Power systems with fuzzy logic

This paper describes a ranking method for ordering fuzzy numbers based on Area, Mode, Spreads and Weights of generalized fuzzy numbers. The area used in this method is obtained from the generalized trapezoidal fuzzy number, first by splitting the generalized trapezoidal fuzzy numbers into three triangles and then calculating the Centroids of each triangle followed by the centroid of these Centroids and then finding the area of this centroid from the original point. In this paper, we also apply mode and spreads in those cases where the discrimination is not possible. Some important results like linearity of ranking function and other properties are proved which are useful for proposed approach. This method is simple in evaluation and can rank various types of fuzzy numbers and also crisp numbers which are considered to be a special case of fuzzy numbers.

International Journal of Artificial Intelligence and Soft Computing (IJAISC) 
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Scope
International Journal of Artificial Intelligence and Soft Computing (IJAISC) 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. ________________________________________
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• Artificial Intelligence
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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
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• Submission Deadline : August 31, 2018
• Notification :September 30, 2018
• Final Manuscript Due : October 06, 2018
• Publication Date : Determined by the Editor-in-Chief
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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.

As a clean renewable energy, wind energy has been focused on an alternative source of fossil fuels. Evaluating wind energy investment projects including multiple attributes is a multi-attribute decision making (MADM) problem. Due to the strong ability in expressing the fuzziness and uncertainty, this paper  uses intuitionistic trapezoidal fuzzy numbers to describe the evaluation information from decision-makers. Considering the interaction among attributes and the influence of subjective risk, this paper proposes an
intuitionistic trapezoidal fuzzy aggregation operator based on Choquet integral and prospect theory. We use the new operator to aggregate the prospect value functions. And then we generate a grey relationprojection pursuit dynamic cluster method to rank the alternatives. At last, an illustrative example has been taken to demonstrate the validity and feasibility of the proposed method.

The increasing demand of World Wide Web raises the need of predicting the user’s web page request. The
most widely used approach to predict the web pages is the pattern discovery process of Web usage mining.
This process involves inevitability of many techniques like Markov model, association rules and clustering.
Fuzzy theory with different techniques has been introduced for the better results. Our focus is on Markov
models. This paper is introducing the vague Rules with Markov models for more accuracy using the vague
set theory.

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 wireless sensor networks, sensor nodes are easily compromised due to their restricted hardware resources. These compromised nodes inject fabricated votes into legitimate reports, and generate false report and false vote injection attacks. These attacks deplete energy resources and block report transmission. A probabilistic voting-based filtering scheme was proposed to detect the bogus votes in reports en-route to protect against attacks. Although this method detects false votes in intermediate nodes, the sensor network needs to be effectively operated in consideration of a node's conditions. In this paper, the proposed method selects effective verification nodes by considering the condition of nodes based on a fuzzy logic system. In the proposed method, the intermediate node selects between two next hop nodes in its range through a fuzzy logic system before forwarding the report. Experimental results suggest that, compared to the original method, the proposed method improves energy savings up to 11% while maintaining a high security level.

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 this paper is to investigate the trend of the return of a portfolio formed randomly or for any specific technique. The approach is made using two techniques fuzzy: fuzzy c-means (FCM) algorithm and the fuzzy transform, where the rules used at fuzzy transform arise from the application of the FCM algorithm. The results show that the proposed methodology is able to predict the trend of the return of a stock portfolio, as well as the tendency of the market index. Real data of the financial market are used from 2004 until 2007.