Type-2 Fuzzy Systems

Supplier selection is one of the most critical activities of purchasing management in a supply chain because of the key role of supplier’s performance in achieving the objectives of a supply chain. Supplier selection problem requires a trade-off between multiple criteria exhibiting vagueness and imprecision with the involvement of a group of experts. This paper presents a multiple criteria group decision-making approach for supplier selection problem in the context of interval type-2 fuzzy sets. A new method for ranking interval type-2 fuzzy numbers, based on the centroid of fuzzy sets, is proposed and compared with some methods. The proposed ranking method is used for extending complex proportional assessment (COPRAS) method for group decision-making with interval type-2 fuzzy numbers. The developed method uses a stepwise procedure for ranking and evaluating the alternatives, in terms of significance and utility degree, and selects the best solution considering both the positive-ideal and the negative-ideal solutions. To demonstrate the applicability of the proposed approach in supplier selection problems, an illustrative example is presented and the results are analyzed.

ABSTRACT In this paper, we define a differentiability of the type-2 fuzzy number-valued functions. The definition is based on type-2 Hukuhara difference which is defined in the paper as well. The related theorem of the differentiability of the type-2 fuzzy number-valued functions is derived. In addition, a parametric closed form of the perfect triangular quasi type-2 fuzzy numbers is introduced, and finally, the applicability and an approach to solving type-2 fuzzy differential equations are illustrated using some examples and cases.

International Journal of Wireless Networks Systems (IJWNS)is a bi monthly open access peer-reviewed journal that publishes articles which contribute new results in all areas of Wireless & Mobile Networks. The journal focuses on all technical and practical aspects of Wireless Networks Systems .

Ranking applicants for a given job is one of the most important processes for Human Resources (HR) systems. The ranking of job applicants involves two main processes which are the specification of the requirements criteria for a given job (experience, skills, etc) and the matching between the applicants’ profiles and the job requirements. There is currently a strong move towards automating these two processes to generate an applicants’ ranking system that gives consistent and fair results. However there is a high level of uncertainty involved in these two processes as they involve the input of several experts. These experts will have different opinions, expectations the interpretations for the requirements specification as well as for the applicants matching and ranking. This paper presents a novel approach for ranking job applicants by employing type-2 fuzzy sets for handling the uncertainties in group decisions in a panel of experts. Hence the presented system will enable automating the processes of requirements specification and applicants matching/ranking. We have performed real world experiments in the care domain where our system handled the uncertainties and produced ranking decisions that were consistent with those of the human experts. To the authors’ knowledge, this will be the first type-2 based commercial software system.

Fuzzy Logic Systems: an International Journal(FLSIJ)
http://flyccs.com/jounals/FLSIJ/Home.html
Scope
Fuzzy Logic Systems: an International Journal(FLSIJ) is a forum for presenting new advances and research results in the fields of Fuzzy Logic Systems. Authors are solicited to contribute to the conference by submitting articles that illustrate research results, projects, surveying works and industrial experiences.All submissions must describe original research, not published or currently under review for another conference or journal.
Topics of interest include, but are not limited to, the following
• Fuzzy logic Techniques & Algorithm
• Fuzzy Mathematics
• Fuzzy Measure and Integral
• Type 2 Fuzzy Logic
• Possibility Theory and Imprecise Probability
• Fuzzy Database
• Fuzzy Clustering
• Fuzzy Decision Support System
• Fuzzy Expert System
• Fuzzy Mathematical Programming
• Fuzzy Decision Making and Decision Support Systems
• Fuzzy Modeling and Fuzzy Control of Biotechnological Processes
• Fuzzy Neural Systems, Neuro-Fuzzy Systems
• Fuzzy Systems Modeling and Identification
• Fuzzy Pattern Recognition
• Fuzzy Process Control
• Fuzzy Reasoning System
• Fuzzy-Rule based System
• Fuzzy System in Multimedia and Web-based Applications
• Fuzzy System Applications in Computer Vision
• Hybrid Fuzzy Systems (fuzzy-neuro-evolutionary-rough)
• Fuzzy System Applications in E-commerce
• Fuzzy Sets in Bioinformatics
• Fuzzy System Applications in Human-Machine Interface
• Fuzzy System Applications in Robotics
• Fuzzy System Applications in System and Control Engineering
Paper Submission
Authors are invited to submit papers for this journal through Submission System. Submissions must be original and should not have been published previously or be under consideration for publication while being evaluated for this Journal.

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In this research, a Type 2 adaptive fuzzy controller approach is formulated and designed to be applied to variable speed doubly fed induction generator-based wind turbines directly connected to the grid. It brings this study to evaluate the whole operation of the system to capture the highest rate of power in the wind turbines. The controlling approach is considered to keep the stator reactive power to the ideal value. In contrast to the other researches, here the controlling technique is developed through the nonlinear systems.  By the aim of making progress in system operation, in contrast with the Type 1 adaptive fuzzy system, type two adaptive fuzzy theory is proposed to approximate a large number of uncertainties and the dynamic nonlinearities, exists in tracking errors which may limit the system performance. Feedback linearization control approach helps us to algebraically alter the system into a linearized plant. Thanks to the Lyapunov theorem, the introduced type two adaptive fuzzy approach is proved to meet the uniformly ultimately boundness (UUB) property. On the other hand, it results better tracking function. The simulation outputs represent that the proposed technique is robust enough in presence of parameter variations and unstructured uncertainties.

In this paper a new interval Type-2 fuzzy neural network will be presented for function approximation. The proposed neural network is based on Locally Linear Model Tree (LOLIMOT) which is a fast learning algorithm for Locally Linear Neuro-Fuzzy Models (LLNFM). In this research, main measures are to be robust in presence of outlier data and be fast in refining steps. The proposed combination between LOLIMOT learning algorithm and interval type 2 fuzzy logic systems presents a good performance both in robustness and speed measures. The results show that the proposed method has good robustness in presence of noise as we can see in experiments conducted using corrupted data. Also this method has eligible speed as it can be seen in the results.

International Journal an Fuzzy Systems (IJFS) is a forum for presenting new advances and research results in the fields of Fuzzy Logic Systems. Authors are solicited to contribute to the conference by submitting articles that illustrate research results, projects, surveying works and industrial experiences.All submissions must describe original research, not published or currently under review for another conference or journal.

Frank t-norms are parametric family of continuous Archimedean t-norms whose members are also strict functions. Very often, this family of t-norms is also called the family of fundamental t-norms because of the role it plays in several applications. In this paper, optimization of a linear objective function with fuzzy relational inequality constraints is investigated. The feasible region is formed as the intersection of two inequality fuzzy systems defined by frank family of t-norms is considered as fuzzy composition. First, the resolution of the feasible solutions set is studied where the two fuzzy inequality systems are defined with max-Frank composition. Second, some related basic and theoretical properties are derived. Then, a necessary and sufficient condition and three other necessary conditions are presented to conceptualize the feasibility of the problem. Subsequently, it is shown that a lower bound is always attainable for the optimal objective value. Also, it is proved that the optimal solution of the problem is always resulted from the unique maximum solution and a minimal solution of the feasible region. Finally, an algorithm is presented to solve the problem and an example is described to illustrate the algorithm. Additionally, a method is proposed to generate random feasible max-Frank fuzzy relational inequalities. By this method, we can easily generate a feasible test problem and employ our algorithm to it.

International Journal an Fuzzy Systems (IJFS) is a forum for presenting new advances and research results in the fields of Fuzzy Logic Systems. Authors are solicited to contribute to the conference by submitting articles that illustrate research results, projects, surveying works and industrial experiences.All submissions must describe original research, not published or currently under review for another conference or journal.

— Type-2 fuzzy logic systems are the systems that use the three dimensional fuzzy membership functions in order to minimize the effects of the indefiniteness not modelled by type-1 fuzzy systems. However, in type-2 fuzzy systems, the complexity in terms of calculation is much higher compared to type-1 fuzzy systems. Particularly the defuzzification stage of type-2 fuzzy systems is a complex calculation process. In this study, an image process based algorithm was suggested for the defuzzification process of type-2 fuzzy systems. The suggested algorithm is both faster and easier to realize compared to those used in the literature. The method is based on converting the area composed of the active rules into black-white image and finding the weighted mean of the black parts in the image. The effectiveness of the defuzzification method suggested was shown in the simulation results provided. Keywords—Type-2 fuzzy system, defuzzification method, image processing, KM algorithm.

In this paper, we describe a novel system for learning and adapting type-2 fuzzy controllers for intelligent agents that are embedded in Ubiquitous Computing Environments (UCE). Our type-2 agents learn online in a non intrusive manner and in a life long learning mode the user behaviours to control the UCE on the user’s behalf. We have performed unique experiments in which the type-2 intelligent agent has learnt and adapted online to the user’s behaviour during a stay of five days in the intelligent Dormitory (iDorm) which is a real ubiquitous computing environment test bed. We will show how our type-2 agents deal with the uncertainty and imprecision present in UCE to give a very good response that outperforms the type-1 fuzzy agents while using a smaller number of rules.

This article presents a methodological proposal for implementing interval type-2 fuzzy processors over digital signal controller technology. We describe the main considerations that a practitioner or an engineer should follow when implementing an interval type-2 fuzzy system over an embedded processor. These considerations guide the implementation study of eight interval type-2 fuzzy processors, which are fully characterized and tested. Results show that by combining fast computing strategies and technologies like digital signal controllers, the inference time of an embedded type-2 fuzzy processor can be set to hundreds of microseconds.

Frank t-norms are parametric family of continuous Archimedean t-norms whose members are also strict functions. Very often, this family of t-norms is also called the family of fundamental t-norms because of the role it plays in several applications. In this paper, optimization of a linear objective function with fuzzy relational inequality constraints is investigated. The feasible region is formed as the intersection of two inequality fuzzy systems defined by frank family of t-norms is considered as fuzzy composition. First, the resolution of the feasible solutions set is studied where the two fuzzy inequality systems are defined with max-Frank composition. Second, some related basic and theoretical properties are derived. Then, a necessary and sufficient condition and three other necessary conditions are presented to conceptualize the feasibility of the problem. Subsequently, it is shown that a lower bound is always attainable for the optimal objective value. Also, it is proved that the optimal solution of the problem is always resulted from the unique maximum solution and a minimal solution of the feasible region. Finally, an algorithm is presented to solve the problem and an example is described to illustrate the algorithm. Additionally, a method is proposed to generate random feasible max-Frank fuzzy relational inequalities. By this method, we can easily generate a feasible test problem and employ our algorithm to it.

In this paper we have designed and applied to mixed (continuous and categorical) data in multiobjective partitional clustering problem. external validity indexes Adjusted Rand Index (ARI) and Minkowski Score (MS). performing multiobjective partitional clustering with mixed data, modeled with fuzzy logic, allowing for clusters, was obtained as a result of the resea CCS Concepts • Theory of computation➝ Unsupervised optimization and decision-making • Computing metho designed and implemented multiplepossible stochastic hill climbing alternatives, applied to mixed (continuous and categorical) data in a fuzzy context, as proposed solution partitional clustering problem. To validate the efficacy of this approach we selected the external validity indexes Adjusted Rand Index (ARI) and Minkowski Score (MS). An approach rforming multiobjective partitional clustering with mixed data, which also provides solutions allowing for a better description of the distribution of objects among the was obtained as a result of the research. Unsupervised learning and clustering • Applied computing➝ Multi Computing methodologies➝ Cluster analysis. Partitional clustering; multiobjective hill climbing ;fuzzy domain; mixed data. c hill climbing alternatives, solutions to a efficacy of this approach we selected the n approach capable of also provides solutions of objects among the

Natural outdoor environments present major navigation challenges to autonomous mobile robots as they involve large amounts of complex sensory data with uncertainties that are inherent to the sensing systems and the unstructured highly dynamic environments involved. The traditional type-1 Fuzzy Logic Controller (FLC) using crisp type-1 fuzzy sets cannot directly model and handle all such uncertainties. A Type-2 Fuzzy Logic Controller (FLC) using type-2 fuzzy sets can model and minimise the effect of uncertainties to produce a better performance. In this paper we present a novel control architecture for autonomous mobile robots based on a hierarchy of Type-2 FLCs to implement the basic navigation behaviours and the coordination between robot control behaviours. We have implemented this architecture in different types of mobile robots navigating in indoor and outdoor unstructured and challenging environments. The type-2 based control system dealt with the linguistic and numerical uncertainties associated with the changing environment and robot conditions and resulted in very good performance that out-performed the type-1 based control system whilst achieving a significant rule reduction compared to the type-1 system.

A novel technique is investigated for PID controller adaptation in order to control the temperature of a liquid-phase reactor tank by using a heat exchanger system. As for nonlinearity, time delay problems and model uncertainties introduced by the heat exchanger, an interval type-2 fuzzy system (IT2FS) is implemented to enhance and improve the total control performance. Moreover, the fuzzy inference rules which enable the adaptive adjustment of PID parameters are established based on error and error variations. Target tracking, oscillation control and error evaluation for the proposed controller are compared with previously performed control strategies on the mentioned heat exchanger system. The results show that the adaptive technique for PID gain based on IT2FS has lower error and strengthened capacity for external oscillation control and also an acceptable tracking capability.

International Journal of Wireless Networks Systems (IJWNS)is a bi monthly open access peer-reviewed journal that publishes articles which contribute new results in all areas of Wireless & Mobile Networks. The journal focuses on all technical and practical aspects of Wireless Networks Systems .

Scope International Journal of Artificial Intelligence and Soft Computing (IJAISC) is an open access peer-reviewed journal that provides an excellent international forum for sharing knowledge and results in theory, methodology and applications of Artificial Intelligence, Soft Computing. The Journal looks for significant contributions to all major fields of the Artificial Intelligence, Soft Computing in theoretical and practical aspects. The aim of the Journal is to provide a platform to the researchers and practitioners from both academia as well as industry to meet and share cutting-edge development in the field. Authors are solicited to contribute to the journal by submitting articles that illustrate research results, projects, surveying works and industrial experiences that describe significant advances in the areas of Soft Computing, Artificial Intelligence and Applications.

International Journal an Fuzzy Systems (IJFS) is a forum for presenting new advances and research results in the fields of Fuzzy Logic Systems. Authors are solicited to contribute to the conference by submitting articles that illustrate research results, projects, surveying works and industrial experiences.All submissions must describe original research, not published or currently under review for another conference or journal.

International Journal an Fuzzy Systems (IJFS) is a forum for presenting new advances and research results in the fields of Fuzzy Logic Systems. Authors are solicited to contribute to the conference by submitting articles that illustrate research results, projects, surveying works and industrial experiences.All submissions must describe original research, not published or currently under review for another conference or journal.

International Journal of Wireless Network Security (IJWNS) is a open access journal that publishes articles which contribute new results in all areas of Computer Networks & Communications. The journal focuses on all technical and practical aspects of Computer Networks & data Communications. Authors are solicited to contribute to this journal by submitting articles that illustrate research results, projects, surveying works and industrial experiences that describe significant advances in the Computer Networks & Communications.

International Journal of Wireless Network Security (IJWNS) 
http://flyccs.com/jounals/IJWNS/Home.html
Scope
International Journal of Wireless Network Security (IJWNS) is a open access journal that publishes articles which contribute new results in all areas of Computer Networks & Communications. The journal focuses on all technical and practical aspects of Computer Networks & data Communications. Authors are solicited to contribute to this journal by submitting articles that illustrate research results, projects, surveying works and industrial experiences that describe significant advances in the Computer Networks & Communications. ________________________________________
Topics of interest include, but are not limited to, the following
• Cryptographic protocols
• Cryptography and Coding
• Untraceability
• Privacy and authentication
• Key management
• Authentication
• Trust Management
• Quantum cryptography
• Computational Intelligence in Security
• Artificial Immune Systems
• Biological & Evolutionary Computation
• Intelligent Agents and Systems
• Reinforcement & Unsupervised Learning
• Autonomy-Oriented Computing
• Coevolutionary Algorithms
• Fuzzy Systems
• Biometric Security
• Trust models and metrics
• Regulation and Trust Mechanisms
• Data Integrity
• Models for Authentication, Trust and Authorization
• Information Hiding • Information Hiding
• Data & System Integrity
• E- Commerce
• Access Control and Intrusion Detection
• Intrusion Detection and Vulnerability Assessment
• Authentication and Non-repudiation
• Identification and Authentication
• Insider Threats and Countermeasures
• Intrusion Detection & Prevention
• Secure Cloud Computing
• Security Information Systems Architecture and Design and Security Patterns
• Security Management
• Cryptography and Information Security
• Security Requirements (threats, vulnerabilities, risk, formal methods, etc.)
• Sensor and Mobile Ad Hoc Network Security
• Service and Systems Design and QoS Network Security
• Software Security
• Security and Privacy in Mobile Systems
• Security and Privacy in Pervasive/Ubiquitous Computing
• Security and Privacy in Web Sevices
• Security and Privacy Policies
• Security Area Control
• Security Deployment•
Paper Submission
Authors are invited to submit papers for this journal through Submission System. Submissions must be original and should not have been published previously or be under consideration for publication while being evaluated for this Journal. For paper format download the template in this page Manuscript Template
Important Dates
• Submission Deadline : August 31, 2018
• Notification :September 30, 2018
• Final Manuscript Due : October 06, 2018
• Publication Date : Determined by the Editor-in-Chief
TO SUBMIT YOUR PAPER, PLEASE CLICK THE FOLLOWING LINK Submit
Contacts
Here's where you can reach us : jwnsjournal@yahoo.com(or) jwnsjjournal@gmail.com

The video data is one of the most important and useful information in computer vision applications. Since vibration of the video occurs unavoidably when camera is moving, video stabilization is an important function for computer vision and its removes the unwanted motion from the camera. This image sequence enhancement is necessary to improve the performance of the subsequently complicated image processing algorithms. With consumer video cameras, video stabilization has been developed in several years. For video stabilization system, various techniques have been proposed to estimate motion vectors of view and the robust method use optical flow technique to obtain motion vectors. The key challenge in optical flow is pre-setting parameters of Gaussian pyramid. In this research enhancement optical flow (OF) method based on Type 2 Fuzzy (T2FOF) for video stabilization is presented. The proposed method is comparing with state of the art based on well-known benchmarks such as, SINTEL and Middlebury benchmark. T2FOF method outperformed Farneback’s method after experimenting on MPI Sintel datasets with 4.54 and 7.19 of average spatiotemporal angular errors consecutively.

International Journal of Artificial Intelligence and Soft Computing (IJAISC) is an open access peer-reviewed journal that provides an excellent international forum for sharing knowledge and results in theory, methodology and applications of Artificial Intelligence, Soft Computing. The Journal looks for significant contributions to all major fields of the Artificial Intelligence, Soft Computing in theoretical and practical aspects.

International Journal of Artificial Intelligence and Soft Computing (IJAISC) is an open access peer-reviewed journal that provides an excellent international forum for sharing knowledge and results in theory, methodology and applications of Artificial Intelligence, Soft Computing. The Journal looks for significant contributions to all major fields of the Artificial Intelligence, Soft Computing in theoretical and practical aspects.

International Journal of Wireless Networks Systems (IJWNS)is a bi monthly open access peer-reviewed journal that publishes articles which contribute new results in all areas of Wireless & Mobile Networks. The journal focuses on all technical and practical aspects of Wireless Networks Systems .

International Journal of Artificial Intelligence and Soft Computing (IJAISC) is an open access peer-reviewed journal that provides an excellent international forum for sharing knowledge and results in theory, methodology and applications of Artificial Intelligence, Soft Computing. The Journal looks for significant contributions to all major fields of the Artificial Intelligence, Soft Computing in theoretical and practical aspects.

International Journal an Fuzzy Systems (IJFS) is a forum for presenting new advances and research results in the fields of Fuzzy Logic Systems. Authors are solicited to contribute to the conference by submitting articles that illustrate research results, projects, surveying works and industrial experiences.All submissions must describe original research, not published or currently under review for another conference or journal.

International Journal an Fuzzy Systems (IJFS) is a forum for presenting new advances and research results in the fields of Fuzzy Logic Systems. Authors are solicited to contribute to the conference by submitting articles that illustrate research results, projects, surveying works and industrial experiences.All submissions must describe original research, not published or currently under review for another conference or journal.

Social network is a group of individuals with diverse social interactions amongst them. The network large scale and distributed due to Quantitative analysis of networks is need of and in turn the society. Clustering helps us to group people with similar characteristics dense social networks. We have considered similarity measures for statistical When a social network is represented as a graph with members as nodes and their relation as edges, graph mining would be suitable for statistical analysis. We have chosen academic social network nodesto simplify network analysis. similaritybetween unstructured data elements extracted from social network.