusting fluid resuscitation rates can require, especially in austere or mass casualty situations. Closed-loop control systems have the potential to simplify hemorrhagic shock resuscitation if properly tuned for the application. We have previously compared 4 different controller types using a hardware-in-loop test platform that simulates hemorrhagic shock conditions, and we found that a dual input—(1) error from target and (2) rate of error change—fuzzy logic (DFL) controller performed best. Here, we highlight a range of DFL designs to showcase the tunability the controller can have for different hemorrhage scenarios. Five different controller setups were configured with different membership function logic to create more and less aggressive controller designs. Overall, the results for the different controller designs ranged from reaching the setup rapidly but often overshooting the target to more conservatively approaching the target, resulting in not reaching the target during high active hemorrhage rates. In conclusion, DFL controllers are well-suited for hemorrhagic shock resuscitation and can be tuned to meet the response rates set by clinical practice guidelines for this application.

Hierarchical Routing with Optimal Clustering Using Fuzzy Approach for Network Lifetime Enhancement in Wireless Sensor Networks

Wireless sensor network (WSN) is one of the major emerging fields in real-time scenarios due to the features of sensors. Sensors are used in remote areas because of their usability. Energy is the major issue to be addressed in sensor networks. This energy affects the entire network lifetime. To resolve the energy issues of WSN, clustering and proper routing are the two major procedures that have to be focused on. In this paper, hierarchical routing with optimal clustering using a fuzzy system (HROCF) is proposed for network lifetime enhancement in WSNs. This proposed scheme uses two phases: selection of a cluster leader with a fuzzy inference system and the hierarchical routing strategy are implemented for routing to consume the energy of the sensors resourcefully. During the first phase, a proper cluster leader can be nominated through the fuzzy inputs, namely: residual energy, cost, and position. In the second phase, hierarchical routing can communicate the information from the lower region to the sink node through the cluster leaders in higher regions. These two phases help to prolong the lifetime. The simulation analysis demonstrates that the HROCF scheme reduces the overall energy spent in the network and supports an increase in the lifespan in terms of more alive sensors compared to the existing schemes.

Robust self-organising fuzzy sliding mode-based path-following control for autonomous underwater vehicles

A robust self-organising fuzzy sliding mode control law steers autonomous underwater vehicles (AUVs) to track a predefined planar path at a constant speed without temporal specifications. An intelligent methodology has been adopted for path-following control to handle varying parametric uncertainties in vehicle dynamics and also conquers stringent preliminary condition constraints in several path-following control strategies illustrated in the literature. Robust controller design builds on a fusion of sliding mode control theory and fuzzy logic technique with an adaptation mechanism to tune boundary layer width and hitting gain. This novel strategy proposes two distinct tuning procedures: the first method commonly uses absolute error and their derivative as fuzzy input variables in a two-dimensional fuzzy logic rule structure. Herein, skew symmetry property is utilised in rule base structure to derive a single input fuzzy variable based on the signed distance technique, drastically reducing two-dimensional fuzzy logic rules. Since the second method provides substantial reductions in rule inferences through the use of the fuzzy rule's mirror image and the Lyapunov approach for tuning purposes, the resulting guidance control law yields fast convergence of the path-following error trajectory towards zero along with the elimination of chattering problem. Simulation results illustrate the effectiveness and robustness of the proposed control law to achieve favourable tracking performance with a high accuracy.

Neuro‐fuzzy Elman wavelet control for nonlinear uncertain systems with fuzzy input and unknown fuzzy disturbances: Application to robotics

SummaryKeeping into view the irregular existence of uncertainties and disturbances in nonlinear systems, a novel adaptive control scheme based on neuro‐fuzzy Elman wavelet neural network (NFEWNN) has been proposed in this work which is capable of accurately modeling system uncertainties. In addition to this, an improvement in an evolutionary algorithm named particle swarm optimization has also been introduced to optimize the initial parameters of the controller. Uncertain non‐linear discrete‐time systems in the non‐strict feedback form with unknown disturbances are taken into consideration. This work aims to design an efficient adaptive control scheme for the proposed systems in the case of fuzzy input and fuzzy external disturbance which makes the control of the system more complex and difficult. The proposed NFEWNN control scheme is capable of tracking the desired trajectory successfully along with accurately modeling system uncertainties with high computational power, low computational burden, and rapid convergence rate. The stability of the controller has been proved using Lyapunov theory and guaranteed convergence of the controller is proved. Finally, real‐world applications of the work have been found in robotics. The controller has been implemented on the robotic manipulator and the dynamic model of aerial robotic vehicles to prove its effectiveness and efficiency.

Global Reliability Sensitivity of the Structure Under Fuzzy Uncertainty and Its Estimations

Global reliability sensitivity (GRS) index under fuzzy uncertainty is defined as the mean of the absolute difference between unconditional failure credibility and conditional failure credibility, which is important to quantify the effect of fuzzy inputs on failure credibility of the structure. The solution of this index requires nested analysis of conditional failure credibility and fuzzy mean, which introduces a large amount of computation. To efficiently estimate the GRS under fuzzy uncertainty, a method combining fuzzy simulation (FS) with fuzzy first-order and second moment (FS–FFOSM) method as well as a fuzzy sequential optimization and reliability assessment (FSORA) method are proposed. Compared with the direct double-loop FS method, in which both the conditional failure credibility and the fuzzy mean are estimated by FS, the proposed FS–FFOSM uses a more efficient FFOSM method to estimate the conditional failure credibility in the inner loop, which improves the computational efficiency of estimating GRS under fuzzy uncertainty. To further improve the computational efficiency, this paper also transforms the estimation of GRS into the solution of the optimization model under the constraint of failure credibility and establishes the FSORA method to solve the optimization model. The FSORA method uses a series of cycles of deterministic optimization and inverse design point analysis corresponding to the required fuzzy reliability index by GRS to reduce the iterations, and then the computational efficiency is further improved. Several examples illustrate the computational efficiency and accuracy of the proposed methods.

The model of SCRUM master selection using fuzzy sets

Problem. The complexity of software is constantly growing. Today a user is interested in software that is integrated into all aspects of their life. Creating a large number of diverse software applications is impossible without the formation of a software development team. Agile methodologies are used to develop most modern programs. The most common is the SCRUM methodology. Team working is managed by a SCRUM master, which is the interface between the owner and the project team. Therefore, choosing an effective SCRUM master is an urgent task. Aim The work aims to increase the efficiency of software development projects by developing the SCRUM master selection model using fuzzy sets, which will consider the individual characteristics and advantages of the SCRUM project team members. Methodology. The methods and principles of fuzzy mathematics are used. Decision-making methods in the conditions of fuzzy input data were used to develop a model for choosing a SCRUM master for a software development project. Results. Today, there are many variants of models for software development projects. The choice of one or another option depends on the features and requirements of the project. A fuzzy model for choosing a SCRUM master for managing a software development project has been developed. An example of work distribution between performers using the theory of fuzzy sets is given. Calculations were made in Microsoft Excel. Originality. The developed model allows choosing a SCRUM master in the conditions of fuzzy information about the individual preferences of the areas of the software development project team. Practical value. Using the proposed SCRUM master selection model for a software development project will improve project team communication and reduce the development time. In addition, the proposed model can be used to select a SCRUM master for other projects.

Time-Dependent Failure Possibility-Based Design Optimization by Using Kriging Model and Fuzzy Simulation

Time-dependent failure possibility-based design optimization (T-PBDO) can minimize the general cost while meeting the failure possibility requirement of aircraft structure in the service life. The accuracy of the T-PBDO solution obtained by existing efficient methods may be problematic in the case of nonlinear performance functions or multiple minimum performance target points. To overcome this limitation, this paper proposes a new double-loop method based on the adaptive kriging (AK) model and fuzzy simulation (FS), referred to as DL-AK-FS, is used to efficiently solve T-PBDO. In DL-AK-FS, to replace the real constraint performance function for dealing with the time-dependent failure possibility (TDFP) constraint, the inner loop is to adaptively construct a single-loop kriging model of the constraint performance function in the FS candidate sample pool. The outer loop is to search the optimal design parameters by optimization algorithm. The kriging model is first built in an augmented space that is spanned by design parameters and fuzzy inputs, and then it is adaptively and timely updated during the optimization iteration. Moreover, the strategy of reducing the size of the FS candidate sample pool is adopted to further improve the efficiency of analyzing the inner TDFP while ensuring the accuracy of the optimization solution. The strategy of combining FS with the AK model can extend the engineering applicability of the DL-AK-FS in estimating the inner TDFP, which is not limited by the complexity of the time-dependent performance function. The optimization results show that the proposed DL-AK-FS method in this paper is efficient and accurate for solving T-PBDO.

Design and application of Nagar-Bardini structure-based interval type-2 fuzzy logic systems optimized with the combination of backpropagation algorithms and recursive least square algorithms

This paper designs a kind of Nagar-Bardini structure based on interval type-2 (IT2) fuzzy logic systems for permanent magnetic drive uncertain parameter forecasting. For each fuzzy rule, the consequent, antecedent and input measurement of IT2 membership functions (MFs) are selected as the Gaussian type-2 MFs with uncertain standard deviations. Backpropagation algorithms and recursive least square algorithms are used to optimize the antecedent, input measurement and consequent parameters of fuzzy logic system (FLS) forecasters, respectively. Compared with the corresponding singleton and non-singleton type-1 (T1) FLSs, two Monte Carlo simulation instances on the basis of data of PMD process show the effectiveness and superiority of non-singleton IT2 FLSs according to the convergence analysis.

Window Analysis and MPI for Efficiency and Productivity Assessment Under Fuzzy Data

This research develops a procedure for DEA window analysis and MPI evaluation of a manufacturing process with fuzzy inputs and outputs. A real case study was provided to illustrate relative efficiency and MPI assessment of a blowing machine over a period of one a year. The proposed approach was implemented to measure the technical, pure technical, and scale efficiency scores for decision making unit. The results showed that the blowing process was technically inefficient due to scale inefficiency. Therefore, management should optimize the size of operations and better utilize resources. Then, the lower and upper MPI values and their corresponding technology change and efficiency change were calculated. The MPI results revealed the reasons behind MPI progress or regress in current period measured with respect to next period. This procedure provides great assistance to process engineering in obtaining reliable feedback on process performance and guide them to take proper actions.

An Evolutionary Neuro-Fuzzy-Based Approach to Estimate the Compressive Strength of Eco-Friendly Concrete Containing Recycled Construction Wastes

There has been a significant increase in construction and demolition (C&D) waste due to the growth of cities and the need for new construction, raising concerns about the impact on the environment of these wastes. By utilising recycled C&D waste, especially in concretes used in construction, further environmental damage can be prevented. By using these concretes, energy consumption and environmental impacts of concrete production can be reduced. The behaviour of these types of concrete in laboratories has been extensively studied, but reliable methods for estimating their behaviour based on the available data are required. Consequently, this research proposes a hybrid intelligent system, Fuzzy Group Method of Data Handling (GMDH)–Horse herd Optimisation Algorithm (HOA), for predicting one of the most important parameters in concrete structure design, compressive strength. In order to avoid uncertainty in the modelling process, crisp input values were converted to Fuzzy values (Fuzzification). Next, using Fuzzy input variables, the group method of data handling is used to predict the compressive strength of recycled aggregate concrete. The HOA algorithm is one of the newest metaheuristic algorithms being used to optimise the Fuzzy GMDH structure. Several databases containing experimental mix design records containing mixture components are gathered from published documents for compressive strength to assess the accuracy and reliability of the proposed hybrid Fuzzy-based model. Compared to other original approaches, the proposed Fuzzy GMDH model with the HOA optimiser outperformed them in terms of accuracy. A Monte Carlo simulation is also employed for uncertainty analysis of the empirical, standalone, and hybridised models in order to demonstrate that the evolutionary Fuzzy-based approach has less uncertainty than the standalone methods when simulating compressive strength.

Fuzzy First-Order and Second Moment Method for Failure Credibility Analysis in the Presence of Fuzzy Uncertainty

A fuzzy first-order and second moment (FFOSM) method is proposed to efficiently estimate time-independent and time-dependent failure credibility in the presence of fuzzy uncertainty. For the time-independent linear performance function with independently, regularly, and identically distributed fuzzy inputs, the analytical relationship is first derived between the failure credibility and fuzzy safety index, and the relation form is established between fuzzy safety index and fuzzy first two moments of the performance function. Second, in the standard space of fuzzy inputs, the minimum infinite norm distance from the coordinate origin to the linear limit state surface is analytically derived, and the point with the smallest infinite norm distance located on the limit state surface is defined as the fuzzy design point. Based on this definition, the geometric and physical meanings are explained for the fuzzy safety index and fuzzy design point, and the FFOSM method for time-independent failure credibility analysis is established for both linear and nonlinear performance function. Third, for time-dependent performance functions, the equivalence is proved between time-dependent fuzzy safety index and the minimum value of transient fuzzy safety indices, on which the iteration steps of the FFOSM method is designed for time-dependent fuzzy safety index and failure credibility estimation. Since the proposed FFOSM method is an approximately analytical approach, it has higher efficiency than the fuzzy simulation method and the cut level optimization method. Finally, the effectiveness and applicability of the proposed method are demonstrated by different examples.

Data Envelopment Analysis on the Context of Spherical Fuzzy Inputs and Outputs

In this study, Data Envelopment Analysis (DEA) models are improved by employing spherical fuzzy sets (SFSs), which is an extension of generalized fuzzy sets. SFSs were recently introduced as a novel type of fuzzy set that allows decision-makers to express their level of uncertainty directly. As a result, SFSs provide a more preferred domain for decision-makers. Fundamental Charnes-Cooper-Rhodes (CCR) model is discussed on the context of spherical trapezoidal fuzzy numbers (STrFNs), which consider each data value’s truth, indeterminacy, and falsehood degrees, and a unique solution technique is implemented. This method converts a spherical fuzzy DEA(SF-DEA) model into three pair of crisp DEA model, which may then be solved using one of many existing approaches. The largest optimal interval is determined for each DMU such that the efficiency score lies inside that interval. Furthermore, an example demonstrates this novel method and clearly explains the DMUs’ ranking technique.

Assessing most productive scale size with fuzzy stochastic data envelopment analysis

Classic data envelopment analysis (DEA) is a linear programming method for evaluating the relative efficiency of decision making units (DMUs) that uses multiple inputs to produce multiple outputs. In the classic DEA model inputs and outputs of DMUs are deterministic, while in the real world, are often fuzzy, random, or fuzzy-random. Many researchers have proposed different approaches to evaluate the relative efficiency with fuzzy and random data in DEA. In many studies, the most productive scale size (mpss) of decision making units has been estimated with fuzzy and random inputs and outputs. Also, the concept of fuzzy random variable is used in the DEA literature to describe events or occurrences in which fuzzy and random changes occur simultaneously. This paper has proposed the fuzzy stochastic DEA model to assess the most productive scale size of DMUs that produce multiple fuzzy random outputs using multiple fuzzy random inputs with respect to the possibility-probability constraints. For solving the fuzzy stochastic DEA model, we obtained a nonlinear deterministic equivalent for the probability constraints using chance constrained programming approaches (CCP). Then, using the possibility theory the possibilities of fuzzy events transformed to the deterministic equivalents with definite data. In the final section, the fuzzy stochastic DEA model, proposed model, has been used to evaluate the most productive scale size of sixteen Iranian hospitals with four fuzzy random inputs and two fuzzy random outputs with symmetrical triangular membership functions.

Time-dependent failure possibility of structures involving epistemic uncertainty

Time-dependent failure possibility (TDFP) is the possibility of performance less than zero under fuzzy uncertainty in the given time interval. Failure possibility of structures gets accelerated by improper consideration of input variables, mathematical and structural models, and environmental factors. To explore the environmental factors in structures, the time-variant creep and shrinkage model for concrete structures, and deterioration of steel cross-section and other material properties due to corrosion, and increment of action on the structure with time are considered. A real-time data has been collected from the city of Addis Ababa, Ethiopia for the case study to substantiate the methodology presented in this paper. In this paper, identification the possible failure modes, estimation of the time-dependent performance of failure modes, generating membership function (MF) of fuzzy input and output variables, and finally, the TDFP evaluated from MF of output quantities. Because of its simplicity and frequent application, triangular MF is used to characterize epistemic uncertainty of parameters. However, the failure possibility analysis is a complex problem for practicing engineering that needs a rigorous procedure, a single-loop optimization method (SLOM) is employed to evaluate TDFP of structures involving epistemic uncertainty. The effectives of SLOM has been checked by adaptive SORM Breitung’s algorithm. To enhance the validation of the proposed approach, several examples are thoroughly carried out that indicate the SLOM is bit conservative compared with SORM Breitung’s algorithm in such a way that their maximum recorded percentage difference is 9.464%.

Aggressive maneuvering of a quadcopter via differential flatness-based fuzzy controllers: From tuning to experiments

In recent years, the rapid development in contemporary technology has brought nano quadcopters with high agility. This paper presents a new differential flatness-based Single Input Fuzzy Logic Controller (SFLC) structure for aggressive maneuvering control alongside its real-world application on Crazyflie 2.1 nano quadcopter. We propose both Type-1 and Interval Type-2 SFLCs as the primary controllers in the flight control system, which are built on the concept of differential flatness. We investigate how the design parameters of SFLCs shape the characteristics of the fuzzy mapping through a geometric approach by analyzing the region and level of aggressiveness/smoothness. Based on the analysis, we present simple tuning guidelines and then design fuzzy logic-based flight control systems, which were implemented as onboard real-time controllers. Finally, we evaluate the performance of SFLCs in comparison with their crisp differential flatness-based nonlinear counterparts for four trajectories with distinct dynamics and shapes in the real world. The presented comparative experimental results clearly show the performance improvements when the proposed T1 and IT2 SFLCs are deployed for real-time aggressive maneuvering.

Design of a fuzzy-based proportional integral derivative controller with optimal membership function scaling for respiratory ventilation system

In this paper, an optimum membership function based fuzzy proportional integral derivative (Fuzzy-PID) controller is proposed to improve the pressure tracking profile of pressure controlled ventilator (PCV) system. A PCV structure as a respiratory support system is spatially designed for seated patients with homogenous lung system. During anesthesia procedure, such type of ventilator is used. For the comfort and safety of patients, the airway pressure of such device must exactly track the desired pressure of patients. As, PID based control structure is well accepted in industry due to the simple structure, easy implementation and efficiency to work robustly. Therefore, a Fuzzy-PID control structure is considered for PCV system. The fuzzy concept is used to tune the parameters of the PID controller. The novelty of the present work is that the membership function of fuzzy inputs (error and change in error in terms of airway pressure) and the outputs (parameters of the PID controller) are optimized with a swarm based optimizer. Therefore, a boosted class topper optimization (BCTO) algorithm is developed. With the proposed control scheme, the presser tracking profile of PCV system is improved in terms of response time, settling time and overshoot and compared with existing one.

PENGOLAHAN CITRA DIGITAL DAN LOGIKA FUZZY DALAM IDENTIFIKASI TINGKAT KEMATANGAN BUAH

This study aims to implement digital image processing and fuzzy logic in identifying the maturity level of red dragon fruit and describing the level of accuracy. The process carried out is to change the type of red dragon fruit image from the red green blue (RGB) type to the grayscale type. Furthermore, extraction is carried out using MATLAB to obtain information from the image. Information from the image is contrast, correlation, energy, homogeneity, mean, variance, entropy. There are 7 information used as fuzzy input. The input fuzzy model uses triangular and trapezoidal membership functions to construct fuzzy rules on 28 data, so there are 28 fuzzy rules. After the fuzzy rules are obtained, then the inference and defuzzification processes are carried out. The inference used is the mamdani method. The result of defuzzification is the value for the maturity level of red dragon fruit which is divided into four categories, namely raw, half-ripe, ripe, rotten. The fuzzy model that has been built will test the model by determining the level of accuracy and error of the model. With the results of 96.42% with an error of 3.57%.