Sharif Digital Repository

In the past decade, fuzzy logic has been widely used to manage uncertainty in games. In this paper, we employ fuzzy logic to determine the priority of a payoff using linguistic preference relation. Preferences are derived according to the difference between payoffs using fuzzy IF-THEN rule. We introduce the concept of linguistic Nash equilibrium based on priority of each payoff. We assign a fuzzy set type2 to each cell to determine how much a cell has possibility to have a specific degree of being Nash. © 2008 IEEE  

Due to the various contents of images, the stego images produced by a steganography method may have different levels of undetectability against steganalyzers. In other words, a steganography method may cause less detectable statistical artifacts on some images compared to other images. In this paper, we analyze different features of images to find the similarity between proper cover images for each steganography method Similarity between images is modeled in form of fuzzy if-then rules using an evolutionary algorithm. Subsequently for hiding secret data in a cover image, we suggest a reliable steganography method that results in an undetectable stego image against most recently reported... 

This paper presents a new interpretable fuzzy classification system. Simulated annealing heuristic is employed to effectively investigate the large search space usually associated with classification problem. Here, two criteria are used to evaluate the proposed method. The first criterion is accuracy of extracted fuzzy if-then rules, and the other is comprehensibility of obtained rules. Experiments are performed with some data sets from UCI machine learning repository. Results are compared with several well-known classification algorithms, and show that the proposed approach provides more accurate and interpretable classification system. © 2009 IEEE  

In this paper, a novel classification approach is presented. This approach uses fuzzy if-then rules for classification task and employs a hybrid optimization method to improve the accuracy and comprehensibility of obtained outcome. The mentioned optimization method has been formulated by simulated annealing and genetic algorithm. In fact, the genetic operators have been used as perturb functions at the core of simulated annealing heuristic. Results of proposed approach have been compared with several well-known methods such as Naïve Bayes, Support Vector Machine, Decision Tree, k-NN, and GBML, and show that our method performs the classification task as well as other famous algorithms. ©... 

This study proposes a novel non-invasive method for estimating the utility harmonic impedance. Since the major concern about non-invasive methods is the dependency of the accuracy of the results on background harmonic fluctuations, proper measured samples are selected using a three-point data selection technique to increase the method accuracy. Then, a new non-invasive method for the evaluation of the utility harmonic impedance at a point of common coupling (PCC) based on fuzzy logic is presented. In the proposed method, fuzzy logic is applied to the constrained recursive least squares algorithm (CRLS) by designing a set of fuzzy if-then rules. Due to the changes in the quantities at a... 

Data mining is a very popular technique which is successfully used in many areas. The aim of this paper is to present a Hybrid model for data classification from input datasets. The proposed model extracts knowledge using fuzzy rule based systems and performs classification task by fuzzy if-then rules. The proposed method performs the classification task and extracts required knowledge using fuzzy rule based systems which consists of fuzzy if-then rules. In order to do so the hybrid ant colony and simulated annealing algorithms have been used to optimize extracted fuzzy rule set. "ACSA", a self development data mining software system based on swarm intelligence, is applied to experiment on... 

A novel approach for selecting proper cover images in steganography is presented in this paper. The proposed approach consists of two stages. The first stage is an evolutionary algorithm that extracts the signature of cover images against stego images in the form of fuzzy if-then rules. This algorithm is based on an iterative rule learning approach to construct an accurate fuzzy rule base. The rule base is generated in an incremental way by optimizing one fuzzy rule at a time using an evolutionary algorithm. In the second stage of the proposed approach, the fuzzy rules generated in the first stage are used for selecting suitable cover images for steganography. We applied our approach to some... 

Intrusion detection systems (IDS) are tools located inside computer networks that analyze the network traffics. In this paper, a novel fuzzy-evolutionary system is presented to effectively detect the intrusion in computer networks. This system utilizes a hybridization of simulated annealing heuristic and tabu search algorithm to improve the accuracy of fuzzy if-then rules as intrusion detectors. Each of these algorithms has its advantageous and disadvantageous. Using the hybrid model of both algorithms, the proposed system employs the good features of them to improve the accuracy of obtained rules. Evaluation of the proposed system is done on the KDDCup99 Dataset which has information about... 

In this paper, we use simulated annealing heuristics for constructing an intrusion detection system (IDS). The proposed IDS combines the learning ability of simulated annealing heuristics with the approximate reasoning method of fuzzy systems. The use of simulated annealing is an effort to effectively explore the large search space related to intrusion detection problems, and find the optimum set of fuzzy if-then rules. The aim of this paper is to present the capability of simulated annealing based fuzzy system to deal with intrusion detection classification problem as a new real-world application area. Experiments were performed with KDD-Cup99 intrusion detection benchmark data set. © 2008... 

In this paper we propose to control a bipedal robot in an unstable position by means of a PID controller that gains are turned by a fuzzy logic system. For that, a model of planar 3 linked segment consisting of limb, trunk and extended arms with fixed base is used. Fuzzy if-then rules are constructed based on human expert knowledge and biomechanics studies for tuning of PID's gain. For construction of tuning rules, we have developed an optical measuring system to record experimental data of balance keeping of a human in an unstable position. The control model is based on three sets of different global variables: (1) limb orientation and its derivative, (2) trunk/upper attitude and its...