Sharif Digital Repository

The evolution of communication and its consequences on living objects is a challenging subject for many researches. Due to lack of our knowledge about the real trend of this evolution, artificial life simulations can shed light on many dark points of this process. In this paper, we have used Zamin artificial life environment to test the emergence of a very simple form of communication. The usability of this new capability is tested by a simple test of coping with new environment  

The author utilized neuro-fuzzy based group method of data handling (NF-GMDH) to predict the local scour depth around pile groups under clear-water conditions. They collected the datasets from literature. To predict the local scour by using NF-GMDH, nine dimensional parameters were considered to define a functional relationship between input and output variables. The results of NF- GMDH networks were compared with that of the empirical equations. However, the collected datasets for pile group scouring, the method of implementing the empirical formula to calculate scour depth, and using the equation of Sheppard et al. (2004) suggested for single pier to predict local scouring around pile... 

An adaptive fuzzy sliding mode control (AFSMC) for uncertain dynamic systems is presented in this paper. Chattering problem in classical sliding mode control (CSMC) is highly undesirable because it may excite unmodeled high frequency plant dynamics and leads to unforeseen instabilities. The proposed approach in this paper completely eliminates the chattering by using adaptive fuzzy system to calculate continuous control gain vector instead of discontinuous one in classical sliding mode control. Mathematical proof for the stability and the convergence of the system is also presented to show its validity theoretically. The proposed approach and the classical sliding mode control are both... 

Using the combination of fuzzy clustering estimation and sliding mode control, a technique for controlling the magnetic levitation (ML) systems is introduced. This technique is applied to an experimental setup of an ML system for investigating the method derived. The system considered, is a symmetric rotor supported by a cantilever load cell beam and excited by only one electromagnet of a 4-pole magnetic bearing setup. After demonstrating the experimental setup instruction and the specifications of its parts, the clustering, and the sliding mode control methods are explained briefly, then the quality of implementing the techniques to the setup is described step by step. Finally, the results... 

This paper presents a centralized structure for optimum navigation of emergency vehicles with emphasis on ambulances in disaster situations. In this system, an enhanced fuzzy controlled ant colony system (FCACS) based search engine is proposed for optimum route selection. Performance of the system is evaluated by simulating it on a part on London, UK  

This paper describes a genetic-fuzzy system adapted to find efficient partitions on data domains for IDS (Ink Drop Spread). IDS is the engine of Active Learning Method (ALM), a methodology of soft computing. IDS extracts useful information from a system subjected to modeling. Proposed method, called GIDS (Genetic IDS), uses genetic algorithm which optimizes the parameters of membership functions that represent the partitions on data planes. Obtained Results showed that using genetic algorithm to find the partitions has better accuracy than the previous generic IDS methods. © 2008 IEEE  

Fuzzy rule-based classifiers are one of the famous forms of the classification systems particularly in the data mining field. Genetic algorithm is a useful technique for discovering this kind of classifiers and it has been used for this purpose in some studies. In this paper, we propose a new symbiotic evolutionary approach to find desired fuzzy rulebased classifiers. For this purpose, a symbiotic combination operator has been designed as an alternative to the recombination operator (crossover) in the genetic algorithms. In the proposed approach, the evolution starts from simple chromosomes and the structure of chromosomes gets complex gradually during the evolutionary process. Experimental... 

A new approach for PID tuning, based on GA (Genetic algorithm) and Internal Model Control (IMC) technique, is presented in this paper. PID tuning is based on using Method. The IMC technique reduces the number of parameters that must be tuned for a multivariable system using PID controller. The algorithm uses GA for optimal determination of IMC variables. Simulation results present the good performance of the proposed method. © 2007 IEEE  

Improving the methods of identification and control of nuclear power reactors core is an important area in nuclear engineering. Controlling the nuclear reactor core during load following operation encounters some difficulties in control of core thermal power while considering the core limitations in local power peaking and safety margins. In this paper, a nuclear power reactor core (VVER) is identified using a multi nonlinear autoregressive with exogenous inputs (NARX) structure, including neural networks with different time steps and a heuristic compound learning method, consisting of off- and on-line batch learning. An intelligent nuclear reactor core controller, is designed which... 

Many actions have been taken by researchers to design an appropriate controller for a quadruped robot. These efforts can be divided into two main categories: analytical and intelligent methods. The analytical method is based on the motion equations of the robot which are mostly complicated. These complication leads to difficulty in implementation. In contrast, there exists intelligent method which is based on the learning algorithms. Despite lots of improvements in the controlling of the quadruped robots, their ability of walking is still far less from the four legged animals' capability. Many researchers believe that four legged animals owe this priority to the learning attribute. Therefore... 

In this paper, we have proposed a dual-channel supply chain model in uncertain environment to analyze the demand of manufacturer and retailer demand in which the profit being maximized. Linguistic terms are also utilized to establish two fuzzy systems for estimating the demand in direct and retail channels. In order to do that, a mathematical model is proposed based on decentralized situation of supply chain. To solve the model, we have developed a hybrid solution method of genetic algorithm, fuzzy system, and L-P metric. Finally, several test problems are first generated; then, the computational results are analyzed. © 2017 World Scientific Publishing Company  

The design and evaluation of a novel approach to reactor core power control based on emotional learning is described. The controller includes a neuro-fuzzy system with power error and its derivative as inputs. A fuzzy critic evaluates the present situation, and provides the emotional signal (stress). The controller modifies its characteristics so that the critic's stress is reduced. Simulation results show that the controller has good convergence and performance robustness characteristics over a wide range of operational parameters. © 2008 Elsevier Ltd. All rights reserved  

In this paper, the use of simulated annealing (SA) metaheuristic for constructing a fuzzy classification system is presented. In several previous investigations, the capability of fuzzy systems to solve different kinds of problems has been demonstrated. Simulated annealing based fuzzy classification system (SAFCS), hybridizes the learning capability of SA metaheuristic with the approximate reasoning method of fuzzy systems. The objective of this paper is to illustrate the ability of SA to develop an accurate fuzzy classifier. The use of SA in classification is an attempt to effectively explore and exploit the large search space usually associated with classification problems, and find the... 

This paper presents a novel hybrid approach for intrusion detection in computer networks. The proposed approach combines an evolutionary based fuzzy system with an artificial immune system to generate high quality fuzzy classification rules. The performance of final fuzzy classification system has been investigated using the KDD-Cup99 benchmark dataset. The results indicate that in comparison to several traditional techniques, such as C4.5, Naïve Bayes, k-NN and SVM, the proposed hybrid approach achieves better classification accuracies for most of the classes of the intrusion detection classification problem. Therefore, the resulted fuzzy classification rules can be used to produce a... 

The bandwidth of the E-shaped microstrip antenna is optimized using the genetic algorithm (GA) based on fuzzy decision-making, The method of moments is employed for the analysis of the microstrip antenna at the frequency band of 1.8GHz to 2.6GHz by the optimization parameters of supply locations and slot dimensions. Fuzzy inference system is used for the control of the parameters of the genetic algorithm. In the implemented fuzzy system, inputs are parameters like population, and outputs are parameters such as crossover and mutation rates. Simulation results show the genetic algorithm to optimize the bandwidth of the E-shaped microstrip patch by 33.3%. The measured results of the optimized... 

A novel approach is proposed to combine the strengths of GA and GP to optimize rule sets and membership functions of fuzzy systems in a co-evolutionary strategy in order to avoid the problem of dual representation in fuzzy systems. The novelty of proposed algorithm is twofold. One is that GP is used for the structural part (Rule sets) and GA for the string part (Membership functions). The goal is to reduce/eliminate the problem of competing conventions by co-evolving pieces of the problem separately and then in combination. Second is exploiting the synergism between rules sets and membership functions by imitating the effect of "matching" and friendship in cooperating teams of humans,... 

Accurate forecasting of outpatient visits aids in decision-making and planning for the future and is the foundation for greater and better utilization of resources and increased levels of outpatient care. It provides the ability to better manage the ways in which outpatient's needs and aspirations are planned and delivered. This study presents a hybrid artificial intelligence (AI) model to develop a Mamdani type fuzzy rule based system to forecast outpatient visits with high accuracy. The hybrid model uses genetic algorithm for evolving knowledge base of fuzzy system. Actually it extracts useful patterns of information with a descriptive rule induction approach based on Genetic Fuzzy Systems...