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This paper aims to propose a novel computational framework called EvoPOL (EVOlving POLicies) to support governmental policy analysis in restricting recruitment of smokers. EvoPOL is a fuzzy inference‐based decision support system that uses an evolutionary algorithm (EA) to optimize the if‐then rules and its parameters. The performance of the proposed method is compared with a fuzzy inference method adapted using neural network learning technique (neuro‐fuzzy).

EA is a population‐based adaptive method, which may be used to solve optimization problems, based on the genetic processes of biological organisms. The Takagi‐Sugeno fuzzy decision support system was developed based on three sub‐systems: fuzzification, fuzzy knowledge base (if‐then rules) and defuzzification. The fine‐tuning of the fuzzy rule base and membership function parameters is achieved by using an EA.

The proposed EvoPOL technique is simple and efficient when compared to the neuro‐fuzzy approach. However, EvoPOL attracts extra computational cost due to the population‐based hierarchical search process. When compared to neuro‐fuzzy model the error values on the test sets have improved considerably. Hence, when policy makers require more accuracy EvoPOL seems to be a good solution.

When policy makers require more accuracy EvoPOL seems to be a good solution. For complicated decision support systems involving more input variables, EvoPOL would be an excellent candidate for framing if‐then rules with precise decision scores that could help the government representatives as to what extent to concentrate on available social regulation measures in restricting the recruitment of smokers.

The aim of this paper is to propose a robust robot fuzzy logic proportional-derivative (PD) controller for trajectory tracking of autonomous nonholonomic differential drive wheeled mobile robot (WMR) of the type Quanser Qbot.

Fuzzy robot control approach is used for developing a robust fuzzy PD controller for trajectory tracking of a nonholonomic differential drive WMR. The linear/angular velocity of the differential drive mobile robot are formulated such that the tracking errors between the robot’s trajectory and the reference path converge asymptotically to zero. Here, a new controller zero-order Takagy–Sugeno trajectory tracking (ZTS-TT) controller is deduced for robot’s speed regulation based on the fuzzy PD controller. The WMR used for the experimental implementation is Quanser Qbot which has two differential drive wheels; therefore, the right/left wheel velocity of the differential wheels of the robot are worked out using inverse kinematics model. The controller is implemented using MATLAB Simulink with QUARC framework, downloaded and compiled into executable (.exe) on the robot based on the WIFI TCP/IP connection.

Compared to other fuzzy proportional-integral-derivative (PID) controllers, the proposed fuzzy PD controller was found to be robust, stable and consuming less resources on the robot. The comparative results of the proposed ZTS-TT controller and the conventional PD controller demonstrated clearly that the proposed ZTS-TT controller provides better tracking performances, flexibility, robustness and stability for the WMR.

The proposed fuzzy PD controller can be improved using hybrid techniques. The proposed approach can be developed for obstacle detection and collision avoidance in combination with trajectory tracking for use in environments with obstacles.

A robust fuzzy logic PD is developed and its performances are compared to the existing fuzzy PID controller. A ZTS-TT controller is deduced for trajectory tracking of an autonomous nonholonomic differential drive mobile robot (i.e. Quanser Qbot).

Expert systems are computer-based systems that mimic the logical processes of human experts or organizations to give advice in a specific domain of knowledge. Fuzzy expert systems use fuzzy logic to handle uncertainties generated by imprecise, incomplete and/or vague information. The purpose of this paper is to present a comprehensive review of the methods and applications in fuzzy expert systems.

The authors have carefully reviewed 281 journal publications and 149 conference proceedings published over the past 37 years since 1982. The authors grouped the journal publications and conference proceedings separately accordingly to the methods, application domains, tools and inference systems.

The authors have synthesized the findings and proposed useful suggestions for future research directions. The authors show that the most common use of fuzzy expert systems is in the medical field.

Fuzzy logic can be used to manage uncertainty in expert systems and solve problems that cannot be solved effectively with conventional methods. In this study, the authors present a comprehensive review of the methods and applications in fuzzy expert systems which could be useful for practicing managers developing expert systems under uncertainty.

This paper aims to design and implement the multiple adaptive neuro-fuzzy inference system (MANFIS) architecture-based sensor-actuator (motor) control technique for mobile robot navigation in different two-dimensional environments with the presence of static and moving obstacles.

The three infrared range sensors have been mounted on the front, left and right side of the robot, which reads the forward, left forward and right forward static and dynamic obstacles in the environment. This sensor data information is fed as inputs into the MANFIS architecture to generate appropriate speed control commands for right and left motors of the robot. In this study, we have taken one assumption for moving obstacle avoidance in different scenarios the speed of the mobile robot is at least greater than or equal to the speed of moving obstacles and goal.

Graphical simulations have designed through MATLAB and virtual robot experimentation platform (V-REP) software and experiments have been done on Arduino MEGA 2560 microcontroller-based mobile robot. Simulation and experimental studies demonstrate the effectiveness and efficiency of the proposed MANFIS architecture.

This paper designs and implements MANFIS architecture for mobile robot navigation between a static and moving obstacle in different simulation and experimental environments. Also, the authors have compared this developed architecture to the other navigational technique and found that our developed architecture provided better results in terms of path length in the same environment.

Successful implementation of infrastructure projects has been a controversial issue in recent years, particularly in developing countries. This study aims to propose a decision support system (DSS) for the evaluation and prediction of project success while considering sustainability criteria.

To predict sustainable success factor, the study first developed its sustainable success factors and sustainable success criteria. These then formed a decision table. A rough set theory (RST) was then implemented for rules generation. The decision table was used as the input for the rough set, which returned a set of rules as the output. The generated rulesets were then filtered in fuzzy inference system (FIS), before serving as the basis for the DSS. The developed prediction tool was tested and validated by applying data from a real infrastructure project.

The results show that the developed rough set fuzzy method has strong ability in evaluation and prediction of the project success. Hence, the efficacy of the DSS is greatly related to the rule-based system, which applies RST to generate the rules and the result of the FIS was found to be valid via running a case study.

Use of DSS for predicting the sustainable success of the construction projects is gaining progressive interest. Integration of RST and FIS has also been advocated by the seminal literature in terms of developing robust rulesets for impeccable prediction. However, there is no preceding study adopting this integration for predicting project success from the sustainability perspective. The developed system in this study can serve as a tool to assist the decision-makers to dynamically evaluate and predict the success of their own projects based on different sustainability criteria throughout the project life cycle.

The purpose of this paper is to attempt to shift away from an exclusive probabilistic viewpoint or a pure network theory-based perspective for vulnerability assessment of infrastructure networks (INs), toward an integrated framework that accounts for joint considerations of the consequences of component failure as well as the component reliability.

This work introduces a fuzzy inference system (FIS) model that deals with the problem of vulnerability analysis by mapping reliability and centrality to vulnerability. In the presented model, reliability and centrality are first fuzzified, then 16 different rules are defined and finally, a defuzzification process is conducted to obtain the model output, termed the vulnerability score. The FIS model developed herein attempts to explain the linkage between reliability and centrality so as to evaluate the degree of vulnerability for INs elements.

This paper compared the effectiveness of the vulnerability score in criticality ranking of the components against the conventional vulnerability analysis methods. Comparison of the output of the proposed FIS model with the conventional vulnerability indices reveals the effectiveness of the vulnerability score in identifying the criticality of components. The model result showed the vulnerability score decreases by increasing reliability and decreasing centrality.

Two key practical implications for vulnerability analysis of INs can be drawn from the suggested FIS model in this research. First, the maintenance strategy based on the vulnerability analysis proposed herein will provide an expert facilitator that helps infrastructure utilities to identify and prioritize the vulnerabilities. The second practical implication is especially valuable for designing an effective risk management framework, which allows for least cost decisions to be made for the protection of INs.

As part of the first contribution, we propose a novel fuzzy-based vulnerability assessment model in building a qualitative and quantitative picture of the vulnerability of INs. The second contribution is especially valuable for vulnerability analysis of INs by virtue of offering a key to understanding the component vulnerability principle as being constituted by the component likely behavior as well as the component importance in the network.

This paper aims to investigate using scanner-based adaptive neuro-fuzzy inference system (ANFIS), artificial neural networks (ANNs) and polynomial regression methods for prediction of silver nanoparticles (AgNPs) and dye concentrations on AgNP-treated silk fabrics.

For estimation of the dye and AgNPs concentration using image processing, the silk fabrics were scanned under the condition of 200 pixels per inch. The red green blue (RGB) values of scanned images were obtained after applying the median filter. Then, the relationship between scanner RGB values and dye and AgNPs concentrations were obtained by using artificial intelligence methods such as ANFIS and ANNs.

The best result was achieved by the ANFIS system for calculation concentration of dye with 0.07% error and concentration of AgNPs with 0.008 (gr/l) error. The obtained results indicate that the performance of the ANFIS system method is better than the other methods.

Using a scanner-based artificial intelligence technique for prediction of nanosilver and dye content on silk fabric.

The objective of this paper is to develop a fast modelling technique for predicting magneto-rheological fluid damper behaviour under impact loading applications.

The adaptive neuro-fuzzy inference system (ANFIS) technique was adopted to predict the behaviour of a magneto-rheological fluid (MRF) damper through experimental characterisation data. In this study, an MRF damper manufactured by Lord Corporation was used for characterisation using an impact pendulum test rig. The experimental characterisation was carried out with various impact energies and constant input currents applied to the MRF damper.

This research provided a fast modelling technique with relatively less error in predicting MRF damper behaviour for the development of control strategies. Accordingly, the ANFIS model was able to predict MRF damper behaviour under impact loading and showed better performance than the modified Bouc–Wen model.

This study only focused on modelling technique for a single type of MRF damper used for impact loading applications. It is possible for other applications, such as cyclic loading, random loadings and system identification, to be studied in future experiments.

Future researchers could apply the ANFIS model as an actuator model for the development of control strategies and analyse the control performance. The model also can be replicated in other industries with minor modifications to suit different needs.

Artificial intelligence (AI) methods, such as genetic algorithm (GA) and adaptive neuro-fuzzy inference system (ANFIS), are capable of providing superior solutions for the simulation and the modeling of complex problems. The purpose of this study is to estimate the dye and the silver nanoparticle (AgNP) concentrations of silver nanoparticle-treated silk fabrics by the aforementioned methods.

In this study, the color and the antimicrobial properties of silver nanoparticle-treated silk fabrics were matched by using the GA technique based on spectrophotometric color matching. The ANFIS method was also used; this method is based on the grid partitioning algorithm across four different methods. The first and second methods are provided for dye concentration prediction, and the third and the fourth methods are given for AgNP concentration prediction.

The mean of absolute error and root mean square (RMS) of the best dye concentration prediction by the ANFIS method based on the second method are 0.087 and 0.103, respectively. In addition, the mean of the absolute error and the RMS of the best results for AgNP concentration prediction by the ANFIS method by using the third method is 0.002 and 0.003, respectively. The obtained results indicate that the performance of the ANFIS method is better than the GA method.

The simultaneous prediction of the color and the antimicrobial properties of silver nanoparticle-treated silk fabrics was performed by using the GA and the ANFIS. The suggested method led to acceptable accuracy for color and antibacterial matching.

This paper aims to discuss a comprehensive survey on fuzzy-based clustering techniques. The determination of an appropriate sensor node as a cluster head straightforwardly affects a network’s lifetime. Clustering often possesses some uncertainties in determining suitable sensor nodes as a cluster head. Owing to various variables, selection of a suitable node as a cluster head is a perplexing decision. Fuzzy logic is capable of handling uncertainties and improving decision-making processes even with insufficient information. Then, state-of-the-art research in the field of clustering techniques has been reviewed.

The literature is presented in a tabular form with merits and limitations of each technique. Furthermore, the various techniques are compared graphically and classified in a tabular form and the flowcharts of important algorithms are presented with pseudocodes.

This paper comprehends the importance and distinction of different fuzzy-based clustering methods which are further supportive in designing more efficient clustering protocols.

This paper fulfills the need of a review paper in the field of fuzzy-based clustering techniques because no other paper has reviewed all the fuzzy-based clustering techniques. Furthermore, none of them has presented literature in a tabular form or presented flowcharts with pseudocodes of important techniques.