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This paper presents optimization technique to develop type-2 fuzzy systems (FSs) through hybrid genetic algorithms (HGAs). The proposed optimization technique works as follows: (i) Optimize the type-2 membership functions (ii) Learn the rule base through genetic algorithms (iii) Apply the reducing technique to reduce the rule base. (iv) Build the FSs based on type-2 membership functions and the reduced...
The stability of blast furnace gas (BFG) system is of great importance in steel manufacturing process. This paper proposes a multi-objective hierarchical genetic method for building a fuzzy system to measure the pressure of BFG network in complex industrial environments. In order to improve the accuracy of the model, the fuzzy system is divided into four layers with the optimization target of mean...
Decision making and/or Decision Support Systems (DSS) using intelligent techniques like Genetic Algorithm and fuzzy logic is becoming popular in many new applications. Combining these techniques provides an enhanced capability of any decision support systems (DSS. This paper discusses a modular approach toward implementing Genetic Fuzzy system termed as “Genetic Fuzzimetric Technique” (GFT). The technique...
The problem of Software Reliability Prediction is attracting the attention of several researchers during the last few years. Various classification techniques are proposed in current literature which involve the use of metrics drawn from version control systems in order to classify software components as defect-prone or defect-free. In this paper, we create a novel genetic fuzzy rule-based system...
Participatory evolution is a learning paradigm recently introduced in the realm of fuzzy system modeling and system optimization. The paradigm benefits from the concept of participatory learning, genetic algorithms and differential evolution. In this paper we address two distinct participatory evolutionary learning algorithms. The first combines participatory learning and the processing steps of differential...
Genetic Fuzzy Systems have been successfully used as a modeling approach for numerous applications. There is an increasing interest on how to construct fuzzy models for different types of complex systems such as highly nonlinear, large-scale, multiobjective, and high-dimensional systems. Current state of the art indicates the use of fast and scalable evolutionary algorithms in complex fuzzy modeling...
Fuzzy classification systems have been widely researched in the literature. Genetic fuzzy systems combine the power of the global search of genetic algorithms with fuzzy systems to provide accurate and interpretable rule-based systems. In this paper, we present a new approach for the genetic generation of fuzzy systems. The novelty of our proposal, named FCA-Based method, is a hybrid combination of...
The paper proposes a fuzzy control method with a real-time genetic algorithm for an uncertain DC server motor with a Buck converter. The parameters of the fuzzy system are online adjusted by the real-time genetic algorithm in order to generate appropriate control input. For the purpose of on-line evaluating the stability of the closed-loop system, an energy fitness function derived from backstepping...
Sensor Localization is a crucial part of many location??]dependent applications that is utilized in wireless sensor networks (WSNs). Several approaches, including range??]based and range??]free, have been proposed to calculate the position of randomly deployed sensor nodes. With specific hardware, the range??]based schemes typically achieve high accuracy based on either node??]to-node distances or...
Wireless sensor networks (WSNs) are composed of sensor nodes in order to detect and transmit features from the physical environment. Generally, the sensor nodes transmit information to a special node called sink. Some recent researches have led to the selection of routes in sensor networks with multiple sink nodes. The approach proposed by this paper presents the application of Genetic Fuzzy System...
In this article, a Genetic Algorithm-based fuzzy clustering method (GOGA), which incorporates Gene Ontology (GO) knowledge in the clustering process, has been proposed for clustering microarray gene expression data. The proposed technique combines the expression-based and GO-based gene dissimilarity measures for this purpose. Both expression-based and GO-based clustering objectives have been incorporated...
This paper concentrates on studying the use of interval type-2 fuzzy sets for the pattern classification problem. Even though researchers recognize that type-2 fuzzy sets are more difficult to understand and use than type-1 fuzzy sets, the interest in the study is motivated by the additional power to represent uncertainty in different levels. The work developed here relies on the recent advances concerning...
Among the various approaches to determine the true domain of fuzzy inference rules in the designing of fuzzy controller, the important one is to solve a system of fuzzy relation equations corresponding to the fuzzy inference sentence. In our earlier work, we gave a new method to construct the system and presented a method to solve the system when the system is consistent. But when the system is inconsistent,...
Forecasting stock price time series is very important and challenging in the real world because they are affected by many highly interrelated economic, social, political and even psychological factors, and these factors interact with each other in a very complicated manner. This article presents an approach based on Genetic Fuzzy Systems (GFS) for constructing a stock price forecasting expert system...
In this paper we apply bio-inspired optimization methods to design type-2 fuzzy logic controllers (FLC) to minimize the steady state error of linear systems. We test the optimal FLC obtained by the genetic algorithms and the PSO using benchmark plants. The bio-inspired methods are used to find the parameters of the membership functions of the FLC to obtain the optimal controller. Simulation results...
Nowadays, the BP network algorithm has achieved a great success and many nonlinear problems can be solved well. However, standard BP network algorithm has some Shortcomings. Such as local minimum, low convergence and oscillation effects etc. GA has a strong macro-search capability. It has some advantages. Such as simple and universal, robust, parallel computing features, so use it to complete the...
In this paper a method is proposed for constructing hierarchical fuzzy rule bases in order to model black box systems defined by input-output pairs, i.e. to solve supervised machine learning problems. The resultant hierarchical rule base is the knowledge base, which is constructed by using structure constructing evolutionary techniques, namely, Genetic and Bacterial Programming Algorithms. Applying...
A computer aided detection (CAD) system suffers from vagueness and imprecision in both medical science and image processing techniques. These uncertainty issues in the classification components of a CAD system directly influence the accuracy. This paper takes advantage of type-2 fuzzy sets as three-dimensional fuzzy sets with high potential for managing uncertainty issues in vague environments. In...
When there is a substantial difference between the number of cases of the majority and minority classes, minimum error-based classification systems tend to overlook these last instances. This can be corrected either by preprocessing the dataset or by altering the objective function of the classifier. In this paper we analyze the first approach, in the context of genetic fuzzy systems (GFS), and in...
In this paper we present a new approach for laser-based environment device control systems by laser pointer for handicapped people. The paper proposes the design of a Fuzzy Rule Base System for laser pointer detection. The idea is to improve the success rate of the previous approaches decreasing as much as possible the false offs, i.e., the detection of a false laser spot (since this could lead to...
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