Chen, Guanrong
2005-01-01
Introduction to Fuzzy Systems provides students with a self-contained introduction that requires no preliminary knowledge of fuzzy mathematics and fuzzy control systems theory. Simplified and readily accessible, it encourages both classroom and self-directed learners to build a solid foundation in fuzzy systems. After introducing the subject, the authors move directly into presenting real-world applications of fuzzy logic, revealing its practical flavor. This practicality is then followed by basic fuzzy systems theory. The book also offers a tutorial on fuzzy control theory, based mainly on th
Multiple Fuzzy Classification Systems
Scherer, Rafał
2012-01-01
Fuzzy classiﬁers are important tools in exploratory data analysis, which is a vital set of methods used in various engineering, scientiﬁc and business applications. Fuzzy classiﬁers use fuzzy rules and do not require assumptions common to statistical classiﬁcation. Rough set theory is useful when data sets are incomplete. It deﬁnes a formal approximation of crisp sets by providing the lower and the upper approximation of the original set. Systems based on rough sets have natural ability to work on such data and incomplete vectors do not have to be preprocessed before classiﬁcation. To achieve better performance than existing machine learning systems, fuzzy classifiers and rough sets can be combined in ensembles. Such ensembles consist of a ﬁnite set of learning models, usually weak learners. The present book discusses the three aforementioned ﬁelds – fuzzy systems, rough sets and ensemble techniques. As the trained ensemble should represent a single hypothesis, a lot of attention is placed o...
Syropoulos, Apostolos
2011-01-01
Dialectica categories are a very versatile categorical model of linear logic. These have been used to model many seemingly different things (e.g., Petri nets and Lambek's calculus). In this note, we expand our previous work on fuzzy petri nets to deal with fuzzy topological systems. One basic idea is to use as the dualizing object in the Dialectica categories construction, the unit real interval [0,1], which has all the properties of a {\\em lineale}. The second basic idea is to generalize Vickers's notion of a topological system.
Entropy of Fuzzy Partitions and Entropy of Fuzzy Dynamical Systems
Directory of Open Access Journals (Sweden)
Dagmar Markechová
2016-01-01
Full Text Available In the paper we define three kinds of entropy of a fuzzy dynamical system using different entropies of fuzzy partitions. It is shown that different definitions of the entropy of fuzzy partitions lead to different notions of entropies of fuzzy dynamical systems. The relationships between these entropies are studied and connections with the classical case are mentioned as well. Finally, an analogy of the Kolmogorov–Sinai Theorem on generators is proved for fuzzy dynamical systems.
Probability representations of fuzzy systems
Institute of Scientific and Technical Information of China (English)
LI Hongxing
2006-01-01
In this paper, the probability significance of fuzzy systems is revealed. It is pointed out that COG method, a defuzzification technique used commonly in fuzzy systems, is reasonable and is the optimal method in the sense of mean square. Based on different fuzzy implication operators, several typical probability distributions such as Zadeh distribution, Mamdani distribution, Lukasiewicz distribution, etc. are given. Those distributions act as "inner kernels" of fuzzy systems. Furthermore, by some properties of probability distributions of fuzzy systems, it is also demonstrated that CRI method, proposed by Zadeh, for constructing fuzzy systems is basically reasonable and effective. Besides, the special action of uniform probability distributions in fuzzy systems is characterized. Finally, the relationship between CRI method and triple I method is discussed. In the sense of construction of fuzzy systems, when restricting three fuzzy implication operators in triple I method to the same operator, CRI method and triple I method may be related in the following three basic ways: 1) Two methods are equivalent; 2) the latter is a degeneration of the former; 3) the latter is trivial whereas the former is not. When three fuzzy implication operators in triple I method are not restricted to the same operator, CRI method is a special case of triple I method; that is, triple I method is a more comprehensive algorithm. Since triple I method has a good logical foundation and comprises an idea of optimization of reasoning, triple I method will possess a beautiful vista of application.
A Novel Weak Fuzzy Solution for Fuzzy Linear System
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Soheil Salahshour
2016-03-01
Full Text Available This article proposes a novel weak fuzzy solution for the fuzzy linear system. As a matter of fact, we define the right-hand side column of the fuzzy linear system as a piecewise fuzzy function to overcome the related shortcoming, which exists in the previous findings. The strong point of this proposal is that the weak fuzzy solution is always a fuzzy number vector. Two complex and non-complex linear systems under uncertainty are tested to validate the effectiveness and correctness of the presented method.
Duality in Dynamic Fuzzy Systems
Yoshida, Yuji
1995-01-01
This paper shows the resolvent equation, the maximum principle and the co-balayage theorem for a dynamic fuzzy system. We define a dual system for the dynamic fuzzy system, and gives a duality for Snell's optimal stopping problem by the dual system.
Fuzzy Modeling for Uncertainty Nonlinear Systems with Fuzzy Equations
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Raheleh Jafari
2017-01-01
Full Text Available The uncertain nonlinear systems can be modeled with fuzzy equations by incorporating the fuzzy set theory. In this paper, the fuzzy equations are applied as the models for the uncertain nonlinear systems. The nonlinear modeling process is to find the coefficients of the fuzzy equations. We use the neural networks to approximate the coefficients of the fuzzy equations. The approximation theory for crisp models is extended into the fuzzy equation model. The upper bounds of the modeling errors are estimated. Numerical experiments along with comparisons demonstrate the excellent behavior of the proposed method.
Design of interpretable fuzzy systems
Cpałka, Krzysztof
2017-01-01
This book shows that the term “interpretability” goes far beyond the concept of readability of a fuzzy set and fuzzy rules. It focuses on novel and precise operators of aggregation, inference, and defuzzification leading to flexible Mamdani-type and logical-type systems that can achieve the required accuracy using a less complex rule base. The individual chapters describe various aspects of interpretability, including appropriate selection of the structure of a fuzzy system, focusing on improving the interpretability of fuzzy systems designed using both gradient-learning and evolutionary algorithms. It also demonstrates how to eliminate various system components, such as inputs, rules and fuzzy sets, whose reduction does not adversely affect system accuracy. It illustrates the performance of the developed algorithms and methods with commonly used benchmarks. The book provides valuable tools for possible applications in many fields including expert systems, automatic control and robotics.
Modelling on fuzzy control systems
Institute of Scientific and Technical Information of China (English)
LI; Hongxing(李洪兴); WANG; Jiayin(王加银); MIAO; Zhihong(苗志宏)
2002-01-01
A kind of modelling method for fuzzy control systems is first proposed here, which is calledmodelling method based on fuzzy inference (MMFI). It should be regarded as the third modelling method thatis different from two well-known modelling methods, that is, the first modelling method, mechanism modellingmethod (MMM), and the second modelling method, system identification modelling method (SlMM). Thismethod can, based on the interpolation mechanism on fuzzy logic system, transfer a group of fuzzy inferencerules describing a practice system into a kind of nonlinear differential equation with variable coefficients, calledHX equations, so that the mathematical model of the system can be obtained. This means that we solve thedifficult problem of how to get a model represented as differential equations on a complicated or fuzzy controlsystem.
DEFF Research Database (Denmark)
Roy, Mallarika Sinha
2009-01-01
The Naxalbari movement, a radical Maoist movement, marks a significant moment in the postcolonial history of West Bengal, as well as in the larger context of India. Beginning as an armed peasant movement in 1967 in the Naxalbari area of northern West Bengal, the movement soon was spread in differ......The Naxalbari movement, a radical Maoist movement, marks a significant moment in the postcolonial history of West Bengal, as well as in the larger context of India. Beginning as an armed peasant movement in 1967 in the Naxalbari area of northern West Bengal, the movement soon was spread...... in different districts of West Bengal and several provinces of India. Even though it has been one of the well-studied political and social events in postcolonial West Bengal, the gender dimension, particularly the history of women's participation, remains neglected in the historiography of the movement....... A critical review from the point of view of gender requires contextualisation of gender relations according to class, ethnicity, spatial locations, and cultural environments of men and women Naxalites. Through a discussion of the centrality of Calcutta - the metropolitan centre - in the dominant memory...
13. workshop fuzzy systems. Proceedings; 13. Workshop Fuzzy Systeme. Beitraege
Energy Technology Data Exchange (ETDEWEB)
Mikut, R.; Reischl, M. (eds.)
2003-11-01
This volume contains the papers presented at the 13th workshop on fuzzy systems of TC 5.2.2 'Fuzzy Control' of the VDI/VDE-Gesellschaft fuer Mess- und Automatisierungstechnik (GMA) and the TG 'Fuzzy Systems and Soft Computing' of the Gesellschaft fuer Informatik (GI), which took place at Dortmund on November 19-21, 2003. New methods and applications of fuzzy logic, artificial neuronal nets and evolutionary algorithms were presented. The focus was on automation, e.g. in chemical engineering, energy engineering, motor car engineering, robotics and medical engineering. Other applications, e.g. data mining for technical and non-technical applications, were gone into as well. [German] Dieser Tagungsband enthaelt die Beitraege des 13. Workshops ''Fuzzy System'' des Fachausschusses 5.22 ''Fuzzy Control'' der VDI/VDE-Gesellschaft fuer Mess- und Automatisierungstechnik (GMA) und der Fachgruppe ''Fuzzy-Systeme und Soft-Computing'' der Gesellschaft fuer Informatik (GI), der vom 19.-21. November 2003 im Haus Bommerholz, Dortmund, stattfindet. Der jaehrliche Workshop unseres Fachausschusses bietet ein Forum zur Diskussion neuer methodischer Ansaetze und industrieller Anwendungen auf dem Gebiet der Fuzzy-Logik und in angrenzenden Gebieten wie Kuenstlichen Neuronalen Netzen und Evolutionaeren Algorithmen. Besondere Schwerpunkte sind automatisierungstechnische Anwendungen, z.B. in der Verfahrenstechnik, Energietechnik, Kfz-Technik, Robotik und Medizintechnik, aber auch Loesungen in anderen Problemgebieten (z.B. Data Mining fuer technische und nichttechnische Anwendungen) sind von Interesse. (orig.)
Neuro-fuzzy system modeling based on automatic fuzzy clustering
Institute of Scientific and Technical Information of China (English)
Yuangang TANG; Fuchun SUN; Zengqi SUN
2005-01-01
A neuro-fuzzy system model based on automatic fuzzy clustering is proposed.A hybrid model identification algorithm is also developed to decide the model structure and model parameters.The algorithm mainly includes three parts:1) Automatic fuzzy C-means (AFCM),which is applied to generate fuzzy rules automatically,and then fix on the size of the neuro-fuzzy network,by which the complexity of system design is reducesd greatly at the price of the fitting capability;2) Recursive least square estimation (RLSE).It is used to update the parameters of Takagi-Sugeno model,which is employed to describe the behavior of the system;3) Gradient descent algorithm is also proposed for the fuzzy values according to the back propagation algorithm of neural network.Finally,modeling the dynamical equation of the two-link manipulator with the proposed approach is illustrated to validate the feasibility of the method.
Fuzzy Logic Indoor Positioning System
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Roberto García Sánz
2008-12-01
Full Text Available The GPS system is not valid for positioning indoors, thus positioning systems are designed using Wi-Fi technology that allows location of a device inside buildings. The use of fuzzy logic is argued by the failure to find positioning systems based on this technology, which seeks toobserve how their use in this field
Function Approximation Using Probabilistic Fuzzy Systems
J.H. van den Berg (Jan); U. Kaymak (Uzay); R.J. Almeida e Santos Nogueira (Rui Jorge)
2011-01-01
textabstractWe consider function approximation by fuzzy systems. Fuzzy systems are typically used for approximating deterministic functions, in which the stochastic uncertainty is ignored. We propose probabilistic fuzzy systems in which the probabilistic nature of uncertainty is taken into account.
Learning fuzzy logic control system
Lung, Leung Kam
1994-01-01
The performance of the Learning Fuzzy Logic Control System (LFLCS), developed in this thesis, has been evaluated. The Learning Fuzzy Logic Controller (LFLC) learns to control the motor by learning the set of teaching values that are generated by a classical PI controller. It is assumed that the classical PI controller is tuned to minimize the error of a position control system of the D.C. motor. The Learning Fuzzy Logic Controller developed in this thesis is a multi-input single-output network. Training of the Learning Fuzzy Logic Controller is implemented off-line. Upon completion of the training process (using Supervised Learning, and Unsupervised Learning), the LFLC replaces the classical PI controller. In this thesis, a closed loop position control system of a D.C. motor using the LFLC is implemented. The primary focus is on the learning capabilities of the Learning Fuzzy Logic Controller. The learning includes symbolic representation of the Input Linguistic Nodes set and Output Linguistic Notes set. In addition, we investigate the knowledge-based representation for the network. As part of the design process, we implement a digital computer simulation of the LFLCS. The computer simulation program is written in 'C' computer language, and it is implemented in DOS platform. The LFLCS, designed in this thesis, has been developed on a IBM compatible 486-DX2 66 computer. First, the performance of the Learning Fuzzy Logic Controller is evaluated by comparing the angular shaft position of the D.C. motor controlled by a conventional PI controller and that controlled by the LFLC. Second, the symbolic representation of the LFLC and the knowledge-based representation for the network are investigated by observing the parameters of the Fuzzy Logic membership functions and the links at each layer of the LFLC. While there are some limitations of application with this approach, the result of the simulation shows that the LFLC is able to control the angular shaft position of the
Fuzzy logic control and optimization system
Lou, Xinsheng [West Hartford, CT
2012-04-17
A control system (300) for optimizing a power plant includes a chemical loop having an input for receiving an input signal (369) and an output for outputting an output signal (367), and a hierarchical fuzzy control system (400) operably connected to the chemical loop. The hierarchical fuzzy control system (400) includes a plurality of fuzzy controllers (330). The hierarchical fuzzy control system (400) receives the output signal (367), optimizes the input signal (369) based on the received output signal (367), and outputs an optimized input signal (369) to the input of the chemical loop to control a process of the chemical loop in an optimized manner.
Fuzzy logic control for camera tracking system
Lea, Robert N.; Fritz, R. H.; Giarratano, J.; Jani, Yashvant
1992-01-01
A concept utilizing fuzzy theory has been developed for a camera tracking system to provide support for proximity operations and traffic management around the Space Station Freedom. Fuzzy sets and fuzzy logic based reasoning are used in a control system which utilizes images from a camera and generates required pan and tilt commands to track and maintain a moving target in the camera's field of view. This control system can be implemented on a fuzzy chip to provide an intelligent sensor for autonomous operations. Capabilities of the control system can be expanded to include approach, handover to other sensors, caution and warning messages.
Bayesian system reliability assessment under fuzzy environments
Energy Technology Data Exchange (ETDEWEB)
Wu, H.-C
2004-03-01
The Bayesian system reliability assessment under fuzzy environments is proposed in this paper. In order to apply the Bayesian approach, the fuzzy parameters are assumed as fuzzy random variables with fuzzy prior distributions. The (conventional) Bayes estimation method will be used to create the fuzzy Bayes point estimator of system reliability by invoking the well-known theorem called 'Resolution Identity' in fuzzy sets theory. On the other hand, we also provide the computational procedures to evaluate the membership degree of any given Bayes point estimate of system reliability. In order to achieve this purpose, we transform the original problem into a nonlinear programming problem. This nonlinear programming problem is then divided into four subproblems for the purpose of simplifying computation. Finally, the subproblems can be solved by using any commercial optimizers, e.g. GAMS or LINGO.
Terrorism Event Classification Using Fuzzy Inference Systems
Inyaem, Uraiwan; Meesad, Phayung; Tran, Dat
2010-01-01
Terrorism has led to many problems in Thai societies, not only property damage but also civilian casualties. Predicting terrorism activities in advance can help prepare and manage risk from sabotage by these activities. This paper proposes a framework focusing on event classification in terrorism domain using fuzzy inference systems (FISs). Each FIS is a decision-making model combining fuzzy logic and approximate reasoning. It is generated in five main parts: the input interface, the fuzzification interface, knowledge base unit, decision making unit and output defuzzification interface. Adaptive neuro-fuzzy inference system (ANFIS) is a FIS model adapted by combining the fuzzy logic and neural network. The ANFIS utilizes automatic identification of fuzzy logic rules and adjustment of membership function (MF). Moreover, neural network can directly learn from data set to construct fuzzy logic rules and MF implemented in various applications. FIS settings are evaluated based on two comparisons. The first evaluat...
An Intelligent Trading System with Fuzzy Rules and Fuzzy Capital Management
Naranjo, Rodrigo; Meco, Albert; Arroyo Gallardo, Javier; Santos Peñas, Matilde
2015-01-01
In this work we are proposing a trading system where fuzzy logic is applied not only for defining the trading rules, but also for managing the capital to invest. In fact, two fuzzy decision support systems are developed. The first one uses fuzzy logic to design the trading rules and to apply the stock market technical indicators. The second one enhances this fuzzy trading system adding a fuzzy strategy to manage the capital to trade. Additionally, a new technical market indicator that produce...
Stability of Cascaded Fuzzy Systems and Observers
Lendek, Z.; Babuska, R.; De Schutter, B.
2009-01-01
A large class of nonlinear systems can be well approximated by Takagi-Sugeno (TS) fuzzy models with linear or affine consequents. It is well known that the stability of these consequent models does not ensure the stability of the overall fuzzy system. Therefore, several stability conditions have bee
Decomposed fuzzy systems and their application in direct adaptive fuzzy control.
Hsueh, Yao-Chu; Su, Shun-Feng; Chen, Ming-Chang
2014-10-01
In this paper, a novel fuzzy structure termed as the decomposed fuzzy system (DFS) is proposed to act as the fuzzy approximator for adaptive fuzzy control systems. The proposed structure is to decompose each fuzzy variable into layers of fuzzy systems, and each layer is to characterize one traditional fuzzy set. Similar to forming fuzzy rules in traditional fuzzy systems, layers from different variables form the so-called component fuzzy systems. DFS is proposed to provide more adjustable parameters to facilitate possible adaptation in fuzzy rules, but without introducing a learning burden. It is because those component fuzzy systems are independent so that it can facilitate minimum distribution learning effects among component fuzzy systems. It can be seen from our experiments that even when the rule number increases, the learning time in terms of cycles is still almost constant. It can also be found that the function approximation capability and learning efficiency of the DFS are much better than that of the traditional fuzzy systems when employed in adaptive fuzzy control systems. Besides, in order to further reduce the computational burden, a simplified DFS is proposed in this paper to satisfy possible real time constraints required in many applications. From our simulation results, it can be seen that the simplified DFS can perform fairly with a more concise decomposition structure.
Combined heuristic with fuzzy system to transmission system expansion planning
Energy Technology Data Exchange (ETDEWEB)
Silva Sousa, Aldir; Asada, Eduardo N. [University of Sao Paulo, Sao Carlos School of Engineering, Department of Electrical Engineering Av. Trabalhador Sao-carlense, 400, 13566-590 Sao Carlos, SP (Brazil)
2011-01-15
A heuristic algorithm that employs fuzzy logic is proposed to the power system transmission expansion planning problem. The algorithm is based on the divide to conquer strategy, which is controlled by the fuzzy system. The algorithm provides high quality solutions with the use of fuzzy decision making, which is based on nondeterministic criteria to guide the search. The fuzzy system provides a self-adjusting mechanism that eliminates the manual adjustment of parameters to each system being solved. (author)
Minimal solution of singular LR fuzzy linear systems.
Nikuie, M; Ahmad, M Z
2014-01-01
In this paper, the singular LR fuzzy linear system is introduced. Such systems are divided into two parts: singular consistent LR fuzzy linear systems and singular inconsistent LR fuzzy linear systems. The capability of the generalized inverses such as Drazin inverse, pseudoinverse, and {1}-inverse in finding minimal solution of singular consistent LR fuzzy linear systems is investigated.
Fuzzy stability and synchronization of hyperchaos systems
Energy Technology Data Exchange (ETDEWEB)
Wang Junwei [School of Mathematics and Computational Science, Zhongshan University Guangzhou 510275 (China)], E-mail: wangjunweilj@yahoo.com.cn; Xiong Xiaohua [School of Mathematics and Computational Science, Zhongshan University Guangzhou 510275 (China); Department of Computer Science, Jiangxi Normal University, Nanchang 330027 (China); Zhao Meichun [School of Mathematics and Computational Science, Zhongshan University Guangzhou 510275 (China); Department of Mathematics, Guangdong University of Finance, Gunangzhou 510521 (China); Zhang Yanbin [School of Mathematics and Computational Science, Zhongshan University Guangzhou 510275 (China)
2008-03-15
This paper studies stability and synchronization of hyperchaos systems via a fuzzy-model-based control design methodology. First, we utilize a Takagi-Sugeno fuzzy model to represent a hyperchaos system. Second, we design fuzzy-model-based controllers for stability and synchronization of the system, based on so-called 'parallel distributed compensation (PDC)'. Third, we reduce a question of stabilizing and synchronizing hyperchaos systems to linear matrix inequalities (LMI) so that convex programming techniques can solve these LMIs efficiently. Finally, the generalized Lorenz hyperchaos system is employed to illustrate the effectiveness of our designing controller.
Artificial Hydrocarbon Networks Fuzzy Inference System
Directory of Open Access Journals (Sweden)
Hiram Ponce
2013-01-01
Full Text Available This paper presents a novel fuzzy inference model based on artificial hydrocarbon networks, a computational algorithm for modeling problems based on chemical hydrocarbon compounds. In particular, the proposed fuzzy-molecular inference model (FIM-model uses molecular units of information to partition the output space in the defuzzification step. Moreover, these molecules are linguistic units that can be partially understandable due to the organized structure of the topology and metadata parameters involved in artificial hydrocarbon networks. In addition, a position controller for a direct current (DC motor was implemented using the proposed FIM-model in type-1 and type-2 fuzzy inference systems. Experimental results demonstrate that the fuzzy-molecular inference model can be applied as an alternative of type-2 Mamdani’s fuzzy control systems because the set of molecular units can deal with dynamic uncertainties mostly present in real-world control applications.
Adaptive Fuzzy Systems in Computational Intelligence
Berenji, Hamid R.
1996-01-01
In recent years, the interest in computational intelligence techniques, which currently includes neural networks, fuzzy systems, and evolutionary programming, has grown significantly and a number of their applications have been developed in the government and industry. In future, an essential element in these systems will be fuzzy systems that can learn from experience by using neural network in refining their performances. The GARIC architecture, introduced earlier, is an example of a fuzzy reinforcement learning system which has been applied in several control domains such as cart-pole balancing, simulation of to Space Shuttle orbital operations, and tether control. A number of examples from GARIC's applications in these domains will be demonstrated.
A fuzzy expert system for diabetes decision support application.
Lee, Chang-Shing; Wang, Mei-Hui
2011-02-01
An increasing number of decision support systems based on domain knowledge are adopted to diagnose medical conditions such as diabetes and heart disease. It is widely pointed that the classical ontologies cannot sufficiently handle imprecise and vague knowledge for some real world applications, but fuzzy ontology can effectively resolve data and knowledge problems with uncertainty. This paper presents a novel fuzzy expert system for diabetes decision support application. A five-layer fuzzy ontology, including a fuzzy knowledge layer, fuzzy group relation layer, fuzzy group domain layer, fuzzy personal relation layer, and fuzzy personal domain layer, is developed in the fuzzy expert system to describe knowledge with uncertainty. By applying the novel fuzzy ontology to the diabetes domain, the structure of the fuzzy diabetes ontology (FDO) is defined to model the diabetes knowledge. Additionally, a semantic decision support agent (SDSA), including a knowledge construction mechanism, fuzzy ontology generating mechanism, and semantic fuzzy decision making mechanism, is also developed. The knowledge construction mechanism constructs the fuzzy concepts and relations based on the structure of the FDO. The instances of the FDO are generated by the fuzzy ontology generating mechanism. Finally, based on the FDO and the fuzzy ontology, the semantic fuzzy decision making mechanism simulates the semantic description of medical staff for diabetes-related application. Importantly, the proposed fuzzy expert system can work effectively for diabetes decision support application.
Structural health monitoring using genetic fuzzy systems
Pawar, Prashant M
2014-01-01
The high profile of structural health monitoring (SHM) will add urgency to this detailed treatment of intelligent SHM development and implementation via the evolutionary system, which uses a genetic algorithm to automate the development of the fuzzy system.
Gender Classification by Fuzzy Inference System
2013-01-01
Gender classification from face images has many applications and is thus an important research topic. This paper presents an approach to gender classification based on shape and texture information gathered to design a fuzzy decision making system. Beside face shape features, Zernik moments are applied as system inputs to improve the system output which is considered as the probability of being male face image. After parameters tuning of the proposed fuzzy decision making system, 85.05% class...
Fuzzy PID controller combines with closed-loop optimal fuzzy reasoning for pitch control system
Li, Yezi; Xiao, Cheng; Sun, Jinhao
2013-03-01
PID and fuzzy PID controller are applied into the pitch control system. PID control has simple principle and its parameters setting are rather easy. Fuzzy control need not to establish the mathematical of the control system and has strong robustness. The advantages of fuzzy PID control are simple, easy in setting parameters and strong robustness. Fuzzy PID controller combines with closed-loop optimal fuzzy reasoning (COFR), which can effectively improve the robustness, when the robustness is special requirement. MATLAB software is used for simulations, results display that fuzzy PID controller which combines with COFR has better performances than PID controller when errors exist.
Fuzzy modeling and synchronization of hyper chaotic systems
Energy Technology Data Exchange (ETDEWEB)
Zhang Hongbin [Center for Nonlinear and Complex Systems, School of Electronic Engineering, University of Electronic Science and Technology of China, Chengdu 610054 (China)] e-mail: zhanghb@uestc.edu.cn; Liao Xiaofeng [Center for Nonlinear and Complex Systems, School of Electronic Engineering, University of Electronic Science and Technology of China, Chengdu 610054 (China); Institute of Computer Science, Chongqing University, Chongqing 400044 (China); Yu Juebang [Center for Nonlinear and Complex Systems, School of Electronic Engineering, University of Electronic Science and Technology of China, Chengdu 610054 (China)
2005-11-01
This paper presents fuzzy model-based designs for synchronization of hyper chaotic systems. The T-S fuzzy models for hyper chaotic systems are exactly derived. Based on the T-S fuzzy hyper chaotic models, the fuzzy controllers for hyper chaotic synchronization are designed via the exact linearization techniques. Numerical examples are given to demonstrate the effectiveness of the proposed method.
Fuzzy Case-Based Reasoning System
Directory of Open Access Journals (Sweden)
Jing Lu
2016-06-01
Full Text Available In this paper, we propose a fuzzy case-based reasoning system, using a case-based reasoning (CBR system that learns from experience to solve problems. Different from a traditional case-based reasoning system that uses crisp cases, our system works with fuzzy ones. Specifically, we change a crisp case into a fuzzy one by fuzzifying each crisp case element (feature, according to the maximum degree principle. Thus, we add the “vague” concept into a case-based reasoning system. It is these somewhat vague inputs that make the outcomes of the prediction more meaningful and accurate, which illustrates that it is not necessarily helpful when we always create accurate predictive relations through crisp cases. Finally, we prove this and apply this model to practical weather forecasting, and experiments show that using fuzzy cases can make some prediction results more accurate than using crisp cases.
A New Method for Solving General Dual Fuzzy Linear Systems
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M. Otadi
2013-09-01
Full Text Available . According to fuzzy arithmetic, general dual fuzzy linear system (GDFLS cannot be replaced by a fuzzy linear system (FLS. In this paper, we use new notation of fuzzy numbers and convert a GDFLS to two linear systems in crisp case, then we discuss complexity of the proposed method. Conditions for the existence of a unique fuzzy solution to n × n GDFLS are derived
Universal fuzzy models and universal fuzzy controllers for discrete-time nonlinear systems.
Gao, Qing; Feng, Gang; Dong, Daoyi; Liu, Lu
2015-05-01
This paper investigates the problems of universal fuzzy model and universal fuzzy controller for discrete-time nonaffine nonlinear systems (NNSs). It is shown that a kind of generalized T-S fuzzy model is the universal fuzzy model for discrete-time NNSs satisfying a sufficient condition. The results on universal fuzzy controllers are presented for two classes of discrete-time stabilizable NNSs. Constructive procedures are provided to construct the model reference fuzzy controllers. The simulation example of an inverted pendulum is presented to illustrate the effectiveness and advantages of the proposed method. These results significantly extend the approach for potential applications in solving complex engineering problems.
Temperature Control System Using Fuzzy Logic Technique
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Isizoh A N
2012-06-01
Full Text Available Fuzzy logic technique is an innovative technology used in designing solutions for multi-parameter and non-linear control models for the definition of a control strategy. As a result, it delivers solutions faster than the conventional control design techniques. This paper thus presents a fuzzy logic based-temperature control system, which consists of a microcontroller, temperature sensor, and operational amplifier, Analogue to Digital Converter, display interface circuit and output interface circuit. It contains a design approach that uses fuzzy logic technique to achieve a controlled temperature output function.
Japanese advances in fuzzy systems research
Schwartz, Daniel G.
1992-07-01
During this past summer (1991), I spent two months on an appointment as visiting researcher at Kansai University, Osaka, Japan, and five weeks at the Laboratory for International Fuzzy Engineering Research (LIFE), in Yokohama. Part of the expenses for the time in Osaka, and all the expenses for the visit at LIFE, were covered by ONR. While there I met with most of the key researchers in both fuzzy systems and case-based reasoning. This involved trips to numerous universities and research laboratories at Matsushita/Panasonic, Omron, and Hitachi Corporations. In addition, I spent three days at the Fuzzy Logic Systems Institute (FLSI), Iizuka, and I attended the annual meeting of the Japan Society for Fuzzy Theory and Research (SOFT-91) in Nagoya. The following report elaborates what I learned as a result of those activities.
A note on the solution of fuzzy transportation problem using fuzzy linear system
Directory of Open Access Journals (Sweden)
P. Senthilkumar
2013-08-01
Full Text Available In this paper, we discuss the solution of a fuzzy transportation problem, with fuzzy quantities. The problem is solved in two stages. In the first stage, the fuzzy transportation problem is reduced to crisp system by using the lower and upper bounds of fuzzy quantities. In the second stage, the crisp transportation problems are solved by usual simplex method. The procedure is illustrated with numerical examples.
A Fuzzy Control Irrigation System For Cottonfield
Zhang, Jun; Zhao, Yandong; Wang, Yiming; Li, Jinping
A fuzzy control irrigation system for cotton field is presented in this paper. The system is composed of host computer, slave computer controller, communication module, soil water sensors, valve controllers, and system software. A fuzzy control model is constructed to control the irrigation time and irrigation quantity for cotton filed. According to the water-required rules of different cotton growing periods, different irrigation strategies can be carried out automatically. This system had been used for precision irrigation of the cotton field in Langfang experimental farm of Soil and Fertilizer Institute, Chinese Academy of Agricultural Sciences in 2006. The results show that the fuzzy control irrigation system can improve cotton yield and save much water quantity than the irrigation system based on simple on-off control algorithm.
Digital Image Enhancement with Fuzzy Interface System
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Prabhpreet Kaur
2012-09-01
Full Text Available Present day application requires various version kinds of images and pictures as sources of information for interpretation and analysis. Whenever an image is converted from one form to another, such as, digitizing, scanning, transmitting, storing, etc. Some form of degradation occurs at the output. Hence, the output image has to undergo a process called image enhancement which consist of a collection of techniques that seeks to improve the visual appearances of an image. Image enhancement technique is basically improving the perception of information in images for human viewers and providing 'better' input for other automated image processing techniques. This thesis presents a new approach for image enhancement with fuzzy interface system. Fuzzy techniques can manage the vagueness and ambiguity efficiently (an image can be represented as fuzzy set. Fuzzy logic is a powerful tool to represent and process human knowledge in form of fuzzy if-then rules. Compared to other filtering techniques, fuzzy filter gives the better performance and is able to represent knowledge in a comprehensible way.
A method for solving fully fuzzy linear system with trapezoidal fuzzy numbers
Directory of Open Access Journals (Sweden)
A. Kumar
2010-03-01
Full Text Available Different methods have been proposed for finding the non-negative solution of fully fuzzy linear system (FFLS i.e. fuzzy linear system with fuzzy coefficients involving fuzzy variables. To the best of our knowledge, there is no method in the literature for finding the non-negative solution of a FFLS without any restriction on the coefficient matrix. In this paper a new computational method is proposed to solve FFLS without any restriction on the coefficient matrix by representing all the parameters as trapezoidal fuzzy numbers.
Automobile active suspension system with fuzzy control
Institute of Scientific and Technical Information of China (English)
刘少军; 黄中华; 陈毅章
2004-01-01
A quarter-automobile active suspension model was proposed. High speed on/off solenoid valves were used as control valves and fuzzy control was chosen as control method . Based on force analyses of system parts, a mathematical model of the active suspension system was established and simplified by linearization method. Simulation study was conducted with Matlab and three scale coefficients of fuzzy controller (ke, kec, ku) were acquired. And an experimental device was designed and produced. The results indicate that the active suspension system can achieve better vibration isolation performance than passive suspension system, the displacement amplitude of automobile body can be reduced to 55%. Fuzzy control is an effective control method for active suspension system.
Gender Classification by Fuzzy Inference System
Directory of Open Access Journals (Sweden)
Payman Moallem
2013-02-01
Full Text Available Gender classification from face images has many applications and is thus an important research topic. This paper presents an approach to gender classification based on shape and texture information gathered to design a fuzzy decision making system. Beside face shape features, Zernik moments are applied as system inputs to improve the system output which is considered as the probability of being male face image. After parameters tuning of the proposed fuzzy decision making system, 85.05% classification rate on the FERET face database (including 1199 individuals from different poses and facial expressions shows acceptable results compare to other methods.
Fault detection thermal storage system by expert system using fuzzy abduction
Energy Technology Data Exchange (ETDEWEB)
Yamada, Koichi [Yamatake-Honeywell Co., Ltd, Yokohama (Japan). Advanced Technology Center; Kamimura, Kazuyuki [Yamatake-Honeywell Co., Ltd., Tokyo (Japan). Building Systems Div.
1996-12-31
Fuzzy abduction is a procedure for deriving fuzzy sets of hypotheses which explain a given fuzzy set of events using a set of rules with a truth value. The derived fuzzy sets of hypotheses are called fuzzy explanations. This presentation starts with discussion about diagnosis using conventional expert systems and that using fuzzy relational equations. Then, it proposes a new approach using a fuzzy abduction, and applies the technique to fault detection of a thermal storage system. (orig.)
Weakly linear systems of fuzzy relation inequalities: The heterogeneous case
Ignjatović, Jelena; Damljanović, Nada; Jančić, Ivana
2011-01-01
New types of systems of fuzzy relation inequalities and equations, called weakly linear, have been recently introduced in [J. Ignjatovi\\'c, M. \\'Ciri\\'c, S. Bogdanovi\\'c, On the greatest solutions to weakly linear systems of fuzzy relation inequalities and equations, Fuzzy Sets and Systems 161 (2010) 3081--3113.]. The mentioned paper dealt with homogeneous weakly linear systems, composed of fuzzy relations on a single set, and a method for computing their greatest solutions has been provided. This method is based on the computing of the greatest post-fixed point, contained in a given fuzzy relation, of an isotone function on the lattice of fuzzy relations. Here we adapt this method for computing the greatest solutions of heterogeneous weakly linear systems, where the unknown fuzzy relation relates two possibly different sets. We also introduce and study quotient fuzzy relational systems and establish relationships between solutions to heterogeneous and homogeneous weakly linear systems. Besides, we point out ...
12. workshop fuzzy systems. Proceedings; 12. Workshop Fuzzy Systeme. Proceedings
Energy Technology Data Exchange (ETDEWEB)
Mikut, R.; Reischl, M. (eds.)
2002-11-01
This annual workshop is a forum for discussing new methods and industrial applications in fuzzy logic and related fields like artificial neuronal nets and evolutionary algorithms. The focus is on applications in automation, e.g. in chemical engineering, energy engineering, automobile engineering, robotics and medical engineering. Other areas of interest are, e.g. data mining for technical and non-technical applications. [German] Der jaehrliche Workshop unseres Fachausschusses bietet ein Forum zur Diskussion neuer methodischer Ansaetze und industrieller Anwendungen auf dem Gebiet der Fuzzy-Logik und in angrenzenden Gebieten wie Kuenstlichen Neuronalen Netzen und Evolutionaeren Algorithmen. Besondere Schwerpunkte sind automatisierungstechnische Anwendungen, z.B. in der Verfahrenstechnik, Enegietechnik, Kfz-Technik, Robotik und Medizintechnik, aber auch Loesungen in anderen Problemgebieten (z.B. Data Mining fuer technische und nichttechnische Anwendungen) sind von Interesse. (orig.)
Robust control for a class of uncertain switched fuzzy systems
Institute of Scientific and Technical Information of China (English)
Hong YANG; Jun ZHAO
2007-01-01
A model of uncertain switched fuzzy systems whose subsystems are uncertain fuzzy systems is presented.Robust controllers for a class of switched fuzzy systems are designed by using the Lyapunov function method. Stability conditions for global asymptotic stability are developed and a switching strategy is proposed. An example shows the effectiveness of the method.
Z Number Based Fuzzy Inference System for Dynamic Plant Control
Directory of Open Access Journals (Sweden)
Rahib H. Abiyev
2016-01-01
Full Text Available Frequently the reliabilities of the linguistic values of the variables in the rule base are becoming important in the modeling of fuzzy systems. Taking into consideration the reliability degree of the fuzzy values of variables of the rules the design of inference mechanism acquires importance. For this purpose, Z number based fuzzy rules that include constraint and reliability degrees of information are constructed. Fuzzy rule interpolation is presented for designing of an inference engine of fuzzy rule-based system. The mathematical background of the fuzzy inference system based on interpolative mechanism is developed. Based on interpolative inference process Z number based fuzzy controller for control of dynamic plant has been designed. The transient response characteristic of designed controller is compared with the transient response characteristic of the conventional fuzzy controller. The obtained comparative results demonstrate the suitability of designed system in control of dynamic plants.
Neuro Fuzzy Systems: Sate-of-the-Art Modeling Techniques
Abraham, Ajith
2004-01-01
Fusion of Artificial Neural Networks (ANN) and Fuzzy Inference Systems (FIS) have attracted the growing interest of researchers in various scientific and engineering areas due to the growing need of adaptive intelligent systems to solve the real world problems. ANN learns from scratch by adjusting the interconnections between layers. FIS is a popular computing framework based on the concept of fuzzy set theory, fuzzy if-then rules, and fuzzy reasoning. The advantages of a combination of ANN a...
Fuzzy logic systems are equivalent to feedforward neural networks
Institute of Scientific and Technical Information of China (English)
李洪兴
2000-01-01
Fuzzy logic systems and feedforward neural networks are equivalent in essence. First, interpolation representations of fuzzy logic systems are introduced and several important conclusions are given. Then three important kinds of neural networks are defined, i.e. linear neural networks, rectangle wave neural networks and nonlinear neural networks. Then it is proved that nonlinear neural networks can be represented by rectangle wave neural networks. Based on the results mentioned above, the equivalence between fuzzy logic systems and feedforward neural networks is proved, which will be very useful for theoretical research or applications on fuzzy logic systems or neural networks by means of combining fuzzy logic systems with neural networks.
Yarn Strength Modelling Using Genetic Fuzzy Expert System
Banerjee, Debamalya; Ghosh, Anindya; Das, Subhasis
2013-05-01
This paper deals with the modelling of cotton yarn strength using genetic fuzzy expert system. Primarily a fuzzy expert system has been developed to model the cotton yarn strength from the constituent fibre parameters such as fibre strength, upper half mean length, fibre fineness and short fibre content. A binary coded genetic algorithm has been used to improve the prediction performance of the fuzzy expert system. The experimental validation confirms that the genetic fuzzy expert system has significantly better prediction accuracy and consistency than that of the fuzzy expert system.
Fuzzy Optimization and Normal Simulation for Solving Fuzzy Web Queuing System Problems
Directory of Open Access Journals (Sweden)
Xidong Zheng
2005-04-01
Full Text Available In this paper, we use both fuzzy optimization and normal simulation methods to solve fuzzy web planning model problems, which are queuing system problems for designing web servers. We apply fuzzy probabilities to the queuing system models with customers arrival rate l and servers?service rate m, and then compute fuzzy system performance variables, including Utilization, Number (of requests in the System, Throughput, and Response Time. For the fuzzy optimization method, we apply two-step calculation, first use fuzzy calculation to get the maximum and minimum values of fuzzy steady state probabilities, and then we compute the fuzzy system performance variables. For the simulation method, we use one-step normal queuing theory to simulate the whole system performance and its variables. We deal with queuing systems with a single server and multiple servers?cases, and compare the results of these two cases, giving a mathematical explanation of the difference. Keywords: Fuzzy Optimization, Normal Simulation, Queuing Theory, Web Planning Model.
Distributed intrusion detection system based on fuzzy rules
Qiao, Peili; Su, Jie; Liu, Yahui
2006-04-01
Computational Intelligence is the theory and method solving problems by simulating the intelligence of human using computer and it is the development of Artificial Intelligence. Fuzzy Technique is one of the most important theories of computational Intelligence. Genetic Fuzzy Technique and Neuro-Fuzzy Technique are the combination of Fuzzy Technique and novel techniques. This paper gives a distributed intrusion detection system based on fuzzy rules that has the characters of distributed parallel processing, self-organization, self-learning and self-adaptation by the using of Neuro-Fuzzy Technique and Genetic Fuzzy Technique. Specially, fuzzy decision technique can be used to reduce false detection. The results of the simulation experiment show that this intrusion detection system model has the characteristics of distributed, error tolerance, dynamic learning, and adaptation. It solves the problem of low identifying rate to new attacks and hidden attacks. The false detection rate is low. This approach is efficient to the distributed intrusion detection.
Fuzzy Expert System to Characterize Students
Van Hecke, T.
2011-01-01
Students wanting to succeed in higher education are required to adopt an adequate learning approach. By analyzing individual learning characteristics, teachers can give personal advice to help students identify their learning success factors. An expert system based on fuzzy logic can provide economically viable solutions to help students identify…
Fuzzy Expert System to Characterize Students
Van Hecke, T.
2011-01-01
Students wanting to succeed in higher education are required to adopt an adequate learning approach. By analyzing individual learning characteristics, teachers can give personal advice to help students identify their learning success factors. An expert system based on fuzzy logic can provide economically viable solutions to help students identify…
Fuzzy Sliding Mode Control for Discrete Nonlinear Systems
Institute of Scientific and Technical Information of China (English)
F.Qiao.Q.M.Zhu; A.Winfield; C.Melhuish
2003-01-01
Sliding mode control is introduced into classical model free fuzzy logic control for discrete time nonlinear systems with uncertainty to the design of a novel fuzzy sliding mode control to meet the requirement of necessary and sufficient reaching conditions of sliding mode control. The simulation results show that the proposed controller outperforms the original fuzzy sliding mode controller and the classical fuzzy logic controller in stability, convergence and robustness.
Directory of Open Access Journals (Sweden)
K. A. Halim
2011-01-01
Full Text Available In this article, we consider a single-unit unreliable production system which produces a single item. During a production run, the production process may shift from the in-control state to the out-of-control state at any random time when it produces some defective items. The defective item production rate is assumed to be imprecise and is characterized by a trapezoidal fuzzy number. The production rate is proportional to the demand rate where the proportionality constant is taken to be a fuzzy number. Two production planning models are developed on the basis of fuzzy and stochastic demand patterns. The expected cost per unit time in the fuzzy sense is derived in each model and defuzzified by using the graded mean integration representation method. Numerical examples are provided to illustrate the optimal results of the proposed fuzzy models.
Directory of Open Access Journals (Sweden)
A. Karimi Dizicheh
2016-03-01
Full Text Available In this paper, we firstly introduce system of first order fuzzy differential equations. Then, we convert the problem to two crisp systems of first order differential equations. For numerical aspects, we apply exponentially fitted Runge Kutta method to solve the fuzzy problems. We solve some well-known examples in order to demonstrate the applicability and accuracy of results.
UNCERTAIN KNOWLEDGE MANAGEMENT IN EXPERT SYSTEMS USING FUZZY METAGRAPHS
Institute of Scientific and Technical Information of China (English)
无
2000-01-01
This paper presented a new graph-theoretic construct fuzzy metagraphs and discussed their applications in constructing--fuzzy knowledge base. Fuzzy metagraphs describe the relationships between sets of fuzzy elements but not single fuzzy element and offer some distinct advantages both for visualization of systems, as well as for formal analysis of system structure. In rule-based system, a fuzzy metagraph is a unity of the knowledge base and the reasoning engine. Based on the closure of the adjacency matrix of fuzzy metagraphs, this paper presented an optimized inferential mechanism working mainly by an off-line approach. It can greatly increase the efficiency of inference. Finally, it was applied in a daignostic expert system and satisfactory results were obtained.
Yarn Strength Modelling Using Fuzzy Expert System
Directory of Open Access Journals (Sweden)
Abhijit Majumdar, Ph.D.
2008-12-01
Full Text Available Yarn strength modelling and prediction has remained as the cynosure of research for the textile engineers although the investigation in this domain was first reported around one century ago. Several mathematical, statistical and empirical models have been developed in the past only to yield limited success in terms of prediction accuracy and general applicability. In recent years, soft computing tools like artificial neural networks and neural-fuzzy models have been developed, which have shown remarkable prediction accuracy. However, artificial neural network and neural-fuzzy models are trained using enormous amount of noise free input-output data, which are difficult to collect from the spinning industries. In contrast, fuzzy logic based models could be developed by using the experience of the spinner only and it gives good understanding about the roles played by various inputs on the outputs. This paper deals with the modelling of ring spun cotton yarn strength using a simple fuzzy expert system. The prediction accuracy of the model was found to be very encouraging.
Fuzzy Logic System for Slope Stability Prediction
Directory of Open Access Journals (Sweden)
Tarig Mohamed
2012-01-01
Full Text Available The main goal of this research is to predict the stability of slopes using fuzzy logic system. GeoStudio, a commercially available software was used to compute safety factors for various designs of slope. The general formulation of the software could analyze slope stability using various methods of analysis i.e. Morgenstern-Price, Janbu, Bishop and Ordinary to calculate the safety factors. After analyzing, fuzzy logic was used to predict the slope stability. Fuzzy logic is based on natural language and conceptually easy to understand, flexible, tolerant of imprecise data and able to model nonlinear functions of arbitrary complexity. Several important parameters such as height of slope, unit weight of slope material, angle of slope, coefficient of cohesion and internal angle of friction were used as the input parameters, while the factor of safety was the output parameter. A model to test the stability of the slope was generated from the calculated data. This model presented a relationship between input parameters and stability of the slopes. Results showed that the prediction using fuzzy logic was accurate and close to the target data.
Fuzzy fault diagnosis system of MCFC
Institute of Scientific and Technical Information of China (English)
Wang Zhenlei; Qian Feng; Cao Guangyi
2005-01-01
A kind of fault diagnosis system of molten carbonate fuel cell (MCFC) stack is proposed in this paper. It is composed of a fuzzy neural network (FNN) and a fault diagnosis element. FNN is able to deal with the information of the expert knowledge and the experiment data efficiently. It also has the ability to approximate any smooth system. FNN is used to identify the fault diagnosis model of MCFC stack. The fuzzy fault decision element can diagnose the state of the MCFC generating system, normal or fault, and can decide the type of the fault based on the outputs of FNN model and the MCFC system. Some simulation experiment results are demonstrated in this paper.
A Temporal Neuro-Fuzzy Monitoring System to Manufacturing Systems
Mahdaoui, Rafik; Mouss, Mohamed Djamel; Chouhal, Ouahiba
2011-01-01
Fault diagnosis and failure prognosis are essential techniques in improving the safety of many manufacturing systems. Therefore, on-line fault detection and isolation is one of the most important tasks in safety-critical and intelligent control systems. Computational intelligence techniques are being investigated as extension of the traditional fault diagnosis methods. This paper discusses the Temporal Neuro-Fuzzy Systems (TNFS) fault diagnosis within an application study of a manufacturing system. The key issues of finding a suitable structure for detecting and isolating ten realistic actuator faults are described. Within this framework, data-processing interactive software of simulation baptized NEFDIAG (NEuro Fuzzy DIAGnosis) version 1.0 is developed. This software devoted primarily to creation, training and test of a classification Neuro-Fuzzy system of industrial process failures. NEFDIAG can be represented like a special type of fuzzy perceptron, with three layers used to classify patterns and failures....
Recommendation System Based on Fuzzy Cognitive Map
Directory of Open Access Journals (Sweden)
Wei Liu
2014-07-01
Full Text Available With the increase of data volume and visitor volume, the website faces great challenge in the environment of network. How to know the users’ requirements rapidly and effectively and recommend the required information to the user becomes the research direction of all websites. The researchers of recommendation system propose a series of recommendation system models and algorithms for the user. The common challenge faced by these algorithms is how to judge the user intention and recommend the relevant content by little user action. The paper proposes the user situation awareness and information recommendation system based on fuzzy clustering analysis and fuzzy cognitive maps, and verifies the validity of the algorithm by the application to recommendation site of academic thesis.
Fuzzy controller for an uncertain dynamical system
DEFF Research Database (Denmark)
Kulczycki, P.; Wisniewski, Rafal
2002-01-01
The present paper deals with the time-optimal control for mechanical systems with uncertain load. A fuzzy approach is used in the design of suboptimal feedback controllers, robust with respect to the load. Statistical kernel estimators are used for the specification of crucial parameters. The met......The present paper deals with the time-optimal control for mechanical systems with uncertain load. A fuzzy approach is used in the design of suboptimal feedback controllers, robust with respect to the load. Statistical kernel estimators are used for the specification of crucial parameters....... The methodology proposed in this work may be easily adopted to other modeling uncertainties of mechanical systems, e.g. motion resistance....
Fuzzy Logic Based Power System Contingency Ranking
Directory of Open Access Journals (Sweden)
A. Y. Abdelaziz
2013-02-01
Full Text Available Voltage stability is a major concern in planning and operations of power systems. It is well known that voltage instability and collapse have led to major system failures. Modern transmission networks are more heavily loaded than ever before to meet the growing demand. One of the major consequences resulted from such a stressed system is voltage collapse or instability. This paper presents maximum loadability identification of a load bus in a power transmission network. In this study, Fast Voltage Stability Index (FVSI is utilized as the indicator of the maximum loadability termed as Qmax. In this technique, reactive power loading will be increased gradually at particular load bus until the FVSI reaches close to unity. Therefore, a critical value of FVSI was set as the maximum loadability point. This value ensures the system from entering voltage-collapse region. The main purpose in the maximum loadability assessment is to plan for the maximum allowable load value to avoid voltage collapse; which is important in power system planning risk assessment.The most important task in security analysis is the problem of identifying the critical contingencies from a large list of credible contingencies and ranks them according to their severity. The condition of voltage stability in a power system can be characterized by the use of voltage stability indices. This paper presents fuzzy approach for ranking the contingencies using composite-index based on parallel operated fuzzy inference engine. The Line Flow index (L.F and bus Voltage Magnitude (VM of the load buses are expressed in fuzzy set notation. Further, they are evaluated using Fuzzy rules to obtain overall Criticality Index. Contingencies are ranked based on decreasing order of Criticality Index and then provides the comparison of ranking obtained with FVSI method.
On Controllability and Observability of Fuzzy Dynamical Matrix Lyapunov Systems
Directory of Open Access Journals (Sweden)
M. S. N. Murty
2008-04-01
Full Text Available We provide a way to combine matrix Lyapunov systems with fuzzy rules to form a new fuzzy system called fuzzy dynamical matrix Lyapunov system, which can be regarded as a new approach to intelligent control. First, we study the controllability property of the fuzzy dynamical matrix Lyapunov system and provide a sufficient condition for its controllability with the use of fuzzy rule base. The significance of our result is that given a deterministic system and a fuzzy state with rule base, we can determine the rule base for the control. Further, we discuss the concept of observability and give a sufficient condition for the system to be observable. The advantage of our result is that we can determine the rule base for the initial value without solving the system.
Energy Technology Data Exchange (ETDEWEB)
Castro, Antonio Orestes de Salvo [PETROBRAS, Rio de Janeiro, RJ (Brazil); Ferreira Filho, Virgilio Jose Martins [Universidade Federal do Rio de Janeiro (UFRJ), RJ (Brazil)
2004-07-01
The hydraulic fracture operation is wide used to increase the oil wells production and to reduce formation damage. Reservoir studies and engineer analysis are made to select the wells for this kind of operation. As the reservoir parameters have some diffuses characteristics, Fuzzy Inference Systems (SIF) have been tested for this selection processes in the last few years. This paper compares the performance of a neuro fuzzy system and a genetic fuzzy system used for hydraulic Fracture well selection, with knowledge acquisition from an operational data base to set the SIF membership functions. The training data and the validation data used were the same for both systems. We concluded that, in despite of the genetic fuzzy system would be a younger process, it got better results than the neuro fuzzy system. Another conclusion was that, as the genetic fuzzy system can work with constraints, the membership functions setting kept the consistency of variables linguistic values. (author)
Fuzzy controllers and fuzzy expert systems: industrial applications of fuzzy technology
Bonissone, Piero P.
1995-06-01
We will provide a brief description of the field of approximate reasoning systems, with a particular emphasis on the development of fuzzy logic control (FLC). FLC technology has drastically reduced the development time and deployment cost for the synthesis of nonlinear controllers for dynamic systems. As a result we have experienced an increased number of FLC applications. In a recently published paper we have illustrated some of our efforts in FLC technology transfer, covering projects in turboshaft aircraft engine control, stream turbine startup, steam turbine cycling optimization, resonant converter power supply control, and data-induced modeling of the nonlinear relationship between process variable in a rolling mill stand. These applications will be illustrated in the oral presentation. In this paper, we will compare these applications in a cost/complexity framework, and examine the driving factors that led to the use of FLCs in each application. We will emphasize the role of fuzzy logic in developing supervisory controllers and in maintaining explicit the tradeoff criteria used to manage multiple control strategies. Finally, we will describe some of our FLC technology research efforts in automatic rule base tuning and generation, leading to a suite of programs for reinforcement learning, supervised learning, genetic algorithms, steepest descent algorithms, and rule clustering.
Generating Interpretable Fuzzy Systems for Classification Problems
Directory of Open Access Journals (Sweden)
Juan A. Contreras-Montes
2009-12-01
Full Text Available This paper presents a new method to generate interpretable fuzzy systems from training data to deal with classification problems. The antecedent partition uses triangular sets with 0.5 interpolations avoiding the presence of complex overlapping that happens in another method. Singleton consequents are generated form the projection of the modal values of each triangular membership function into the output space. Least square method is used to adjust the consequents. The proposed method gets a higher average classification accuracy rate than the existing methods with a reduced number of rules andparameters and without sacrificing the fuzzy system interpretability. The proposed approach is applied to two classical classification problems: Iris data and the Wisconsin Breast Cancer classification problem.
Fuzzy system dynamics and optimization with application to manpower systems
Directory of Open Access Journals (Sweden)
C. Mbohwa
2012-10-01
Full Text Available The dynamics of human resource recruitment and training in an uncertain environment creates a challenge for many policy makers in various organisations. In the presence of fuzzy manpower demand and training capacity, many companies fear losing critical human resources when their employees leave. As such, the development of effective dynamic policies for recruitment and training in a fuzzy dynamic environment is imperative. In this frame of mind, a fuzzy systems dynamics modelling approach is proposed to enable the policy maker to develop reliable dynamic policies relating recruitment, training, and available skills, from a systems perspective. It is anticipated in this study that fuzzy system dynamics and optimization approach would help organizations to design effective manpower policies and strategies.
FUZZY ALGEBRA IN TRIANGULAR NORM SYSTEM
Institute of Scientific and Technical Information of China (English)
宋晓秋; 潘志
1994-01-01
Triangular norm is a powerful tool in the theory research and application development of fuzzy sets. In this paper, using the triangular norm, we introduce some concepts such as fuzzy algebra, fuzzy o algebra and fuzzy monotone class, and discuss the relations among them, obtaining the following main conclusions.
Fuzzy Rule Base System for Software Classification
Directory of Open Access Journals (Sweden)
Adnan Shaout
2013-07-01
Full Text Available Given the central role that software development plays in the delivery and application of informationtechnology, managers have been focusing on process improvement in the software development area. Thisimprovement has increased the demand for software measures, or metrics to manage the process. Thismetrics provide a quantitative basis for the development and validation of models during the softwaredevelopment process. In this paper a fuzzy rule-based system will be developed to classify java applicationsusing object oriented metrics. The system will contain the following features:Automated method to extract the OO metrics from the source code,Default/base set of rules that can be easily configured via XML file so companies, developers, teamleaders,etc, can modify the set of rules according to their needs,Implementation of a framework so new metrics, fuzzy sets and fuzzy rules can be added or removeddepending on the needs of the end user,General classification of the software application and fine-grained classification of the java classesbased on OO metrics, andTwo interfaces are provided for the system: GUI and command.
Uncertainty in Interval Type-2 Fuzzy Systems
Directory of Open Access Journals (Sweden)
Sadegh Aminifar
2013-01-01
Full Text Available This paper studies uncertainty and its effect on system response displacement. The paper also describes how IT2MFs (interval type-2 membership functions differentiate from T1MFs (type-1 membership functions by adding uncertainty. The effect of uncertainty is modeled clearly by introducing a technique that describes how uncertainty causes membership degree reduction and changing the fuzzy word meanings in fuzzy logic controllers (FLCs. Several criteria are discussed for the measurement of the imbalance rate of internal uncertainty and its effect on system behavior. Uncertainty removal is introduced to observe the effect of uncertainty on the output. The theorem of uncertainty avoidance is presented for describing the role of uncertainty in interval type-2 fuzzy systems (IT2FSs. Another objective of this paper is to derive a novel uncertainty measure for IT2MFs with lower complexity and clearer presentation. Finally, for proving the affectivity of novel interpretation of uncertainty in IT2FSs, several investigations are done.
Genetic fuzzy system modeling and simulation of vascular behaviour
DEFF Research Database (Denmark)
Tang, Jiaowei; Boonen, Harrie C.M.
and find the optimal parameters in a Fuzzy Control set that can control the fluctuation of physical features in a blood vessel, based on experimental data (training data). Our solution is to create chromosomes or individuals composed of a sequence of parameters in the fuzzy system and find the best...... chromosome or individual to define the fuzzy system. The model is implemented by combining the Matlab Genetic algorithm and Fuzzy system toolboxes, respectively. To test the performance of this method, experimental data sets about calculated pressure change in different blood vessels after several chemical...... treatments are chosen as training and testing data sets. In the simulation, the fuzzy control system is trained by pressure data of one blood vessel and tested with pressure data of other blood vessels. Results: Right now, some rough results show that trained fuzzy control system can be used to predict...
Indian Academy of Sciences (India)
Diptiranjan Behera; S Chakraverty
2015-02-01
This paper proposes two new methods to solve fully fuzzy system of linear equations. The fuzzy system has been converted to a crisp system of linear equations by using single and double parametric form of fuzzy numbers to obtain the non-negative solution. Double parametric form of fuzzy numbers is defined and applied for the first time in this paper for the present analysis. Using single parametric form, the $n \\times n$ fully fuzzy system of linear equations have been converted to a $2n \\times 2n$ crisp system of linear equations. On the other hand, double parametric form of fuzzy numbers converts the n×n fully fuzzy system of linear equations to a crisp system of same order. Triangular and trapezoidal convex normalized fuzzy sets are used for the present analysis. Known example problems are solved to illustrate the efficacy and reliability of the proposed methods.
Fuzzy Control Method with Application for Functional Neuromuscular Stimulation System
Institute of Scientific and Technical Information of China (English)
吴怀宇; 周兆英; 熊沈蜀
2001-01-01
A fuzzy control technique is applied to a functional neuromuscular stimulation (FNS) physicalmultiarticular muscle control system. The FNS multiarticular muscle control system based on the fuzzy controllerwas developed with the fuzzy control rule base. Simulation experiments were then conducted for the joint angletrajectories of both the elbow flexion and the wrist flexion using the proposed fuzzy control algorithm and aconventional PID control algorithm with the FNS physical multiarticular muscle control system. The simulationresults demonstrated that the proposed fuzzy control method is more suitable for the physiologicalcharacteristics than conventional PID control. In particular, both the trajectory-following and the stability of theFNS multiarticular muscle control system were greatly improved. Furthermore, the stimulating pulse trainsgenerated by the fuzzy controller were stable and smooth.``
Wang, Lijie; Li, Hongyi; Zhou, Qi; Lu, Renquan
2017-09-01
This paper investigates the problem of observer-based adaptive fuzzy control for a category of nonstrict feedback systems subject to both unmodeled dynamics and fuzzy dead zone. Through constructing a fuzzy state observer and introducing a center of gravity method, unmeasurable states are estimated and the fuzzy dead zone is defuzzified, respectively. By employing fuzzy logic systems to identify the unknown functions. And combining small-gain approach with adaptive backstepping control technique, a novel adaptive fuzzy output feedback control strategy is developed, which ensures that all signals involved are semi-globally uniformly bounded. Simulation results are given to demonstrate the effectiveness of the presented method.
Fuzzy Dynamic Discrimination Algorithms for Distributed Knowledge Management Systems
Directory of Open Access Journals (Sweden)
Vasile MAZILESCU
2010-12-01
Full Text Available A reduction of the algorithmic complexity of the fuzzy inference engine has the following property: the inputs (the fuzzy rules and the fuzzy facts can be divided in two parts, one being relatively constant for a long a time (the fuzzy rule or the knowledge model when it is compared to the second part (the fuzzy facts for every inference cycle. The occurrence of certain transformations over the constant part makes sense, in order to decrease the solution procurement time, in the case that the second part varies, but it is known at certain moments in time. The transformations attained in advance are called pre-processing or knowledge compilation. The use of variables in a Business Rule Management System knowledge representation allows factorising knowledge, like in classical knowledge based systems. The language of the first-degree predicates facilitates the formulation of complex knowledge in a rigorous way, imposing appropriate reasoning techniques. It is, thus, necessary to define the description method of fuzzy knowledge, to justify the knowledge exploiting efficiency when the compiling technique is used, to present the inference engine and highlight the functional features of the pattern matching and the state space processes. This paper presents the main results of our project PR356 for designing a compiler for fuzzy knowledge, like Rete compiler, that comprises two main components: a static fuzzy discrimination structure (Fuzzy Unification Tree and the Fuzzy Variables Linking Network. There are also presented the features of the elementary pattern matching process that is based on the compiled structure of fuzzy knowledge. We developed fuzzy discrimination algorithms for Distributed Knowledge Management Systems (DKMSs. The implementations have been elaborated in a prototype system FRCOM (Fuzzy Rule COMpiler.
Output-back fuzzy logic systems and equivalence with feedback neural networks
Institute of Scientific and Technical Information of China (English)
无
2000-01-01
A new idea, output-back fuzzy logic systems, is proposed. It is proved that output-back fuzzy logic systems must be equivalent to feedback neural networks. After the notion of generalized fuzzy logic systems is defined, which contains at least a typical fuzzy logic system and an output-back fuzzy logic system, one important conclusion is drawn that generalized fuzzy logic systems are almost equivalent to neural networks.
A proposed method for solving fuzzy system of linear equations.
Kargar, Reza; Allahviranloo, Tofigh; Rostami-Malkhalifeh, Mohsen; Jahanshaloo, Gholam Reza
2014-01-01
This paper proposes a new method for solving fuzzy system of linear equations with crisp coefficients matrix and fuzzy or interval right hand side. Some conditions for the existence of a fuzzy or interval solution of m × n linear system are derived and also a practical algorithm is introduced in detail. The method is based on linear programming problem. Finally the applicability of the proposed method is illustrated by some numerical examples.
Life insurance risk assessment using a fuzzy logic expert system
Carreno, Luis A.; Steel, Roy A.
1992-01-01
In this paper, we present a knowledge based system that combines fuzzy processing with rule-based processing to form an improved decision aid for evaluating risk for life insurance. This application illustrates the use of FuzzyCLIPS to build a knowledge based decision support system possessing fuzzy components to improve user interactions and KBS performance. The results employing FuzzyCLIPS are compared with the results obtained from the solution of the problem using traditional numerical equations. The design of the fuzzy solution consists of a CLIPS rule-based system for some factors combined with fuzzy logic rules for others. This paper describes the problem, proposes a solution, presents the results, and provides a sample output of the software product.
Reliable fuzzy control with domain guaranteed cost for fuzzy systems with actuator failures
Institute of Scientific and Technical Information of China (English)
JIA Xinchun; ZHENG Nanning
2004-01-01
The reliable fuzzy control with guaranteed cost for T-S fuzzy systems with actuator failure is proposed in this paper. The cost function is a quadratic function with failure input. When the initial state of such systems is known, a design method of the reliable fuzzy controller with reliable guaranteed cost is presented, and the formula of the guaranteed cost is established. When the initial state of such systems is unknown but belongs to a known bounded closed domain, a notion of the reliable domain guaranteed cost (RDGC) for such systems is proposed. For two classes of initial state domain, polygon domain and ellipsoid domain, some design methods for reliable fuzzy controllers with the RDGC are provided. The efficiency of our design methods is finally verified by numerical design and simulation on the Rossler chaotic system.
Advanced Concepts in Fuzzy Logic and Systems with Membership Uncertainty
Starczewski, Janusz T
2013-01-01
This book generalizes fuzzy logic systems for different types of uncertainty, including - semantic ambiguity resulting from limited perception or lack of knowledge about exact membership functions - lack of attributes or granularity arising from discretization of real data - imprecise description of membership functions - vagueness perceived as fuzzification of conditional attributes. Consequently, the membership uncertainty can be modeled by combining methods of conventional and type-2 fuzzy logic, rough set theory and possibility theory. In particular, this book provides a number of formulae for implementing the operation extended on fuzzy-valued fuzzy sets and presents some basic structures of generalized uncertain fuzzy logic systems, as well as introduces several of methods to generate fuzzy membership uncertainty. It is desirable as a reference book for under-graduates in higher education, master and doctor graduates in the courses of computer science, computational intelligence, or...
FUZZY LOGIC MULTI-AGENT SYSTEM
Atef GHARBI; Ben Ahmed, Samir
2014-01-01
The paper deals with distributed planning in a Multi-Agent System (MAS) constituted by several intelligent agents each one has to interact with the other autonomous agents. The problem faced is how to ensure a distributed planning through the cooperation in our multi-agent system. To do so, we propose the use of fuzzy logic to represent the response of the agent in case of interaction with the other. Finally, we use JADE platform to create agents and ensure the communication be...
Fuzzy Control of Chaotic System with Genetic Algorithm
Institute of Scientific and Technical Information of China (English)
FANG Jian-an; GUO Zhao-xia; SHAO Shi-huang
2002-01-01
A novel approach to control the unpredictable behavior of chaotic systems is presented. The control algorithm is based on fuzzy logic control technique combined with genetic algorithm. The use of fuzzy logic allows for the implementation of human "rule-of-thumb" approach to decision making by employing linguistic variables. An improved Genetic Algorithm (GA) is used to learn to optimally select the fuzzy membership functions of the linguistic labels in the condition portion of each rule,and to automatically generate fuzzy control actions under each condition. Simulation results show that such an approach for the control of chaotic systems is both effective and robust.
Zhao, Tao; Dian, Songyi
2017-09-01
This paper addresses a fuzzy dynamic output feedback H∞ control design problem for continuous-time nonlinear systems via T-S fuzzy model. The stability of the fuzzy closed-loop system which is formed by a T-S fuzzy model and a fuzzy dynamic output feedback H∞ controller connected in a closed loop is investigated with Lyapunov stability theory. The proposed fuzzy controller does not share the same membership functions and number of rules with T-S fuzzy systems, which can enhance design flexibility. A line-integral fuzzy Lyapunov function is utilized to derive the stability conditions in the form of linear matrix inequalities (LMIs). The boundary information of membership functions is considered in the stability analysis to reduce the conservativeness of the imperfect premise matching design technique. Two simulation examples are provided to demonstrate the effectiveness of the proposed approach. Copyright © 2017 ISA. Published by Elsevier Ltd. All rights reserved.
HyFIS: adaptive neuro-fuzzy inference systems and their application to nonlinear dynamical systems.
Kim, J; Kasabov, N
1999-11-01
This paper proposes an adaptive neuro-fuzzy system, HyFIS (Hybrid neural Fuzzy Inference System), for building and optimising fuzzy models. The proposed model introduces the learning power of neural networks to fuzzy logic systems and provides linguistic meaning to the connectionist architectures. Heuristic fuzzy logic rules and input-output fuzzy membership functions can be optimally tuned from training examples by a hybrid learning scheme comprised of two phases: rule generation phase from data; and rule tuning phase using error backpropagation learning scheme for a neural fuzzy system. To illustrate the performance and applicability of the proposed neuro-fuzzy hybrid model, extensive simulation studies of nonlinear complex dynamic systems are carried out. The proposed method can be applied to an on-line incremental adaptive learning for the prediction and control of nonlinear dynamical systems. Two benchmark case studies are used to demonstrate that the proposed HyFIS system is a superior neuro-fuzzy modelling technique.
Fuzzy Lyapunov Reinforcement Learning for Non Linear Systems.
Kumar, Abhishek; Sharma, Rajneesh
2017-03-01
We propose a fuzzy reinforcement learning (RL) based controller that generates a stable control action by lyapunov constraining fuzzy linguistic rules. In particular, we attempt at lyapunov constraining the consequent part of fuzzy rules in a fuzzy RL setup. Ours is a first attempt at designing a linguistic RL controller with lyapunov constrained fuzzy consequents to progressively learn a stable optimal policy. The proposed controller does not need system model or desired response and can effectively handle disturbances in continuous state-action space problems. Proposed controller has been employed on the benchmark Inverted Pendulum (IP) and Rotational/Translational Proof-Mass Actuator (RTAC) control problems (with and without disturbances). Simulation results and comparison against a) baseline fuzzy Q learning, b) Lyapunov theory based Actor-Critic, and c) Lyapunov theory based Markov game controller, elucidate stability and viability of the proposed control scheme.
Fuzzy expert system for diagnosing diabetic neuropathy.
Rahmani Katigari, Meysam; Ayatollahi, Haleh; Malek, Mojtaba; Kamkar Haghighi, Mehran
2017-02-15
To design a fuzzy expert system to help detect and diagnose the severity of diabetic neuropathy. The research was completed in 2014 and consisted of two main phases. In the first phase, the diagnostic parameters were determined based on the literature review and by investigating specialists' perspectives (n = 8). In the second phase, 244 medical records related to the patients who were visited in an endocrinology and metabolism research centre during the first six months of 2014 and were primarily diagnosed with diabetic neuropathy, were used to test the sensitivity, specificity, and accuracy of the fuzzy expert system. The final diagnostic parameters included the duration of diabetes, the score of a symptom examination based on the Michigan questionnaire, the score of a sign examination based on the Michigan questionnaire, the glycolysis haemoglobin level, fasting blood sugar, blood creatinine, and albuminuria. The output variable was the severity of diabetic neuropathy which was shown as a number between zero and 10, had been divided into four categories: absence of the disease, (the degree of severity) mild, moderate, and severe. The interface of the system was designed by ASP.Net (Active Server Pages Network Enabled Technology) and the system function was tested in terms of sensitivity (true positive rate) (89%), specificity (true negative rate) (98%), and accuracy (a proportion of true results, both positive and negative) (93%). The system designed in this study can help specialists and general practitioners to diagnose the disease more quickly to improve the quality of care for patients.
Minimal solution of general dual fuzzy linear systems
Energy Technology Data Exchange (ETDEWEB)
Abbasbandy, S. [Department of Mathematics, Science and Research Branch, Islamic Azad University, Tehran 14778 (Iran, Islamic Republic of); Department of Mathematics, Faculty of Science, Imam Khomeini International University, Qazvin 34194-288 (Iran, Islamic Republic of)], E-mail: abbasbandy@yahoo.com; Otadi, M.; Mosleh, M. [Department of Mathematics, Science and Research Branch, Islamic Azad University, Tehran 14778 (Iran, Islamic Republic of); Department of Mathematics, Islamic Azad University, Firuozkooh Branch, Firuozkooh (Iran, Islamic Republic of)
2008-08-15
Fuzzy linear systems of equations, play a major role in several applications in various area such as engineering, physics and economics. In this paper, we investigate the existence of a minimal solution of general dual fuzzy linear equation systems. Two necessary and sufficient conditions for the minimal solution existence are given. Also, some examples in engineering and economic are considered.
Iterative Feedback Tuning in Fuzzy Control Systems. Theory and Applications
Directory of Open Access Journals (Sweden)
Stefan Preitl
2006-07-01
Full Text Available The paper deals with both theoretical and application aspects concerningIterative Feedback Tuning (IFT algorithms in the design of a class of fuzzy controlsystems employing Mamdani-type PI-fuzzy controllers. The presentation is focused on twodegree-of-freedom fuzzy control system structures resulting in one design method. Thestability analysis approach based on Popov’s hyperstability theory solves the convergenceproblems associated to IFT algorithms. The suggested design method is validated by realtimeexperimental results for a fuzzy controlled nonlinear DC drive-type laboratoryequipment.
Fuzzy Expert System for Heart Attack Diagnosis
Hassan, Norlida; Arbaiy, Nureize; Shah, Noor Aziyan Ahmad; Afizah Afif@Afip, Zehan
2017-08-01
Heart attack is one of the serious illnesses and reported as the main killer disease. Early prevention is significant to reduce the risk of having the disease. The prevention efforts can be strengthen through awareness and education about risk factor and healthy lifestyle. Therefore the knowledge dissemination is needed to play role in order to distribute and educate public in health care management and disease prevention. Since the knowledge dissemination in medical is important, there is a need to develop a knowledge based system that can emulate human intelligence to assist decision making process. Thereby, this study utilized hybrid artificial intelligence (AI) techniques to develop a Fuzzy Expert System for Diagnosing Heart Attack Disease (HAD). This system integrates fuzzy logic with expert system, which helps the medical practitioner and people to predict the risk and as well as diagnosing heart attack based on given symptom. The development of HAD is expected not only providing expert knowledge but potentially become one of learning resources to help citizens to develop awareness about heart-healthy lifestyle.
Diagnosis of arthritis through fuzzy inference system.
Singh, Sachidanand; Kumar, Atul; Panneerselvam, K; Vennila, J Jannet
2012-06-01
Expert or knowledge-based systems are the most common type of AIM (artificial intelligence in medicine) system in routine clinical use. They contain medical knowledge, usually about a very specifically defined task, and are able to reason with data from individual patients to come up with reasoned conclusion. Although there are many variations, the knowledge within an expert system is typically represented in the form of a set of rules. Arthritis is a chronic disease and about three fourth of the patients are suffering from osteoarthritis and rheumatoid arthritis which are undiagnosed and the delay of detection may cause the severity of the disease at higher risk. Thus, earlier detection of arthritis and treatment of its type of arthritis and related locomotry abnormalities is of vital importance. Thus the work was aimed to design a system for the diagnosis of Arthitis using fuzzy logic controller (FLC) which is, a successful application of Zadeh's fuzzy set theory. It is a potential tool for dealing with uncertainty and imprecision. Thus, the knowledge of a doctor can be modelled using an FLC. The performance of an FLC depends on its knowledge base which consists of a data base and a rule base. It is observed that the performance of an FLC mainly depends on its rule base, and optimizing the membership function distributions stored in the data base is a fine tuning process.
A Recursive Fuzzy System for Efficient Digital Image Stabilization
Directory of Open Access Journals (Sweden)
Nikolaos Kyriakoulis
2008-01-01
Full Text Available A novel digital image stabilization technique is proposed in this paper. It is based on a fuzzy Kalman compensation of the global motion vector (GMV, which is estimated in the log-polar plane. The GMV is extracted using four local motion vectors (LMVs computed on respective subimages in the logpolar plane. The fuzzy Kalman system consists of a fuzzy system with the Kalman filter's discrete time-invariant definition. Due to this inherited recursiveness, the output results into smoothed image sequences. The proposed stabilization system aims to compensate any oscillations of the frame absolute positions, based on the motion estimation in the log-polar domain, filtered by the fuzzy Kalman system, and thus the advantages of both the fuzzy Kalman system and the log-polar transformation are exploited. The described technique produces optimal results in terms of the output quality and the level of compensation.
Comparative Analysis of Fuzzy Inference Systems for Air Conditioner
Directory of Open Access Journals (Sweden)
Swati R. Chaudhari
2014-12-01
Full Text Available In today’s world there is exponential increase in the use of air conditioning devices. The enhancement in utilization of such devices makes it essential for them to work with their full capability and efficiency. The fuzzy inference systems are best suited for the applications requiring easy interpretation, human reasoning, accurate decision making and control. The fuzzy inference systems resemble human decision making and generate precise solutions from approximate information. A comprehensive review of fuzzy inference systems with weighted average and defuzzification is covered in this paper. The objective of the paper is to provide the comparative analysis of fuzzy inference systems. This paper is a quick reference for the researchers in studying the characteristics of fuzzy inference system in air conditioner.
Model Reduction of Fuzzy Logic Systems
Directory of Open Access Journals (Sweden)
Zhandong Yu
2014-01-01
Full Text Available This paper deals with the problem of ℒ2-ℒ∞ model reduction for continuous-time nonlinear uncertain systems. The approach of the construction of a reduced-order model is presented for high-order nonlinear uncertain systems described by the T-S fuzzy systems, which not only approximates the original high-order system well with an ℒ2-ℒ∞ error performance level γ but also translates it into a linear lower-dimensional system. Then, the model approximation is converted into a convex optimization problem by using a linearization procedure. Finally, a numerical example is presented to show the effectiveness of the proposed method.
System control fuzzy neural sewage pumping stations using genetic algorithms
Directory of Open Access Journals (Sweden)
Владлен Николаевич Кузнецов
2015-06-01
Full Text Available It is considered the system of management of sewage pumping station with regulators based on a neuron network with fuzzy logic. Linguistic rules for the controller based on fuzzy logic, maintaining the level of effluent in the receiving tank within the prescribed limits are developed. The use of genetic algorithms for neuron network training is shown.
Optimization of Fuzzy Logic Controller for Supervisory Power System Stabilizers
Directory of Open Access Journals (Sweden)
Y. A. Al-Turki
2012-01-01
Full Text Available This paper presents a powerful supervisory power system stabilizer (PSS using an adaptive fuzzy logic controller driven by an adaptive fuzzy set (AFS. The system under study consists of two synchronous generators, each fitted with a PSS, which are connected via double transmission lines. Different types of PSS-controller techniques are considered. The proposed genetic adaptive fuzzy logic controller (GAFLC-PSS, using 25 rules, is compared with a static fuzzy logic controller (SFLC driven by a fixed fuzzy set (FFS which has 49 rules. Both fuzzy logic controller (FLC algorithms utilize the speed error and its rate of change as an input vector. The adaptive FLC algorithm uses a genetic algorithmto tune the parameters of the fuzzy set of each PSS. The FLC’s are simulated and tested when the system is subjected to different disturbances under a wide range of operating points. The proposed GAFLC using AFS reduced the computational time of the FLC, where the number of rules is reduced from 49 to 25 rules. In addition, the proposed adaptive FLC driven by a genetic algorithm also reduced the complexity of the fuzzy model, while achieving a good dynamic response of the system under study.
Fuzzy Simulation Human Intelligent Control System Design on Gyratory Breaker
Institute of Scientific and Technical Information of China (English)
Wen,Ruchun; Zhao,Shuling; Zhu,Jianwu; Wang,Xiaoyan
2005-01-01
In order to deal with the complex process that incurs serious time delay, enormous inertia and nonlinear problems,fuzzy simulation human intelligent control algorithm rules are established. The fuzzy simulation human intelligent controller and the hardware with the single-chip microcomputer are designed and the anti-interference measures to the whole system are provided.
A first course in fuzzy logic, fuzzy dynamical systems, and biomathematics theory and applications
de Barros, Laécio Carvalho; Lodwick, Weldon Alexander
2017-01-01
This book provides an essential introduction to the field of dynamical models. Starting from classical theories such as set theory and probability, it allows readers to draw near to the fuzzy case. On one hand, the book equips readers with a fundamental understanding of the theoretical underpinnings of fuzzy sets and fuzzy dynamical systems. On the other, it demonstrates how these theories are used to solve modeling problems in biomathematics, and presents existing derivatives and integrals applied to the context of fuzzy functions. Each of the major topics is accompanied by examples, worked-out exercises, and exercises to be completed. Moreover, many applications to real problems are presented. The book has been developed on the basis of the authors’ lectures to university students and is accordingly primarily intended as a textbook for both upper-level undergraduates and graduates in applied mathematics, statistics, and engineering. It also offers a valuable resource for practitioners such as mathematical...
Incomplete fuzzy data processing systems using artificial neural network
Patyra, Marek J.
1992-01-01
In this paper, the implementation of a fuzzy data processing system using an artificial neural network (ANN) is discussed. The binary representation of fuzzy data is assumed, where the universe of discourse is decartelized into n equal intervals. The value of a membership function is represented by a binary number. It is proposed that incomplete fuzzy data processing be performed in two stages. The first stage performs the 'retrieval' of incomplete fuzzy data, and the second stage performs the desired operation on the retrieval data. The method of incomplete fuzzy data retrieval is proposed based on the linear approximation of missing values of the membership function. The ANN implementation of the proposed system is presented. The system was computationally verified and showed a relatively small total error.
Customization Using Fuzzy Recommender Systems
Institute of Scientific and Technical Information of China (English)
Ronald R. Yager
2004-01-01
We discuss some methods for constructing recommender systems. An important feature of the methods studied here is that we assume the availability of a description, representation, of the objects being considered for recommendation. The approaches studied here differ from collaborative filtering in that we only use pReferences information from the individual for whom we are providing the recommendation and make no use the preferences of other collaborators. We provide a detailed discussion of the construction of the representation schema used. We consider two sources of information about the users preferences. The first are direct statements about the type of objects the user likes. The second source of information comes from ratings of objects which the user has experienced.
A New Fuzzy System Based on Rectangular Pyramid
Jiang, Mingzuo; Yuan, Xuehai; Li, Hongxing; Wang, Jiaxia
2015-01-01
A new fuzzy system is proposed in this paper. The novelty of the proposed system is mainly in the compound of the antecedents, which is based on the proposed rectangular pyramid membership function instead of t-norm. It is proved that the system is capable of approximating any continuous function of two variables to arbitrary degree on a compact domain. Moreover, this paper provides one sufficient condition of approximating function so that the new fuzzy system can approximate any continuous function of two variables with bounded partial derivatives. Finally, simulation examples are given to show how the proposed fuzzy system can be effectively used for function approximation. PMID:25874253
A kind of fuzzy control for chaotic systems
Institute of Scientific and Technical Information of China (English)
WANG Hong-wei; MA Guang-fu
2007-01-01
With a T-S fuzzy dynamic model approximating to a non-linear system, the nonlinear system can be decomposed into some local linear models. A variable structure controller based on Lyapunov theories is designed to guarantee the global stability of the T-S fuzzy model. The controlling problems of a nonlinear system can be solved by means of consisting of linear system variable structure control and fuzzy control. The validity of the control method based on the simulating result of two kinds of chaotic systems is shown here.
A New Approach for Solving Fully Fuzzy Linear Systems
Directory of Open Access Journals (Sweden)
Amit Kumar
2011-01-01
Full Text Available Several authors have proposed different methods to find the solution of fully fuzzy linear systems (FFLSs that is, fuzzy linear system with fuzzy coefficients involving fuzzy variables. But all the existing methods are based on the assumption that all the fuzzy coefficients and the fuzzy variables are nonnegative fuzzy numbers. In this paper a new method is proposed to solve an FFLS with arbitrary coefficients and arbitrary solution vector, that is, there is no restriction on the elements that have been used in the FFLS. The primary objective of this paper is thus to introduce the concept and a computational method for solving FFLS with no non negative constraint on the parameters. The method incorporates the principles of linear programming in solving an FFLS with arbitrary coefficients and is not only easier to understand but also widens the scope of fuzzy linear equations in scientific applications. To show the advantages of the proposed method over existing methods we solve three FFLSs.
Modeling and control of an unstable system using probabilistic fuzzy inference system
Directory of Open Access Journals (Sweden)
Sozhamadevi N.
2015-09-01
Full Text Available A new type Fuzzy Inference System is proposed, a Probabilistic Fuzzy Inference system which model and minimizes the effects of statistical uncertainties. The blend of two different concepts, degree of truth and probability of truth in a unique framework leads to this new concept. This combination is carried out both in Fuzzy sets and Fuzzy rules, which gives rise to Probabilistic Fuzzy Sets and Probabilistic Fuzzy Rules. Introducing these probabilistic elements, a distinctive probabilistic fuzzy inference system is developed and this involves fuzzification, inference and output processing. This integrated approach accounts for all of the uncertainty like rule uncertainties and measurement uncertainties present in the systems and has led to the design which performs optimally after training. In this paper a Probabilistic Fuzzy Inference System is applied for modeling and control of a highly nonlinear, unstable system and also proved its effectiveness.
Fuzzy expert system for diagnosing diabetic neuropathy
Rahmani Katigari, Meysam; Ayatollahi, Haleh; Malek, Mojtaba; Kamkar Haghighi, Mehran
2017-01-01
AIM To design a fuzzy expert system to help detect and diagnose the severity of diabetic neuropathy. METHODS The research was completed in 2014 and consisted of two main phases. In the first phase, the diagnostic parameters were determined based on the literature review and by investigating specialists’ perspectives (n = 8). In the second phase, 244 medical records related to the patients who were visited in an endocrinology and metabolism research centre during the first six months of 2014 and were primarily diagnosed with diabetic neuropathy, were used to test the sensitivity, specificity, and accuracy of the fuzzy expert system. RESULTS The final diagnostic parameters included the duration of diabetes, the score of a symptom examination based on the Michigan questionnaire, the score of a sign examination based on the Michigan questionnaire, the glycolysis haemoglobin level, fasting blood sugar, blood creatinine, and albuminuria. The output variable was the severity of diabetic neuropathy which was shown as a number between zero and 10, had been divided into four categories: absence of the disease, (the degree of severity) mild, moderate, and severe. The interface of the system was designed by ASP.Net (Active Server Pages Network Enabled Technology) and the system function was tested in terms of sensitivity (true positive rate) (89%), specificity (true negative rate) (98%), and accuracy (a proportion of true results, both positive and negative) (93%). CONCLUSION The system designed in this study can help specialists and general practitioners to diagnose the disease more quickly to improve the quality of care for patients. PMID:28265346
Supplier Selection Using Fuzzy Inference System
Directory of Open Access Journals (Sweden)
hamidreza kadhodazadeh
2014-01-01
Full Text Available Suppliers are one of the most vital parts of supply chain whose operation has significant indirect effect on customer satisfaction. Since customer's expectations from organization are different, organizations should consider different standards, respectively. There are many researches in this field using different standards and methods in recent years. The purpose of this study is to propose an approach for choosing a supplier in a food manufacturing company considering cost, quality, service, type of relationship and structure standards of the supplier organization. To evaluate supplier according to the above standards, the fuzzy inference system has been used. Input data of this system includes supplier's score in any standard that is achieved by AHP approach and the output is final score of each supplier. Finally, a supplier has been selected that although is not the best in price and quality, has achieved good score in all of the standards.
Adaptive neural-based fuzzy modeling for biological systems.
Wu, Shinq-Jen; Wu, Cheng-Tao; Chang, Jyh-Yeong
2013-04-01
The inverse problem of identifying dynamic biological networks from their time-course response data set is a cornerstone of systems biology. Hill and Michaelis-Menten model, which is a forward approach, provides local kinetic information. However, repeated modifications and a large amount of experimental data are necessary for the parameter identification. S-system model, which is composed of highly nonlinear differential equations, provides the direct identification of an interactive network. However, the identification of skeletal-network structure is challenging. Moreover, biological systems are always subject to uncertainty and noise. Are there suitable candidates with the potential to deal with noise-contaminated data sets? Fuzzy set theory is developed for handing uncertainty, imprecision and complexity in the real world; for example, we say "driving speed is high" wherein speed is a fuzzy variable and high is a fuzzy set, which uses the membership function to indicate the degree of a element belonging to the set (words in Italics to denote fuzzy variables or fuzzy sets). Neural network possesses good robustness and learning capability. In this study we hybrid these two together into a neural-fuzzy modeling technique. A biological system is formulated to a multi-input-multi-output (MIMO) Takagi-Sugeno (T-S) fuzzy system, which is composed of rule-based linear subsystems. Two kinds of smooth membership functions (MFs), Gaussian and Bell-shaped MFs, are used. The performance of the proposed method is tested with three biological systems.
Stability and Sensitivity Analysis of Fuzzy Control Systems. Mechatronics Applications
Directory of Open Access Journals (Sweden)
Radu-Emil Precup
2006-01-01
Full Text Available The development of fuzzy control systems is usually performed by heuristicmeans, incorporating human skills, the drawback being in the lack of general-purposedevelopment methods. A major problem, which follows from this development, is theanalysis of the structural properties of the control system, such as stability, controllabilityand robustness. Here comes the first goal of the paper, to present a stability analysismethod dedicated to fuzzy control systems with mechatronics applications based on the useof Popov’s hyperstability theory. The second goal of this paper is to perform the sensitivityanalysis of fuzzy control systems with respect to the parametric variations of the controlledplant for a class of servo-systems used in mechatronics applications based on theconstruction of sensitivity models. The stability and sensitivity analysis methods provideuseful information to the development of fuzzy control systems. The case studies concerningfuzzy controlled servo-systems, accompanied by digital simulation results and real-timeexperimental results, validate the presented methods.
Secondary systems modeled as fuzzy sub-structures
DEFF Research Database (Denmark)
Tarp-Johansen, Niels Jacob; Ditlevsen, Ove Dalager; Lin, Y.K.
1998-01-01
in the simplest case be modeled by attaching random single degree of freedom oscillators, called fuzzies, to the master structure at randomly distributed points of the structure. Each of these fuzzies are characterized by a random triplet of mass, eigenfrequency, and damping ratio. This characterization can...... be combined with a model of the random distribution of the fuzzies over the structure by letting the entire system of fuzzies be characterized as a triplet of random fields over the structure. Two specific examples, a Poisson point pulse field and a Poisson square wave field, of such a triplet field...... the probabilistic properties of the impulse response function, say, or of the nonergodic steady state response to stationary excitation, say. The study prepares for a finite element model of a flexible master structure with a fuzzy subsystem attached to it....
Fuzzy modelling and impulsive control of the hyperchaotic Lü system
Institute of Scientific and Technical Information of China (English)
Zhang Xiao-Hong; Li Dong
2009-01-01
This paper presents a novel approach to hyperchvos control of hyperchaotic systems based on impulsive control and the Takagi-Sugeno (T-S) fuzzy model. In this study, the hyperchaotic Lü system is exactly represented by the T-S fuzzy model and an impulsive control framework is proposed for stabilizing the hyperchaotic Lü system, which is also suitable for classes of T-S fuzzy hyperchaotic systems, such as the hyperchaotic Rossler, Chen, Chua systems and so on. Sufficient conditions for achieving stability in impulsive T-S fuzzy hyperchaotic systems are derived by using Lyapunov stability theory in the form of the linear matrix inequality, and axe less conservative in comparison with existing results. Numerical simulations are given to demonstrate the effectiveness of the proposed method.
Applied intelligent systems: blending fuzzy logic with conventional control
Filev, Dimitar; Syed, Fazal U.
2010-05-01
The aim of this paper is to show that design of applied intelligent control systems requires different types of blending between fuzzy logic and conventional control systems. Two alternative automotive applications - a manufacturing process control problem and an advisory system for fuel efficient driving - that benefit from both fuzzy and control theories are reviewed and different levels of prioritisations of both approaches are discussed based on the specificity of the applications.
FUZZY CONTROLLED AUTOMATION SYSTEM FOR THE MAIN COAL BUNKER
Institute of Scientific and Technical Information of China (English)
邵良杉; 叶景楼; 付华
1997-01-01
A fuzzy control scheme is presented according to the coal quantity in the main coal bunker, this method has a good dynamic response characteristic and is suited for complex nonlinear systems. The designation of self-adopting fuzzy controller, the working principle and functions of this system are also proposed, with the hardware and the main flow diagram of this system introduced in this paper.
Hybrid Fuzzy Sliding Mode Controller for Timedelay System
Yadav, N K; R. K. Singh,
2013-01-01
This paper is concerned with the problems of stability analysis and stabilization control design for a class of discrete-time T-S fuzzy systems with state-delay for multi-input and multi-output. The nonlinear fuzzy controller helps to overcome the problems of the ill - defined model of the systems, which are creating the undesirable performance. . Here sliding surface is being designed for error function of nonlinear system and sliding mode control is being designed here. The swit...
Stability and Sensitivity Analysis of Fuzzy Control Systems. Mechatronics Applications
Radu-Emil Precup; Stefan Preitl
2006-01-01
The development of fuzzy control systems is usually performed by heuristicmeans, incorporating human skills, the drawback being in the lack of general-purposedevelopment methods. A major problem, which follows from this development, is theanalysis of the structural properties of the control system, such as stability, controllabilityand robustness. Here comes the first goal of the paper, to present a stability analysismethod dedicated to fuzzy control systems with mechatronics applications bas...
Fault Diagnosis in Dynamic Systems Using Fuzzy Interacting Observers
Directory of Open Access Journals (Sweden)
N. V. Kolesov
2013-01-01
Full Text Available A method of fault diagnosis in dynamic systems based on a fuzzy approach is proposed. The new method possesses two basic specific features which distinguish it from the other known fuzzy methods based on the application of fuzzy logic and a bank of state observers. First, this method uses a bank of interacting observers instead of traditional independent observers. The second specific feature of the proposed method is the assumption that there is no strict boundary between the serviceable and disabled technical states of the system, which makes it possible to specify a decision making rule for fault diagnosis.
Generalized multidirectional fuzzy map model of the logistics system networks
Ji, Chun-Rong; Liu, Ming-Yuan; Li, Yan; He, Yue M.
1997-07-01
By conducting [0, 1] treatment to time consuming of logistics system network key links, and regarding the time consumed by manufacture, inspection, storage, assembling, packing and market as a kind of existent extent of the joint and the time consumed by materials handling, transportation and logistics information as the connection strength between joints in a generalized multi-directional fuzzy map, a generalized multi-directional fuzzy map model of logistics system networks is built. The mutual flow among network joints and the special form of generalized fuzzy matrix is analyzed. Finally, an example of model building is given.
FUZZY NEURAL NETWORK FOR MACHINE PARTS RECOGNITION SYSTEM
Institute of Scientific and Technical Information of China (English)
Luo Xiaobin; Yin Guofu; Chen Ke; Hu Xiaobing; Luo Yang
2003-01-01
The primary purpose is to develop a robust adaptive machine parts recognition system. A fuzzy neural network classifier is proposed for machine parts classifier. It is an efficient modeling method. Through learning, it can approach a random nonlinear function. A fuzzy neural network classifier is presented based on fuzzy mapping model. It is used for machine parts classification. The experimental system of machine parts classification is introduced. A robust least square back-propagation (RLSBP) training algorithm which combines robust least square (RLS) with back-propagation (BP) algorithm is put forward. Simulation and experimental results show that the learning property of RLSBP is superior to BP.
FUZZY MAPPING IN DATA SONIFICATION SYSTEM OF WIRELESS SENSOR NETWORK
Directory of Open Access Journals (Sweden)
Arseny A. Markhotin
2016-11-01
Full Text Available Problem Statement. This paper describes the modeling of sonification system with possible types of wireless sensor network data. Fuzzy logic is used for the data-to-sound mapping. Methods. Devised sonification system includes input data model and sound synthesis core. It was created in Pure Data. For fuzzy output of mapped data the Fuzzy Logic Toolboxof MATLABwas used. Moreover, the system model has an ability to send data to the side application via UDP protocol. Results. We offer the method of timbre space organization for sonification system output and the following output of control sound characteristics depending on the type of input data. Practical Relevance. The offered approach of using fuzzy logic in sonification systems can be applied in development of new applications when the formalization of data-to-sound mapping is difficult and also complicated timbal space organization is required.
Genetic fuzzy system modeling and simulation of vascular behaviour
DEFF Research Database (Denmark)
Tang, Jiaowei; Boonen, Harrie C.M.
in cardiovascular disease and ultimately improve pharmacotherapy. For this purpose, novel computational approaches incorporating adaptive properties, auto-regulatory control and rule sets will be assessed, properties that are commonly lacking in deterministic models based on differential equations. We hypothesize...... in principle for any physiological system that is characterized by auto-regulatory control and adaptation. Methods: Currently, one modeling approach is being investigated, Genetic Fuzzy System (GFS). In Genetic Fuzzy Systems, the model algorithm mimics the biologic genetic evolutionary process to learn...... chromosome or individual to define the fuzzy system. The model is implemented by combining the Matlab Genetic algorithm and Fuzzy system toolboxes, respectively. To test the performance of this method, experimental data sets about calculated pressure change in different blood vessels after several chemical...
Variable universe stable adaptive fuzzy control of nonlinear system
Institute of Scientific and Technical Information of China (English)
李洪兴; 苗志宏; 王加银
2002-01-01
A kind of stable adaptive fuzzy control of nonlinear system is implemented using variable universe method. First of all, the basic structure of variable universe adaptive fuzzy controllers is briefly introduced. Then the contraction-expansion factor that is a key tool of variable universe method is defined by means of integral regulation idea, and a kind of adaptive fuzzy controllers is designed by using such a contraction-expansion factor. The simulation on first order nonlinear system is done. Secondly, it is proved that the variable universe adaptive fuzzy control is asymptotically stable by use of Lyapunov theory. The simulation on the second order nonlinear system shows that its simulation effect is also quite good. Finally a useful tool, called symbolic factor, is proposed, which may be of universal significance. It can greatly reduce the settling time and enhance the robustness of the system.
Fuzzy rank functions in the set of all binary systems.
Kim, Hee Sik; Neggers, J; So, Keum Sook
2016-01-01
In this paper, we introduce fuzzy rank functions for groupoids, and we investigate their roles in the semigroup of binary systems by using the notions of right parallelisms and [Formula: see text]-shrinking groupoids.
CENTRIC MANAGEMENT SYSTEM BASED ON NEURO - FUZZY TOPOLOGY
Directory of Open Access Journals (Sweden)
Shumkov Y. A.
2014-11-01
Full Text Available The article describes the network-centric approach to a building control system based on the "inner teacher" neuro - fuzzy topology, which uses the principles of reinforcement learning
Genetic fuzzy system predicting contractile reactivity patterns of small arteries
DEFF Research Database (Denmark)
Tang, J; Sheykhzade, Majid; Clausen, B F;
2014-01-01
strategies. Results show that optimized fuzzy systems (OFSs) predict contractile reactivity of arteries accurately. In addition, OFSs identified significant differences that were undetectable using conventional analysis in the responses of arteries between groups. We concluded that OFSs may be used...
Robust support vector machine-trained fuzzy system.
Forghani, Yahya; Yazdi, Hadi Sadoghi
2014-02-01
Because the SVM (support vector machine) classifies data with the widest symmetric margin to decrease the probability of the test error, modern fuzzy systems use SVM to tune the parameters of fuzzy if-then rules. But, solving the SVM model is time-consuming. To overcome this disadvantage, we propose a rapid method to solve the robust SVM model and use it to tune the parameters of fuzzy if-then rules. The robust SVM is an extension of SVM for interval-valued data classification. We compare our proposed method with SVM, robust SVM, ISVM-FC (incremental support vector machine-trained fuzzy classifier), BSVM-FC (batch support vector machine-trained fuzzy classifier), SOTFN-SV (a self-organizing TS-type fuzzy network with support vector learning) and SCLSE (a TS-type fuzzy system with subtractive clustering for antecedent parameter tuning and LSE for consequent parameter tuning) by using some real datasets. According to experimental results, the use of proposed approach leads to very low training and testing time with good misclassification rate.
CASCADED FUNZZY SYSTEM AND ITS ROBUST ANALYSIS BASED ON SYLLOGISTIC FUZZY REASONING
Institute of Scientific and Technical Information of China (English)
Wang Shitong; Korris F. L. Chung
2004-01-01
Syllogistic fuzzy reasoning is introduced into fizzy system, and the new Cascaded Fuzzy System(CFS) is presented. The thoroughly theoretical analysis and experimental results show that syllogistic fuzzy reasoning is more robust than all other implication inferences for noise data and that CFS has better robustness than conventional fuzzy systems, which provide the solid foundation for CFS's potential application in fuzzy control and modeling and so on.
MODELLING OF AIR CONDITIONING SYSTEM BY FUZZY LOGIC APPROACH
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Ahmet ÖZEK
2004-03-01
Full Text Available One of the main problems in control systems is the difficulty to form the mathematical model associated with the control mechanism. Even though this model can be formed, to realize the application with conventional logic may cause very complex problems. The fuzzy logic without using mathematical model of control system can create control mechanism only with the help of linguistic variables. In this article the modeling has been realized by fuzzy logic.
PERFORMANCE EVALUATION OF PENSION FUNDS WITH FUZZY EXPERT SYSTEM
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SERDAR KORUKOĞLU
2013-06-01
Full Text Available Financial rating and ranking firms often use linguistic instead of numerical values. When input data are mostly qualitative and are based on subjective knowledge of experts, the Fuzzy Set Theory provides a solid mathematical model to represent and handle these data. The aim of this study is developing a fuzzy expert model to evaluate the performance of the pension funds by using their risk and return values. The method is used for evaluating the performance of the randomly selected of twenty seven Turkish pension funds. The obtained results proved that the fuzzy expert system is appropriate and consistent for performance evaluation.
Marginal linearization method in modeling on fuzzy control systems
Institute of Scientific and Technical Information of China (English)
无
2003-01-01
Marginal linearization method in modeling on fuzzy control systems is proposed, which is to deal with the nonlinear model with variable coefficients. The method can turn a nonlinear model with variable coefficients into a linear model with variable coefficients in the way that the membership functions of the fuzzy sets in fuzzy partitions of the universes are changed from triangle waves into rectangle waves. However, the linearization models are incomplete in their forms because of their lacking some items. For solving this problem, joint approximation by using linear models is introduced. The simulation results show that marginal linearization models are of higher approximation precision than their original nonlinear models.
Fuzzy Backstepping Sliding Mode Control for Mismatched Uncertain System
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H. Q. Hou
2014-06-01
Full Text Available Sliding mode controllers have succeeded in many control problems that the conventional control theories have difficulties to deal with; however it is practically impossible to achieve high-speed switching control. Therefore, in this paper an adaptive fuzzy backstepping sliding mode control scheme is derived for mismatched uncertain systems. Firstly fuzzy sliding mode controller is designed using backstepping method based on the Lyapunov function approach, which is capable of handling mismatched problem. Then fuzzy sliding mode controller is designed using T-S fuzzy model method, it can improve the performance of the control systems and their robustness. Finally this method of control is applied to nonlinear system as a case study; simulation results are also provided the performance of the proposed controller.
Robust adaptive fuzzy control scheme for nonlinear system with uncertainty
Institute of Scientific and Technical Information of China (English)
Mingjun ZHANG; Huaguang ZHANG
2006-01-01
In this paper, a robust adaptive fuzzy control scheme for a class of nonlinear system with uncertainty is proposed. First, using prior knowledge about the plant we obtain a fuzzy model, which is called the generalized fuzzy hyperbolic model (GFHM). Secondly, for the case that the states of the system are not available an observer is designed and a robust adaptive fuzzy output feedback control scheme is developed. The overall control system guarantees that the tracking error converges to a small neighborhood of origin and that all signals involved are uniformly bounded. The main advantages of the proposed control scheme are that the human knowledge about the plant under control can be used to design the controller and only one parameter in the adaptive mechanism needs to be on-line adjusted.
Fuzzy logic applications to expert systems and control
Lea, Robert N.; Jani, Yashvant
1991-01-01
A considerable amount of work on the development of fuzzy logic algorithms and application to space related control problems has been done at the Johnson Space Center (JSC) over the past few years. Particularly, guidance control systems for space vehicles during proximity operations, learning systems utilizing neural networks, control of data processing during rendezvous navigation, collision avoidance algorithms, camera tracking controllers, and tether controllers have been developed utilizing fuzzy logic technology. Several other areas in which fuzzy sets and related concepts are being considered at JSC are diagnostic systems, control of robot arms, pattern recognition, and image processing. It has become evident, based on the commercial applications of fuzzy technology in Japan and China during the last few years, that this technology should be exploited by the government as well as private industry for energy savings.
Fuzzy Variable Structure Control of Photovoltaic MPPT System
Institute of Scientific and Technical Information of China (English)
LI Wei; ZHU Xin-jian; CAO Guang-yi
2006-01-01
In order to reduce chattering phenomenon of variable structure control, a fuzzy variable structure control method is adopted and applied in the photovoitaic maximum power point tracking (MPPT) control system. Firstly, the electric features of PV cells and a dynamic model of photovoltaic system with a DC-DC buck converter are analysed. Then a hybrid fuzzy variable structure controller is designed. The controller is composed of a fuzzy variable structure control term and a supervisory control term. The former is the main part of the controller and the latter is used to ensure the stability of the system. Finally, the conventional variable structure control method and the fuzzy variable structure control method are applied respectively. The comparing of simulation results shows the superiority of the latter.
An Automatic KANSEI Fuzzy Rule Creating System Using Thesaurus
Hotta, Hajime; Hagiwara, Masafumi
In this paper, we propose an automatic Kansei fuzzy rule creating system using thesaurus. In general, there are a lot of words that express impressions. However, conventional approaches of Kansei engineering are not suitable to use many impression words because it is difficult to collect enough data. The proposed system is an enhanced algorithm of the conventional method that the authors proposed before. The proposed system extracts fuzzy rules for many words defined in the thesaurus dictionary while the conventional one can extract rules of specified words which user defined. The flow of the system consists of 3 steps: (1) construction of thesaurus networks; (2) data collection by web questionnaire sheets; (3) Extraction of fuzzy rules. In order to extract Kansei fuzzy rules, the system employs enhanced GRNN(general regression neural network) which can treat relative words of the thesaurus network. Using a Japanese thesaurus dictionary in the experiments, the sets of fuzzy rules for 1,195 impression words are extracted, and the fuzzy rules extracted by the proposed system obtained higher accuracy than those extracted by the conventional one.
Uncertain rule-based fuzzy systems introduction and new directions
Mendel, Jerry M
2017-01-01
The second edition of this textbook provides a fully updated approach to fuzzy sets and systems that can model uncertainty — i.e., “type-2” fuzzy sets and systems. The author demonstrates how to overcome the limitations of classical fuzzy sets and systems, enabling a wide range of applications from time-series forecasting to knowledge mining to control. In this new edition, a bottom-up approach is presented that begins by introducing classical (type-1) fuzzy sets and systems, and then explains how they can be modified to handle uncertainty. The author covers fuzzy rule-based systems – from type-1 to interval type-2 to general type-2 – in one volume. For hands-on experience, the book provides information on accessing MatLab and Java software to complement the content. The book features a full suite of classroom material. Presents fully updated material on new breakthroughs in human-inspired rule-based techniques for handling real-world uncertainties; Allows those already familiar with type-1 fuzzy se...
Enhanced adaptive fuzzy sliding mode control for uncertain nonlinear systems
Roopaei, Mehdi; Zolghadri, Mansoor; Meshksar, Sina
2009-09-01
In this article, a novel Adaptive Fuzzy Sliding Mode Control (AFSMC) methodology is proposed based on the integration of Sliding Mode Control (SMC) and Adaptive Fuzzy Control (AFC). Making use of the SMC design framework, we propose two fuzzy systems to be used as reaching and equivalent parts of the SMC. In this way, we make use of the fuzzy logic to handle uncertainty/disturbance in the design of the equivalent part and provide a chattering free control for the design of the reaching part. To construct the equivalent control law, an adaptive fuzzy inference engine is used to approximate the unknown parts of the system. To get rid of the chattering, a fuzzy logic model is assigned for reaching control law, which acting like the saturation function technique. The main advantage of our proposed methodology is that the structure of the system is unknown and no knowledge of the bounds of parameters, uncertainties and external disturbance are required in advance. Using Lyapunov stability theory and Barbalat's lemma, the closed-loop system is proved to be stable and convergence properties of the system is assured. Simulation examples are presented to verify the effectiveness of the method. Results are compared with some other methods proposed in the past research.
Advances in type-2 fuzzy sets and systems theory and applications
Mendel, Jerry; Tahayori, Hooman
2013-01-01
This book explores recent developments in the theoretical foundations and novel applications of general and interval type-2 fuzzy sets and systems, including: algebraic properties of type-2 fuzzy sets, geometric-based definition of type-2 fuzzy set operators, generalizations of the continuous KM algorithm, adaptiveness and novelty of interval type-2 fuzzy logic controllers, relations between conceptual spaces and type-2 fuzzy sets, type-2 fuzzy logic systems versus perceptual computers; modeling human perception of real world concepts with type-2 fuzzy sets, different methods for generating membership functions of interval and general type-2 fuzzy sets, and applications of interval type-2 fuzzy sets to control, machine tooling, image processing and diet. The applications demonstrate the appropriateness of using type-2 fuzzy sets and systems in real world problems that are characterized by different degrees of uncertainty.
Fuzzy rule-based support vector regression system
Institute of Scientific and Technical Information of China (English)
Ling WANG; Zhichun MU; Hui GUO
2005-01-01
In this paper,we design a fuzzy rule-based support vector regression system.The proposed system utilizes the advantages of fuzzy model and support vector regression to extract support vectors to generate fuzzy if-then rules from the training data set.Based on the first-order linear Tagaki-Sugeno (TS) model,the structure of rules is identified by the support vector regression and then the consequent parameters of rules are tuned by the global least squares method.Our model is applied to the real world regression task.The simulation results gives promising performances in terms of a set of fuzzy rules,which can be easily interpreted by humans.
Research and Design of a Fuzzy Neural Expert System
Institute of Scientific and Technical Information of China (English)
王仕军; 王树林
1995-01-01
We have developed a fuzzy neural expert system that has the precision and learning ability of a neural network.Knowledge is acquired from domain experts as fuzzy rules and membership functions.Then,they are converted into a neural network which implements fuzzy inference without rule matching.The neural network is applied to problem-solving and learns from the data obtained during operation to enhance the accuracy.The learning ability of the neural network makes it easy to modify the membership functions defined by domain experts.Also,by modifying the weights of neural networks adaptively,the problem of belief propagation in conventional expert systems can be solved easily.Converting the neural network back into fuzzy rules and membership functions helps explain the inner representation and operation of the neural network.
Identification of uncertain nonlinear systems for robust fuzzy control.
Senthilkumar, D; Mahanta, Chitralekha
2010-01-01
In this paper, we consider fuzzy identification of uncertain nonlinear systems in Takagi-Sugeno (T-S) form for the purpose of robust fuzzy control design. The uncertain nonlinear system is represented using a fuzzy function having constant matrices and time varying uncertain matrices that describe the nominal model and the uncertainty in the nonlinear system respectively. The suggested method is based on linear programming approach and it comprises the identification of the nominal model and the bounds of the uncertain matrices and then expressing the uncertain matrices into uncertain norm bounded matrices accompanied by constant matrices. It has been observed that our method yields less conservative results than the other existing method proposed by Skrjanc et al. (2005). With the obtained fuzzy model, we showed the robust stability condition which provides a basis for different robust fuzzy control design. Finally, different simulation examples are presented for identification and control of uncertain nonlinear systems to illustrate the utility of our proposed identification method for robust fuzzy control.
GA-Based Fuzzy Sliding Mode Controller for Nonlinear Systems
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W. L. Chiang
2008-11-01
Full Text Available Generally, the greatest difficulty encountered when designing a fuzzy sliding mode controller (FSMC or an adaptive fuzzy sliding mode controller (AFSMC capable of rapidly and efficiently controlling complex and nonlinear systems is how to select the most appropriate initial values for the parameter vector. In this paper, we describe a method of stability analysis for a GA-based reference adaptive fuzzy sliding model controller capable of handling these types of problems for a nonlinear system. First, we approximate and describe an uncertain and nonlinear plant for the tracking of a reference trajectory via a fuzzy model incorporating fuzzy logic control rules. Next, the initial values of the consequent parameter vector are decided via a genetic algorithm. After this, an adaptive fuzzy sliding model controller, designed to simultaneously stabilize and control the system, is derived. The stability of the nonlinear system is ensured by the derivation of the stability criterion based upon Lyapunov's direct method. Finally, an example, a numerical simulation, is provided to demonstrate the control methodology.
Fuzzy logic controllers: A knowledge-based system perspective
Bonissone, Piero P.
1993-01-01
Over the last few years we have seen an increasing number of applications of Fuzzy Logic Controllers. These applications range from the development of auto-focus cameras, to the control of subway trains, cranes, automobile subsystems (automatic transmissions), domestic appliances, and various consumer electronic products. In summary, we consider a Fuzzy Logic Controller to be a high level language with its local semantics, interpreter, and compiler, which enables us to quickly synthesize non-linear controllers for dynamic systems.
Simulation Study of IMC and Fuzzy Controller for HVAC System
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Umamaheshwari
2009-06-01
Full Text Available This paper presents how the fuzzy logic controller is used to solve the control problems of complex and non linear process and show that it is more robust and their performance are less sensitive to parametric variations than conventional controllers. These systems will yield a linear response when compared to ordinary controllers. The main advantage of Fuzzy control over conventional controllers is regulation can be done without over shoot.
Diagnosa Gangguan Perkembangan Anak Dengan Metode Fuzzy Expert System
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Diki Arisandi
2017-05-01
Full Text Available AbstrakAnak-anak dibawah umur 10 tahun merupakan fase yang sangat perlu diperhatikan perkembangannya oleh orang tua dan dibantu oleh pakar, apakah mengalami gangguan perkembangan atau tidak. Gangguan perkembangan anak dapat didiagnosis dari perilaku yang diperlihatkan oleh anak dengan cara observasi oleh seorang pakar psikologi anak. Hasil diagnosa dari observasi yang dilakukan beberapa pakar bisa saja berbeda. Hal ini membuat para orang tua menjadi kebingungan terhadap tindak lanjut yang harus dilakukan kepada anak mereka. Untuk mempermudah mendiagnosis gangguan perkembangan pada anak perlu adanya sebuah sistem pakar berbasis Fuzzy. Metode Fuzzy yang diterapkan didasari atas rentang logika berpikir manusia seperti dingin dan panas, tinggi dan rendah, dan lainnya. Diharapkan dengan adanya sistem pakar berbasis fuzzy ini, hasil diagnosa dapat menghasilkan solusi seperti nalar manusia dari sehingga didapatkan solusi untuk tindak lanjut pada gangguan anak. Kata kunci: Diagnosa, Fuzzy, Fungsi Keanggotaan, Gangguan perkembangan, Sistem Pakar. AbstractChildren under 10 years is a critical phase of their developmental and should be noticed by parents and assisted by experts, whether experiencing developmental disruption or not. Children developmental disruption can be diagnosed from behaviors shown by children by observation by a psychologist. Diagnosis results from observations made by some experts may be different. This makes the parents become confused about the follow-up to be done to their children. A Fuzzy-based expert system is needed to overcome the children developmental disruption. The applied Fuzzy method is based on the logical range of human thinking such as cold and hot, high and low, and others. With the fuzzy-based expert system, the diagnostic results can produce solutions such as human reasoning from that obtained a solution to following up on children disruption. Keywords: Diagnosis, Fuzzy, Membership Function, Developmental
2014-01-01
The purpose of this paper is to create an interval estimation of the fuzzy system reliability for the repairable multistate series–parallel system (RMSS). Two-sided fuzzy confidence interval for the fuzzy system reliability is constructed. The performance of fuzzy confidence interval is considered based on the coverage probability and the expected length. In order to obtain the fuzzy system reliability, the fuzzy sets theory is applied to the system reliability problem when dealing with uncertainties in the RMSS. The fuzzy number with a triangular membership function is used for constructing the fuzzy failure rate and the fuzzy repair rate in the fuzzy reliability for the RMSS. The result shows that the good interval estimator for the fuzzy confidence interval is the obtained coverage probabilities the expected confidence coefficient with the narrowest expected length. The model presented herein is an effective estimation method when the sample size is n ≥ 100. In addition, the optimal α-cut for the narrowest lower expected length and the narrowest upper expected length are considered. PMID:24987728
Fuzzy Adaptive Control System of a Non-Stationary Plant
Nadezhdin, Igor S.; Goryunov, Alexey G.; Manenti, Flavio
2016-08-01
This paper proposes a hybrid fuzzy PID control logic, whose tuning parameters are provided in real time. The fuzzy controller tuning is made on the basis of Mamdani controller. In addition, this paper compares a fuzzy logic based PID with PID regulators whose tuning is performed by standard and well-known methods. In some cases the proposed tuning methodology ensures a control performance that is comparable to that guaranteed by simpler and more common tuning methods. However, in case of dynamic changes in the parameters of the controlled system, conventionally tuned PID controllers do not show to be robust enough, thus suggesting that fuzzy logic based PIDs are definitively more reliable and effective.
Automatic control of biomass gasifiers using fuzzy inference systems
Energy Technology Data Exchange (ETDEWEB)
Sagues, C. [Universidad de Zaragoza (Spain). Dpto. de Informatica e Ingenieria de Sistemas; Garcia-Bacaicoa, P.; Serrano, S. [Universidad de Zaragoza (Spain). Dpto. de Ingenieria Quimica y Medio Ambiente
2007-03-15
A fuzzy controller for biomass gasifiers is proposed. Although fuzzy inference systems do not need models to be tuned, a plant model is proposed which has turned out very useful to prove different combinations of membership functions and rules in the proposed fuzzy control. The global control scheme is shown, including the elements to generate the set points for the process variables automatically. There, the type of biomass and its moisture content are the only data which need to be introduced to the controller by a human operator at the beginning of operation to make it work autonomously. The advantages and good performance of the fuzzy controller with the automatic generation of set points, compared to controllers utilising fixed parameters, are demonstrated. (author)
Automatic control of biomass gasifiers using fuzzy inference systems.
Sagüés, C; García-Bacaicoa, P; Serrano, S
2007-03-01
A fuzzy controller for biomass gasifiers is proposed. Although fuzzy inference systems do not need models to be tuned, a plant model is proposed which has turned out very useful to prove different combinations of membership functions and rules in the proposed fuzzy control. The global control scheme is shown, including the elements to generate the set points for the process variables automatically. There, the type of biomass and its moisture content are the only data which need to be introduced to the controller by a human operator at the beginning of operation to make it work autonomously. The advantages and good performance of the fuzzy controller with the automatic generation of set points, compared to controllers utilising fixed parameters, are demonstrated.
Li, Shih-Yu; Tam, Lap-Mou; Tsai, Shang-En; Ge, Zheng-Ming
2015-09-11
Ge and Li proposed an alternative strategy to model and synchronize two totally different nonlinear systems in the end of 2011, which provided a new version for fuzzy modeling and has been applied to several fields to simplify their modeling works and solve the mismatch problems [1]-[17]. However, the proposed model limits the number of nonlinear terms in each equation so that this model could not be used in all kinds of nonlinear dynamic systems. As a result, in this paper, a more efficient and comprehensive advanced-Ge-Li fuzzy model is given to further release the limitation and improve the effectiveness of the original one. The novel fuzzy model can be applied to all kinds of complex nonlinear systems--this is the universal strategy and only m x 2 fuzzy rules as well as two linear subsystems are needed to simulate nonlinear behaviors (m is the number of states in a nonlinear dynamic system), whatever the nonlinear terms are copious or complicated. Further, the fuzzy synchronization of two nonlinear dynamic systems with totally distinct structures can be achieved via only two sets of control gains designed through the novel fuzzy model as well as its corresponding fuzzy synchronization scheme. Two complicated dynamic systems are designed to be the illustrations, Mathieu-Van der pol system with uncertainties and Quantum-cellular neural networks nano system with uncertainties, to show the effectiveness and feasibility of the novel fuzzy model.
Robust controller for a class of uncertain switched fuzzy systems
Institute of Scientific and Technical Information of China (English)
YANG Hong; ZHAO Jun
2007-01-01
A robustness control of uncertain switched fuzzy systems is presented.Using the switching technique and the Lyapunov function method,a continuous state feedback controller is built to ensure that for all allowable uncertainties the relevant closed-loop system is asymptotically stable.Furthermore,a switching strategy that achieves system global asymptotic stability of the uncertain switched fuzzy system is given.In this model,each subsystem of the switched system is an uncertain fuzzy system,and a common parallel distributed compensation controller is presented.The main condition is given in the form of convex combinations which are more solvable.This method transforms a certain switched system and has strong robustness for various system parameters.Simulations show the feasibility and the effectiveness of this method.
Hair mercury concentrations in residents of Sundarban and Calcutta, India.
Gibb, Herman; O'Leary, Keri Grace; Sarkar, Santosh Kumar; Wang, Jing; Liguori, Lisa; Rainis, Holly; Smith, Katy A; Chatterjee, Mousumi
2016-10-01
Few studies on hair mercury have been conducted in India despite the fact that India is the world's third largest producer of coal and coal is India's primary energy source. No studies have been conducted in the Indian state of West Bengal which has a coastline with the Bay of Bengal. This study examined the concentration of mercury in hair in two diverse populations in West Bengal, India: Sundarban, a mangrove wetland where fishing is a common occupation, and Calcutta, a megacity and India's oldest functioning port. Individuals from whom scalp hair was collected (N=100) were asked a series of questions on occupation, education, age, smoking and alcohol consumption, and fish consumption. SAS was utilized to generate descriptive statistics including frequency and univariate analyses and to perform regression analyses to determine significant predictors of hair mercury in this population. The mean hair mercury increased across the first three age categories (45). Hair mercury concentration was significantly higher among residents of Sundarban compared to Calcutta (p=0.0005). In multivariable analysis, location (Sundarban vs. Calcutta) and age were significant predictors of hair mercury concentration (p=0.0120 and p=0.0161, respectively). Average hair mercury concentrations in this study were not particularly elevated. Smoking and alcohol consumption were predictors of hair mercury concentration. The hair mercury in Sundarban residents compared to Calcutta residents may be elevated due to greater consumption of fish and type of fish consumed. Copyright © 2016 Elsevier Inc. All rights reserved.
Directory of Open Access Journals (Sweden)
Zahra Mohammadi
2011-07-01
Full Text Available This study presents a novel controller of magnetic levitation system by using new neuro-fuzzy structures which called flexible neuro-fuzzy systems. In this type of controller we use sliding mode control with neuro-fuzzy to eliminate the Jacobian of plant. At first, we control magnetic levitation system with Mamdanitype neuro-fuzzy systems and logical-type neuro-fuzzy systems separately and then we use two types of flexible neuro-fuzzy systems as controllers. Basic flexible OR-type neuro-fuzzy inference system and basic compromise AND-type neuro-fuzzy inference system are two new flexible neuro-fuzzy controllers which structure of fuzzy inference system (Mamdani or logical is determined in the learning process. We can investigate with these two types of controllers which of the Mamdani or logical type systems has better performance for control of this plant. Finally we compare performance of these controllers with sliding mode controller and RBF sliding mode controller.
Fuzzy logic in indoor position determination system
Directory of Open Access Journals (Sweden)
Michał Socha
2016-12-01
Full Text Available The article outlines how to use the convergence of collections to determine the position of a mobile device based on the WiFi radio signal strength with the use of fuzzy sets. The main aim is the development of the method for indoor position determination based on existing WiFi network infrastructure indoors. The approach is based on the WiFi radio infrastructure existing inside the buildings and requires operating mobile devices such as smartphones or tablets. An SQL database engine is also necessary as a widespread data interface. The SQL approach is not limited to the determination of the position but also to the creation of maps in which the system defining the position of the mobile device will operate. In addition, implementation issues are presented along with the distribution of the burden of performing calculations and the benefits of such an approach for determining the location. The authors describe how to decompose the task of determining the position in a client-server architecture.
Fuzzy Logic Temperature Control System For The Induction Furnace
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Lei Lei Hnin
2015-08-01
Full Text Available This research paper describes the fuzzy logic temperature control system of the induction furnace. Temperature requirement of the heating system varies during the heating process. In the conventional control schemes the switching losses increase with the change in the load. A closed loop control is required to have a smooth control on the system. In this system pulse width modulation based power control scheme for the induction heating system is developed using the fuzzy logic controller. The induction furnace requires a good voltage regulation to have efficient response. The controller controls the temperature depending upon weight of meat water and time. This control system is implemented in hardware system using microcontroller. Here the fuzzy logic controller is designed and simulated in MATLAB to get the desire condition.
Composite fuzzy sliding mode control of nonlinear singularly perturbed systems.
Nagarale, Ravindrakumar M; Patre, B M
2014-05-01
This paper deals with the robust asymptotic stabilization for a class of nonlinear singularly perturbed systems using the fuzzy sliding mode control technique. In the proposed approach the original system is decomposed into two subsystems as slow and fast models by the singularly perturbed method. The composite fuzzy sliding mode controller is designed for stabilizing the full order system by combining separately designed slow and fast fuzzy sliding mode controllers. The two-time scale design approach minimizes the effect of boundary layer system on the full order system. A stability analysis allows us to provide sufficient conditions for the asymptotic stability of the full order closed-loop system. The simulation results show improved system performance of the proposed controller as compared to existing methods. The experimentation results validate the effectiveness of the proposed controller.
Applications of Fuzzy Sliding Mode Control for a Gyroscope System
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Shih-Chung Chen
2013-01-01
Full Text Available The study proposed the application of the fuzzy sliding mode for a gyroscope system status control. The state response analysis of the gyroscope system revealed highly nonlinear and chaotic subharmonic motions of 2T during state formation. The current study discussed the use of tracking control on the sliding mode control and fuzzy sliding mode control of a gyroscope control system. Consequently, the gyroscope system drives from chaotic motion to periodic motion. The numerical simulation results confirm that the proposed controller provides good system stability and convergence without chattering phenomena.
Fuzzy Controllers for a Gantry Crane System with Experimental Verifications
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Naif B. Almutairi
2016-01-01
Full Text Available The control problem of gantry cranes has attracted the attention of many researchers because of the various applications of these cranes in the industry. In this paper we propose two fuzzy controllers to control the position of the cart of a gantry crane while suppressing the swing angle of the payload. Firstly, we propose a dual PD fuzzy controller where the parameters of each PD controller change as the cart moves toward its desired position, while maintaining a small swing angle of the payload. This controller uses two fuzzy subsystems. Then, we propose a fuzzy controller which is based on heuristics. The rules of this controller are obtained taking into account the knowledge of an experienced crane operator. This controller is unique in that it uses only one fuzzy system to achieve the control objective. The validity of the designed controllers is tested through extensive MATLAB simulations as well as experimental results on a laboratory gantry crane apparatus. The simulation results as well as the experimental results indicate that the proposed fuzzy controllers work well. Moreover, the simulation and the experimental results demonstrate the robustness of the proposed control schemes against output disturbances as well as against uncertainty in some of the parameters of the crane.
Adaptive Fuzzy Dynamic Surface Control for Uncertain Nonlinear Systems
Institute of Scientific and Technical Information of China (English)
Xiao-Yuan Luo; Zhi-Hao Zhu; Xin-Ping Guan
2009-01-01
In this paper, a robust adaptive fuzzy dynamic surface control for a class of uncertain nonlinear systems is proposed. A novel adaptive fuzzy dynamic surface model is built to approximate the uncertain nonlinear functions by only one fuzzy logic system. The approximation capability of this model is proved and the model is implemented to solve the problem that too many approximators are used in the controller design of uncertain nonlinear systems. The shortage of "explosion of complexity" in backstepping design procedure is overcome by using the proposed dynamic surface control method. It is proved by constructing appropriate Lyapunov candidates that all signals of closed-loop systems are semi-globaily uniformly ultimate bounded. Also, this novel controller stabilizes the states of uncertain nonlinear systems faster than the adaptive sliding mode controller (SMC). Two simulation examples are provided to illustrate the effectiveness of the control approach proposed in this paper.
Fuzzy logic based variable speed wind generation system
Energy Technology Data Exchange (ETDEWEB)
Simoes, M.G. [Sao Paulo Univ., SP (Brazil). Escola Politecnica. PMC - Mecatronica; Bose, B.K. [Tennessee Univ., Knoxville, TN (United States). Dept. of Electrical Engineering; Spiegel, Ronal J. [Environmental Protection Agency, Research Triangle Park, NC (United States). Air and Energy Engineering Research Lab.
1996-12-31
This work demonstrates the successful application of fuzzy logic to enhance the performance and control of a variable speed wind generation system. A maximum power point tracker control is performed with three fuzzy controllers, without wind velocity measurement, and robust to wind vortex and turbine torque ripple. A squirrel cage induction generator feeds the power to a double-sided PWM converter system which pumps the power to a utility grid or supplies to an autonomous system. The fuzzy logic controller FLC-1 searches on-line the generator speed so that the aerodynamic efficiency of the wind turbine is optimized. A second fuzzy controller FLC-2 programs the machine flux by on-line search so as to optimize the machine-converter system wind vortex. Detailed analysis and simulation studies were performed for development of the control strategy and fuzzy algorithms, and a DSP TMS320C30 based hardware with C control software was built for the performance evaluation of a laboratory experimental set-up. The theoretical development was fully validated and the system is ready to be reproduced in a higher power installation. (author) 7 refs., 3 figs., 1 tab.
Row Reduced Echelon Form for Solving Fully Fuzzy System with Unknown Coefficients
Directory of Open Access Journals (Sweden)
Ghassan Malkawi
2014-08-01
Full Text Available This study proposes a new method for finding a feasible fuzzy solution in positive Fully Fuzzy Linear System (FFLS, where the coefficients are unknown. The fully fuzzy system is transferred to linear system in order to obtain the solution using row reduced echelon form, thereafter; the crisp solution is restricted in obtaining the positive fuzzy solution. The fuzzy solution of FFLS is included crisp intervals, to assign alternative values of unknown entries of fuzzy numbers. To illustrate the proposed method, numerical examples are solved, where the entries of coefficients are unknown in right or left hand side, to demonstrate the contributions in this study.
Rule weights in a neuro-fuzzy system with a hierarchical domain partition
National Research Council Canada - National Science Library
Krzysztof Siminski
2010-01-01
Rule weights in a neuro-fuzzy system with a hierarchical domain partition The paper discusses the problem of rule weight tuning in neuro-fuzzy systems with parameterized consequences in which rule...
Indirect Adaptive Fuzzy and Impulsive Control of Nonlinear Systems
Institute of Scientific and Technical Information of China (English)
Hai-Bo Jiang
2010-01-01
The problem of indirect adaptive fuzzy and impulsive control for a class of nonlinear systems is investigated.Based on the approximation capability of fuzzy systems,a novel adaptive fuzzy and impulsive control strategy with supervisory controller is developed.With the help of a supervisory controller,global stability of the resulting closed-loop system is established in the sense that all signals involved are uniformly bounded.Furthermore,the adaptive compensation term of the upper bound function of the sum of residual and approximation error is adopted to reduce the effects of modeling error.By the generalized Barbalat's lemma,the tracking error between the output of the system and the reference signal is proved to be convergent to zero asymptotically.Simulation results illustrate the effectiveness of the proposed approach.
New Asymmetric Fuzzy PID Control for Pneumatic Position Control System
Institute of Scientific and Technical Information of China (English)
薛阳; 彭光正; 范萌; 伍清河
2004-01-01
A fuzzy control algorithm of asymmetric fuzzy strategy is introduced for a servo-pneumatic position system. It can effectively solve the difficult problems of single rod low friction cylinders, which are mainly caused by asymmetric structures and different friction characteristics in two directions. On the basis of this algorithm, a traditional PID control is used to improve dynamic performance. Furthermore, a new asymmetric fuzzy PID control with α factor is advanced to improve the self-adaptability and robustness of the system. Both the theoretical analyses and experimental results prove that, with this control strategy, the dynamic performance of the system can be greatly improved. The system using this control algorithm has strong robustness and it obtains desired overshoot and repeatability in both transient and steady-state responses.
A Novel Evolutionary-Fuzzy Control Algorithm for Complex Systems
Institute of Scientific and Technical Information of China (English)
王攀; 徐承志; 冯珊; 徐爱华
2002-01-01
This paper presents an adaptive fuzzy control scheme based on modified genetic algorithm. In the control scheme, genetic algorithm is used to optimze the nonlinear quantization functions of the controller and some key parameters of the adaptive control algorithm. Simulation results show that this control scheme has satisfactory performance in MIMO systems, chaotic systems and delay systems.
ASSESSING THE SUSTAINABILITY OF AGRICULTURAL PRODUCTION SYSTEMS USING FUZZY LOGIC
Directory of Open Access Journals (Sweden)
Moslem Sami
2013-09-01
Full Text Available First stage for attaining sustainability in a system is the measurement of current state of sustainability. Indicators are widely used as tools for measurement of sustainability. In this study, a comprehensive index was proposed to measure sustainability in agricultural production systems. This index takes advantage of fuzzy logic to combine all six indexes which were selected as the representative of three dimensions of sustainability. A set of models and sub-models based on the fuzzy inference system were employed to define the index. A case study conducted in two large production farms of maize and wheat, in Iran, proved the feasibility and usability of the model.
Fuzzy synthetic assessment of building fire safety system
Institute of Scientific and Technical Information of China (English)
YANG Gao-shang; PENG Li-min
2005-01-01
A multistage assessment index set is chosen based on the analysis of building fire safety system, whereby the weight of each index is determined through an analy tie.hierarchy process; a fuzzy synthetic assessment model for the building fire safety system is constructed, and the quantified result was obtained by using hierarchy parameter judgment. This fuzzy synthetic assessment method can quantify assessment result of the building fire safety system, so thatthe fire precautions may be accurately adopted, and the serious potential risk may be avoided. The application shows that this method possesses both objectivity and feasibility.
Lagrangian Fuzzy Dynamics of Physical and Non-Physical Systems
Sandler, Uziel
2014-01-01
In this paper, we show how to study the evolution of a system, given imprecise knowledge about the state of the system and the dynamics laws. Our approach is based on Fuzzy Set Theory, and it will be shown that the \\emph{Fuzzy Dynamics} of a $n$-dimensional system is equivalent to Lagrangian (or Hamiltonian) mechanics in a $n+1$-dimensional space. In some cases, however, the corresponding Lagrangian is more general than the usual one and could depend on the action. In this case, Lagrange's eq...
Fuzzy fractional order sliding mode controller for nonlinear systems
Delavari, H.; Ghaderi, R.; Ranjbar, A.; Momani, S.
2010-04-01
In this paper, an intelligent robust fractional surface sliding mode control for a nonlinear system is studied. At first a sliding PD surface is designed and then, a fractional form of these networks PDα, is proposed. Fast reaching velocity into the switching hyperplane in the hitting phase and little chattering phenomena in the sliding phase is desired. To reduce the chattering phenomenon in sliding mode control (SMC), a fuzzy logic controller is used to replace the discontinuity in the signum function at the reaching phase in the sliding mode control. For the problem of determining and optimizing the parameters of fuzzy sliding mode controller (FSMC), genetic algorithm (GA) is used. Finally, the performance and the significance of the controlled system two case studies (robot manipulator and coupled tanks) are investigated under variation in system parameters and also in presence of an external disturbance. The simulation results signify performance of genetic-based fuzzy fractional sliding mode controller.
A TWO-PHASE APPROACH TO FUZZY SYSTEM IDENTIFICATION
Institute of Scientific and Technical Information of China (English)
Ta-Wei HUNG; Shu-Cherng FANG; Henry L.W.NUTTLE
2003-01-01
A two-phase approach to fuzzy system identification is proposed. The first phase produces a baseline design to identify a prototype fuzzy system for a target system from a coIlection of input-output data pairs. It uses two easily implemented clustering techniques: the subtractive clustering method and the fuzzy c-means (FCM) clustering algorithm. The second phase (fine tuning)is executed to adjust the parameters identified in the baseline design. This phase uses the steepest descent and recursive least-squares estimation methods. The proposed approach is validated by applying it to both a function approximation type of problem and a classification type of problem. An analysis of the learning behavior of the proposed approach for the two test problems is conducted for further confirmation.
Advanced Fuzzy Logic Based Admission Control for UMTS System
Directory of Open Access Journals (Sweden)
P. Kejik
2010-12-01
Full Text Available The capacity of CDMA (Code Division Multiple Access systems is interference limited. Therefore radio resources management (RRM functions are used. They are responsible for supplying optimum coverage, ensuring efficient use of physical resources, and providing the maximum planned capacity. This paper deals with admission control techniques for UMTS (Universal Mobile Telecommunication System. A UMTS system model and four fuzzy logic based admission control algorithms are presented in this paper. Two new versions of fuzzy logic based admission control algorithms are presented there. All algorithms are mutually compared via simulations. Simulations show that the novel advanced fuzzy algorithm outperforms the other simulated algorithms (in terms of blocking probability, dropping probability and the number of active UEs in cell.
The design of thermoelectric footwear heating system via fuzzy logic.
Işik, Hakan; Saraçoğlu, Esra
2007-12-01
In this study, Heat Control of Thermoelectric Footwear System via Fuzzy Logic has been implemented in order to use efficiently in cold weather conditions. Temperature control is very important in domestic as well as in many industrial applications. The final product is seriously affected from the changes in temperature. So it is necessary to reach some desired temperature points quickly and avoid large overshoot. Here, fuzzy logic acts an important role. PIC 16F877 microcontroller has been designed to act as fuzzy logic controller. The designed system provides energy saving and has better performance than proportional control that was implemented in the previous study. The designed system takes into consideration so appropriate parameters that it can also be applied to the people safely who has illnesses like diabetes, etc.
Directory of Open Access Journals (Sweden)
Guo Haigang
2012-01-01
Full Text Available Combining adaptive fuzzy sliding mode control with fuzzy or variable universe fuzzy switching technique, this study develops two novel direct adaptive schemes for a class of MIMO nonlinear systems with uncertainties and external disturbances. The proposed control schemes consist of fuzzy equivalent control terms, fuzzy switching control terms (in scheme one or variable universe fuzzy switching control terms (in scheme two, and compensation control terms. The compensation control terms are used to relax the assumption on fuzzy approximation error. Based on Lyapunov stability theory, the parameters update laws are adaptively tuned online and the global asymptotic stability of the closed-loop system can be guaranteed. The major contribution of this study is to develop a novel framework for designing direct adaptive fuzzy sliding mode control scheme facing model uncertainties and external disturbances. The derived schemes can effectively solve the chattering problem and the equivalent control calculation in that environment. Simulation results performed on a two-link robotic manipulator demonstrate the feasibility of the proposed control schemes.
Applications of fuzzy sets to rule-based expert system development
Lea, Robert N.
1989-01-01
Problems of implementing rule-based expert systems using fuzzy sets are considered. A fuzzy logic software development shell is used that allows inclusion of both crisp and fuzzy rules in decision making and process control problems. Results are given that compare this type of expert system to a human expert in some specific applications. Advantages and disadvantages of such systems are discussed.
Studying on the Fuzzy-QFD System Based on Database Class Encapsulation Technology
Institute of Scientific and Technical Information of China (English)
FANG Xifeng; ZHANG Shengwen; LU Yuping; WU Hongtao
2006-01-01
Complicated product QFD system design information including design and manufacturing, operation and maintenance as well as relative supply information, all are tightly related to the product life cycle cooperative design and the process of establishing the QFD system. In the early stage of product design, we can only get the fuzzy and unreliable information. With design going, the fuzzy and unreliable information become less and less. The defect of the traditional QFD is not deal with the fuzzy contents very well. Adopt database class encapsulation and fuzzy inference technology, and then discuss the realization of QFD system based on VFP database. The structure of the fuzzy QFD system based on database class's encapsulation is built and the work flow of fuzzy algorithm based on VFP software is presented. In the analysis of fuzzy QFD process, fuzzy inference is adopted. A developed prototype system and an example have verified some presented techniques and the research results are the basis of the future development.
Novel Approach to Fuzzy Logic Controller Design for Systems With Deadzones
Kim, Jong-Hwan; Park, Jong-Hwan; Lee, Seon-Woo; Chong, Edwin K. P.
1992-01-01
Existing fuzzy control methods do not perform well when applied to systems containing nonlinearities arising from unknown deadzones. In particular, we show that a conventional fuzzy logic controller applied to a system with a deadzone suffers from poor transient performance and a large steady-state error. In this report, we propose a novel two-layered fuzzy logic controller for controlling systems with deadzones. The two-layered control structure consists of a fuzzy logic-based precompensator...
Genetic fuzzy system predicting contractile reactivity patterns of small arteries
DEFF Research Database (Denmark)
Tang, J; Sheykhzade, Majid; Clausen, B F;
2014-01-01
information. We developed a genetic fuzzy system (GFS) algorithm that is capable of learning all information in time-domain physiological data. Data on isometric force development of isolated small arteries were used as a framework for developing and optimizing a GFS. GFS performance was improved by several...... strategies. Results show that optimized fuzzy systems (OFSs) predict contractile reactivity of arteries accurately. In addition, OFSs identified significant differences that were undetectable using conventional analysis in the responses of arteries between groups. We concluded that OFSs may be used...
Fuzzy-Immune PID Control for AMB Systems
Institute of Scientific and Technical Information of China (English)
SU Yixin; LI Xuan; ZHOU Zude; CHEN Youping; ZHANG Danhong
2006-01-01
In order to improve the dynamic performance of active magnetic bearing systems with highly nonlinear and naturally unstable dynamics, a new nonlinear fuzzy-immune proportional-integral-derivative (PID) controller is proposed by combining the immune feedback law with linear PID control. This controller consists of a PID controller and a basic immune proportional controller in cascaded connection, the nonlinear function of the immune proportional controller is realized by using fuzzy reasoning. Simulation results demonstrate that the active magnetic bearing system with the proposed controller has better dynamic performance and disturbance rejection ability than using the linear PID controller.
Quality determination of Mozafati dates using Mamdani fuzzy inference system
Directory of Open Access Journals (Sweden)
N. Alavi
2013-06-01
Full Text Available The date fruit, which is produced mostly in the hot arid regions of Southern Asia and North Africa, in large quantities, is marketed all over the world as an important crop. Date grading is an important process for producers and affects the fruit quality evaluation and export market. In this research Mamdani fuzzy inference system (MFIS was applied as a decision making technique to classify the Mozafati dates based on quality. Two date parameters including the length and freshness were measured for 500 date fruits. These dates were graded by both a human expert and MFIS. Grading results obtained from fuzzy system showed 91% general conformity with the experimental results.
Performance evaluation of the distance education system with fuzzy logic
Armaǧan, Hamit; Yiǧit, Tuncay
2017-07-01
Distance education is a kind of education that brought together course advisor, student and educational materials in a different time and place through communicational technologies. In this educational system the success of education is directly related to audio, video and interaction. In this study, a model is created by using fuzzy logic with the success of distance education students and the components of distance education. This study is made by MATLAB fuzzy logic toolbox. Audio, video, educational technology, student achievement are used as parameters in the evaluation. System assessment is carried out depending on parameter.
Fault Detection in Systems-A Fuzzy Approach
Directory of Open Access Journals (Sweden)
Ashok Kumar
2004-04-01
Full Text Available The task of fault detection is important when dealing with failures of crucial nature. After detection of faults in a system, it is advisable to suggest maintenance action before occurrenceof a failure. Fault detection may be done by observing various symptoms of the system during its operational stage. Sometimes, symptoms cannot be quantified easily but can be expressedin linguistic terms. Since linguistic terms are fuzzy quantifiers, these can be represented by fuzzy numbers. In this paper, two cases have been discussed, where a fault likely to affect a particular systemlsystems, is detected. In the first case, this is done by means of a compositional rule of inference. The second case is based on modified similarity measure. For both these cases, linguistic terms have been expressed as trapezoidal fuzzy numbers
Application of genetic algorithms to tuning fuzzy control systems
Espy, Todd; Vombrack, Endre; Aldridge, Jack
1993-01-01
Real number genetic algorithms (GA) were applied for tuning fuzzy membership functions of three controller applications. The first application is our 'Fuzzy Pong' demonstration, a controller that controls a very responsive system. The performance of the automatically tuned membership functions exceeded that of manually tuned membership functions both when the algorithm started with randomly generated functions and with the best manually-tuned functions. The second GA tunes input membership functions to achieve a specified control surface. The third application is a practical one, a motor controller for a printed circuit manufacturing system. The GA alters the positions and overlaps of the membership functions to accomplish the tuning. The applications, the real number GA approach, the fitness function and population parameters, and the performance improvements achieved are discussed. Directions for further research in tuning input and output membership functions and in tuning fuzzy rules are described.
Recent Advances in Interval Type-2 Fuzzy Systems
Castillo, Oscar
2012-01-01
This book reviews current state of the art methods for building intelligent systems using type-2 fuzzy logic and bio-inspired optimization techniques. Combining type-2 fuzzy logic with optimization algorithms, powerful hy-brid intelligent systems have been built using the advantages that each technique offers. This book is intended to be a reference for scientists and engineers interested in applying type-2 fuzzy logic for solving problems in pattern recognition, intelligent control, intelligent manufacturing, robotics and automation. This book can also be used as a reference for graduate courses like the following: soft computing, intelligent pattern recognition, computer vision, applied artificial intelligence, and similar ones. We con-sider that this book can also be used to get novel ideas for new lines of re-search, or to continue the lines of research proposed by the authors.
Advanced Takagi‒Sugeno fuzzy systems delay and saturation
Benzaouia, Abdellah
2014-01-01
This monograph puts the reader in touch with a decade’s worth of new developments in the field of fuzzy control specifically those of the popular Takagi-Sugeno (T-S) type. New techniques for stabilizing control analysis and design based on multiple Lyapunov functions and linear matrix inequalities (LMIs), are proposed. All the results are illustrated with numerical examples and figures and a rich bibliography is provided for further investigation. Control saturations are taken into account within the fuzzy model. The concept of positive invariance is used to obtain sufficient asymptotic stability conditions for the fuzzy system with constrained control inside a subset of the state space. The authors also consider the non-negativity of the states. This is of practical importance in many chemical, physical and biological processes that involve quantities that have intrinsically constant and non-negative sign: concentration of substances, level of liquids, etc. Results for linear systems are then extended to l...
Improving Computer Based Speech Therapy Using a Fuzzy Expert System
Ovidiu Andrei Schipor; Stefan Gheorghe Pentiuc; Maria Doina Schipor
2012-01-01
In this paper we present our work about Computer Based Speech Therapy systems optimization. We focus especially on using a fuzzy expert system in order to determine specific parameters of personalized therapy, i.e. the number, length and content of training sessions. The efficiency of this new approach was tested during an experiment performed with our CBST, named LOGOMON.
New approach to solve symmetric fully fuzzy linear systems
Indian Academy of Sciences (India)
P Senthilkumar; G Rajendran
2011-12-01
In this paper, we present a method to solve fully fuzzy linear systems with symmetric coefﬁcient matrix. The symmetric coefﬁcient matrix is decomposed into two systems of equations by using Cholesky method and then a solution can be obtained. Numerical examples are given to illustrate our method.
Evaluation of Combined Heat and Power (CHP Systems Using Fuzzy Shannon Entropy and Fuzzy TOPSIS
Directory of Open Access Journals (Sweden)
Fausto Cavallaro
2016-06-01
Full Text Available Combined heat and power (CHP or cogeneration can play a strategic role in addressing environmental issues and climate change. CHP systems require less fuel than separate heat and power systems in order to produce the same amount of energy saving primary energy, improving the security of the supply. Because less fuel is combusted, greenhouse gas emissions and other air pollutants are reduced. If we are to consider the CHP system as “sustainable”, we must include in its assessment not only energetic performance but also environmental and economic aspects, presenting a multicriteria issue. The purpose of the paper is to apply a fuzzy multicriteria methodology to the assessment of five CHP commercial technologies. Specifically, the combination of the fuzzy Shannon’s entropy and the fuzzy Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS approach will be tested for this purpose. Shannon’s entropy concept, using interval data such as the α-cut, is a particularly suitable technique for assigning weights to criteria—it does not require a decision-making (DM to assign a weight to the criteria. To rank the proposed alternatives, a fuzzy TOPSIS method has been applied. It is based on the principle that the chosen alternative should be as close as possible to the positive ideal solution and be as far as possible from the negative ideal solution. The proposed approach provides a useful technical–scientific decision-making tool that can effectively support, in a consistent and transparent way, the assessment of various CHP technologies from a sustainable point of view.
Efficient Fuzzy Logic Controller for Magnetic Levitation Systems
Directory of Open Access Journals (Sweden)
D. S. Shu’aibu
2016-12-01
Full Text Available Magnetic levitation is a system of suspending a body or a complete system against gravity. Suspending a system in air against gravity without using fixed structure for supporting is highly unstable and complex. In the previous research many techniques of stabilizing magnetic levitation systems were discussed. In this paper magnetic levitation controller using fuzzy logic is proposed. The proposed Fuzzy logic controller (FLC is designed, and developed using triangular membership function with 7×7 rules. The system model was implemented in MATLAB/SIMULINK and the system responses to Fuzzy controller with different input signals were investigated. Using unit step input signal, the proposed controller has a settling time of 0.35 secs, percentage overshoot of 0% and there is no oscillation. The proposed controller is validated with a model of an existing practical conventional proportional plus derivatives (PD controller. The PD controller has a settling time of 0.45 secs, percentage overshoot of 7% and with oscillation. Similarly, with sinusoidal input, the FLC has a phase shift and peak response of 0^0 and 0.9967 respectively, while PD controller has a phase shift and peak response of 24.48o and 0.9616 respectively. A disturbance signal was applied to the input of the control system. Fuzzy controller succeeded in rejecting the disturbance signal without further turning of the parameters whereby PD controller failed.
Hybrid TS fuzzy modelling and simulation for chaotic Lorenz system
Institute of Scientific and Technical Information of China (English)
Li De-Quan
2006-01-01
The projection of the chaotic attractor observed from the Lorenz system in the X-Z plane is like a butterfly, hence the classical Lorenz system is widely known as the butterfly attractor, and has served as a prototype model for studying chaotic behaviour since it was coined. In this work we take one step further to investigate some fundamental dynamic behaviours of a novel hybrid Takagi-Sugeno (TS) fuzzy Lorenz-type system, which is essentially derived from the delta-operator-based TS fuzzy modelling for complex nonlinear systems, and contains the original Lorenz system of continuous-time TS fuzzy form as a special case. By simply and appropriately tuning the additional parametric perturbations in the two-rule hybrid TS fuzzy Lorenz-type system, complex (two-wing) butterfly attractors observed from this system in the three dimensional (3D) X-Y-Z space are created, which have not yet been reported in the literature, and the forming mechanism of the compound structures have been numerically investigated.
Fuzzy stochastic neural network model for structural system identification
Jiang, Xiaomo; Mahadevan, Sankaran; Yuan, Yong
2017-01-01
This paper presents a dynamic fuzzy stochastic neural network model for nonparametric system identification using ambient vibration data. The model is developed to handle two types of imprecision in the sensed data: fuzzy information and measurement uncertainties. The dimension of the input vector is determined by using the false nearest neighbor approach. A Bayesian information criterion is applied to obtain the optimum number of stochastic neurons in the model. A fuzzy C-means clustering algorithm is employed as a data mining tool to divide the sensed data into clusters with common features. The fuzzy stochastic model is created by combining the fuzzy clusters of input vectors with the radial basis activation functions in the stochastic neural network. A natural gradient method is developed based on the Kullback-Leibler distance criterion for quick convergence of the model training. The model is validated using a power density pseudospectrum approach and a Bayesian hypothesis testing-based metric. The proposed methodology is investigated with numerically simulated data from a Markov Chain model and a two-story planar frame, and experimentally sensed data from ambient vibration data of a benchmark structure.
The Development of a Fuzzy Predictive Control System for Automotive Anti-lock Braking System
Institute of Scientific and Technical Information of China (English)
HUANGFU Shihui; BAO Xiangying
2006-01-01
This paper presents the model of one-tire kinetics、tires、the braking system and the model of control system. On virtual road, this paper builds a fuzzy predictive control system to insure the best attachment coefficient between tires and road. And it turns out to be that this fuzzy predictive control method has achieved good performances.
Application of Fuzzy Clustering in Modeling of a Water Hydraulics System
DEFF Research Database (Denmark)
Zhou, Jianjun; Kroszynski, Uri
2000-01-01
This article presents a case study of applying fuzzy modeling techniques for a water hydraulics system. The obtained model is intended to provide a basis for model-based control of the system. Fuzzy clustering is used for classifying measured input-output data points into partitions. The fuzzy mo...
A Fuzzy Rule-Based Expert System for Evaluating Intellectual Capital
Directory of Open Access Journals (Sweden)
Mohammad Hossein Fazel Zarandi
2012-01-01
Full Text Available A fuzzy rule-based expert system is developed for evaluating intellectual capital. A fuzzy linguistic approach assists managers to understand and evaluate the level of each intellectual capital item. The proposed fuzzy rule-based expert system applies fuzzy linguistic variables to express the level of qualitative evaluation and criteria of experts. Feasibility of the proposed model is demonstrated by the result of intellectual capital performance evaluation for a sample company.
Generating Fuzzy Rule-based Systems from Examples Based on Robust Support Vector Machine
Institute of Scientific and Technical Information of China (English)
JIA Jiong; ZHANG Hao-ran
2006-01-01
This paper firstly proposes a new support vector machine regression (SVR) with a robust loss function, and designs a gradient based algorithm for implementation of the SVR,then uses the SVR to extract fuzzy rules and designs fuzzy rule-based system. Simulations show that fuzzy rule-based system technique based on robust SVR achieves superior performance to the conventional fuzzy inference method, the proposed method provides satisfactory performance with excellent approximation and generalization property than the existing algorithm.
MI-ANFIS: A Multiple Instance Adaptive Neuro-Fuzzy Inference System
2015-08-02
16. SECURITY CLASSIFICATION OF: 1. REPORT DATE (DD-MM-YYYY) 4. TITLE AND SUBTITLE 13. SUPPLEMENTARY NOTES 12. DISTRIBUTION AVAILIBILITY STATEMENT 6...Instance AdaptiveNeuro-Fuzzy Inference System We introduce a novel adaptive neuro -fuzzy architecture based on the framework of Multiple Instance Fuzzy...Inference. The new architecture called Multiple Instance-ANFIS (MI-ANFIS), is an extension of the standard Adaptive Neuro Fuzzy Inference System (ANFIS
A Novel Web-based Human Advisor Fuzzy Expert System
Directory of Open Access Journals (Sweden)
Vahid Rafe
2013-02-01
Full Text Available The applications of the Internet-based technologies and the concepts of fuzzy expert systems (FES have created new methods for sharing and distributing knowledge. However, there has been a general lack of investigation in the area of web-based fuzzy expert systems. In this paper, the issues associated with the design, development, and use of web-based applications from a standpoint of the benefits and challenges of development and utilization are investigated. The original theory and concepts in conventional FES are reviewed and a knowledge engineering framework for developing such systems is revised. For a human advisor to have a satisfying performance, expertise is a must. In addition, some of advisory rules are subject to change because of domain knowledge update. The human requests may have linguistic or crisp forms and a conventional expert system (ES is not able to overcome the fuzziness in the problem nature. In this research, a Web-based fuzzy expert system for Common Human Advisor (FES-CHA is developed and implemented to be used as a student advisor at the department‘s web portal. The system is implemented by using Microsoft Visual Studio .NET 2010, MVC and Microsoft SQL Server 2012.
Rough Set Fuzzy Optimum Selecting in Multidisciplinary System
Institute of Scientific and Technical Information of China (English)
LIU Xu-lin; SONG Bao-wei; WANG Jin-hua; CHEN Jie
2008-01-01
Scheme evaluation and selection is an optimum selecting and sequencing problem with multi-objective and multi- level. It can't follow single objective function or rule. Meanwhile, these objectives are coupled with each other and the at- tribution information is fuzzy also. It is necessary to find an effective evaluation method which can consider all conditions and restrictions. In this paper, AHP and rough set theory are applied to fuzzy optimization to determine important weight of each attribution. The rough set fuzzy optimum selection is used to eliminate the useless information. Autonomous un- derwater vehicle (AUV) is large-scah systems with many coupled design variables and objective functions. Their scheme evaluation and selection are very important, which relate to multiple factors, such as reliability;security, service time; the lifeeyele, etc. Results of application in torpedo design indicate that this method is feasible.
Genetic Algorithm Based Hybrid Fuzzy System for Assessing Morningness
Directory of Open Access Journals (Sweden)
Animesh Biswas
2014-01-01
Full Text Available This paper describes a real life case example on the assessment process of morningness of individuals using genetic algorithm based hybrid fuzzy system. It is observed that physical and mental performance of human beings in different time slots of a day are majorly influenced by morningness orientation of those individuals. To measure the morningness of people various self-reported questionnaires were developed by different researchers in the past. Among them reduced version of Morningness-Eveningness Questionnaire is mostly accepted. Almost all of the linguistic terms used in questionnaires are fuzzily defined. So, assessing them in crisp environments with their responses does not seem to be justifiable. Fuzzy approach based research works for assessing morningness of people are very few in the literature. In this paper, genetic algorithm is used to tune the parameters of a Mamdani fuzzy inference model to minimize error with their predicted outputs for assessing morningness of people.
Revamping Grooving Process for Sustainability using Fuzzy Expert System
Directory of Open Access Journals (Sweden)
Iqba Asif
2016-01-01
Full Text Available The article presents an application of a fuzzy expert system for renovating a metal cutting process to cope with the sustainability requirements. The work seeks a sustainable balance between energy consumption, productivity and tool damage. Cylindrical grooving experiments were performed to generate data related to quantification of the effects of material hardness, cutting speed, width of cut and feed rate on the aforementioned sustainability measures. A fuzzy knowledge-base was developed that suggests the most suitable adjustments of the controlled variables that would lead to achievement of various combinations of the objectives.
Performance Enhancement of Intrusion Detection using Neuro - Fuzzy Intelligent System
Directory of Open Access Journals (Sweden)
Dr. K. S. Anil Kumar
2014-10-01
Full Text Available This research work aims at developing hybrid algorithms using data mining techniques for the effective enhancement of anomaly intrusion detection performance. Many proposed algorithms have not addressed their reliability with varying amount of malicious activity or their adaptability for real time use. The study incorporates a theoretical basis for improvement in performance of IDS using K-medoids Algorithm, Fuzzy Set Algorithm, Fuzzy Rule System and Neural Network techniques. Also statistical significance of estimates has been looked into for finalizing the best one using DARPA network traffic datasets.
Turbine speed control system based on a fuzzy-PID
Institute of Scientific and Technical Information of China (English)
SUN Jian-hua; WANG Wei; YU Hai-yan
2008-01-01
The flexibility demand of marine nuclear power plant is very high,the multiple parameters of the marine nuclear power plant with the once-through steam generator are strongly coupled,and the normal PID control of the turbine speed can't meet the control demand. This paper introduces a turbine speed Fuzzy-PID controller to coordinately control the steam pressure and thus realize the demand for quick tracking and steady state control over the turbine speed by using the Fuzzy control's quick dynamic response and PID control's steady state performance. The simulation shows the improvement of the response time and steady state performance of the control system.
Simulation of fuzzy control systems for nonferrous alloy vacuum counter-pressure casting
Institute of Scientific and Technical Information of China (English)
YAN Qing-song; CAI Qi-zhou; WEI Bo-kang; YU Huan; YU Zi-rong
2005-01-01
Through simulation analyses of vacuum counter-pressure casting fuzzy control systems based on MATLAB, fuzzy control systems designed by simulation can track technical route established well. When transmission functions of vacuum counter-pressure casting controlled objects are changed in operation, fuzzy control systems can carry on self-regulation and stabilize quickly, and embody the advantages of fleet response velocity and little adjusting quantity. The design of vacuum counter-pressure casting fuzzy control systems is accelerated and improved greatly by simulation based on MATLAB. Meanwhile, their design is accurate and reliable. Moreover, microstructure and properties of thin-wall aluminum alloy castings are improved effectively by using fuzzy control systems.
Energy Technology Data Exchange (ETDEWEB)
Guimaraes, Antonio Cesar Ferreira [Instituto de Engenharia Nuclear (IEN), Rio de Janeiro, RJ (Brazil)
2002-04-01
This work consists of the analysis of natural circulation in a thermal hydraulics loop to a system of passive cooling of a nuclear reactor. The loop in reduced scale is similar to a passive heat removal system of a Pressurized Water Reactor. Using some experts of the area and of the system simulator, a set of fuzzy rules are defined to represent the problem and the associated uncertainties. The results are satisfactory if compared for example to experimental ones. With this model, inferences can be accomplished by the engineer, for adjustment and control of the problem variables. (author)
Towards a Fuzzy Expert System on Toxicological Data Quality Assessment.
Yang, Longzhi; Neagu, Daniel; Cronin, Mark T D; Hewitt, Mark; Enoch, Steven J; Madden, Judith C; Przybylak, Katarzyna
2013-01-01
Quality assessment (QA) requires high levels of domain-specific experience and knowledge. QA tasks for toxicological data are usually performed by human experts manually, although a number of quality evaluation schemes have been proposed in the literature. For instance, the most widely utilised Klimisch scheme1 defines four data quality categories in order to tag data instances with respect to their qualities; ToxRTool2 is an extension of the Klimisch approach aiming to increase the transparency and harmonisation of the approach. Note that the processes of QA in many other areas have been automatised by employing expert systems. Briefly, an expert system is a computer program that uses a knowledge base built upon human expertise, and an inference engine that mimics the reasoning processes of human experts to infer new statements from incoming data. In particular, expert systems have been extended to deal with the uncertainty of information by representing uncertain information (such as linguistic terms) as fuzzy sets under the framework of fuzzy set theory and performing inferences upon fuzzy sets according to fuzzy arithmetic. This paper presents an experimental fuzzy expert system for toxicological data QA which is developed on the basis of the Klimisch approach and the ToxRTool in an effort to illustrate the power of expert systems to toxicologists, and to examine if fuzzy expert systems are a viable solution for QA of toxicological data. Such direction still faces great difficulties due to the well-known common challenge of toxicological data QA that "five toxicologists may have six opinions". In the meantime, this challenge may offer an opportunity for expert systems because the construction and refinement of the knowledge base could be a converging process of different opinions which is of significant importance for regulatory policy making under the regulation of REACH, though a consensus may never be reached. Also, in order to facilitate the implementation
ICD 10 Based Medical Expert System Using Fuzzy Temporal Logic
Directory of Open Access Journals (Sweden)
P.Chinniah
2009-12-01
Full Text Available Medical diagnosis process involves many levels and considerable amount of time and money are invariably spent for the first level of diagnosis usually made by the physician for all the patients every time. Hence there is a need for a computer based system which not only asks relevant questions to the patients but also aids the physician by giving a set of possible diseases from the symptoms obtained using logic at inference. In this work, an ICD10 based Medical Expert System that provides advice, information and recommendation to the physician using fuzzy temporal logic. The knowledge base used in this system consists of facts of symptoms and rules on diseases. It also provides fuzzy severity scale and weight factor for symptom and disease and can vary with respect to time. The system generates the possible disease conditions based on modified Euclidean metric using Elder’s algorithm for effective clustering. The minimum similarity value is used as the decision parameter to identify a disease. Keywords -Fuzzy clustering, symptoms, fuzzy severity scale, weight factor, Minkowski distance, ICD, WHO, Rules Base, TSQL
Fuzzy self-learning control for magnetic servo system
Tarn, J. H.; Kuo, L. T.; Juang, K. Y.; Lin, C. E.
1994-01-01
It is known that an effective control system is the key condition for successful implementation of high-performance magnetic servo systems. Major issues to design such control systems are nonlinearity; unmodeled dynamics, such as secondary effects for copper resistance, stray fields, and saturation; and that disturbance rejection for the load effect reacts directly on the servo system without transmission elements. One typical approach to design control systems under these conditions is a special type of nonlinear feedback called gain scheduling. It accommodates linear regulators whose parameters are changed as a function of operating conditions in a preprogrammed way. In this paper, an on-line learning fuzzy control strategy is proposed. To inherit the wealth of linear control design, the relations between linear feedback and fuzzy logic controllers have been established. The exercise of engineering axioms of linear control design is thus transformed into tuning of appropriate fuzzy parameters. Furthermore, fuzzy logic control brings the domain of candidate control laws from linear into nonlinear, and brings new prospects into design of the local controllers. On the other hand, a self-learning scheme is utilized to automatically tune the fuzzy rule base. It is based on network learning infrastructure; statistical approximation to assign credit; animal learning method to update the reinforcement map with a fast learning rate; and temporal difference predictive scheme to optimize the control laws. Different from supervised and statistical unsupervised learning schemes, the proposed method learns on-line from past experience and information from the process and forms a rule base of an FLC system from randomly assigned initial control rules.
Using Fuzzy Association Rules to Design E-commerce Personalized Recommendation System
Guofang Kuang; Yuanchen Li
2013-01-01
In order to improve the efficiency of fuzzy association rule mining, the paper defines the redundant fuzzy association rules, and strong fuzzy association rules redundant nature. As much as possible for more information in the e-commerce environment, and in the right form is a prerequisite for personalized recommendation. Personalized recommendation technology is a core issue of e-commerce automated recommendation system. Higher complexity than ordinary association rules algorithm fuzzy assoc...
An Adaptive Fuzzy-Logic Traffic Control System in Conditions of Saturated Transport Stream
Marakhimov, A. R.; Igamberdiev, H. Z.; Umarov, Sh. X.
2016-01-01
This paper considers the problem of building adaptive fuzzy-logic traffic control systems (AFLTCS) to deal with information fuzziness and uncertainty in case of heavy traffic streams. Methods of formal description of traffic control on the crossroads based on fuzzy sets and fuzzy logic are proposed. This paper also provides efficient algorithms for implementing AFLTCS and develops the appropriate simulation models to test the efficiency of suggested approach. PMID:27517081
Power system damping using fuzzy controlled facts devices
Energy Technology Data Exchange (ETDEWEB)
Kazemi, Ahad; Sohrforouzani, Mahmoud Vakili [Department of Electrical Engineering, Iran University of Science and Technology, Narmak, Tehran (Iran)
2006-06-15
This paper presents a new approach to the implementation of the effect of FACTS devices on damping local modes and inter-area modes of oscillations based on a simple fuzzy logic proportional plus conventional integral controller in a multi-machine power system. The proposed controller uses a combination of a FLC and a PI controller. In comparison with the existing fuzzy controllers, the proposed fuzzy controller combines the advantages of a FLC and a conventional PI controller. By applying this controller to the FACTS devices such as UPFC, TCSC and SVC the damping of local modes and inter-area modes of oscillations in a multi-machine power system will be handled properly. In addition, the paper considers the conventional PI controller and compares its performance with respect to the proposed fuzzy controller. Also the effects of the auxiliary signals in damping multimodal oscillation have been shown. Finally, several fault and load disturbance simulation results are presented to highlight the effectiveness of the proposed FACTS controller in a multi-machine power system. (author)
On the quasi-controllability of continuous-time dynamic fuzzy control systems
Energy Technology Data Exchange (ETDEWEB)
Feng Yuhu [Department of Applied Mathematics, Dong Hua University, Shanghai 200051 (China)]. E-mail: yhfeng@dhu.edu.cn; Hu Liangjian [Department of Applied Mathematics, Dong Hua University, Shanghai 200051 (China)
2006-10-15
This paper gives the controllability analysis of continuous-time dynamic fuzzy control system from the aspect of fuzzy differential equations. The fuzzy state is different from the crisp state, as the counterpart of the controllability concept in the classical control theory, the controllable target state must be restricted within some limits. Hence, the concepts of admissible controllable state subset and quasi-controllability are introduced to describe the controllability property for fuzzy control system. The sufficient and necessary conditions for the fuzzy control system to be quasi-controllable are obtained and some examples are given to demonstrate the problems discussed in this paper.
Liu, Chuang; Lam, H. K.
2015-01-01
In this paper, we propose a polynomial fuzzy observer controller for nonlinear systems, where the design is achieved through the stability analysis of polynomial-fuzzy-model-based (PFMB) observer-control system. The polynomial fuzzy observer estimates the system states using estimated premise variables. The estimated states are then employed by the polynomial fuzzy controller for the feedback control of nonlinear systems represented by the polynomial fuzzy model. The system stability of the P...
Fuzzy Sliding Mode Control for Hyper Chaotic Chen System
Directory of Open Access Journals (Sweden)
SARAILOO, M.
2012-02-01
Full Text Available In this paper, a fuzzy sliding mode control method is proposed for stabilizing hyper chaotic Chen system. The main objective of the control scheme is to stabilize unstable equilibrium point of the system by controlling the states of the system so that they converge to a pre-defined sliding surface and remain on it. A fuzzy control technique is also utilized in order to overcome the main disadvantage of sliding mode control methods, i.e. chattering problem. It is shown that the equilibrium point of the system is stabilized by using the proposed method. A stability analysis is also performed to prove that the states of the system converge to the sliding surface and remain on it. Simulations show that the control method can be effectively applied to Chen system when it performs hyper chaotic behavior.
A fuzzy recommendation system for daily water intake
Directory of Open Access Journals (Sweden)
Bin Dai
2016-05-01
Full Text Available Water is one of the most important constituents of the human body. Daily consumption of water is thus necessary to protect human health. Daily water consumption is related to several factors such as age, ambient temperature, and degree of physical activity. These factors are generally difficult to express with exact numerical values. The main objective of this article is to build a daily water intake recommendation system using fuzzy methods. This system will use age, physical activity, and ambient temperature as the input factors and daily water intake values as the output factor. The reasoning mechanism of the fuzzy system can calculate the recommended value of daily water intake. Finally, the system will compare the actual recommended values with our system to determine the usefulness. The experimental results show that this recommendation system is effective in actual application.
Human Disease Diagnosis Using a Fuzzy Expert System
Hasan, Mir Anamul; Chowdhury, Ahsan Raja
2010-01-01
Human disease diagnosis is a complicated process and requires high level of expertise. Any attempt of developing a web-based expert system dealing with human disease diagnosis has to overcome various difficulties. This paper describes a project work aiming to develop a web-based fuzzy expert system for diagnosing human diseases. Now a days fuzzy systems are being used successfully in an increasing number of application areas; they use linguistic rules to describe systems. This research project focuses on the research and development of a web-based clinical tool designed to improve the quality of the exchange of health information between health care professionals and patients. Practitioners can also use this web-based tool to corroborate diagnosis. The proposed system is experimented on various scenarios in order to evaluate it's performance. In all the cases, proposed system exhibits satisfactory results.
FHESMM: Fuzzy Hybrid Expert System for Marketing Mix Model
Directory of Open Access Journals (Sweden)
Mehdi Neshat
2011-11-01
Full Text Available Increasing customers satisfaction in this developed world is the most important factor to have a successful trade and production. New marketing methods and supervising the marketing choices will have a key role to increase the profit of a company. This paper investigates an expert system through four main principles of marketing (price, product, Place and Promotion and their composition with a logic fuzzy system and benefiting from the experiences of marketing specialists. Comparing with the other systems, this one has special properties such as investigating and extracting different fields in which affect the customers satisfaction directly or indirectly as input parameters (26, using knowledge of experts to design inference system rule, composing the results of five fuzzy expert systems and calculating final result(customers satisfaction and finally creating a high function expert system on management and guiding the managers to do a successful marketing in dynamic markets.
Expert,Neural and Fuzzy Systems in Process Planning
Institute of Scientific and Technical Information of China (English)
无
1999-01-01
Computer aided process planning (CAPP) aims at improving efficiency, quali t y, and productivity in a manufacturing concern through reducing lead-times and costs by utilizing better manufacturing practices thus improving competitiveness in the market. CAPP attempts to capture the thoughts and methods of the experie nced process planner. Variant systems are understandable, generative systems can plan new parts. Expert systems increase flexibility, fuzzy logic captures vague knowledge while neural networks learn. The combination of fuzzy, neural and exp ert system technologies is necessary to capture and utilize the process planning logic. A system that maintains the dependability and clarity of variant systems , is capable of planning new parts, and improves itself through learning is neede d by industry.
LMI-based output feedback fuzzy control of chaotic system with uncertainties
Institute of Scientific and Technical Information of China (English)
Tan Wen; Wang Yao-Nan; Duan Feng; Li Xiao-Hui
2006-01-01
This paper studies the robust fuzzy control for nonlinear chaotic system in the presence of parametric uncertainties. An uncertain Takagi-Sugeno (T-S) fuzzy model is employed for fuzzy modelling of an unknown chaotic system. A sufficient condition formulated in terms of linear matrix inequality (LMI) for the existence of fuzzy controller is obtained. Then the output feedback fuzzy-model-based regulator derived from the LMI solutions can guarantee the stability of the closed-loop overall fuzzy system. The T-S fuzzy model of the chaotic Chen system is developed as an example for illustration. The effectiveness of the proposed controller design methodology is finally demonstrated through computer simulations on the uncertain Chen chaotic system.
Fuzzy Timing Petri Net for Fault Diagnosis in Power System
Directory of Open Access Journals (Sweden)
Alireza Tavakholi Ghainani
2012-01-01
Full Text Available A model-based system for fault diagnosis in power system is presented in this paper. It is based on fuzzy timing Petri net (FTPN. The ordinary Petri net (PN tool is used to model the protective components, relays, and circuit breakers. In addition, fuzzy timing is associated with places (token/transition to handle the uncertain information of relays and circuits breakers. The received delay time information of relays and breakers is mapped to fuzzy timestamps, π(τ, as initial marking of the backward FTPN. The diagnosis process starts by marking the backward sub-FTPNs. The final marking is found by going through the firing sequence, σ, of each sub-FTPN and updating fuzzy timestamp in each state of σ. The final marking indicates the estimated fault section. This information is then in turn used in forward FTPN to evaluate the fault hypothesis. The FTPN will increase the speed of the inference engine because of the ability of Petri net to describe parallel processing, and the use of time-tag data will cause the inference procedure to be more accurate.
Fuzzy Logic Control of a Ball on Sphere System
Directory of Open Access Journals (Sweden)
Seyed Alireza Moezi
2014-01-01
Full Text Available The scope of this paper is to present a fuzzy logic control of a class of multi-input multioutput (MIMO nonlinear systems called “system of ball on a sphere,” such an inherently nonlinear, unstable, and underactuated system, considered truly to be two independent ball and wheel systems around its equilibrium point. In this work, Sugeno method is investigated as a fuzzy controller method, so it works in a good state with optimization and adaptive techniques, which makes it very attractive in control problems, particularly for such nonlinear dynamic systems. The system’s dynamic is described and the equations are illustrated. The outputs are shown in different figures so as to be compared. Finally, these simulation results show the exactness of the controller’s performance.
Precision control of inverter welding power sources by using T-S fuzzy systems
Institute of Scientific and Technical Information of China (English)
Zhou Yiqing; Huang Shisheng; Zhang Hongbing; Wang Zhenmin; Xie Shengmian
2007-01-01
The functional relationship of approximation accuracy and number of fuzzy sets is used to find the rational balance point between the control accuracy and the control cost of fuzzy systems. This approach efficiently eliminates the drawback of rapid control cost increase caused by blind increase of fuzzy set number in practical engineering. The sufficient conditions for T-S fuzzy systems as universal approximators are derived. A special T-S fuzzy system that satisfied these conditions is analyzed, and the simulation results show that when the number of fuzzy sets is increased moderately, the model parameters' training epochs can be effectually decreased while the model accuracy improved significantly. A practical welding power source controlled by a T-S fuzzy system is developed with satisfactory experimental results.
Fuzzy Logic Applied to an Oven Temperature Control System
Directory of Open Access Journals (Sweden)
Nagabhushana KATTE
2011-10-01
Full Text Available The paper describes the methodology of design and development of fuzzy logic based oven temperature control system. As simple fuzzy logic controller (FLC structure with an efficient realization and a small rule base that can be easily implemented in existing underwater control systems is proposed. The FLC has been designed using bell-shaped membership function for fuzzification, 49 control rules in its rule base and centre of gravity technique for defuzzification. Analog interface card with 16-bits resolution is designed to achieve higher precision in temperature measurement and control. The experimental results of PID and FLC implemented system are drawn for a step input and presented in a comparative fashion. FLC exhibits fast response and it has got sharp rise time and smooth control over conventional PID controller. The paper scrupulously discusses the hardware and software (developed using ‘C’ language features of the system.
Fuzzy Logic Control for Suspension Systems of Tracked Vehicles
Institute of Scientific and Technical Information of China (English)
YU Yang; WEI Xue-xia; ZHANG Yong-fa
2009-01-01
A scheme of fuzzy logic control for the suspension system of a tracked vehicle is presented.A mechanical model for the whole body of a tracked vehicle,which is totally a fifteen-degree-of-freedom system,is established.The model includes the vertical motion,the pitch motion as well as the roll motion of the tracked vehicle.In contrast to most previous studies,the coupling effect among the vertical,the pitch and the roll motions of the suspension system of a tracked vehicle is considered simultaneously.The simulation of fuzzy logic control under road surface with random excitation shows that the acceleration,pitch angle and roll angle of suspension system can be efficiently controlled.
ICD 10 Based Medical Expert System Using Fuzzy Temporal Logic
Chinniah, P
2010-01-01
Medical diagnosis process involves many levels and considerable amount of time and money are invariably spent for the first level of diagnosis usually made by the physician for all the patients every time. Hence there is a need for a computer based system which not only asks relevant questions to the patients but also aids the physician by giving a set of possible diseases from the symptoms obtained using logic at inference. In this work, an ICD10 based Medical Expert System that provides advice, information and recommendation to the physician using fuzzy temporal logic. The knowledge base used in this system consists of facts of symptoms and rules on diseases. It also provides fuzzy severity scale and weight factor for symptom and disease and can vary with respect to time. The system generates the possible disease conditions based on modified Euclidean metric using Elders algorithm for effective clustering. The minimum similarity value is used as the decision parameter to identify a disease.
Hamdy, M; Hamdan, I
2015-07-01
In this paper, a robust H∞ fuzzy output feedback controller is designed for a class of affine nonlinear systems with disturbance via Takagi-Sugeno (T-S) fuzzy bilinear model. The parallel distributed compensation (PDC) technique is utilized to design a fuzzy controller. The stability conditions of the overall closed loop T-S fuzzy bilinear model are formulated in terms of Lyapunov function via linear matrix inequality (LMI). The control law is robustified by H∞ sense to attenuate external disturbance. Moreover, the desired controller gains can be obtained by solving a set of LMI. A continuous stirred tank reactor (CSTR), which is a benchmark problem in nonlinear process control, is discussed in detail to verify the effectiveness of the proposed approach with a comparative study.
Keller, James M; Fogel, David B
2016-01-01
This book covers the three fundamental topics that form the basis of computational intelligence: neural networks, fuzzy systems, and evolutionary computation. The text focuses on inspiration, design, theory, and practical aspects of implementing procedures to solve real-world problems. While other books in the three fields that comprise computational intelligence are written by specialists in one discipline, this book is co-written by current former Editor-in-Chief of IEEE Transactions on Neural Networks and Learning Systems, a former Editor-in-Chief of IEEE Transactions on Fuzzy Systems, and the founding Editor-in-Chief of IEEE Transactions on Evolutionary Computation. The coverage across the three topics is both uniform and consistent in style and notation. Discusses single-layer and multilayer neural networks, radial-basi function networks, and recurrent neural networks Covers fuzzy set theory, fuzzy relations, fuzzy logic interference, fuzzy clustering and classification, fuzzy measures and fuzz...
Intelligent micro blood typing system using a fuzzy algorithm
Kang, Taeyun; Lee, Seung-Jae; Kim, Yonggoo; Lee, Gyoo-Whung; Cho, Dong-Woo
2010-01-01
ABO typing is the first analysis performed on blood when it is tested for transfusion purposes. The automated machines used in hospitals for this purpose are typically very large and the process is complicated. In this paper, we present a new micro blood typing system that is an improved version of our previous system (Kang et al 2004 Trans. ASME, J. Manuf. Sci. Eng. 126 766, Lee et al 2005 Sensors Mater. 17 113). This system, fabricated using microstereolithography, has a passive valve for controlling the flow of blood and antibodies. The intelligent micro blood typing system has two parts: a single-line micro blood typing device and a fuzzy expert system for grading the strength of agglutination. The passive valve in the single-line micro blood typing device makes the blood stop at the entrance of a micro mixer and lets it flow again after the blood encounters antibodies. Blood and antibodies are mixed in the micro mixer and agglutination occurs in the chamber. The fuzzy expert system then determines the degree of agglutination from images of the agglutinated blood. Blood typing experiments using this device were successful, and the fuzzy expert system produces a grading decision comparable to that produced by an expert conducting a manual analysis.
Designing Fuzzy Rule Based Expert System for Cyber Security
Goztepe, Kerim
2016-01-01
The state of cyber security has begun to attract more attention and interest outside the community of computer security experts. Cyber security is not a single problem, but rather a group of highly different problems involving different sets of threats. Fuzzy Rule based system for cyber security is a system consists of a rule depository and a mechanism for accessing and running the rules. The depository is usually constructed with a collection of related rule sets. The aim of this study is to...
Subway Train Braking System: A Fuzzy Based Hardware Approach
Directory of Open Access Journals (Sweden)
Mamun B.I. Reaz
2011-01-01
Full Text Available Problem statement: Automated subway train-braking system require perfection, efficiency and fast response. In order to cope with this concerns, an appropriate algorithm need to be developed which need to be implemented in hardware for faster response. Approach: In this research, the FPGA realization of fuzzy based subway train braking system has been presented on an Alter FLEX10K device to provide an accurate and increased speed of convergence of the network. The fuzzy based subway train braking system is comprised of fusilier, inference, rule selector and defuzzifier modules. Sixteen rules are identified for the rule selector module. After determining the membership functions and its fuzzy variables, the Max-Min Composition method and Madman-Min implication operator are used for the inference module and the Centre of Gravity method is used for the defuzzification module. Each module is modeled individually using behavioral VHDL. The layers are then connected using structural VHDL. Two 8-bit and one 8-bit unsigned digital signals are used for input and output respectively. Six ROMs are defined in order to decrease the chances of processing and increasing the throughput of the system. Results: Functional simulations were commenced to verify the functionality of the individual modules and the system as well. We have validated the hardware implementation of the proposed approach through comparison, verification and analysis. The design has utilized 2372 units of LC with a system frequency of 139.8MHz. Conclusion: In this research, the FPGA realization of fuzzy brake system of subway train has been successfully implemented with minimum usage of logic cells. The validation study with C model shows that the hardware model is appropriate and the hardware approach shows faster and accurate response with full automatic control.
A Behavioral Distance for Fuzzy-Transition Systems
Cao, Yongzhi; Sun, Sherry X; Chen, Guoqing
2011-01-01
In contrast to the existing approaches to bisimulation for fuzzy systems, we introduce a behavioral distance to measure the behavioral similarity of states in a nondeterministic fuzzy-transition system. This behavioral distance is defined as the greatest fixed point of a suitable monotonic function and provides a quantitative analogue of bisimilarity. The behavioral distance has the important property that two states are at zero distance if and only if they are bisimilar. Moreover, for any given threshold, we find that states with behavioral distances bounded by the threshold are equivalent. In addition, we show that two system combinators---parallel composition and product---are non-expansive with respect to our behavioral distance, which makes compositional verification possible.
A Novel Web-based Human Advisor Fuzzy Expert System
Directory of Open Access Journals (Sweden)
Vahid Rafe
2013-01-01
Full Text Available The applications of the Internet-based technologies and the concepts of fuzzy expert systems (FES have creatednew methods for sharing and distributing knowledge. However, there has been a general lack of investigation in thearea of web-based fuzzy expert systems. In this paper, the issues associated with the design, development, and useof web-based applications from a standpoint of the benefits and challenges of development and utilization areinvestigated. The original theory and concepts in conventional FES are reviewed and a knowledge engineeringframework for developing such systems is revised. For a human advisor to have a satisfying performance, expertise isa must. In addition, some of advisory rules are subject to change because of domain knowledge update. The humanrequests may have linguistic or crisp forms and a conventional expert system (ES is not able to overcome thefuzziness in the problem nature. In this research, a Web-based fuzzy expert system for Common Human Advisor(FES-CHA is developed and implemented to be used as a student advisor at the department's web portal. Thesystem is implemented by using Microsoft Visual Studio .NET 2010, MVC and Microsoft SQL Server 2012.
The application of fuzzy Delphi and fuzzy inference system in supplier ranking and selection
Tahriri, Farzad; Mousavi, Maryam; Hozhabri Haghighi, Siamak; Zawiah Md Dawal, Siti
2014-06-01
In today's highly rival market, an effective supplier selection process is vital to the success of any manufacturing system. Selecting the appropriate supplier is always a difficult task because suppliers posses varied strengths and weaknesses that necessitate careful evaluations prior to suppliers' ranking. This is a complex process with many subjective and objective factors to consider before the benefits of supplier selection are achieved. This paper identifies six extremely critical criteria and thirteen sub-criteria based on the literature. A new methodology employing those criteria and sub-criteria is proposed for the assessment and ranking of a given set of suppliers. To handle the subjectivity of the decision maker's assessment, an integration of fuzzy Delphi with fuzzy inference system has been applied and a new ranking method is proposed for supplier selection problem. This supplier selection model enables decision makers to rank the suppliers based on three classifications including "extremely preferred", "moderately preferred", and "weakly preferred". In addition, in each classification, suppliers are put in order from highest final score to the lowest. Finally, the methodology is verified and validated through an example of a numerical test bed.
Study on Missile Intelligent Fault Diagnosis System Based on Fuzzy NN Expert System
Institute of Scientific and Technical Information of China (English)
无
2001-01-01
In order to study intelligent fault diagnosis methods based on fuzzy neural network (NN) expert systemand build up intelligent fault diagnosis for a type of mis-sile weapon system, the concrete implementation of a fuzzyNN fault diagnosis expert system is given in this paper. Based on thorough research of knowledge presentation, theintelligent fault diagnosis system is implemented with artificial intelligence for a large-scale missile weapon equipment.The method is an effective way to perform fuzzy fault diagnosis. Moreover, it provides a new way of the fault diagnosisfor large-scale missile weapon equipment.
An Improved Self-Organizing CPN-Based Fuzzy System
Institute of Scientific and Technical Information of China (English)
ZHANG Zhiming; WANG Yue; TAO Ran; ZHOU Siyong
2001-01-01
An improved self-organizing CPN-based fuzzy system is proposed in this paper.Asso-ciated with the neuro-fuzzy system,there is a two-phase hybrid learning algorithm,which utilizes aCPN-based nearest-neighborhood clustering schemefor both structure learning and initial parameters set-ting,and a gradient descent method with variablelearning rate for parameters fine-tuning.By combin-ing the above two methods,the learning speed is muchfaster than that of the original back-propagation al-gorithms.The comparative results on the examplessuggested that the method has the merits of simplestructure,fast learning speed and good modeling ac-curacy.
Advanced biofeedback from surface electromyography signals using fuzzy system
DEFF Research Database (Denmark)
Samani, Afshin; Holtermann, Andreas; Søgaard, Karen
2010-01-01
The aims of this study were to develop a fuzzy inference-based biofeedback system and investigate its effects when inducing active (shoulder elevation) and passive (relax) pauses on the trapezius muscle electromyographic (EMG) activity during computer work. Surface EMG signals were recorded from...... clavicular, descending (bilateral) and ascending parts of the trapezius muscles during computer work. The fuzzy system readjusted itself based on the history of previous inputs. The effect of feedback was assessed in terms of muscle activation regularity and amplitude. Active pause resulted in non......-uniform muscle activity changes in the trapezius muscle depicted by increase and decrease of permuted sample entropy in ascending and clavicular parts of trapezius, respectively (P
Using Fuzzy Inference Systems to Optimize Highway Alignments
Directory of Open Access Journals (Sweden)
Gianluca Dell’Acqua
2012-03-01
Full Text Available The general objective of the research project is to explore innovations in integrating infrastructure and land use planning for transportation corridors. In contexts with environmental impact, the choice of transportation routes must address the sensitivity of current and preexisting conditions. Multi-criteria analyses are used to solve problems of this nature, but they do not define an objective approach on a quantitative basis taking into account some important, but often intrinsically unmeasurable parameters. Fuzzy logic becomes a more effective model as systems become more complex. During the preliminary design phase, fuzzy inference systems offer a contribution to decision-making which is much more complete than a benefits/and costs analysis. In this study, alternative alignment options are considered, combining engineering, social, environmental, and economic factors in the decision-making. The research formalizes a general method useful for analyzing different case studies. The method can be used to justify highway alignment choices in environmental impact study analysis.
Application of Improved Fuzzy Controller in Networked Control System
Institute of Scientific and Technical Information of China (English)
ZHANG Qian; GUO Xi-jin; WANG Zhen; TIAN Xi-lan
2006-01-01
Aiming at the problem of network-induced delay and data dropout in networked control system, an improved fuzzy controller is proposed in this paper. Considering the great influence of a controller on the performance of control system, an improved controller with a second order fuzzy controller and network-induced delay compensator being added to the basic fuzzy controller is proposed to realize self-regulation on-line. For this type of controller, neither plant model nor measurement of network delay is required. So it is capable of automatically adjusting quantified factor, proportional factor, and integral factor according to the control system error and its derivative. The design makes full use of the advantages of quickness in operation and reduction of steady state error because of its integral function. The controller has a good control effect on time-delay and can keep a better performance by self-regulation on-line in the network with data dropout and interference. It is good in quickness, adaptability, and robustness, which is favorable for controlling the long time-delay system.
Document Retrieval Using A Fuzzy Knowledge-Based System
Subramanian, Viswanath; Biswas, Gautam; Bezdek, James C.
1986-03-01
This paper presents the design and development of a prototype document retrieval system using a knowledge-based systems approach. Both the domain-specific knowledge base and the inferencing schemes are based on a fuzzy set theoretic framework. A query in natural language represents a request to retrieve a relevant subset of documents from a document base. Such a query, which can include both fuzzy terms and fuzzy relational operators, is converted into an unambiguous intermediate form by a natural language interface. Concepts that describe domain topics and the relationships between concepts, such as the synonym relation and the implication relation between a general concept and more specific concepts, have been captured in a knowledge base. The knowledge base enables the system to emulate the reasoning process followed by an expert, such as a librarian, in understanding and reformulating user queries. The retrieval mechanism processes the query in two steps. First it produces a pruned list of documents pertinent to the query. Second, it uses an evidence combination scheme to compute a degree of support between the query and individual documents produced in step one. The front-end component of the system then presents a set of document citations to the user in ranked order as an answer to the information request.
Evolutionary Computation and Its Applications in Neural and Fuzzy Systems
Directory of Open Access Journals (Sweden)
Biaobiao Zhang
2011-01-01
Full Text Available Neural networks and fuzzy systems are two soft-computing paradigms for system modelling. Adapting a neural or fuzzy system requires to solve two optimization problems: structural optimization and parametric optimization. Structural optimization is a discrete optimization problem which is very hard to solve using conventional optimization techniques. Parametric optimization can be solved using conventional optimization techniques, but the solution may be easily trapped at a bad local optimum. Evolutionary computation is a general-purpose stochastic global optimization approach under the universally accepted neo-Darwinian paradigm, which is a combination of the classical Darwinian evolutionary theory, the selectionism of Weismann, and the genetics of Mendel. Evolutionary algorithms are a major approach to adaptation and optimization. In this paper, we first introduce evolutionary algorithms with emphasis on genetic algorithms and evolutionary strategies. Other evolutionary algorithms such as genetic programming, evolutionary programming, particle swarm optimization, immune algorithm, and ant colony optimization are also described. Some topics pertaining to evolutionary algorithms are also discussed, and a comparison between evolutionary algorithms and simulated annealing is made. Finally, the application of EAs to the learning of neural networks as well as to the structural and parametric adaptations of fuzzy systems is also detailed.
Risk Mapping of Cutaneous Leishmaniasis via a Fuzzy C Means-based Neuro-Fuzzy Inference System
Akhavan, P.; Karimi, M.; Pahlavani, P.
2014-10-01
Finding pathogenic factors and how they are spread in the environment has become a global demand, recently. Cutaneous Leishmaniasis (CL) created by Leishmania is a special parasitic disease which can be passed on to human through phlebotomus of vector-born. Studies show that economic situation, cultural issues, as well as environmental and ecological conditions can affect the prevalence of this disease. In this study, Data Mining is utilized in order to predict CL prevalence rate and obtain a risk map. This case is based on effective environmental parameters on CL and a Neuro-Fuzzy system was also used. Learning capacity of Neuro-Fuzzy systems in neural network on one hand and reasoning power of fuzzy systems on the other, make it very efficient to use. In this research, in order to predict CL prevalence rate, an adaptive Neuro-fuzzy inference system with fuzzy inference structure of fuzzy C Means clustering was applied to determine the initial membership functions. Regarding to high incidence of CL in Ilam province, counties of Ilam, Mehran, and Dehloran have been examined and evaluated. The CL prevalence rate was predicted in 2012 by providing effective environmental map and topography properties including temperature, moisture, annual, rainfall, vegetation and elevation. Results indicate that the model precision with fuzzy C Means clustering structure rises acceptable RMSE values of both training and checking data and support our analyses. Using the proposed data mining technology, the pattern of disease spatial distribution and vulnerable areas become identifiable and the map can be used by experts and decision makers of public health as a useful tool in management and optimal decision-making.
Risk Mapping of Cutaneous Leishmaniasis via a Fuzzy C Means-based Neuro-Fuzzy Inference System
Directory of Open Access Journals (Sweden)
P. Akhavan
2014-10-01
Full Text Available Finding pathogenic factors and how they are spread in the environment has become a global demand, recently. Cutaneous Leishmaniasis (CL created by Leishmania is a special parasitic disease which can be passed on to human through phlebotomus of vector-born. Studies show that economic situation, cultural issues, as well as environmental and ecological conditions can affect the prevalence of this disease. In this study, Data Mining is utilized in order to predict CL prevalence rate and obtain a risk map. This case is based on effective environmental parameters on CL and a Neuro-Fuzzy system was also used. Learning capacity of Neuro-Fuzzy systems in neural network on one hand and reasoning power of fuzzy systems on the other, make it very efficient to use. In this research, in order to predict CL prevalence rate, an adaptive Neuro-fuzzy inference system with fuzzy inference structure of fuzzy C Means clustering was applied to determine the initial membership functions. Regarding to high incidence of CL in Ilam province, counties of Ilam, Mehran, and Dehloran have been examined and evaluated. The CL prevalence rate was predicted in 2012 by providing effective environmental map and topography properties including temperature, moisture, annual, rainfall, vegetation and elevation. Results indicate that the model precision with fuzzy C Means clustering structure rises acceptable RMSE values of both training and checking data and support our analyses. Using the proposed data mining technology, the pattern of disease spatial distribution and vulnerable areas become identifiable and the map can be used by experts and decision makers of public health as a useful tool in management and optimal decision-making.
CHEBYSHEV ACCELERATION TECHNIQUE FOR SOLVING FUZZY LINEAR SYSTEM
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S.H. Nasseri
2009-10-01
Full Text Available In this paper, Chebyshev acceleration technique is used to solve the fuzzy linear system (FLS. This method is discussed in details and followed by summary of some other acceleration techniques. Moreover, we show that in some situations that the methods such as Jacobi, Gauss-Sidel, SOR and conjugate gradient is divergent, our proposed method is applicable and the acquired results are illustrated by some numerical examples.
Switch Reluctance Motor Control Based on Fuzzy Logic System
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S. Aleksandrovsky
2012-01-01
Full Text Available Due to its intrinsic simplicity and reliability, the switched reluctance motor (SRM has now become a promising candidate for variable-speed drive applications as an alternative induction motor in various industrial application. However, the SRM has the disadvantage of nonlinear characteristic and control. It is suggested to use controller based on fuzzy logic system. Design of FLS controller and simulation model presented.
Adaptive Fractional Fuzzy Sliding Mode Control for Multivariable Nonlinear Systems
Junhai Luo; Heng Liu
2014-01-01
This paper presents a robust adaptive fuzzy sliding mode control method for a class of uncertain nonlinear systems. The fractional order calculus is employed in the parameter updating stage. The underlying stability analysis as well as parameter update law design is carried out by Lyapunov based technique. In the simulation, two examples including a comparison with the traditional integer order counterpart are given to show the effectiveness of the proposed method. The main contribution of th...
A Fuzzy Control System for Inductive Video Games
Lara-Alvarez, Carlos; Mitre-Hernandez, Hugo; Flores, Juan; Fuentes, Maria
2017-01-01
It has been shown that the emotional state of students has an important relationship with learning; for instance, engaged concentration is positively correlated with learning. This paper proposes the Inductive Control (IC) for educational games. Unlike conventional approaches that only modify the game level, the proposed technique also induces emotions in the player for supporting the learning process. This paper explores a fuzzy system that analyzes the players' performance and their emotion...
Properties of fuzzy hyperplanes
Institute of Scientific and Technical Information of China (English)
ZHANG Zhong; LI Chuandong; WU Deyin
2004-01-01
Some properties of closed fuzzy matroid and those of its hyperplanes are investigated. A fuzzy hyperplane property,which extends the analog of a crisp matroid from crisp set systems to fuzzy set systems, is proved.
Periodicity of a class of nonlinear fuzzy systems with delays
Energy Technology Data Exchange (ETDEWEB)
Yu Jiali [Computational Intelligence Laboratory, School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu 610054 (China)], E-mail: yujiali@uestc.edu.cn; Yi Zhang [Computational Intelligence Laboratory, School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu 610054 (China)], E-mail: zhangyi@uestc.edu.cn; Zhang Lei [Computational Intelligence Laboratory, School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu 610054 (China)], E-mail: leilazhang@uestc.edu.cn
2009-05-15
The well known Takagi-Sugeno (T-S) model gives an effective method to combine some simple local systems with their linguistic description to represent complex nonlinear dynamic systems. By using the T-S method, a class of local nonlinear systems having nice dynamic properties can be employed to represent some global complex nonlinear systems. This paper proposes to study the periodicity of a class of global nonlinear fuzzy systems with delays by using T-S method. Conditions for guaranteeing periodicity are derived. Examples are employed to illustrate the theory.
Fuzzy Aided Application Layer Semantic Intrusion Detection System - FASIDS
Sangeetha, S; 10.5121/ijnsa.2010.2204
2010-01-01
The objective of this is to develop a Fuzzy aided Application layer Semantic Intrusion Detection System (FASIDS) which works in the application layer of the network stack. FASIDS consist of semantic IDS and Fuzzy based IDS. Rule based IDS looks for the specific pattern which is defined as malicious. A non-intrusive regular pattern can be malicious if it occurs several times with a short time interval. For detecting such malicious activities, FASIDS is proposed in this paper. At application layer, HTTP traffic's header and payload are analyzed for possible intrusion. In the proposed misuse detection module, the semantic intrusion detection system works on the basis of rules that define various application layer misuses that are found in the network. An attack identified by the IDS is based on a corresponding rule in the rule-base. An event that doesn't make a 'hit' on the rule-base is given to a Fuzzy Intrusion Detection System (FIDS) for further analysis.
Detection of Coal Mine Spontaneous Combustion by Fuzzy Inference System
Institute of Scientific and Technical Information of China (English)
SUN Ji-ping; SONG Shu; MA Feng-ying; ZHANG Ya-li
2006-01-01
The spontaneous combustion is a smoldering process and characterized by a slow burning speed and a long duration. Therefore, it is a hazard to coal mines. Early detection of coal mine spontaneous combustion is quite difficult because of the complexity of different coal mines. And the traditional threshold discriminance is not suitable for spontaneous combustion detection due to the uncertainty of coalmine combustion. Restrictions of the single detection method will also affect the detection precision in the early time of spontaneous combustion. Although multiple detection methods can be adopted as a complementarity to improve the accuracy of detection, the synthesized method will increase the complicacy of criterion, making it difficult to estimate the combustion. To solve this problem, a fuzzy inference system based on CRI (Compositional Rule of Inference) and fuzzy reasoning method FITA (First Infer Then Aggregate) are presented. And the neural network is also developed to realize the fuzzy inference system. Finally, the effectiveness of the inference system is demonstrated by means of an experiment.
Anderson, H C; Lotfi, A; Westphal, L C; Jang, J R
1998-01-01
The above paper claims that under a set of minor restrictions radial basis function networks and fuzzy inference systems are functionally equivalent. The purpose of this letter is to show that this set of restrictions is incomplete and that, when it is completed, the said functional equivalence applies only to a small range of fuzzy inference systems. In addition, a modified set of restrictions is proposed which is applicable for a much wider range of fuzzy inference systems.
FUZZY IDENTIFIER WITH EXPONENTIAL RATE OF CONVERGENCE FOR NONLINEAR DYNAMIC SYSTEMS
Institute of Scientific and Technical Information of China (English)
2000-01-01
In this paper,fuzzy systems are used as identifiers for unknown nonlinear dynamic systems.The fuzzy identifier can incorporate linguistic knowledge of nonlinear dynamic systems with input-output pairs directly into the design.In the case where there is the modelling error,a new identification algorithm is proposed.It is proved that the fuzzy identifier is globally stable and the identification error converges to zero exponentially fast.
TECHNICAL ANALYSIS OF FUZZY METAGRAPH BASED DECISION SUPPORT SYSTEM FOR CAPITAL MARKET
Anbalagan Thirunavukarasu; Uma Maheswari
2013-01-01
This study proposes a Fuzzy Metagraph based Decision Support System (DSS) for short term and long term investment in share market. This rule base decision system will help traders to make correct decision at very low risk. Relative Strength Index (RSI), Moving Average Convergence Divergence (MACD) and WILLIAM- %R are some of the Technical Indicators which are used as input to train the system which is integrated with Fuzzy Metagraph. This approach of incorporating Fuzzy Metagraph with RSI, MA...
Takagi Sugeno fuzzy expert model based soft fault diagnosis for two tank interacting system
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Manikandan Pandiyan
2014-09-01
Full Text Available The inherent characteristics of fuzzy logic theory make it suitable for fault detection and diagnosis (FDI. Fault detection can benefit from nonlinear fuzzy modeling and fault diagnosis can profit from a transparent reasoning system, which can embed operator experience, but also learn from experimental and/or simulation data. Thus, fuzzy logic-based diagnostic is advantageous since it allows the incorporation of a-priori knowledge and lets the user understand the inference of the system. In this paper, the successful use of a fuzzy FDI based system, based on dynamic fuzzy models for fault detection and diagnosis of an industrial two tank system is presented. The plant data is used for the design and validation of the fuzzy FDI system. The validation results show the effectiveness of this approach.
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Mr.D. V. Kodavade
2014-09-01
Full Text Available With the acceptance of artificial intelligence paradigm, a number of successful artificial intelligence systems were created. Fault diagnosis in microprocessor based boards needs lot of empirical knowledge and expertise and is a true artificial intelligence problem. Research on fault diagnosis in microprocessor based system boards using new fuzzy-object oriented approach is presented in this paper. There are many uncertain situations observed during fault diagnosis. These uncertain situations were handled using fuzzy mathematics properties. Fuzzy inference mechanism is demonstrated using one case study. Some typical faults in 8085 microprocessor board and diagnostic procedures used is presented in this paper.
Autonomous Navigation System Using a Fuzzy Adaptive Nonlinear H∞ Filter
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Fariz Outamazirt
2014-09-01
Full Text Available Although nonlinear H∞ (NH∞ filters offer good performance without requiring assumptions concerning the characteristics of process and/or measurement noises, they still require additional tuning parameters that remain fixed and that need to be determined through trial and error. To address issues associated with NH∞ filters, a new SINS/GPS sensor fusion scheme known as the Fuzzy Adaptive Nonlinear H∞ (FANH∞ filter is proposed for the Unmanned Aerial Vehicle (UAV localization problem. Based on a real-time Fuzzy Inference System (FIS, the FANH∞ filter continually adjusts the higher order of the Taylor development thorough adaptive bounds and adaptive disturbance attenuation , which significantly increases the UAV localization performance. The results obtained using the FANH∞ navigation filter are compared to the NH∞ navigation filter results and are validated using a 3D UAV flight scenario. The comparison proves the efficiency and robustness of the UAV localization process using the FANH∞ filter.
Classification of toddler nutritional status using fuzzy inference system (FIS)
Permatasari, Dian; Azizah, Isnaini Nur; Hadiat, Hanifah Latifah; Abadi, Agus Maman
2017-08-01
Nutrition is a major health problem and concern for parents when it is relating with their toddler. The nutritional status is an expression of the state caused by the status of the balance between the number of intake of nutrients and the amount needed by the body for a variety of biological functions. The indicators that often used to determine the nutritional status is the combination of Weight (W) and Height (H) symbolized by W/H, because it describe a sensitive and specific nutritional status. This study aims to apply the Fuzzy Inference System Mamdani method to classify the nutritional status of toddler. The inputs are weight and height of the toddler. There are nine rules that used and the output is nutritional status classification consisting of four criteria: stunting, wasting, normal, and overweight. Fuzzy Inference System that be used is Mamdani method and the defuzzification use Centroid Method. The result of this study is compared with Assessment Anthropometric Standard of Toddler Nutritional Status by Ministry of Health. The accuracy level of this fuzzy model is about 84%.
Fuzzy assessment of health information system users' security awareness.
Aydın, Özlem Müge; Chouseinoglou, Oumout
2013-12-01
Health information systems (HIS) are a specific area of information systems (IS), where critical patient data is stored and quality health service is only realized with the correct use and efficient dissemination of this data to health workers. Therefore, a balance needs to be established between the levels of security and flow of information on HIS. Instead of implementing higher levels and further mechanisms of control to increase the security of HIS, it is preferable to deal with the arguably weakest link on HIS chain with respect to security: HIS users. In order to provide solutions and approaches for transforming users to the first line of defense in HIS but also to employ capable and appropriate candidates from the pool of newly graduated students, it is important to assess and evaluate the security awareness levels and characteristics of these existing and future users. This study aims to provide a new perspective to understand the phenomenon of security awareness of HIS users with the use of fuzzy analysis, and to assess the present situation of current and future HIS users of a leading medical and educational institution of Turkey, with respect to their security characteristics based on four different security scales. The results of the fuzzy analysis, the guide on how to implement this fuzzy analysis to any health institution and how to read and interpret these results, together with the possible implications of these results to the organization are provided.
CLASSIFICATION OF MAMMOGRAPHIC MASSES USING FUZZY INFERENCE SYSTEM
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K. Divyadarshini
2015-10-01
Full Text Available Computer aided detection (CAD intends to provide assistance to the mammography detection, reducing breast cancer misdiagnosis, thus allowing better diagnosis and more efficient treatments. In this work the task of automatically classifying the mass tissue into Breast Imaging Reporting and Data System (BI-RADS shape categories: round, oval, lobular, irregular and also as benign or malignant is investigated. Geometrical shape and margin features based on maximum and minimum radius of mass are used in this work to classify the masses. These geometric features are found to be good in discriminating regular shapes from irregular shapes. For the purpose of classification, the masses are segmented from the mammogram using gray level thresholding. Finally, the classification is performed using fuzzy inference system. The fuzzy rules are used to construct the generalized fuzzy membership function for classifying the shape and severity of masses. The images were collected from Mammographic Image Analysis Society (MIAS Database and Digital Database for Screening Mammography (DDSM. The experiments were implemented in MATLAB.
A Temporal Fuzzy Logic Formalism for Knowledge Based Systems
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Vasile MAZILESCU
2012-11-01
Full Text Available This paper shows that the influence of knowledge on new forms of work organisation can be described as mutual relationships. Different changes in work organisation also have a strong influence on the increasing importance of knowledge of different individual and collective actors in working situations. After that, we characterize a piece of basic formal system, an Extended Fuzzy Logic System (EFLS with temporal attributes, to conceptualize future DKMSs based on human imprecise for distributed just in time decisions. The approximate reasoning is perceived as a derivation of new formulas with the corresponding temporal attributes, within a fuzzy theory defined by the fuzzy set of special axioms. In a management application, the reasoning is evolutionary because of unexpected events which may change the state of the DKMS. In this kind of situations it is necessary to elaborate certain mechanisms in order to maintain the coherence of the obtained conclusions, to figure out their degree of reliability and the time domain for which these are true. These last aspects stand as possible further directions of development at a basic logic level for future technologies that must automate knowledge organizational processes.
Knowledge Representation and Fuzzy Reasoning of an Agricultural Expert System
Institute of Scientific and Technical Information of China (English)
吴顺祥; 倪子伟; 李茂青
2002-01-01
The design scheme of an agricultural expert system based on longan and cauliflower planting techniques is presented. Using an object-oriented design and a combination of the techniques in multimedia, database, expert system and artificial intelligence, an in-depth analysis and summary are made of the knowledge features of the agricultural multimedia expert system and data models involved. According to the practical problems in agricultural field, the architectures and functions of the system are designed, and some design ideas about the hybrid knowledge representation and fuzzy reasoning are proposed.
Robust Sliding Mode Fuzzy Control of a Car Suspension System
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Ayman A. Aly
2013-07-01
Full Text Available Different characteristics can be considered in a suspension system design like: ride comfort, body travel, road handling and suspension travel. No suspension system can optimize all these parameters together but a better tradeoff among these parameters can be achieved in active suspension system.Objective of this paper is to establish a robust control technique of the active suspension system for a quarter-car model. The paper describes also the model and controller used in the study and discusses the vehicle response results obtained from a range of road input simulations. A comparison of robust suspension sliding fuzzy control and passive control is shown using MATLAB simulations.
Feedforward Tracking Control of Flat Recurrent Fuzzy Systems
Gering, Stefan; Adamy, Jürgen
2014-12-01
Flatness based feedforward control has proven to be a feasible solution for the problem of tracking control, which may be applied to a broad class of nonlinear systems. If a flat output of the system is known, the control is often based on a feedforward controller generating a nominal input in combination with a linear controller stabilizing the linearized error dynamics around the trajectory. We show in this paper that the very same idea may be incorporated for tracking control of MIMO recurrent fuzzy systems. Their dynamics is given by means of linguistic differential equations but may be converted into a hybrid system representation, which then serves as the basis for controller synthesis.
Improved adaptive fuzzy control for MIMO nonlinear time-delay systems
Institute of Scientific and Technical Information of China (English)
无
2011-01-01
This paper presents an improved observer-based indirect adaptive fuzzy control scheme for multiinput-multioutput (MIMO) nonlinear time-delay systems.The control scheme synthesizes adaptive fuzzy control with adaptive fuzzy identification.An observer is designed to observe the system state,and an identifier is developed to identify the unknown parts of the system.The update laws for parameters utilize two types of errors in the adaptive time-delay fuzzy logic systems,the observation error and the identificat...
Chaotic System Identification Based on a Fuzzy Wiener Model with Particle Swarm Optimization
Institute of Scientific and Technical Information of China (English)
LI Yong; TANG Ying-Gan
2010-01-01
@@ A fuzzy Wiener model is proposed to identify chaotic systems.The proposed fuzzy Wiener model consists of two parts,one is a linear dynamic subsystem and the other is a static nonlinear part,which is represented by the Takagi-Sugeno fuzzy model Identification of chaotic systems is converted to find optimal parameters of the fuzzy Wiener model by minimizing the state error between the original chaotic system and the fuzzy Wiener model.Particle swarm optimization algorithm,a global optimizer,is used to search the optimal parameter of the fuzzy Wiener model.The proposed method can identify the parameters of the linear part and nonlinear part simultaneously.Numerical simulations for Henón and Lozi chaotic system identification show the effectiveness of the proposed method.
Synthesis of nonlinear discrete control systems via time-delay affine Takagi-Sugeno fuzzy models.
Chang, Wen-Jer; Chang, Wei
2005-04-01
The affine Takagi-Sugeno (TS) fuzzy model played a more important role in nonlinear control because it can be used to approximate the nonlinear systems more than the homogeneous TS fuzzy models. Besides, it is known that the time delays exist in physical systems and the previous works did not consider the time delay effects in the analysis of affine TS fuzzy models. Hence a parallel distributed compensation based fuzzy controller design issue for discrete time-delay affine TS fuzzy models is considered in this paper. The time-delay effect is considered in the discrete affine TS fuzzy models and the stabilization issue is developed for the nonlinear time-delay systems. Finally, a numerical simulation for a time-delayed nonlinear truck-trailer system is given to show the applications of the present approach.
Directory of Open Access Journals (Sweden)
Sivagowry shathesh
2015-11-01
Full Text Available To unravel hidden relationships and diagnose diseases efficiently, Data Mining along with Soft Computing Techniques are used in several researches. Cardio Vascular Disease is a condition which leads to severe disability and death. Since the diagnosis involves vague symptoms and tedious procedures, diagnosis is usually time-consuming and erroneous. For the healthier analysis and treatment of heart disease based on brutality, an Intellectual, accurate and proficient investigative system is needed. For diagnosing heart disease with improved effectiveness, an Intelligent Fuzzy Inference System is needed. This paper illustrates how Fuzzy Inference System is used to envisage the severity of disease by constructing an effective Fuzzy Rule Base. It is also proved that a precision of 95.23% is obtained when Fuzzy System is used in severity prediction
Robust Takagi-Sugeno Fuzzy Dynamic Regulator for Trajectory Tracking of a Pendulum-Cart System
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Miguel A. Llama
2015-01-01
Full Text Available Starting from a nonlinear model for a pendulum-cart system, on which viscous friction is considered, a Takagi-Sugeno (T-S fuzzy augmented model (TSFAM as well as a TSFAM with uncertainty (TSFAMwU is proposed. Since the design of a T-S fuzzy controller is based on the T-S fuzzy model of the nonlinear system, then, to address the trajectory tracking problem of the pendulum-cart system, three T-S fuzzy controllers are proposed via parallel distributed compensation: (1 a T-S fuzzy servo controller (TSFSC designed from the TSFAM; (2 a robust TSFSC (RTSFSC designed from the TSFAMwU; and (3 a robust T-S fuzzy dynamic regulator (RTSFDR designed from the RTSFSC with the addition of a T-S fuzzy observer, which estimates cart and pendulum velocities. Both TSFAM and TSFAMwU are comprised of two fuzzy rules and designed via local approximation in fuzzy partition spaces technique. Feedback gains for the three fuzzy controllers are obtained via linear matrix inequalities approach. A swing-up controller is developed to swing the pendulum up from its pendant position to its upright position. Real-time experiments validate the effectiveness of the proposed schemes, keeping the pendulum in its upright position while the cart follows a reference signal, standing out the RTSFDR.
An overview of the fuzzy axiomatic systems and characterizations proposed at Ghent University
ETIENNE E. KERRE; Lynn D´eer; Bart Van Gasse
2016-01-01
During the past 40 years of fuzzy research at the Fuzziness and Uncertainty Modeling research unit of Ghent University several axiomatic systems and characterizations have been introduced. In this paper we highlight some of them. The main purpose of this paper consists of an invitation to continue research on these first attempts to axiomatize important concepts and systems in fuzzy set theory. Currently, these attempts are spread over many journals; with this paper they are now collected in ...
A New Approach of Fuzzy Theory with Uncertainties in Geographic Information Systems
Mohammad Bazmara; Fereshteh Mohammadi
2013-01-01
Until now, fuzzy logic has been extensively used to better analyze and design controllers for chemical processes. It has also been used for other applications like parameter estimation of nonlinear continuous-time systems but in general fuzzy logic has been intensively used for heuristics based system. Recently, fuzzy logic has been applied successfully in many areas where conventional model based approaches are difficult or not cost effective to implement. Mechanistic modeling of physical sy...
Almaraashia, M.; John, Robert; Hopgood, A.; S. Ahmadi
2016-01-01
This paper reports the use of simulated annealing to design more efficient fuzzy logic systems to model problems with associated uncertainties. Simulated annealing is used within this work as a method for learning the best configurations of interval and general type-2 fuzzy logic systems to maximize their modeling ability. The combination of simulated annealing with these models is presented in the modeling of four benchmark problems including real-world problems. The type-2 fuzzy logic syste...
Fuzzy Logic Based Autonomous Parallel Parking System with Kalman Filtering
Panomruttanarug, Benjamas; Higuchi, Kohji
This paper presents an emulation of fuzzy logic control schemes for an autonomous parallel parking system in a backward maneuver. There are four infrared sensors sending the distance data to a microcontroller for generating an obstacle-free parking path. Two of them mounted on the front and rear wheels on the parking side are used as the inputs to the fuzzy rules to calculate a proper steering angle while backing. The other two attached to the front and rear ends serve for avoiding collision with other cars along the parking space. At the end of parking processes, the vehicle will be in line with other parked cars and positioned in the middle of the free space. Fuzzy rules are designed based upon a wall following process. Performance of the infrared sensors is improved using Kalman filtering. The design method needs extra information from ultrasonic sensors. Starting from modeling the ultrasonic sensor in 1-D state space forms, one makes use of the infrared sensor as a measurement to update the predicted values. Experimental results demonstrate the effectiveness of sensor improvement.
General System theory, Like-Quantum Semantics and Fuzzy Sets
Licata, Ignazio
2006-01-01
It is outlined the possibility to extend the quantum formalism in relation to the requirements of the general systems theory. It can be done by using a quantum semantics arising from the deep logical structure of quantum theory. It is so possible taking into account the logical openness relationship between observer and system. We are going to show how considering the truth-values of quantum propositions within the context of the fuzzy sets is here more useful for systemics . In conclusion we propose an example of formal quantum coherence.
Fuzzy Modeling and Synchronization of a New Hyperchaotic Complex System with Uncertainties
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Hadi Delavari
2015-07-01
Full Text Available In this paper, the synchronization of a new hyperchaotic complex system based on T-S fuzzy model is proposed. First the considered hyperchaotic system is represented by T-S fuzzy model equivalently. Then by using the parallel distributed compensation (PDC method and by applying linear system theory and exact linearization (EL technique, a fuzzy controller is designed to realize the synchronization. Finally, simulation results are carried out to demonstrate the performance of our proposed control scheme, and also the robustness of the designed fuzzy controller to uncertainties.
Fuzzy inference systems with no any base and linearly parameter growth
Institute of Scientific and Technical Information of China (English)
Shitong WANG; Korris F. L. CHUNG; Jieping LU; Bin HAN; Dewen HU
2004-01-01
A class of new fuzzy inference systems New-FISs is presented. Compared with the standard fuzzy system,New-FIS is still a universal approximator and has no fuzzy rule base and linearly parameter growth. Thus, it effectively overcomes the second "curse of dimensionality": there is an exponential growth in the number of parameters of a fuzzy system as the number of input variables, resulting in surprisingly reduced computational complexity and being especially suitable for applications, where the complexity is of the first importance with respect to the approximation accuracy.
Unknown Input Observer Design for Fuzzy Bilinear System: An LMI Approach
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D. Saoudi
2012-01-01
Full Text Available A new method to design a fuzzy bilinear observer (FBO with unknown inputs is developed for a class of nonlinear systems. The nonlinear system is modeled as a fuzzy bilinear model (FBM. This kind of T-S fuzzy model is especially suitable for a nonlinear system with a bilinear term. The proposed fuzzy bilinear observer subject to unknown inputs is developed to ensure the asymptotic convergence of the error dynamic using the Lyapunov method. The proposed design conditions are given in linear matrix inequality (LMI formulation. The paper studies also the problem of fault detection and isolation. An unknown input fuzzy bilinear fault diagnosis observer design is proposed. This work is given for both continuous and discrete cases of fuzzy bilinear models. Illustrative examples are chosen to provide the effectiveness of the given methodology.
Directory of Open Access Journals (Sweden)
Wen-Jer Chang
2014-01-01
Full Text Available For nonlinear discrete-time stochastic systems, a fuzzy controller design methodology is developed in this paper subject to state variance constraint and passivity constraint. According to fuzzy model based control technique, the nonlinear discrete-time stochastic systems considered in this paper are represented by the discrete-time Takagi-Sugeno fuzzy models with multiplicative noise. Employing Lyapunov stability theory, upper bound covariance control theory, and passivity theory, some sufficient conditions are derived to find parallel distributed compensation based fuzzy controllers. In order to solve these sufficient conditions, an iterative linear matrix inequality algorithm is applied based on the linear matrix inequality technique. Finally, the fuzzy stabilization problem for nonlinear discrete ship steering stochastic systems is investigated in the numerical example to illustrate the feasibility and validity of proposed fuzzy controller design method.
Grey Prediction Fuzzy Control of the Target Tracking System in a Robot Weapon
Institute of Scientific and Technical Information of China (English)
WANG Jian-zhong; JI Jiang-tao; WANG Hong-ru
2007-01-01
Grey modeling can be used to predict the behavioral development of a system and find out the lead control values of the system. By using fuzzy inference, PID parameters can be adjusted on line by the fuzzy controller with PID parameters self-tuning. According to the characteristics of target tracking system in a robot weapon, grey prediction theory and fuzzy PID control ideas are combined. A grey prediction mathematical model is constructed and a fuzzy PID controller with grey prediction was developed. Simulation result shows fuzzy PID control algorithm with grey prediction is an efficient method that can improve the control equality and robustness of traditional PID control and fuzzy PID control, and has much better performance for target tracking.
Using Fuzzy Association Rules to Design E-commerce Personalized Recommendation System
Directory of Open Access Journals (Sweden)
Guofang Kuang
2013-09-01
Full Text Available In order to improve the efficiency of fuzzy association rule mining, the paper defines the redundant fuzzy association rules, and strong fuzzy association rules redundant nature. As much as possible for more information in the e-commerce environment, and in the right form is a prerequisite for personalized recommendation. Personalized recommendation technology is a core issue of e-commerce automated recommendation system. Higher complexity than ordinary association rules algorithm fuzzy association rules, the low efficiency become a bottleneck in the practical application of fuzzy association rules algorithm. The paper presents using fuzzy association rules to design E-commerce personalized recommendation system. The experimental results show that the new algorithm to improve the efficiency of the implementation.
Adaptive fuzzy sliding mode control of Lorenz chaotic system
Institute of Scientific and Technical Information of China (English)
WU Ligang; WANG Changhong
2007-01-01
By using the exponential reaching law technology,a sliding mode controller was designed for Lorenz chaotic system subject to an unknown external disturbance.On this basis,considering the unknown disturbance,an adaptive law was introduced to adaptively estimate the parameters of the disturbance bounds.Furthermore,to eliminate the chattering resulting from the discontinuous switch controller and to guarantee system transient performance,a new adaptive fuzzy sliding mode controller was designed.The results of the simulation show the effectiveness of the proposed control scheme.
Fuzzy controller for a system with uncertain load
DEFF Research Database (Denmark)
Kulczycki, P.; Wisniewski, Rafal
2002-01-01
in engineering solutions. The present paper deals with the time-optimal control for mechanical systems with uncertain load. A fuzzy approach is used in the design of suboptimal feedback controllers, robust with respect to the load. The methodology proposed in this work may be easily adapted to other modeling......In many applications of motion control, problems associated with imprecisely measured or changing load (a mass or a moment of inertia) can be a serious obstacle in the formation of satisfactory controlling systems. This barrier compels the designer to include various kinds of uncertainties...
Adaptive Neuro-fuzzy Controller Design for Non-affine Nonlinear Systems
Institute of Scientific and Technical Information of China (English)
JIA Li; GE Shu-zhi; QIU Ming-sen
2008-01-01
An adaptive neuro-fuzzy control is investigated for a class of noa-affine nonlinear systems.To do so,rigorous description and quantification of the approximation error of the neuro-fuzzy controller are firstly discussed.Applying this result and Lyapunov stability theory,a novel updating algorithm to adapt the weights,centers,and widths of the neuro-fuzzy controller is presented.Consequently,the proposed design method is able to guaranteg the stability of the closed-loop system and the convergence of the tracking error.Simulation results illustrate the effectiveness of the proposed adaptive neuro-fuzzy control scheme.
A Fuzzy Mathematics Based Fault Auto-diagnosis System for Vacuum Resin Shot Dosing Equipment
Institute of Scientific and Technical Information of China (English)
无
2001-01-01
On the basis of the analysis of faults and their causes of vacuum resin shot dosing equipment, the fuzzy model of fault diagnosis for the equipment is constructed, and the fuzzy relationship matrix, the symptom fuzzy vector, the fuzzy compound arithmetic operator, and the diagnosis principle of the model are determined. Then the fault auto-diagnosis system for the equipment is designed, and the functions for real-time monitoring its operation condition and for fault auto-diagnosis are realized. Finally, the experiments of fault auto-diagnosis are conducted in practical production and the veracity of the system is verified.
Stabilizability of linear quadratic state feedback for uncertain fuzzy time-delay systems.
Wang, Rong-Jyue; Lin, Wei-Wei; Wang, Wen-June
2004-04-01
This paper investigates the problem of designing a fuzzy state feedback controller to stabilize an uncertain fuzzy system with time-varying delay. Based on Lyapunov criterion and Razumikhin theorem, some sufficient conditions are derived under which the parallel-distributed fuzzy control can stabilize the whole uncertain fuzzy time-delay system asymptotically. By Schur complement, these sufficient conditions can be easily transformed into the problem of LMIs. Furthermore, the tolerable bound of the perturbation is also obtained. A practical example based on the continuous stirred tank reactor (CSTR) model is given to illustrate the control design and its effectiveness.
Directory of Open Access Journals (Sweden)
Wei Huang
2013-01-01
Full Text Available We introduce a new category of fuzzy inference systems with the aid of a multiobjective opposition-based space search algorithm (MOSSA. The proposed MOSSA is essentially a multiobjective space search algorithm improved by using an opposition-based learning that employs a so-called opposite numbers mechanism to speed up the convergence of the optimization algorithm. In the identification of fuzzy inference system, the MOSSA is exploited to carry out the parametric identification of the fuzzy model as well as to realize its structural identification. Experimental results demonstrate the effectiveness of the proposed fuzzy models.
Lagrangian Fuzzy Dynamics of Physical and Non-Physical Systems
Sandler, Uziel
2014-01-01
In this paper, we show how to study the evolution of a system, given imprecise knowledge about the state of the system and the dynamics laws. Our approach is based on Fuzzy Set Theory, and it will be shown that the \\emph{Fuzzy Dynamics} of a $n$-dimensional system is equivalent to Lagrangian (or Hamiltonian) mechanics in a $n+1$-dimensional space. In some cases, however, the corresponding Lagrangian is more general than the usual one and could depend on the action. In this case, Lagrange's equations gain a non-zero right side proportional to the derivative of the Lagrangian with respect to the action. Examples of such systems are unstable systems, systems with dissipation and systems which can remember their history. Moreover, in certain situations, the Lagrangian could be a set-valued function. The corresponding equations of motion then become differential inclusions instead of differential equations. We will also show that the principal of least action is a consequence of the causality principle and the loc...
An Interval Type-2 Neural Fuzzy System for Online System Identification and Feature Elimination.
Lin, Chin-Teng; Pal, Nikhil R; Wu, Shang-Lin; Liu, Yu-Ting; Lin, Yang-Yin
2015-07-01
We propose an integrated mechanism for discarding derogatory features and extraction of fuzzy rules based on an interval type-2 neural fuzzy system (NFS)-in fact, it is a more general scheme that can discard bad features, irrelevant antecedent clauses, and even irrelevant rules. High-dimensional input variable and a large number of rules not only enhance the computational complexity of NFSs but also reduce their interpretability. Therefore, a mechanism for simultaneous extraction of fuzzy rules and reducing the impact of (or eliminating) the inferior features is necessary. The proposed approach, namely an interval type-2 Neural Fuzzy System for online System Identification and Feature Elimination (IT2NFS-SIFE), uses type-2 fuzzy sets to model uncertainties associated with information and data in designing the knowledge base. The consequent part of the IT2NFS-SIFE is of Takagi-Sugeno-Kang type with interval weights. The IT2NFS-SIFE possesses a self-evolving property that can automatically generate fuzzy rules. The poor features can be discarded through the concept of a membership modulator. The antecedent and modulator weights are learned using a gradient descent algorithm. The consequent part weights are tuned via the rule-ordered Kalman filter algorithm to enhance learning effectiveness. Simulation results show that IT2NFS-SIFE not only simplifies the system architecture by eliminating derogatory/irrelevant antecedent clauses, rules, and features but also maintains excellent performance.
A Novel Approach to Modeling of Hydrogeologic Systems Using Fuzzy Differential Equations
Faybishenko, B. A.
2003-12-01
The many simultaneously occurring processes in unsaturated-saturated heterogeneous soils and fractured rocks can cause field observations to become imprecise and incomplete. Consequently, the results of predictions using deterministic and stochastic mathematical models are often uncertain, vague or "fuzzy." One of the alternative approaches to modeling hydrogeologic systems is the application of a fuzzy-systems approach, which is already widely used in such fields as engineering, physics, chemistry, and biology. After presenting a hydrogeologic system as a fuzzy system, the author presents a fuzzy form of Darcy's equation. Based on this equation, second-order fuzzy partial differential equations of the elliptic type (analogous to the Laplace equation) and the parabolic type (analogous to the Richards equation) are derived. These equations are then approximated as fuzzy-difference equations and solved using the basic principles of fuzzy arithmetic. The solutions for the fuzzy-difference equations take the form of fuzzy membership functions for each observation point (node). The author gives examples of the solutions of these equations for flow in unsaturated and saturated media and then compares them with those obtained using deterministic and stochastic methods.
Tatari, Farzaneh; Akbarzadeh-T, Mohammad-R; Sabahi, Ahmad
2012-12-01
In this paper, we present an agent-based system for distributed risk assessment of breast cancer development employing fuzzy and probabilistic computing. The proposed fuzzy multi agent system consists of multiple fuzzy agents that benefit from fuzzy set theory to demonstrate their soft information (linguistic information). Fuzzy risk assessment is quantified by two linguistic variables of high and low. Through fuzzy computations, the multi agent system computes the fuzzy probabilities of breast cancer development based on various risk factors. By such ranking of high risk and low risk fuzzy probabilities, the multi agent system (MAS) decides whether the risk of breast cancer development is high or low. This information is then fed into an insurance premium adjuster in order to provide preventive decision making as well as to make appropriate adjustment of insurance premium and risk. This final step of insurance analysis also provides a numeric measure to demonstrate the utility of the approach. Furthermore, actual data are gathered from two hospitals in Mashhad during 1 year. The results are then compared with a fuzzy distributed approach.
Design and Simulation of Fuzzy Logic controller for DSTATCOM In Power System
Anju Gupta; SHARMA, P. R.
2011-01-01
In this paper design of self tuned fuzzy set theory based PI controller is incorporated in typical FACTS device DSTATCOM. Its effects are tested in power systems. The modeling and the controller block diagram for DSTATCOM with detailed design of self tuned fuzzy logic controller is presented. The performance of proposed fuzzy logic DSTATCOM has been simulated for current balancing and harmonic compensation for both linear and non-linear loads. The results show the capability of proposed model...
Employee Likelihood of Purchasing Health Insurance using Fuzzy Inference System
Directory of Open Access Journals (Sweden)
Lazim Abdullah
2012-01-01
Full Text Available Many believe that employees health and economic factors plays an important role in their likelihood to purchase health insurance. However decision to purchase health insurance is not trivial matters as many risk factors that influence decision. This paper presents a decision model using fuzzy inference system to identify the likelihoods of purchasing health insurance based on the selected risk factors. To build the likelihoods, data from one hundred and twenty eight employees at five organizations under the purview of Kota Star Municipality Malaysia were collected to provide input data. Three risk factors were considered as the input of the system including age, salary and risk of having illness. The likelihoods of purchasing health insurance was the output of the system and defined in three linguistic terms of Low, Medium and High. Input and output data were governed by the Mamdani inference rules of the system to decide the best linguistic term. The linguistic terms that describe the likelihoods of purchasing health insurance were identified by the system based on the three risk factors. It is found that twenty seven employees were likely to purchase health insurance at Low level and fifty six employees show their likelihoods at High level. The usage of fuzzy inference system would offer possible justifications to set a new approach in identifying prospective health insurance purchasers.
Location-aware News Recommendation System with Using Fuzzy Logic
Directory of Open Access Journals (Sweden)
Mehdi Nejati
2016-10-01
Full Text Available with release of a huge amount of news on the Internet and the trend of users to Web-based news services.it is necessary to have a recommendation system. To grab attentions to news, news services use a number of criteria that called news values and user location is an important factor for it. In this paper, LONEF is proposed as a tow stage recommendation system. In first stage news are ranked by user’s locations and in second stage news are recommended by location Preferences, recency, Trustworthiness, groups priorities and popularity. To reduce ambiguity these properties is used tow Mamdani fuzzy interference and case-based decision systems. In Mamdani fuzzy interference system, it is tried to increase the system speed by optimizing selection of rules and membership functions and because of ambiguous feedback implementation, a decision making system is used to enable better simulation of user’s activities. Performance of our proposed approach is demonstrated in the experiments on different news groups.
A New Fuzzy-Evidential Controller for Stabilization of the Planar Inverted Pendulum System.
Tang, Yongchuan; Zhou, Deyun; Jiang, Wen
2016-01-01
In order to realize the stability control of the planar inverted pendulum system, which is a typical multi-variable and strong coupling system, a new fuzzy-evidential controller based on fuzzy inference and evidential reasoning is proposed. Firstly, for each axis, a fuzzy nine-point controller for the rod and a fuzzy nine-point controller for the cart are designed. Then, in order to coordinate these two controllers of each axis, a fuzzy-evidential coordinator is proposed. In this new fuzzy-evidential controller, the empirical knowledge for stabilization of the planar inverted pendulum system is expressed by fuzzy rules, while the coordinator of different control variables in each axis is built incorporated with the dynamic basic probability assignment (BPA) in the frame of fuzzy inference. The fuzzy-evidential coordinator makes the output of the control variable smoother, and the control effect of the new controller is better compared with some other work. The experiment in MATLAB shows the effectiveness and merit of the proposed method.
Universal immunization in urban areas: Calcutta's success story.
Chaudhuri, E R
1990-01-01
The Central Government of Calcutta, India aimed to immunize 85% (85,262) of the city's 12 month old infants against polio, diphtheria, measles, tuberculosis, pertussis and tetanus. The Universal Immunization Program (UIP) achieved this target 3 months earlier than intended. In fact, at the end of December 1990, it achieved 110.6% for DPT3, 142.16% for OPV3, 151.96% for BCG, and 97% for measles. UIP was able to surpass its targets by emphasizing team work. Government, the private sector, UNICEF, and the voluntary sector made up the Apex Coordination Committee on Immunization headed up by the mayor. The committee drafted an action plan which included routine immunization sessions on a fixed day and intensive immunization drives. Further the involved organizations pooled together cold chain equipment. In addition, the District Family Welfare Bureau was the distribution center for vaccines, syringes, immunization cards, report formats, vaccine carriers, and ice packs. Health workers administered immunizations from about 300 centers generally on Wednesday, National Immunization Day. Intensive immunization drives focused on measles immunizations. UIP leaders encouraged all center to routinely record coverage and submit monthly progress reports to the District Family Welfare Bureau. The Calcutta Municipal Corporation coordinated promotion activities and social mobilization efforts. Promotion included radio and TV announcements, newspaper advertisements, cinema slides, billboards, and posters. The original UIP plan to use professional communicators to mobilize communities was ineffective, so nongovernmental organizations entered the slums to encourage people to encourage their neighbors to immunize their children. Further Islamic, Protestant, and Catholic leaders encouraged the faithful to immunize their children. A UNICEF officer noted that this success must be sustained, however.
A cloud-based fuzzy approach for spatial site selection in decision support system
Institute of Scientific and Technical Information of China (English)
FU Xiao-xi; Byeong-Seob You; XIA Ying; Gyung-Bae Kim; Hae-Young Bae
2007-01-01
In decision support system for spatial site selection, the fuzzy synthetic evaluation is a useful way. However, the method can't pay attention to the randomness in factors. To remedy the problem, this paper proposes a clouded-base fuzzy approach which combines advantages of cloud transform and fuzzy synthetic evaluation. The cloud transform considers the randomness in the factors and product the higher concept layer for data mining. At the same time, the check mechanism controls the quality of partitions in factors. Then the fuzzy approach was used to get final evaluation value with randomness and fuzziness. It make the final result is optimization. Finally, performance evaluations show that this approach spent less runtime and got more accuracy than the fuzzy synthetic. The experiments prove that the proposed method is faster and more accuracy than the original method.
Fuzzy Adaptive Cubature Kalman Filter for Integrated Navigation Systems
Directory of Open Access Journals (Sweden)
Chien-Hao Tseng
2016-07-01
Full Text Available This paper presents a sensor fusion method based on the combination of cubature Kalman filter (CKF and fuzzy logic adaptive system (FLAS for the integrated navigation systems, such as the GPS/INS (Global Positioning System/inertial navigation system integration. The third-degree spherical-radial cubature rule applied in the CKF has been employed to avoid the numerically instability in the system model. In processing navigation integration, the performance of nonlinear filter based estimation of the position and velocity states may severely degrade caused by modeling errors due to dynamics uncertainties of the vehicle. In order to resolve the shortcoming for selecting the process noise covariance through personal experience or numerical simulation, a scheme called the fuzzy adaptive cubature Kalman filter (FACKF is presented by introducing the FLAS to adjust the weighting factor of the process noise covariance matrix. The FLAS is incorporated into the CKF framework as a mechanism for timely implementing the tuning of process noise covariance matrix based on the information of degree of divergence (DOD parameter. The proposed FACKF algorithm shows promising accuracy improvement as compared to the extended Kalman filter (EKF, unscented Kalman filter (UKF, and CKF approaches.
Fuzzy Adaptive Cubature Kalman Filter for Integrated Navigation Systems.
Tseng, Chien-Hao; Lin, Sheng-Fuu; Jwo, Dah-Jing
2016-07-26
This paper presents a sensor fusion method based on the combination of cubature Kalman filter (CKF) and fuzzy logic adaptive system (FLAS) for the integrated navigation systems, such as the GPS/INS (Global Positioning System/inertial navigation system) integration. The third-degree spherical-radial cubature rule applied in the CKF has been employed to avoid the numerically instability in the system model. In processing navigation integration, the performance of nonlinear filter based estimation of the position and velocity states may severely degrade caused by modeling errors due to dynamics uncertainties of the vehicle. In order to resolve the shortcoming for selecting the process noise covariance through personal experience or numerical simulation, a scheme called the fuzzy adaptive cubature Kalman filter (FACKF) is presented by introducing the FLAS to adjust the weighting factor of the process noise covariance matrix. The FLAS is incorporated into the CKF framework as a mechanism for timely implementing the tuning of process noise covariance matrix based on the information of degree of divergence (DOD) parameter. The proposed FACKF algorithm shows promising accuracy improvement as compared to the extended Kalman filter (EKF), unscented Kalman filter (UKF), and CKF approaches.
A fuzzy logic system based on Schweizer-Sklar t-norm
Institute of Scientific and Technical Information of China (English)
无
2006-01-01
Based on the Schweizer-Sklar t-norm, a fuzzy logic system UL* is established, and its soundness theorem and completeness theorem are proved. The following facts are pointed out: the well-known formal system SBL(~) is a semantic extension of UL*; the fuzzy logic system IMTLΔ is a special case of UL* when two negations in UL* coincide. Moreover, the connections between the system UL* and some fuzzy logic formal systems are investigated. Finally, starting from the concepts of "the strength of an 'AND' operator" by R.R. Yager and "the strength of fuzzy rule interaction" by T. Whalen, the essential meaning of a parameter p in UL* is explained and the use of fuzzy logic system UL* in approximate reasoning is presented.
Fuzzy Modeling, Tracking Control and Synchronization of the Rossler's Chaotic System
Institute of Scientific and Technical Information of China (English)
方建安; 范丹丹
2004-01-01
In this paper, a novel method to model, track control and synchronize the Rossler's chaotic system is proposed. The fuzzy logical system is used so that the fuzzy inference rule is transferred into a type of variable coef ficient nonlinear ordinary differential equation. Consequently the model of the chaotic system is obtained. Then a fuzzy tracking control and a fuzzy synchronization for chaotic systems is proposed as well. First, a known tracking control for the Rossler's system is used in this paper. We represent the Rossler's chaotic and control systems into fuzzy inference rules. Then the variable coefficient nonlinear ordinary differential equation is also got. Simulation results show that such an approach is effective and has a high precision.
Developing a Software for Fuzzy Group Decision Support System: A Case Study
Baba, A. Fevzi; Kuscu, Dincer; Han, Kerem
2009-01-01
The complex nature and uncertain information in social problems required the emergence of fuzzy decision support systems in social areas. In this paper, we developed user-friendly Fuzzy Group Decision Support Systems (FGDSS) software. The software can be used for multi-purpose decision making processes. It helps the users determine the main and…
Exponential stability of Takagi-Sugeno fuzzy systems with impulsive effects and small delays
Institute of Scientific and Technical Information of China (English)
Yu Yong-Bin; Zhong Qi-Shui; Liao Xiao-Feng; Yu Jue-Bang
2008-01-01
This paper deals with the exponential stability of impulsive Takagi-Sugeno fuzzy systems with delay. Impulsive control and delayed fuzzy control are applied to the system, and the criterion on exponential stability expressed in terms of linear matrix inequalities (LMIs) is presented.
A fuzzy logic based network intrusion detection system for predicting the TCP SYN flooding attack
CSIR Research Space (South Africa)
Mkuzangwe, Nenekazi NP
2017-04-01
Full Text Available presents a fuzzy logic based network intrusion detection system to predict neptune which is a type of a Transmission Control Protocol Synchronized (TCP SYN) flooding attack. The performance of the proposed fuzzy logic based system is compared to that of a...
Model-based fuzzy control solutions for a laboratory Antilock Braking System
DEFF Research Database (Denmark)
Precup, Radu-Emil; Spataru, Sergiu; Rǎdac, Mircea-Bogdan;
2010-01-01
This paper gives two original model-based fuzzy control solutions dedicated to the longitudinal slip control of Antilock Braking System laboratory equipment. The parallel distributed compensation leads to linear matrix inequalities which guarantee the global stability of the fuzzy control systems...
THE FUZZY OVERLAY STUDENT MODEL IN AN INTELLIGENT TUTORING SYSTEM
Directory of Open Access Journals (Sweden)
D. I. Popov
2015-01-01
Full Text Available The article is devoted to the development of the student model for use in an intelligent tutoring system (ITS designed for the evaluation of students’ competencies in different Higher Education Facilities. There are classification and examples of the various student models, the most suitable for the evaluation of competencies is selected and finalized. The dynamic overlay fuzzy student model builded on the domain model based on the concept of didactic units is described in this work. The formulas, chart and diagrams are provided.
Adaptive Fractional Fuzzy Sliding Mode Control for Multivariable Nonlinear Systems
Directory of Open Access Journals (Sweden)
Junhai Luo
2014-01-01
Full Text Available This paper presents a robust adaptive fuzzy sliding mode control method for a class of uncertain nonlinear systems. The fractional order calculus is employed in the parameter updating stage. The underlying stability analysis as well as parameter update law design is carried out by Lyapunov based technique. In the simulation, two examples including a comparison with the traditional integer order counterpart are given to show the effectiveness of the proposed method. The main contribution of this paper consists in the control performance is better for the fractional order updating law than that of traditional integer order.
On PID Controller Design Using Knowledge Based Fuzzy System
Directory of Open Access Journals (Sweden)
Jana Nowakova
2012-01-01
Full Text Available The designing of PID controllers is a frequently discussed problem. Many of design methods have been developed, classic (analytical tuning methods, optimization methods etc. or not so common fuzzy knowledge based methods which are designed to achieve good setpoint following, corresponding time response etc. In this case, the new way of designing PID controller parameters is created where the above mentioned knowledge system based on relations of Ziegler-Nichols design methods is used, more precisely the combination of the both Ziegler-Nichols methods. The proof of efficiency of a proposed method and a numerical experiment is presented.
PV grid connected system with fuzzy intelligent control
Directory of Open Access Journals (Sweden)
Florin DRAGOMIR
2009-05-01
Full Text Available This paper proposes an engineering solution for stability control of the low voltage electrical networks with distributed power generation from renewable energy resources. First there are presentedgenerally, the existing problems in this type of systems, capable to be solved with automation intelligent control. In the second part, the paper focuses over fuzzy controller design based on experimentalmonitored data and experts know how. The results of proposed software realized with LabView will be pointed out from the main objective point of view.
Design of Adaptive Fuzzy PID Altitude Control System for Unmanned Aerial Vehicle
Institute of Scientific and Technical Information of China (English)
SHI Gang; YANG Shu-xing; JING Ya-xing; XU Yong
2008-01-01
Based on Matlab/Simulink and Fuzzy Logic toolboxes, the altitude control system is designed and simulated. The validity of conventional PID control method and adaptive fuzzy PID control method is compared. It can be drawn out that the adaptive fuzzy PID control method is superior to the conventional PID in rising time and overshoot etc. The effectiveness of a fuzzy PID controller shows potential application in the future, especially in the presence of model uncertainty or changing dynamics and time-varying parameters.
A Fuzzy Decision Support System for Management of Breast Cancer
Directory of Open Access Journals (Sweden)
Ahmed Abou Elfetouh Saleh
2011-03-01
Full Text Available In the molecular era the management of cancer is no more a plan based on simple guidelines. Clinical findings, tumor characteristics, and molecular markers are integrated to identify different risk categories, based on which treatment is planned for each individual case. This paper aims at developing a fuzzy decision support system (DSS to guide the doctors for the risk stratification of breast cancer, which is expected to have a great impact on treatment decision and to minimize individual variations in selecting the optimal treatment for a particular case. The developed system was based on clinical practice of Oncology Center Mansoura University (OCMU. This system has six input variables (Her2, hormone receptors, age, tumor grade, tumor size, and lymph node and one output variable (risk status. The output variable is a value from 1 to 4; representing low risk status, intermediate risk status and high risk status. This system uses Mamdani inference method and simulation applied in MATLAB R2009b fuzzy logic toolbox.
Fuzzy control applied to nuclear power plant pressurizer system
Energy Technology Data Exchange (ETDEWEB)
Oliveira, Mauro V.; Almeida, Jose C.S., E-mail: mvitor@ien.gov.b, E-mail: jcsa@ien.gov.b [Instituto de Engenharia Nuclear (IEN/CNEN-RJ), Rio de Janeiro, RJ (Brazil)
2011-07-01
In a pressurized water reactor (PWR) nuclear power plants (NPPs) the pressure control in the primary loop is very important for keeping the reactor in a safety condition and improve the generation process efficiency. The main component responsible for this task is the pressurizer. The pressurizer pressure control system (PPCS) utilizes heaters and spray valves to maintain the pressure within an operating band during steady state conditions, and limits the pressure changes, during transient conditions. Relief and safety valves provide overpressure protection for the reactor coolant system (RCS) to ensure system integrity. Various protective reactor trips are generated if the system parameters exceed safe bounds. Historically, a proportional-integral derivative (PID) controller is used in PWRs to keep the pressure in the set point, during those operation conditions. The purpose of this study has two main goals: first is to develop a pressurizer model based on artificial neural networks (ANNs); second is to develop a fuzzy controller for the PWR pressurizer pressure, and compare its performance with the P controller. Data from a simulator PWR plant was used to test the ANN and the controllers as well. The reference simulator is a Westinghouse 3-loop PWR plant with a total thermal output of 2785 MWth. The simulation results show that the pressurizer ANN model response are in reasonable agreement with the simulated power plant, and the fuzzy controller built in this study has better performance compared to the P controller. (author)
Directory of Open Access Journals (Sweden)
Márcio Mendonça
2015-10-01
Full Text Available In this work, it is analyzed a multivariate system control of an alcoholic fermentation process with no minimum phase. The control is made with PID classic controllers associated with a supervisory system based on Fuzzy Systems. The Fuzzy system, a priori, send set-points to PID controllers, but also adds protection functions, such as if the biomass valued is at zero or very close. The Fuzzy controller changes the campaign to prevent or mitigate the paralyzation of the process. Three control architectures based on Fuzzy Control Systems are presented and compared in performance with classic control in different campaigns. The third architecture, in particular, adds an adaptive function. A brief summary of Fuzzy theory and correlated works will be presented. And, finally simulations results, conclusions and future works end the article.
Fuzzy control systems with time-delay and stochastic perturbation analysis and synthesis
Wu, Ligang; Shi, Peng
2015-01-01
This book presents up-to-date research developments and novel methodologies on fuzzy control systems. It presents solutions to a series of problems with new approaches for the analysis and synthesis of fuzzy time-delay systems and fuzzy stochastic systems, including stability analysis and stabilization, dynamic output feedback control, robust filter design, and model approximation. A set of newly developed techniques such as fuzzy Lyapunov function approach, delay-partitioning, reciprocally convex, cone complementary linearization approach are presented. Fuzzy Control Systems with Time-Delay and Stochastic Perturbation: Analysis and Synthesis is a comprehensive reference for researcher and practitioners working in control engineering, system sciences and applied mathematics, and is also a useful source of information for senior undergraduates and graduates in these areas. The readers will benefit from some new concepts, new models and new methodologies with practical significance in control engineering and si...
Application of Fuzzy-PID Control System in Full-Mechanized Coal Face
Institute of Scientific and Technical Information of China (English)
LU Kui; TANG Pei-rong; YANG Wei-min
2005-01-01
The control system, which includes structure, the composition of software and hardware, the form of PID control system and its systematic closed-loop, was used in No.4236 full-mechanized coal face of Xinlongzhuang mine.The typical fuzzy PID control system structure was investigated, and a simplified fuzzy PID control system was taken the place of the complex three-dimension fuzzy controller. Based on the parameter relation between fuzzy controller and normal PID controller, a common method of parameter adjustment of PID controller was summed up and the computer simulation was realized. This system can overcome the problems of large delay, nonlinear, poor running environment and great load change in the full-mechanized coal face. The simulating investigation indicates that the designing method of fuzzy controller is simple and feasible.
Fuzzy Stochastic Petri Nets for Modeling Biological Systems with Uncertain Kinetic Parameters.
Liu, Fei; Heiner, Monika; Yang, Ming
2016-01-01
Stochastic Petri nets (SPNs) have been widely used to model randomness which is an inherent feature of biological systems. However, for many biological systems, some kinetic parameters may be uncertain due to incomplete, vague or missing kinetic data (often called fuzzy uncertainty), or naturally vary, e.g., between different individuals, experimental conditions, etc. (often called variability), which has prevented a wider application of SPNs that require accurate parameters. Considering the strength of fuzzy sets to deal with uncertain information, we apply a specific type of stochastic Petri nets, fuzzy stochastic Petri nets (FSPNs), to model and analyze biological systems with uncertain kinetic parameters. FSPNs combine SPNs and fuzzy sets, thereby taking into account both randomness and fuzziness of biological systems. For a biological system, SPNs model the randomness, while fuzzy sets model kinetic parameters with fuzzy uncertainty or variability by associating each parameter with a fuzzy number instead of a crisp real value. We introduce a simulation-based analysis method for FSPNs to explore the uncertainties of outputs resulting from the uncertainties associated with input parameters, which works equally well for bounded and unbounded models. We illustrate our approach using a yeast polarization model having an infinite state space, which shows the appropriateness of FSPNs in combination with simulation-based analysis for modeling and analyzing biological systems with uncertain information.
Error Correction, Control Systems and Fuzzy Logic
Smith, Earl B.
2004-01-01
This paper will be a discussion on dealing with errors. While error correction and communication is important when dealing with spacecraft vehicles, the issue of control system design is also important. There will be certain commands that one wants a motion device to execute. An adequate control system will be necessary to make sure that the instruments and devices will receive the necessary commands. As it will be discussed later, the actual value will not always be equal to the intended or desired value. Hence, an adequate controller will be necessary so that the gap between the two values will be closed.
Neural-Fuzzy Approach for System Identification.
Tien, B.T.
1997-01-01
Most real-world processes have nonlinear and complex dynamics. Conventional methods of constructing nonlinear models from first principles are time consuming and require a level of knowledge about the internal functioning of the system that is often not available. Consequently, in such cases a nonli
National Research Council Canada - National Science Library
Junxiao Wang
2016-01-01
.... Then, the mathematical model of PMSM is given. Subsequently, a fuzzy adaptive repetitive controller based on repetitive control and fuzzy logic control is designed for the PMSM speed servo system...
Determination of interrill soil erodibility coefficient based on Fuzzy and Fuzzy-Genetic Systems
Directory of Open Access Journals (Sweden)
Habib Palizvan Zand
2017-02-01
Full Text Available Introduction: Although the fuzzy logic science has been used successfully in various sudies of hydrology and soil erosion, but in literature review no article was found about its performance for estimating of interrill erodibility. On the other hand, studies indicate that genetic algorithm techniques can be used in fuzzy models and finding the appropriate membership functions for linguistic variables and fuzzy rules. So this study was conducted to develop the fuzzy and fuzzy–genetics models and investigation of their performance in the estimation of soil interrill erodibility factor (Ki. Materials and Methods: For this reason 36 soil samples with different physical and chemical properties were collected from west of Azerbaijan province . soilsamples were also taken from the Ap or A horizon of each soil profile. The samples were air-dried , sieved and Some soil characteristics such as soil texture, organic matter (OM, cation exchange capacity (CEC, sodium adsorption ratio (SAR, EC and pH were determined by the standard laboratory methods. Aggregates size distributions (ASD were determined by the wet-sieving method and fractal dimension of soil aggregates (Dn was also calculated. In order to determination of soil interrill erodibility, the flume experiment performed by packing soil a depth of 0.09-m in 0.5 × 1.0 m. soil was saturated from the base and adjusted to 9% slope and was subjected to at least 90 min rainfall . Rainfall intensity treatments were 20, 37 and 47 mm h-1. During each rainfall event, runoff was collected manually in different time intervals, being less than 60 s at the beginning, up to 15 min near the end of the test. At the end of the experiment, the volumes of runoff samples and the mass of sediment load at each time interval were measured. Finally interrill erodibility values were calculated using Kinnell (11 Equation. Then by statistical analyses Dn and sand percent of the soils were selected as input variables and Ki as
Energy Technology Data Exchange (ETDEWEB)
Averkin, A.A. [Russian Academy of Sciences, Moscow (Russian Federation). Computer centre
1994-12-31
A new type of fuzzy expert system for assisting the operator`s decisions in nuclear power plant system in non-standard situations is proposed. This expert system is based on new approaches to fuzzy logics acquisition and to fuzzy logics testing. Fuzzy logics can be generated by a T-norms axiomatic system to choose the most suitable to operator`s way of thinking. Then the chosen fuzzy logic is tested by simulation of inference process in expert system. The designed logic is the input of inference module of expert system.
A Simple and Effective Remedial Learning System with a Fuzzy Expert System
Lin, C.-C.; Guo, K.-H.; Lin, Y.-C.
2016-01-01
This study aims at implementing a simple and effective remedial learning system. Based on fuzzy inference, a remedial learning material selection system is proposed for a digital logic course. Two learning concepts of the course have been used in the proposed system: number systems and combinational logic. We conducted an experiment to validate…
A Simple and Effective Remedial Learning System with a Fuzzy Expert System
Lin, C.-C.; Guo, K.-H.; Lin, Y.-C.
2016-01-01
This study aims at implementing a simple and effective remedial learning system. Based on fuzzy inference, a remedial learning material selection system is proposed for a digital logic course. Two learning concepts of the course have been used in the proposed system: number systems and combinational logic. We conducted an experiment to validate…
Directory of Open Access Journals (Sweden)
P. Kalyana Sundaram
2016-11-01
Full Text Available The paper presents a novel method for the assessment of the power quality disturbances in the distribution system using the Kalman filter and fuzzy expert system. In this method the various classes of disturbance signals are developed through the Matlab Simulink on the test system model. The characteristic features of the disturbance signals are extracted based on the Kalman filter technique. The obtained features such as amplitude and slope are given as the two inputs to the fuzzy expert system. It applied some rules on these inputs to assess the various PQ disturbances. Fuzzy classifier has been carried out and tested for various power quality disturbances. The results clearly demonstrate that the proposed method in the distribution system has the ability to detect and classify PQ events.
Status Evaluation of Loose of Jig Bed Based on Fuzzy Inference System
Institute of Scientific and Technical Information of China (English)
CHENG Jian; GUO Yi-nan; SUN Wei; MU Jun-ying
2003-01-01
This paper mainly describes that loose of jig bed affects jig's separation effect, and the corresponding fuzzy rules were built. Using the evaluating index of jig's separation effect--imperfection (I) and total misplaced material (Cz), it evaluates status of loose of jig bed by fuzzy inference system. Experimental simulation and applications in practice prove the method's feasibility.
A GA-fuzzy automatic generation controller for interconnected power system
CSIR Research Space (South Africa)
Boesack, CD
2011-10-01
Full Text Available This paper presents a GA-Fuzzy Automatic Generation Controller for large interconnected power systems. The design of Fuzzy Logic Controllers by means of expert knowledge have typically been the traditional design norm, however, this may not yield...
FUZZY INFERENCE SYSTEM FOR THE IDENTIFICATION OF OVER-THE-COUNTER (OTC DRUGS
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Eduardo E. Zurek
2013-06-01
Full Text Available This document shows the details of the implementation of a fuzzy inference system, for the identification of four over-the-counter drugs (Naproxen, Calcium Carbonate, Muvett and Winadol, by using a Raman Spectroscopy, which output is the characterization of the substance. Data obtained from Raman Spectroscopy are modeled with Matlab®- Fuzzy Logic Toolbox.
Parkinson's disease Assessment using Fuzzy Expert System and Nonlinear Dynamics
Directory of Open Access Journals (Sweden)
GEMAN, O.
2013-02-01
Full Text Available This paper proposes a new screening system for quantitative evaluation and analysis, designed for the early stage detection of Parkinson disease. This has been carried out in the view of improving the diagnosis currently established upon a basis of subjective scores. Parkinson?s disease (PD appears as a result of dopamine loss, a chemical mediator that is responsible for the body?s ability to control movements. The symptoms reflect the loss of nerve cells, due to an unknown. The input parameters of the system are represented by amplitude, frequency, the spectral characteristic and trembling localization. The main symptoms include trembling of hand, arms, movement difficulties, postural instability, disturbance of coordination and equilibrium, sleep disturbance, difficulties in speaking, reducing of voice volume. The medical knowledge in PD field is characterized by imprecision, uncertainty and vagueness. The proposed system (fuzzy expert systems is non-invasive and, easy to use by both physicians and patients at home.
Fuzzy Logic Controller based on geothermal recirculating aquaculture system
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Hanaa M. Farghally
2014-01-01
Full Text Available One of the most common uses of geothermal heat is in recirculation aquaculture systems (RAS where the water temperature is accurately controlled for optimum growing conditions for sustainable and intensive rearing of marine and freshwater fish. This paper presents a design for RAS rearing tank and brazed heat exchanger to be used with geothermal energy as a source of heating water. The heat losses from the RAS tank are calculated using Geo Heat Center Software. Then a plate type heat exchanger is designed using the epsilon – NTU analysis method. For optimal growth and abundance of production, a Fuzzy Logic control (FLC system is applied to control the water temperature (29 °C. A FLC system has several advantages over conventional techniques; relatively simple, fast, adaptive, and its response is better and faster at all atmospheric conditions. Finally, the total system is built in MATLAB/SIMULINK to study the overall performance of control unit.
Predictive functional control based on fuzzy T-S model for HVAC systems temperature control
Institute of Scientific and Technical Information of China (English)
Hongli L(U); Lei JIA; Shulan KONG; Zhaosheng ZHANG
2007-01-01
In heating,ventilating and air-conditioning(HVAC)systems,there exist severe nonlinearity,time-varying nature,disturbances and uncertainties.A new predictive functional control based on Takagi-Sugeno(T-S)fuzzy model was proposed to control HVAC systems.The T-S fuzzy model of stabilized controlled process was obtained using the least squares method,then on the basis of global linear predictive model from T-S fuzzy model,the process was controlled by the predictive functional controller.Especially the feedback regulation part was developed to compensate uncertainties of fuzzy predictive model.Finally simulation test results in HVAC systems control applications showed that the proposed fuzzy model predictive functional control improves tracking effect and robustness.Compared with the conventional PID controller,this control strategy has the advantages of less overshoot and shorter setting time,etc.
Modular Neural Networks and Type-2 Fuzzy Systems for Pattern Recognition
Melin, Patricia
2012-01-01
This book describes hybrid intelligent systems using type-2 fuzzy logic and modular neural networks for pattern recognition applications. Hybrid intelligent systems combine several intelligent computing paradigms, including fuzzy logic, neural networks, and bio-inspired optimization algorithms, which can be used to produce powerful pattern recognition systems. Type-2 fuzzy logic is an extension of traditional type-1 fuzzy logic that enables managing higher levels of uncertainty in complex real world problems, which are of particular importance in the area of pattern recognition. The book is organized in three main parts, each containing a group of chapters built around a similar subject. The first part consists of chapters with the main theme of theory and design algorithms, which are basically chapters that propose new models and concepts, which are the basis for achieving intelligent pattern recognition. The second part contains chapters with the main theme of using type-2 fuzzy models and modular neural ne...
Sampled-Data Fuzzy Control for Nonlinear Coupled Parabolic PDE-ODE Systems.
Wang, Zi-Peng; Wu, Huai-Ning; Li, Han-Xiong
2017-09-01
In this paper, a sampled-data fuzzy control problem is addressed for a class of nonlinear coupled systems, which are described by a parabolic partial differential equation (PDE) and an ordinary differential equation (ODE). Initially, the nonlinear coupled system is accurately represented by the Takagi-Sugeno (T-S) fuzzy coupled parabolic PDE-ODE model. Then, based on the T-S fuzzy model, a novel time-dependent Lyapunov functional is used to design a sampled-data fuzzy controller such that the closed-loop coupled system is exponentially stable, where the sampled-data fuzzy controller consists of the ODE state feedback and the PDE static output feedback under spatially averaged measurements. The stabilization condition is presented in terms of a set of linear matrix inequalities. Finally, simulation results on the control of a hypersonic rocket car are given to illustrate the effectiveness of the proposed design method.
Adaptive Fuzzy Bounded Control for Consensus of Multiple Strict-Feedback Nonlinear Systems.
Wang, Wei; Tong, Shaocheng
2017-01-10
This paper studies the adaptive fuzzy bounded control problem for leader-follower multiagent systems, where each follower is modeled by the uncertain nonlinear strict-feedback system. Combining the fuzzy approximation with the dynamic surface control, an adaptive fuzzy control scheme is developed to guarantee the output consensus of all agents under directed communication topologies. Different from the existing results, the bounds of the control inputs are known as a priori, and they can be determined by the feedback control gains. To realize smooth and fast learning, a predictor is introduced to estimate each error surface, and the corresponding predictor error is employed to learn the optimal fuzzy parameter vector. It is proved that the developed adaptive fuzzy control scheme guarantees the uniformly ultimate boundedness of the closed-loop systems, and the tracking error converges to a small neighborhood of the origin. The simulation results and comparisons are provided to show the validity of the control strategy presented in this paper.
Modeling and Stability Analysis for Non-linear Network Control System Based on T-S Fuzzy Model
Institute of Scientific and Technical Information of China (English)
ZHANG Hong; FANG Huajing
2007-01-01
Based on the T-S fuzzy model, this paper presents a new model of non-linear network control system with stochastic transfer delay. Sufficient criterion is proposed to guarantee globally asymptotically stability of this two-levels T-S fuzzy model. Also a T-S fuzzy observer of NCS is designed base on this two-levels T-S fuzzy model. All these results present a new approach for networked control system analysis and design.
Combined indirect and direct method for adaptive fuzzy output feedback control of nonlinear system
Institute of Scientific and Technical Information of China (English)
Ding Quanxin; Chen Haitong; Jiang Changsheng; Chen Zongji
2007-01-01
A novel control method for a general class of nonlinear systems using fuzzy logic systems (FLSs) is presertted.Indirect and direct methods are combined to design the adaptive fuzzy output feedback controller and a high-gain observer is used to estimate the derivatives of the system output. The closed-loop system is proven to be semiglobally uniformly ultimately bounded. In addition, it is shown that if the approximation accuracy of the fuzzy logic system is high enough and the observer gain is chosen sufficiently large, an arbitrarily small tracking error can be achieved. Simulation results verify the effectiveness of the newly designed scheme and the theoretical discussion.
Selected Aircraft Throttle Controller With Support Of Fuzzy Expert Inference System
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Żurek Józef
2014-12-01
Full Text Available The paper describes Zlin 143Lsi aircraft engine work parameters control support method – hourly fuel flow as a main factor under consideration. The method concerns project of aircraft throttle control support system with use of fuzzy logic (fuzzy inference. The primary purpose of the system is aircraft performance optimization, reducing flight cost at the same time and support proper aircraft engine maintenance. Matlab Software and Fuzzy Logic Toolbox were used in the project. Work of the system is presented with use of twenty test samples, five of them are presented graphically. In addition, system control surface, included in the paper, supports system all work range analysis.
改进的可撤销指纹Fuzzy Vault方案%Improved cancelable fingerprint fuzzy vault system
Institute of Scientific and Technical Information of China (English)
张镕麟; 刘而云; 赵恒; 庞辽军
2011-01-01
Fuzzy vault has been widely applied in biometric encryption domain for key binding, suitable for set feature representation, such as minutiae set of fingerprint image. However, with the real minutia points contained in vault, fuzzy vault is suffered from cross matching problem, which makes attackers easily obtain real minutia information from multiple vault templates and threatens user privacy and secret key security. In this paper, an improved cancelable fuzzy vault scheme is proposed, in which an one-way function based on password is constructed to transform original minutia information and then the transformed minutiae are fed to fuzzy vault system. The attackers can not obtain the original minutia features even though multiple vaults are at hand, and the users can reissue a new vault template by changing password. Experimental results on FVC2002 DB2 show that the proposed method can effectively reduce the hidden troubles and increase security of system.%Fuzzy Vault系统是生物特征加密(Biometric Encryption,BE)领域中广泛应用的密钥绑定框架,适用于集合特征(例如指纹细节点)与密钥的绑定.然而,Vault模板中包含了真实指纹细节点特征,攻击者可以从多个发布的Vault模板中获取真实指纹信息,因而严重威胁到用户的隐私和密钥安全.笔者提出一种改进的可撤销指纹Fuzzy Vault方案.首先,基于口令构造一个不可逆变换函数,应用该变换函数对细节点特征进行变换,然后使用变换后的细节点输入Fuzzy Vault系统.攻击者即使得到多个应用系统上的Vault也不能获得原始指纹细节点特征,且用户可以通过随时更换口令来发布新的Vault模板.在FVC2002 DB2上的实验表明,所提出的方案能有效降低交叉匹配带来的安全隐患,提高系统安全性.
Fuzzy-Neural Automatic Daylight Control System
Directory of Open Access Journals (Sweden)
Grif H. Şt.
2011-12-01
Full Text Available The paper presents the design and the tuning of a CMAC controller (Cerebellar Model Articulation Controller implemented in an automatic daylight control application. After the tuning process of the controller, the authors studied the behavior of the automatic lighting control system (ALCS in the presence of luminance disturbances. The luminance disturbances were produced by the authors in night conditions and day conditions as well. During the night conditions, the luminance disturbances were produced by turning on and off a halogen desk lamp. During the day conditions the luminance disturbances were produced in two ways: by daylight contributions changes achieved by covering and uncovering a part of the office window and by turning on and off a halogen desk lamp. During the day conditions the luminance disturbances, produced by turning on and off the halogen lamp, have a smaller amplitude than those produced during the night conditions. The luminance disturbance during the night conditions was a helpful tool to select the proper values of the learning rate for CMAC controller. The luminance disturbances during the day conditions were a helpful tool to demonstrate the right setting of the CMAC controller.
Relations Among Some Fuzzy Entropy Formulae
Institute of Scientific and Technical Information of China (English)
卿铭
2004-01-01
Fuzzy entropy has been widely used to analyze and design fuzzy systems, and many fuzzy entropy formulae have been proposed. For further in-deepth analysis of fuzzy entropy, the axioms and some important formulae of fuzzy entropy are introduced. Some equivalence results among these fuzzy entropy formulae are proved, and it is shown that fuzzy entropy is a special distance measurement.
On Fuzzy ideal and Fuzzy deductive system in Hilbert algebra%关于Hilbert-代数的Fuzzy理想与Fuzzy演绎系统
Institute of Scientific and Technical Information of China (English)
张小红
2000-01-01
In this paper we prove that a Fuzzy ideal and a Fuzzy deductive system coincide in Hilbert algebra.%研究了Hilbert代数的Fuzzy理想与Fuzzy演绎系统,得到的主要结果是:Hilbert代数的Fuzzy理想与Fuzzy演绎系统等价.
Adaptive Fuzzy Containment Control for Uncertain Nonlinear Multiagent Systems
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Yang Yu
2014-01-01
Full Text Available This paper considers the containment control problem for uncertain nonlinear multiagent systems under directed graphs. The followers are governed by nonlinear systems with unknown dynamics while the multiple leaders are neighbors of a subset of the followers. Fuzzy logic systems (FLSs are used to identify the unknown dynamics and a distributed state feedback containment control protocol is proposed. This result is extended to the output feedback case, where observers are designed to estimate the unmeasurable states. Then, an output feedback containment control scheme is presented. The developed state feedback and output feedback containment controllers guarantee that the states of all followers converge to the convex hull spanned by the dynamic leaders. Based on Lyapunov stability theory, it is proved that the containment control errors are uniformly ultimately bounded (UUB. An example is provided to show the effectiveness of the proposed control method.
Decision Making in Fuzzy Discrete Event Systems1.
Lin, F; Ying, H; Macarthur, R D; Cohn, J A; Barth-Jones, D; Crane, L R
2007-09-15
The primary goal of the study presented in this paper is to develop a novel and comprehensive approach to decision making using fuzzy discrete event systems (FDES) and to apply such an approach to real-world problems. At the theoretical front, we develop a new control architecture of FDES as a way of decision making, which includes a FDES decision model, a fuzzy objective generator for generating optimal control objectives, and a control scheme using both disablement and enforcement. We develop an online approach to dealing with the optimal control problem efficiently. As an application, we apply the approach to HIV/AIDS treatment planning, a technical challenge since AIDS is one of the most complex diseases to treat. We build a FDES decision model for HIV/AIDS treatment based on expert's knowledge, treatment guidelines, clinic trials, patient database statistics, and other available information. Our preliminary retrospective evaluation shows that the approach is capable of generating optimal control objectives for real patients in our AIDS clinic database and is able to apply our online approach to deciding an optimal treatment regimen for each patient. In the process, we have developed methods to resolve the following two new theoretical issues that have not been addressed in the literature: (1) the optimal control problem has state dependent performance index and hence it is not monotonic, (2) the state space of a FDES is infinite.
Model Based Fuzzy Expert System for Measuring Organization Knowledge Management
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Houshang Taghizadeh
2012-02-01
Full Text Available This paper presents a model based on fuzzy set theory for determining the score of knowledge management in organization. The introduced model has five stages. In the first stage, input and output variable of model are characterized by available theories. Inputs are as follows: knowledge acquisition, knowledge storage, knowledge creation, knowledge sharing and knowledge transfer. The output is as follow score of knowledge management in organization. In the second stage, the input and output are converted into fuzzy numbers after classification. Inference rules are explained in the third stage. In the fourth stage, defuzzification is performed, and in the fifth stage, the devised system is tested. The test result shows that the presented model has high validity. Ultimately, by using the designed model, the score of knowledge management for Tabriz Kar machinery industry was calculated. The statistical population consists of 50 members of this organization. All the population has been studied. A questionnaire was devised, and its validity and reliability were confirmed. The result indicated that the score of knowledge management in Tabriz Kar machinery industry with the membership rank of 0.924 was at an average level and with the membership rank of 0.076 was at a high
Borni, A.; Abdelkrim, T.; Zaghba, L.; Bouchakour, A.; Lakhdari, A.; Zarour, L.
2017-02-01
In this paper the model of a grid connected hybrid system is presented. The hybrid system includes a variable speed wind turbine controlled by aFuzzy MPPT control, and a photovoltaic generator controlled with PSO Fuzzy MPPT control to compensate the power fluctuations caused by the wind in a short and long term, the inverter currents injected to the grid is controlled by a decoupled PI current control. In the first phase, we start by modeling of the conversion system components; the wind system is consisted of a turbine coupled to a gearless permanent magnet generator (PMG), the AC/DC and DC-DC (Boost) converter are responsible to feed the electric energy produced by the PMG to the DC-link. The solar system consists of a photovoltaic generator (GPV) connected to a DC/DC boost converter controlled by a PSO fuzzy MPPT control to extract at any moment the maximum available power at the GPV terminals, the system is based on maximum utilization of both of sources because of their complementary. At the end. The active power reached to the DC-link is injected to the grid through a DC/AC inverter, this function is achieved by controlling the DC bus voltage to keep it constant and close to its reference value, The simulation studies have been performed using Matlab/Simulink. It can be concluded that a good control system performance can be achieved.
The matrix representation of fuzzy knowledge and its application to the expert systems design
Directory of Open Access Journals (Sweden)
V. Levchenko
1993-02-01
Full Text Available An approach to the diagnostic type expert systems design based on the special matrix representation of fuzzy predicates in the tribute model of the problem domain is presented. Intensive representation of predicates by means of sectional matrices is an analogue of the conjunctive normal form. Rules, positive examples and negative examples (in general, all fuzzy can be used to form knowledge base. Diagnostics problem is thought of as finding some attribute values provided that the information about other attribute values is available. Logical inference is based on an equivalent transformation of the matrix to that containing all prime disjuncts by using the operation of fuzzy resolution . Two strategies to carry out such transformation are described. On the basis of formalism presented the expert system shell EDIP is developed, the first version of that is non-fuzzy and the second one allows working with fuzzy data and conclusions.
Adaptive Backstepping Output Feedback Control for SISO Nonlinear System Using Fuzzy Neural Networks
Institute of Scientific and Technical Information of China (English)
Shao-Cheng Tong; Yong-Ming Li
2009-01-01
In this paper, a new fuzzy-neural adaptive control approach is developed for a class of single-input and single-output (SISO) nonlinear systems with unmeasured states. Using fuzzy neural networks to approximate the unknown nonlinear functions, a fuzzy-neural adaptive observer is introduced for state estimation as well as system identification. Under the framework of the backstepping design, fuzzy-neural adaptive output feedback control is constructed rccursively. It is proven that the proposed fuzzy adaptive control approach guarantees the global boundedness property for all the signals, driving the tracking error to a small neighbordhood of the origin. Simulation example is included to illustrate the effectiveness of the proposed approach.
Institute of Scientific and Technical Information of China (English)
Zhang Yougang; Xu Bugong
2006-01-01
Decentralized robust stabilization problem of discrete-time fuzzy large-scale systems with parametric uncertainties is considered. This uncertain fuzzy large-scale system consists of N interconnected T-S fuzzy subsystems, and the parametric uncertainties are unknown but norm-bounded. Based on Lyapunov stability theory and decentralized control theory of large-scale system, the design schema of decentralized parallel distributed compensation (DPDC) fuzzy controllers to ensure the asymptotic stability of the whole fuzzy large-scale system is proposed. The existence conditions for these controllers take the forms of LMIs. Finally a numerical simulation example is given to show the utility of the method proposed.
A fuzzy logic system for seizure onset detection in intracranial EEG.
Rabbi, Ahmed Fazle; Fazel-Rezai, Reza
2012-01-01
We present a multistage fuzzy rule-based algorithm for epileptic seizure onset detection. Amplitude, frequency, and entropy-based features were extracted from intracranial electroencephalogram (iEEG) recordings and considered as the inputs for a fuzzy system. These features extracted from multichannel iEEG signals were combined using fuzzy algorithms both in feature domain and in spatial domain. Fuzzy rules were derived based on experts' knowledge and reasoning. An adaptive fuzzy subsystem was used for combining characteristics features extracted from iEEG. For the spatial combination, three channels from epileptogenic zone and one from remote zone were considered into another fuzzy subsystem. Finally, a threshold procedure was applied to the fuzzy output derived from the final fuzzy subsystem. The method was evaluated on iEEG datasets selected from Freiburg Seizure Prediction EEG (FSPEEG) database. A total of 112.45 hours of intracranial EEG recordings was selected from 20 patients having 56 seizures was used for the system performance evaluation. The overall sensitivity of 95.8% with false detection rate of 0.26 per hour and average detection latency of 15.8 seconds was achieved.
Indirect adaptive fuzzy control for a class of nonlinear discrete-time systems
Institute of Scientific and Technical Information of China (English)
无
2008-01-01
An indirect adaptive fuzzy control scheme is developed for a class of nonlinear discrete-time systems. In this method, two fuzzy logic systems are used to approximate the unknown functions, and the parameters of membership functions in fuzzy logic systems are adjusted according to adaptive laws for the purpose of controlling the plant to track a reference trajectory. It is proved that the scheme can not only guarantee the boundedness of the input and output of the closed-loop system, but also make the tracking error converge to a small neighborhood of the origin. Simulation results indicate the effectiveness of this scheme.
Yin, Hui; Yu, Dejie; Yin, Shengwen; Xia, Baizhan
2016-10-01
This paper introduces mixed fuzzy and interval parametric uncertainties into the FE components of the hybrid Finite Element/Statistical Energy Analysis (FE/SEA) model for mid-frequency analysis of built-up systems, thus an uncertain ensemble combining non-parametric with mixed fuzzy and interval parametric uncertainties comes into being. A fuzzy interval Finite Element/Statistical Energy Analysis (FIFE/SEA) framework is proposed to obtain the uncertain responses of built-up systems, which are described as intervals with fuzzy bounds, termed as fuzzy-bounded intervals (FBIs) in this paper. Based on the level-cut technique, a first-order fuzzy interval perturbation FE/SEA (FFIPFE/SEA) and a second-order fuzzy interval perturbation FE/SEA method (SFIPFE/SEA) are developed to handle the mixed parametric uncertainties efficiently. FFIPFE/SEA approximates the response functions by the first-order Taylor series, while SFIPFE/SEA improves the accuracy by considering the second-order items of Taylor series, in which all the mixed second-order items are neglected. To further improve the accuracy, a Chebyshev fuzzy interval method (CFIM) is proposed, in which the Chebyshev polynomials is used to approximate the response functions. The FBIs are eventually reconstructed by assembling the extrema solutions at all cut levels. Numerical results on two built-up systems verify the effectiveness of the proposed methods.
A unified approach to fuzzy modelling and robust synchronization of different hyperchaotic systems
Institute of Scientific and Technical Information of China (English)
Zhang Hua-Guang; Zhao Yan; Yu Wen; Yang Dong-Sheng
2008-01-01
In this paper,a Takagi-Sugeno (T-S) fuzzy model-based method is proposed to deal with the problem of synchronization of two identical or different hyperchaotic systems.The T-S fuzzy models with a small number of fuzzy IF-THEN rules are employed to represent many typical hyperchaotic systems exactly.The benefit of employing the T-S fuzzy models lies in mathematical simplicity of analysis.Based on the T-S fuzzy hyperchaotic models,two fuzzy controllers are designed via parallel distributed compensation (PDC) and exact linearization (EL) techniques to synchronize two identical hyperchaotic systems with uncertain parameters and two different hyperchaotic systems,respectively.The sufficient conditions for the robust synchronization of two identical hyperchaotic systems with uncertain parameters and the asymptotic synchronization of two different hyperchaotic systems are derived by applying the Lyapunov stability theory.This method is a universal one of synchronizing two identical or different hyperchaotic systems.Numerical examples are given to demonstrate the validity of the proposed fuzzy model and hyperchaotic synchronization scheme.
Some Chaotic Properties of Discrete Fuzzy Dynamical Systems
Directory of Open Access Journals (Sweden)
Yaoyao Lan
2012-01-01
Full Text Available Letting (X,d be a metric space, f:X→X a continuous map, and (ℱ(X,D the space of nonempty fuzzy compact subsets of X with the Hausdorff metric, one may study the dynamical properties of the Zadeh's extension f̂:ℱ(X→ℱ(X:u↦f̂u. In this paper, we present, as a response to the question proposed by Román-Flores and Chalco-Cano 2008, some chaotic relations between f and f̂. More specifically, we study the transitivity, weakly mixing, periodic density in system (X,f, and its connections with the same ones in its fuzzified system.
Fuzzy-Rule-Based Object Identification Methodology for NAVI System
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Rosalyn R. Porle
2005-08-01
Full Text Available We present an object identification methodology applied in a navigation assistance for visually impaired (NAVI system. The NAVI has a single board processing system (SBPS, a digital video camera mounted headgear, and a pair of stereo earphones. The captured image from the camera is processed by the SBPS to generate a specially structured stereo sound suitable for vision impaired people in understanding the presence of objects/obstacles in front of them. The image processing stage is designed to identify the objects in the captured image. Edge detection and edge-linking procedures are applied in the processing of image. A concept of object preference is included in the image processing scheme and this concept is realized using a fuzzy-rule base. The blind users are trained with the stereo sound produced by NAVI for achieving a collision-free autonomous navigation.
Recommender System for Sales at Material Store Using Fuzzy Tsukamoto
Directory of Open Access Journals (Sweden)
July Kurniawan
2016-02-01
Full Text Available The retail business has developed very rapidly, especially in Indonesia. One of them is material stores that have not applied the technology and still manual. In this modern era of buying and selling consumers need systems to assist in overcoming problems in terms of recommend items based on customer needs. The aim of this study is to determine the needs of consumers to recommend the necessary consumer goods. This system will simplify these processes, by utilizing information technology using Tsukamoto fuzzy logic. So that consumer demand for faster and more accurate in recommending goods could be accommodated. This research outlines what is needed to overcome the problems that had been experienced by consumers with a lack of information. The recommendations of this study is the form that refers to the percentage of goods from the predictions that have been studied previously.
Robust direct adaptive fuzzy control for nonlinear MIMO systems
Institute of Scientific and Technical Information of China (English)
ZHANG Huaguang; ZHANG Mingjun
2006-01-01
For a class of nonlinear multi-input multi-output systems with uncertainty, a robust direct adaptive fuzzy control scheme was proposed. The feedback control law and adaptive law for parameters were derived based on Lyapunov design approach. The overall control scheme can guarantee that the tracking error converges in the small neighborhood of origin, and all signals of the closed-loop system are uniformly bounded. The main advantage of the proposed control scheme is that in each subsystem only one parameter vector needs to be adjusted on-line in the adaptive mechanism, and so the on-line computing burden is reduced. In addition, the proposed control scheme is a smooth control with no chattering phenomena. A simulation example was proposed to demonstrate the effectiveness of the proposed control algorithm.
Gain Scheduling of PID Controller Based on Fuzzy Systems
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Singh Sandeep
2016-01-01
Full Text Available This paper aims to utilize fuzzy rules and reasoning to determine the controller parameters and the PID controller generates the control signal. The objective of this study is to simulate the proposed scheme on various processes and arrive at results providing better response of the system when compared with best industrial auto-tuning technique: Ziegler-Nichols. The proposed scheme is based upon the Ultimate Gain (Ku and the Period (Tu of the system. The error and rate of change in error gains are tuned manually to get the desired response using LabVIEW. This can also be done with various optimization techniques. A thumb rule for choosing the ranges for Kc, Kd and Ki has been obtained experimentally.
Chen, Liang; Tokuda, N
2002-01-01
By exploiting the Fourier series expansion, we have developed a new constructive method of automatically generating a multivariable fuzzy inference system from any given sample set with the resulting multivariable function being constructed within any specified precision to the original sample set. The given sample sets are first decomposed into a cluster of simpler sample sets such that a single input fuzzy system is constructed readily for a sample set extracted directly from the cluster independent of the other variables. Once the relevant fuzzy rules and membership functions are constructed for each of the variables completely independent of the other variables, the resulting decomposed fuzzy rules and membership functions are integrated back into the fuzzy system appropriate for the original sample set requiring only a moderate cost of computation in the required decomposition and composition processes. After proving two basic theorems which we need to ensure the validity of the decomposition and composition processes of the system construction, we have demonstrated a constructive algorithm of a multivariable fuzzy system. Exploiting an implicit error bound analysis available at each of the construction steps, the present Fourier method is capable of implementing a more stable fuzzy system than the power series expansion method of ParNeuFuz and PolyNeuFuz, covering and implementing a wider range of more robust applications.
Modeling urban air pollution with optimized hierarchical fuzzy inference system.
Tashayo, Behnam; Alimohammadi, Abbas
2016-10-01
Environmental exposure assessments (EEA) and epidemiological studies require urban air pollution models with appropriate spatial and temporal resolutions. Uncertain available data and inflexible models can limit air pollution modeling techniques, particularly in under developing countries. This paper develops a hierarchical fuzzy inference system (HFIS) to model air pollution under different land use, transportation, and meteorological conditions. To improve performance, the system treats the issue as a large-scale and high-dimensional problem and develops the proposed model using a three-step approach. In the first step, a geospatial information system (GIS) and probabilistic methods are used to preprocess the data. In the second step, a hierarchical structure is generated based on the problem. In the third step, the accuracy and complexity of the model are simultaneously optimized with a multiple objective particle swarm optimization (MOPSO) algorithm. We examine the capabilities of the proposed model for predicting daily and annual mean PM2.5 and NO2 and compare the accuracy of the results with representative models from existing literature. The benefits provided by the model features, including probabilistic preprocessing, multi-objective optimization, and hierarchical structure, are precisely evaluated by comparing five different consecutive models in terms of accuracy and complexity criteria. Fivefold cross validation is used to assess the performance of the generated models. The respective average RMSEs and coefficients of determination (R (2)) for the test datasets using proposed model are as follows: daily PM2.5 = (8.13, 0.78), annual mean PM2.5 = (4.96, 0.80), daily NO2 = (5.63, 0.79), and annual mean NO2 = (2.89, 0.83). The obtained results demonstrate that the developed hierarchical fuzzy inference system can be utilized for modeling air pollution in EEA and epidemiological studies.
PENERAPAN FUZZY INFERENCE SYSTEM TAKAGI-SUGENO-KANG PADA SISTEM PAKAR DIAGNOSA PENYAKIT GIGI
Directory of Open Access Journals (Sweden)
Lutfi Salisa Setiawati
2016-04-01
Full Text Available Generally, expert system only show types of disease after user choose symptoms. In the study is done the addition of disease severity level. The method applied in the calculation of the severity is a method of Fuzzy Inference System Takagi-Sugeno-Kang (Method of Sugeno. This study attempts to know whether method Fuzzy Inference System Takagi-Sugeno-Kang can work for expert system in giving the diagnosis diseases of the teeth. The result of this research or severity for diseases of pulpitis reversible 38,53%, pulpitis irreversible 59,64%, periodontitis 69,62%, acute periodontitis 51,43%, gingivitis 45.5%, acute pericoronitis 53,93%, sub acute pericoronitis 52,14%, chronic pericoronitis 46,05%, caries dentist an early stage 37,61%, caries dentist toward an advanced stage 43,89%, caries dentist an advanced stage 51,76%, gangrene pulpa 42,5%, polyps pulpa 56,43%, and periostitis 58,55%. A conclusion that was obtained from the study that is a method of Fuzzy Inference System Takagi-Sugeno-Kang could be applied to expert system of the teeth. Key Word: Teeth , Expert System , Expert System Teeth , Fuzzy Logic , Fuzzy Inference System , Takagi-Sugeno-Kang , Fuzzy Sugeno Pada umumnya, istem pakar hanya menampilkan jenis penyakit setelah user memilih gejala-gejala. Pada penelitian ini dilakukan penambahan tingkat keparahan penyakit. Metode yang diterapkan dalam perhitungan tingkat keparahan ini yaitu Metode Fuzzy Inference System Takagi-Sugeno-Kang (Metode Sugeno. Penelitian ini bertujuan untuk mengetahui apakah metode Fuzzy Inference System Takagi-Sugeno-Kang dapat diterapkan pada sistem pakar dalam memberikan diagnosa penyakit gigi. Hasil dari penelitian ini didapatkan tingkat keparahan untuk penyakit Pulpitis Reversibel 38,53%, Pulpitis Irreversibel 59,64%, Periodontitis 69,62%, Periodontitis Akut 51,43%, Gingivitis 45,5%, Perikoronitis Akut 53,93%, Perikoronitis Sub Akut 52,14%, Perikoronitis Kronis 46,05%, Karies Denties Tahap Awal 37,61%, Karies
Robust Takagi-Sugeno fuzzy control for fractional order hydro-turbine governing system.
Wang, Bin; Xue, Jianyi; Wu, Fengjiao; Zhu, Delan
2016-11-01
A robust fuzzy control method for fractional order hydro-turbine governing system (FOHGS) in the presence of random disturbances is investigated in this paper. Firstly, the mathematical model of FOHGS is introduced, and based on Takagi-Sugeno (T-S) fuzzy rules, the generalized T-S fuzzy model of FOHGS is presented. Secondly, based on fractional order Lyapunov stability theory, a novel T-S fuzzy control method is designed for the stability control of FOHGS. Thirdly, the relatively loose sufficient stability condition is acquired, which could be transformed into a group of linear matrix inequalities (LMIs) via Schur complement as well as the strict mathematical derivation is given. Furthermore, the control method could resist random disturbances, which shows the good robustness. Simulation results indicate the designed fractional order T-S fuzzy control scheme works well compared with the existing method.
Fuzzy double model control for air supply on a PEM fuel cell system
Energy Technology Data Exchange (ETDEWEB)
Hao, Xiaohong; Zhang, Haochen; An, Aimin; Liu, Xin; Chen, Liwen [Lanzhou Univ. of Technology, Gansu (China). College of Electric and Information Engineering
2013-07-01
Oxygen excess ratio control is closely related to the performance and safety of a Proton Exchange Membrane fuel cell system. Some control strategies should be used to regulate the oxygen excess ratio at the suitable value in order to avoid stack starvation and damage. And in this paper, a simple fuzzy double model control has been proposed to adjust the oxygen excess ratio under variation load currents. The double model controller combines a PID controller and a fuzzy logic controller which can be switched based on the fuzzy inference rules during the regulation process. The simulation results demonstrate that the fuzzy double model control can adjust the oxygen excess ratio at the setting point when the current is changed, and improve the dynamic performance of oxygen excess ratio than fuzzy PID control.
THE DEVELOPMENT AND EXPERIMENTAL TESTING OF A FUZZY CONTROL SYSTEM FOR BATCH DISTILLATION
Directory of Open Access Journals (Sweden)
A.M.Frattini Fileti
2002-03-01
Full Text Available The present work describes the development and implementation of fuzzy control algorithms in order to control on-line the overhead product composition of a batch distillation column. Firstly, the influence of design parameters was evaluated through computational simulations and then the algorithms were experimentally tested by monitoring a pilot column. Binary mixtures of n-hexane/n-heptane were distilled. Temperature measurements and vapor-liquid equilibrium data are the basis for the inference of overhead and bottom compositions. Two different operational strategies were used for the experimental runs: constant overhead product composition and previously determined set-point trajectory. Using the first strategy, the performance of the fuzzy controllers is compared to the performance of conventional feedback digital controllers. Experimental results show that fuzzy control presents a better performance than the conventional digital feedback control and also that fuzzy controllers were able to deal successfully with variable set-point strategy, albeit using constant design parameter values. Under conventional control, the average reflux rate implemented was higher than the average reflux rate implemented with fuzzy algorithms. Consequently, the process becomes less time- and energy-consuming under fuzzy control. Since fuzzy methodology is a promising new way of looking at process control problems and their solutions, the results of this work could provide control system designers with a better evaluation of the potential worth of fuzzy control.
Stability analysis and design of fuzzy control system with bounded uncertain delays
Institute of Scientific and Technical Information of China (English)
Jianguo GUO; Juntao LI; Fengqi ZHOU; Jun ZHOU
2005-01-01
Fuzzy control problems for systems with bounded uncertain delays were studied.Based on Lyapunov stability theory and matrix theory,a nonlinear state feedback fuzzy controller was designed by linear matrix inequalities (LMI) approach,and the global exponential stability of the closed-loop system was strictly proved.For a fuzzy control system with bounded uncertain delays,under the global exponential stability condition which is reduced to p linear matrix inequalities,the controller guarantees stability performances of state variables.Finally,the simulation shows the validity of the method in this paper.
Design of a Tele-Control Electrical Vehicle System Using a Fuzzy Logic Control
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M. Boukhnifer
2012-11-01
Full Text Available This paper presents a fuzzy logic design of a tele-control electrical vehicle system. We showed that the application of fuzzy logic control allows the stability of tele-vehicle system in spite of communication delays between the operator and the vehicle. A robust bilateral controller design using fuzzy logic frameworks was proposed. This approach allows a convenient means to trade off robustness and stability for a pre-specified time-delay margin. Both the performance and robustness of the proposed method were demonstrated by simulation results for a constant time delay between the operator and the electrical vehicle system.
Research and Implementation of Automatic Fuzzy Garage Parking System Based on FPGA
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Wang Kaiyu
2016-01-01
Full Text Available Because of many common scenes of reverse parking in real life, this paper presents a fuzzy controller which accommodates front and back adjustment of vehicle’s body attitude, and based on chaotic-genetic arithmetic to optimize the membership function of this controller, and get a vertical parking fuzzy controller whose simulation result is good .The paper makes the hardware-software embedded design for system based on Field-Programmable Gate Array (FPGA, and set up a 1:10 verification platform of smart car to verify the fuzzy garage parking system with real car. Verification results show that, the system can complete the parking task very well.
A Synergistic Effect in the Measurement of Neuro-Fuzzy System
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Gorbachev Sergey
2016-01-01
Full Text Available We consider a new type of hybrid neuro-fuzzy system based on fuzzy and neural computing in hierarchical sequential structure, the total effect exceeds the effect of each component separately. The proposed system can be applied to multi-criteria analysis, automatic classification on signs and obtain evidence-based estimates of the efficiency of scientific and technical solutions and technologies, engineering and robotics. An example of a neuro-fuzzy system measuring the intensity of the emotions of a robot, with the extraction of diagnostic decision rules “If & then”.
Design Method for the Magnetic Bearing Control System with Fuzzy-PID Approach
Institute of Scientific and Technical Information of China (English)
XU Chun-guang; L(U) Dong-ming; HAO Juan
2008-01-01
The five degree freedom magnetic bearing is researched and its structure and working principles are introduced also.Based on the fuzzy control technology,combining fuzzy algorithm and PID control method,identifying the transition process mode of the online system to get the PID parameters'self-adjusting,the magnetic bearing system's Fuzzy-PID nonlinear controller is designed by analyzing the system control demands.The Fuzzy-PID nonlinear controller can deal with the magnetic bearing system's open loop instability and strong nonlinearity,and the approach could improve the system's rapidity,adaptability,stability and dynamic characteristics.Comparative analysis and experiments are conducted between linear PID and nonlinear fuzzyPID control methods,the results show that the fuzzy-PID controller is better,and the five-freedom magnetic bearing's rotary precision experiments are conducted by the fuzzy-PID controller,it satisfies the control rotary precision demands and realizes the bearing's steady floating and rotating.
Directory of Open Access Journals (Sweden)
Tonatiuh Hernández Cortés
2015-01-01
Full Text Available The synchronization of chaotic systems, described by discrete-time T-S fuzzy models, is treated by means of fuzzy output regulation theory. The conditions for designing a discrete-time output regulator are given in this paper. Besides, when the system does not fulfill the conditions for exact tracking, a new regulator based on genetic algorithms is considered. The genetic algorithms are used to approximate the adequate membership functions, which allow the adequate combination of local regulators. As a result, the tracking error is significantly reduced. Both the Complete Synchronization and the Generalized Synchronization problem are studied. Some numerical examples are used to illustrate the effectiveness of the proposed approach.
Designing a fuzzy expert system for selecting knowledge management strategy
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Ameneh Khadivar
2014-12-01
Full Text Available knowledge management strategy is mentioned as one of the most important success factors for implementing knowledge management. The KM strategy selection is a complex decision that requires consideration of several factors. For evaluation and selection of an appropriate knowledge management strategy in organizations, many factors must be considered. The identified factors and their impact on knowledge management strategy are inherently ambiguous. In this study, an overview of theoretical foundations of research regarding the different knowledge management strategies has been done And factors influencing the knowledge management strategy selection have been extracted from conceptual frameworks and models. How these factors influence the knowledge management strategy selection is extracted through the fuzzy Delphi. Next a fuzzy expert system for the selection of appropriate knowledge management strategy is designed with respect to factors that have an impact on knowledge management strategy. The factors which influence the selection of knowledge management strategy include: general business strategy, organizational structure, cultural factors, IT strategy, strategic human resource management, social level, the types of knowledge creation processes and release it. The factors which influence the knowledge management strategy selection include: business strategy general, organizational structure, cultural factors, IT strategy, human resource management strategies, socialization level, knowledge types and its creation and diffusion processes. According to identified factors which affect the knowledge management strategy, the final strategy is recommended based on the range of human-oriented and system-oriented by keep the balance of explicit and implicit knowledge. The Designed system performance is tested and evaluated by the information related to three Iranian organization.
Using fuzzy logic to integrate neural networks and knowledge-based systems
Yen, John
1991-01-01
Outlined here is a novel hybrid architecture that uses fuzzy logic to integrate neural networks and knowledge-based systems. The author's approach offers important synergistic benefits to neural nets, approximate reasoning, and symbolic processing. Fuzzy inference rules extend symbolic systems with approximate reasoning capabilities, which are used for integrating and interpreting the outputs of neural networks. The symbolic system captures meta-level information about neural networks and defines its interaction with neural networks through a set of control tasks. Fuzzy action rules provide a robust mechanism for recognizing the situations in which neural networks require certain control actions. The neural nets, on the other hand, offer flexible classification and adaptive learning capabilities, which are crucial for dynamic and noisy environments. By combining neural nets and symbolic systems at their system levels through the use of fuzzy logic, the author's approach alleviates current difficulties in reconciling differences between low-level data processing mechanisms of neural nets and artificial intelligence systems.
The Self-Adaptive Fuzzy PID Controller in Actuator Simulated Loading System
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Chuanhui Zhang
2013-05-01
Full Text Available This paper analyzes the structure principle of the actuator simulated loading system with variable stiffness, and establishes the simplified model. What’s more, it also does a research on the application of the self-adaptive tuning of fuzzy PID(Proportion Integration Differentiation in actuator simulated loading system with variable stiffness. Because the loading system is connected with the steering system by a spring rod, there must be strong coupling. Besides, there are also the parametric variations accompanying with the variations of the stiffness. Based on compensation from the feed-forward control on the disturbance brought by the motion of steering engine, the system performance can be improved by using fuzzy adaptive adjusting PID control to make up the changes of system parameter caused by the changes of the stiffness. By combining the fuzzy control with traditional PID control, fuzzy adaptive PID control is able to choose the parameters more properly.
Synchronisation of chaotic systems using a novel sampled-data fuzzy controller
Institute of Scientific and Technical Information of China (English)
Feng Yi-Fu; Zhang Qing-Ling
2011-01-01
This paper presents the synchronisation of chaotic systems using a sampled-data fuzzy controller and is meaningful for many physical real-life applications. Firstly, a Takagi-Sugeno (T-S) fuzzy model is employed to represent the chaotic systems that contain some nonlinear terms, then a type of fuzzy sampled-data controller is proposed and an error system formed by the response and drive chaotic system. Secondly, relaxed LMI-based synchronisation conditions are derived by using a new paraneter-dependent Lyapunov-Krasovskii functional and relaxed stabilisation techniques for the underlying error system. The derived LMI-based conditions are used to aid the design of a sampled-data fuzzy controller to achieve the synchronisation of chaotic systems. Finally, a numerical example is provided to illustrate the effectiveness of the proposed results.
A Neuro-Fuzzy System for Characterization of Arm Movements
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Alexandre Balbinot
2013-02-01
Full Text Available The myoelectric signal reflects the electrical activity of skeletal muscles and contains information about the structure and function of the muscles which make different parts of the body move. Advances in engineering have extended electromyography beyond the traditional diagnostic applications to also include applications in diverse areas such as rehabilitation, movement analysis and myoelectric control of prosthesis. This paper aims to study and develop a system that uses myoelectric signals, acquired by surface electrodes, to characterize certain movements of the human arm. To recognize certain hand-arm segment movements, was developed an algorithm for pattern recognition technique based on neuro-fuzzy, representing the core of this research. This algorithm has as input the preprocessed myoelectric signal, to disclosed specific characteristics of the signal, and as output the performed movement. The average accuracy obtained was 86% to 7 distinct movements in tests of long duration (about three hours.
Contribution of a fuzzy expert system to regulatory impact analysis
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Marco Antônio da Cunha
2015-09-01
Full Text Available Regulatory Impact Analysis (RIA has been consolidating in Brazilian regulatory agencies throughout the last decades. The RIA methodology aims to examine the regulatory process, measure the costs and benefits generated, as well as other effects of social, political or economic nature caused by a new or an existing regulation. By analysing each regulatory option, the expert or regulator faces a myriad of variables, usually of qualitative nature, that are difficult to measure and with a high degree of uncertainty. This research complements the existing literature, given the scarcity of decision support models in RIA that – regardless of the problem treated – incorporate the tacit knowledge of the regulation expert. This paper proposes an exploratory approach using a Fuzzy Expert System, which therefore helps to enrich the decision process in the final stage of comparison of the regulatory options.
Human activity recognition based on Evolving Fuzzy Systems.
Iglesias, Jose Antonio; Angelov, Plamen; Ledezma, Agapito; Sanchis, Araceli
2010-10-01
Environments equipped with intelligent sensors can be of much help if they can recognize the actions or activities of their users. If this activity recognition is done automatically, it can be very useful for different tasks such as future action prediction, remote health monitoring, or interventions. Although there are several approaches for recognizing activities, most of them do not consider the changes in how a human performs a specific activity. We present an automated approach to recognize daily activities from the sensor readings of an intelligent home environment. However, as the way to perform an activity is usually not fixed but it changes and evolves, we propose an activity recognition method based on Evolving Fuzzy Systems.
Efficient modeling of vector hysteresis using fuzzy inference systems
Energy Technology Data Exchange (ETDEWEB)
Adly, A.A. [Electrical Power and Machines Department, Faculty of Engineering, Cairo University, Giza 12211 (Egypt)], E-mail: adlyamr@gmail.com; Abd-El-Hafiz, S.K. [Engineering Mathematics Department, Faculty of Engineering, Cairo University, Giza 12211 (Egypt)], E-mail: sabdelhafiz@gmail.com
2008-10-01
Vector hysteresis models have always been regarded as important tools to determine which multi-dimensional magnetic field-media interactions may be predicted. In the past, considerable efforts have been focused on mathematical modeling methodologies of vector hysteresis. This paper presents an efficient approach based upon fuzzy inference systems for modeling vector hysteresis. Computational efficiency of the proposed approach stems from the fact that the basic non-local memory Preisach-type hysteresis model is approximated by a local memory model. The proposed computational low-cost methodology can be easily integrated in field calculation packages involving massive multi-dimensional discretizations. Details of the modeling methodology and its experimental testing are presented.
Kosko, Bart
1991-01-01
Mappings between fuzzy cubes are discussed. This level of abstraction provides a surprising and fruitful alternative to the propositional and predicate-calculas reasoning techniques used in expert systems. It allows one to reason with sets instead of propositions. Discussed here are fuzzy and neural function estimators, neural vs. fuzzy representation of structured knowledge, fuzzy vector-matrix multiplication, and fuzzy associative memory (FAM) system architecture.
Fuzzy Boundary and Fuzzy Semiboundary
Athar, M.; Ahmad, B.
2008-01-01
We present several properties of fuzzy boundary and fuzzy semiboundary which have been supported by examples. Properties of fuzzy semi-interior, fuzzy semiclosure, fuzzy boundary, and fuzzy semiboundary have been obtained in product-related spaces. We give necessary conditions for fuzzy continuous (resp., fuzzy semicontinuous, fuzzy irresolute) functions. Moreover, fuzzy continuous (resp., fuzzy semicontinuous, fuzzy irresolute) functions have been characterized via fuzzy-derived (resp., fuzz...
Fuzzy Pattern Recognition System for Detection of Alga Distribution
Institute of Scientific and Technical Information of China (English)
无
2001-01-01
To realize the on-line measurement and make analysis on the density of algae and their cluster distribution, the fluorescent detection and fuzzy pattern recognition techniques are used. The principle of fluorescent fiber-optic detection is given as well as the method of fuzzy feature extraction using a class of neural network.
A Novel Identification Method for Generalized T-S Fuzzy Systems
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Ling Huang
2012-01-01
Full Text Available In order to approximate any nonlinear system, not just affine nonlinear systems, generalized T-S fuzzy systems, where the control variables and the state variables, are all premise variables are introduced in the paper. Firstly, fuzzy spaces and rules were determined by using ant colony algorithm. Secondly, the state-space model parameters are identified by using genetic algorithm. The simulation results show the effectiveness of the proposed algorithm.
An Evolving Cascade System Based on a Set of Neo - Fuzzy Nodes
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Zhengbing Hu
2016-09-01
Full Text Available Neo-fuzzy elements are used as nodes for an evolving cascade system. The proposed system can tune both its parameters and architecture in an online mode. It can be used for solving a wide range of Data Mining tasks (namely time series forecasting. The evolving cascade system with neo-fuzzy nodes can process rather large data sets with high speed and effectiveness
2015-01-01
In this paper, the problem of robust control of nonlinear fractional-order systems in the presence of uncertainties and external disturbance is investigated. Fuzzy logic systems are used for estimating the unknown nonlinear functions. Based on the fractional Lyapunov direct method and some proposed Lemmas, an adaptive fuzzy controller is designed. The proposed method can guarantee all the signals in the closed-loop systems remain bounded and the tracking errors converge to an arbitrary small ...
Traffic Forecasting Model Based on Takagi-Sugeno Fuzzy Logical System
Institute of Scientific and Technical Information of China (English)
WANG Wei-gong; LI Zheng; CHENG Mei-ling
2005-01-01
The local multiple regression fuzzy(LMRF)model based on Takagi-Sugeno fuzzy logical system and its application in traffic forecasting is proposed. Besides its prediction accuracy is testified and the model is proved much better than conventional forecasting methods. According to the regional traffic system, the model perfectly states the complex non-linear relation of the traffic and the local social economy. The model also efficiently deals with the system lack of enough data.
Fuzzy model of the computer integrated decision support and management system in mineral processing
Miljanović Igor; Vujić Slobodan
2008-01-01
During the research on the subject of computer integrated systems for decision making and management support in mineral processing based on fuzzy logic, realized at the Department of Applied Computing and System Engineering of the Faculty of Mining and Geology, University of Belgrade, for the needs of doctoral thesis of the first author, and wider demands of the mineral industry, the incompleteness of the developed and contemporary computer integrated systems fuzzy models was noticed. The pap...
Z Source Inverter for Photovoltaic System with Fuzzy Logic Controller
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Vijayabalan R
2012-10-01
Full Text Available In this paper, the photovoltaic system is used to extract the maximum power from sun to get the dc voltage. The output dc voltage is boost up into maximum voltage level by using the SEPIC converter. This converter voltage is fed to Z source inverter to get the AC voltage. The Z source inverter system can boost the given input voltage by controlling the boost factor, to obtain the maximum voltage. PWM technique which is used as to given the gating pulse to the inverter switches. Modified system is very promising for residential solar energy system. In stand-alone systems the solar energy yield is matched to the energy demand. Wherever it was not possible to install an electricity supply from the mains utility grid, or desirable, stand-alone photovoltaic systems could be installed. This proposed system is cost-effective for photovoltaic stand-alone applications. This paper describes the design of a rule based Fuzzy Logic Controller (FLC for Z Source inverter. The obtained AC Voltage contains harmonics of both odd and even harmonics of lower and higher order. Higher order harmonics are eliminated with the help of Filters. Here the impedance network act as a filter to reduce the lower order harmonics obtained in the system. So with the help of FFT analysis this value is obtained to be 15.82%.
A Fuzzy-MOORA approach for ERP system selection
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Prasad Karande
2012-07-01
Full Text Available In today’s global and dynamic business environment, manufacturing organizations face the tremendous challenge of expanding markets and meeting the customer expectations. It compels them to lower total cost in the entire supply chain, shorten throughput time, reduce inventory, expand product choice, provide more reliable delivery dates and better customer service, improve quality, and efficiently coordinate demand, supply and production. In order to accomplish these objectives, the manufacturing organizations are turning to enterprise resource planning (ERP system, which is an enterprise-wide information system to interlace all the necessary business functions, such as product planning, purchasing, inventory control, sales, financial and human resources into a single system having a shared database. Thus to survive in the global competitive environment, implementation of a suitable ERP system is mandatory. However, selecting a wrong ERP system may adversely affect the manufacturing organization’s overall performance. Due to limitations in available resources, complexity of ERP systems and diversity of alternatives, it is often difficult for a manufacturing organization to select and install the most suitable ERP system. In this paper, two ERP system selection problems are solved using fuzzy multi-objective optimization on the basis of ratio analysis (MOORA method and it is observed that in both the cases, SAP is the best solution.
Signal frequency based self-tuning fuzzy controller for semi-active suspension system
Institute of Scientific and Technical Information of China (English)
孙涛; 黄震宇; 陈大跃; 汤磊
2003-01-01
A new kind of fuzzy control scheme, based on the identification of the signal's main frequency and the behavior of the ER damper, is proposed to control the semi-active suspension system. This method adjusts the fuzzy controller to achieve the best isolation effect by analyzing the main frequency's characters and inspecting the change of system parameters. The input of the fuzzy controller is the main frequency and the optimal damping ratio is the output. Simulation results indicated that the proposed control method is very effective in isolating the vibration.
Signal frequency based self－tuning fuzzy controller for semi－active suspension system
Institute of Scientific and Technical Information of China (English)
孙涛; 黄震宇; 陈大跃; 汤磊
2003-01-01
A new kind of fuzzy control scheme, based on the identification of the signal' s main frequency and the behavior of the ER damper, is proposed to control the semi-active suspension system. This method ad-justs the fuzzy controller to achieve the best isolation effect by analyzing the main frequency' s characters and inspecting the change of system parameters. The input of the fuzzy controller is the main frequency and the op-timal damping ratio is the output. Simulation results indicated that the proposed control method is very effec-tive in isolating the vibration.
Design of fuzzy sliding mode controller for SISO discrete-time systems
Institute of Scientific and Technical Information of China (English)
Yang MI; Yuanwei JING
2004-01-01
According to a class of nonlinear SISO discrete systems,the fuzzy sliding mode control problem is considered.Based on Takagi-Sugeno fuzzy model method,a fuzzy model is designed to describe the local dynamic performance of the given nonlinear systems.By using the sliding mode control approach,the global controller is constructed by integrating all the local state controllers and the global supervisory sliding mode controller.The tracking problem can be easily dealt with by taking advantage of the combined controller,and the robustness performance is improved finally.A simulation example is given to show the effectiveness and feasibility of the method proposed.
Wang, Chenhui
2016-01-01
In this paper, control of uncertain fractional-order financial chaotic system with input saturation and external disturbance is investigated. The unknown part of the input saturation as well as the system’s unknown nonlinear function is approximated by a fuzzy logic system. To handle the fuzzy approximation error and the estimation error of the unknown upper bound of the external disturbance, fractional-order adaptation laws are constructed. Based on fractional Lyapunov stability theorem, an adaptive fuzzy controller is designed, and the asymptotical stability can be guaranteed. Finally, simulation studies are given to indicate the effectiveness of the proposed method. PMID:27783648
Stable and optimal fuzzy control of a laboratory Antilock Braking System
DEFF Research Database (Denmark)
Precup, Radu-Emil; Spataru, Sergiu; Petriu, Emil M.
2010-01-01
This paper discusse four new Takagi-Sugeno fuzzy controllers (T-S FCs) for the longitudinal slip control of an Antilock Braking System laboratory equipment. Two discretetime dynamic Takagi-Sugeno fuzzy models of the controlled plant are derived based on the parameters in the consequents...... conditions to the fuzzy control systems (FCSs) and the other two T-S FCs are tuned by the linear-quadratic regulator approach applied to each rule. Linear matrix inequalities are solved to guarantee the global stability of the FCSs. Real-time experimental results validate the original T-S FCs and design...
Observed-Based Adaptive Fuzzy Tracking Control for Switched Nonlinear Systems With Dead-Zone.
Tong, Shaocheng; Sui, Shuai; Li, Yongming
2015-12-01
In this paper, the problem of adaptive fuzzy output-feedback control is investigated for a class of uncertain switched nonlinear systems in strict-feedback form. The considered switched systems contain unknown nonlinearities, dead-zone, and immeasurable states. Fuzzy logic systems are utilized to approximate the unknown nonlinear functions, a switched fuzzy state observer is designed and thus the immeasurable states are obtained by it. By applying the adaptive backstepping design principle and the average dwell time method, an adaptive fuzzy output-feedback tracking control approach is developed. It is proved that the proposed control approach can guarantee that all the variables in the closed-loop system are bounded under a class of switching signals with average dwell time, and also that the system output can track a given reference signal as closely as possible. The simulation results are given to check the effectiveness of the proposed approach.
Controller design of uncertain nonlinear systems based on T-S fuzzy model
Institute of Scientific and Technical Information of China (English)
Songtao ZHANG; Shizhen BAI
2009-01-01
A robust control for uncertain nonlinear systems based on T-S fuzzy model is discussed in this paper. First, a T-S fuzzy system is adopted to model the uncertain nonlinear systems. Then, for the system with input variables adopting standard fuzzy partitions, the efficient maximal overlapped-rules group (EMORG) is presented, and a new sufficient condition to check the stability of T-S fuzzy system with uncertainty is derived, which is expressed in terms of Linear Matrix Inequalities. The derived stability condition, which only requires a local common positive definite matrix in each EMORG, can reduce the conservatism and difficulty in existing stability conditions. Finally, a simulation example shows the proposed approach is effective.
Control of Single Axis Magnetic Levitation System Using Fuzzy Logic Control
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Tania Tariq Salim
2013-12-01
Full Text Available This paper presents a fuzzy logic controller design for the stabilization of magnetic levitation system (Maglev 's.Additionally, the investigation on Linear Quadratic Regulator Controller (LQRC also mentioned here. This paper presents the difference between the performance of fuzzy logic control (FLC and LQRC for the same linear model of magnetic levitation system .A magnetic levitation is a nonlinear unstable system and the fuzzy logic controller brings the magnetic levitation system to a stable region by keeping a magnetic ball suspended in the air. The modeling of the system is simulated using Matlab Simulink and connected to Hilink platform and the maglev model of Zeltom company. This paper presents a comparison for both LQRC and FLC to control a ball suspended on the air. The performance results of simulation shows that the fuzzy logic controller had better performance than the LQR control.
Analysis of Helical Gear System Dynamic Response Based on Fuzzy Numbers
Institute of Scientific and Technical Information of China (English)
马亮; 李瑰贤; 杨伟君
2001-01-01
A non-linear dynamic model with the single degree of freedom of a helical gear pair introducing frzzy numbers is developed. In this proposed model, time-variant mesh stiffness, which is a non-linear parameter, mesh damping and composite error of a pair of meshing tooth of the gear pair are all included. Mesh stiffness is calculated by expressing Bo (r) as a Fourier series. Ⅱshape function is introduced as the membership function to characterize the fuzziness of the error. Fuzzy displacement dynamic response of the geared system at A- level, which is a closed interval, is ohtained by removing the fuzziness of the fuzzy differential equations and using Runge-Kutta numerical method. In fact, the fuzzy dynamic response and dynamic loading factor are aH the interval functions related λ. The result obtained here can be used to the fuzzy dynamic optimization design course of the helical gear system. The main advantage of this method is to introduce the concept of fuzzy number for the first time to the analysis of the gear system dynamics.
Fuzzy logic based control system for fresh water aquaculture: A MATLAB based simulation approach
Directory of Open Access Journals (Sweden)
Rana Dinesh Singh
2015-01-01
Full Text Available Fuzzy control is regarded as the most widely used application of fuzzy logic. Fuzzy logic is an innovative technology to design solutions for multiparameter and non-linear control problems. One of the greatest advantages of fuzzy control is that it uses human experience and process information obtained from operator rather than a mathematical model for the definition of a control strategy. As a result, it often delivers solutions faster than conventional control design techniques. The proposed system is an attempt to apply fuzzy logic techniques to predict the stress factor on the fish, based on line data and rule base generated using domain expert. The proposed work includes a use of Data acquisition system, an interfacing device for on line parameter acquisition and analysis, fuzzy logic controller (FLC for inferring the stress factor. The system takes stress parameters on the fish as inputs, fuzzified by using FLC with knowledge base rules and finally provides single output. All the parameters are controlled and calibrated by the fuzzy logic toolbox and MATLAB programming.
Assessment of the Degree of Consistency of the System of Fuzzy Rules
Directory of Open Access Journals (Sweden)
Pospelova Lyudmila Yakovlevna
2013-12-01
Full Text Available The article analyses recent achievements and publications and shows that difficulties of explaining the nature of fuzziness and equivocation arise in socio-economic models that use the traditional paradigm of classical rationalism (computational, agent and econometric models. The accumulated collective experience of development of optimal models confirms prospectiveness of application of the fuzzy set approach in modelling the society. The article justifies the necessity of study of the nature of inconsistency in fuzzy knowledge bases both on the generalised ontology level and on pragmatic functional level of the logical inference. The article offers the method of search for logical and conceptual contradictions in the form of a combination of the abduction and modus ponens. It discusses the key issue of the proposed method: what properties should have the membership function of the secondary fuzzy set, which describes in fuzzy inference models such a resulting state of the object of management, which combines empirically incompatible properties with high probability. The degree of membership of the object of management in several incompatible classes with respect to the fuzzy output variable is the degree of fuzziness of the “Intersection of all results of the fuzzy inference of the set, applied at some input of rules, is an empty set” statement. The article describes an algorithm of assessment of the degree of consistency. It provides an example of the step-by-step detection of contradictions in statistical fuzzy knowledge bases at the pragmatic functional level of the logical output. The obtained results of testing in the form of sets of incompatible facts, output chains, sets of non-crossing intervals and computed degrees of inconsistency allow experts timely elimination of inadmissible contradictions and, at the same time, increase of quality of recommendations and assessment of fuzzy expert systems.
Introduction to Fuzzy Set Theory
Kosko, Bart
1990-01-01
An introduction to fuzzy set theory is described. Topics covered include: neural networks and fuzzy systems; the dynamical systems approach to machine intelligence; intelligent behavior as adaptive model-free estimation; fuzziness versus probability; fuzzy sets; the entropy-subsethood theorem; adaptive fuzzy systems for backing up a truck-and-trailer; product-space clustering with differential competitive learning; and adaptive fuzzy system for target tracking.
Prescribed Performance Fuzzy Adaptive Output-Feedback Control for Nonlinear Stochastic Systems
Directory of Open Access Journals (Sweden)
Lili Zhang
2014-01-01
Full Text Available A prescribed performance fuzzy adaptive output-feedback control approach is proposed for a class of single-input and single-output nonlinear stochastic systems with unmeasured states. Fuzzy logic systems are used to identify the unknown nonlinear system, and a fuzzy state observer is designed for estimating the unmeasured states. Based on the backstepping recursive design technique and the predefined performance technique, a new fuzzy adaptive output-feedback control method is developed. It is shown that all the signals of the resulting closed-loop system are bounded in probability and the tracking error remains an adjustable neighborhood of the origin with the prescribed performance bounds. A simulation example is provided to show the effectiveness of the proposed approach.
Constrained predictive control based on T-S fuzzy model for nonlinear systems
Institute of Scientific and Technical Information of China (English)
Su Baili; Chen Zengqiang; Yuan Zhuzhi
2007-01-01
A constrained generalized predictive control (GPC) algorithm based on the T-S fuzzy model is presented for the nonlinear system. First, a Takagi-Sugeno (T-S) fuzzy model based on the fuzzy cluster algorithm and the orthogonal least square method is constructed to approach the nonlinear system. Since its consequence is linear, it can divide the nonlinear system into a number of linear or nearly linear subsystems. For this T-S fuzzy model, a GPC algorithm with input constraints is presented.This strategy takes into account all the constraints of the control signal and its increment, and does not require the calculation of the Diophantine equations. So it needs only a small computer memory and the computational speed is high. The simulation results show a good performance for the nonlinear systems.
Takagi-Sugeno Fuzzy Systems Non-fragile H-infinity Filtering
Chang, Xiao-Heng
2012-01-01
"Takagi-Sugeno Fuzzy Systems Non-fragile H-infinity Filtering" investigates the problem of non-fragile H-infinity filter design for T-S fuzzy systems. The nonlinear plant is represented by a T-S fuzzy model. Given a T-S fuzzy system, the objective of this book is to design an H-infinity filter with the gain variations such that the filtering error system guarantees a prescribed H-infinity performance level. Furthermore, it demonstrates that the solution of non-fragile H-infinity filter design problem can be obtained by solving a set of linear matrix inequalities (LMIs). The intended audiences are graduate students and researchers both from the fields of engineering and mathematics. Dr. Xiao-Heng Chang is an Associate Professor at the College of Engineering, Bohai University, Jinzhou, Liaoning, China. He is also a Postdoctoral Researcher at the College of Information Science and Engineering, Northeastern University, Shenyang, China.
Efficient neuro-fuzzy system and its Memristor Crossbar-based Hardware Implementation
Merrikh-Bayat, Farnood
2011-01-01
In this paper a novel neuro-fuzzy system is proposed where its learning is based on the creation of fuzzy relations by using new implication method without utilizing any exact mathematical techniques. Then, a simple memristor crossbar-based analog circuit is designed to implement this neuro-fuzzy system which offers very interesting properties. In addition to high connectivity between neurons and being fault-tolerant, all synaptic weights in our proposed method are always non-negative and there is no need to precisely adjust them. Finally, this structure is hierarchically expandable and can compute operations in real time since it is implemented through analog circuits. Simulation results show the efficiency and applicability of our neuro-fuzzy computing system. They also indicate that this system can be a good candidate to be used for creating artificial brain.
Takagi-Sugeno fuzzy modeling and chaos control of partial differential systems.
Vasegh, Nastaran; Khellat, Farhad
2013-12-01
In this paper a unified approach is presented for controlling chaos in nonlinear partial differential systems by a fuzzy control design. First almost all known chaotic partial differential equation systems are represented by Takagi-Sugeno fuzzy model. For investigating design procedure, Kuramoto-Sivashinsky (K-S) equation is selected. Then, all linear subsystems of K-S equation are transformed to ordinary differential equation (ODE) systems by truncated Fourier series of sine-cosine functions. By solving Riccati equation for each ODE systems, parallel stabilizing feedback controllers are determined. Finally, a distributed fuzzy feedback for K-S equation is designed. Numerical simulations are given to show that the distributed fuzzy controller is very easy to design, efficient, and capable to extend.
Takagi-Sugeno fuzzy modeling and chaos control of partial differential systems
Vasegh, Nastaran; Khellat, Farhad
2013-12-01
In this paper a unified approach is presented for controlling chaos in nonlinear partial differential systems by a fuzzy control design. First almost all known chaotic partial differential equation systems are represented by Takagi-Sugeno fuzzy model. For investigating design procedure, Kuramoto-Sivashinsky (K-S) equation is selected. Then, all linear subsystems of K-S equation are transformed to ordinary differential equation (ODE) systems by truncated Fourier series of sine-cosine functions. By solving Riccati equation for each ODE systems, parallel stabilizing feedback controllers are determined. Finally, a distributed fuzzy feedback for K-S equation is designed. Numerical simulations are given to show that the distributed fuzzy controller is very easy to design, efficient, and capable to extend.
Adaptive neuro-fuzzy estimation of optimal lens system parameters
Petković, Dalibor; Pavlović, Nenad T.; Shamshirband, Shahaboddin; Mat Kiah, Miss Laiha; Badrul Anuar, Nor; Idna Idris, Mohd Yamani
2014-04-01
Due to the popularization of digital technology, the demand for high-quality digital products has become critical. The quantitative assessment of image quality is an important consideration in any type of imaging system. Therefore, developing a design that combines the requirements of good image quality is desirable. Lens system design represents a crucial factor for good image quality. Optimization procedure is the main part of the lens system design methodology. Lens system optimization is a complex non-linear optimization task, often with intricate physical constraints, for which there is no analytical solutions. Therefore lens system design provides ideal problems for intelligent optimization algorithms. There are many tools which can be used to measure optical performance. One very useful tool is the spot diagram. The spot diagram gives an indication of the image of a point object. In this paper, one optimization criterion for lens system, the spot size radius, is considered. This paper presents new lens optimization methods based on adaptive neuro-fuzzy inference strategy (ANFIS). This intelligent estimator is implemented using Matlab/Simulink and the performances are investigated.
Bank Customer Credit Scoring by Using Fuzzy Expert System
Directory of Open Access Journals (Sweden)
Ali Bazmara
2014-10-01
Full Text Available Granting banking facility is one of the most important parts of the financial supplies for each bank. So this activity becomes more valuable economically and always has a degree of risk. These days several various developed Artificial Intelligent systems like Neural Network, Decision Tree, Logistic Regression Analysis, Linear Discriminant Analysis and etc, are used in the field of granting facilities that each of this system owns its advantages and disadvantages. But still studying and working are needed to improve the accuracy and performance of them. In this article among other AI methods, fuzzy expert system is selected. This system is based on data and also extracts rules by using data. Therefore the dependency to experts is omitted and interpretability of rules is obtained. Validity of these rules could be confirmed or rejected by banking affair experts. For investigating the performance of proposed system, this system and some other methods were performed on various datasets. Results show that the proposed algorithm obtained better performance among the others.
Energy Technology Data Exchange (ETDEWEB)
Uezato, K.; Senju, T.; Shiroma, T. (University of the Ryukyus, Okinawa (Japan))
1994-03-01
The SVC (static var compensator) control method featured by fuzzy control is proposed to improve the stabilization of power systems. The method is applicable to a simple single-machine infinite bus system, and SVC is allocated at the center of a transmission line to keep the line terminal voltage constant. The SVC controller is composed of the PI controller to keep the terminal voltage constant and the fuzzy controller-1 parallel to the PI controller for determining SVC admittances to suppress system fluctuation. The fuzzy controller-2 switches control between stabilizing control during system fluctuation and constant voltage control in normal operation. The fuzzy rules are remarkably simple because those are constructed qualitatively on the basis of sliding mode control. System fluctuation can be also reduced rapidly by using not only the terminal information such as terminal voltage and power flow on an interconnection line but also the generator information such as load angle and slip. 10 refs., 24 figs., 7 tabs.
Designing an Energy Storage System Fuzzy PID Controller for Microgrid Islanded Operation
Directory of Open Access Journals (Sweden)
Jin-Hong Jeon
2011-09-01
Full Text Available Recently, interest in microgrids, which are composed of distributed generation (DG, distributed storage (DS, and loads, has been growing as a potentially effective clean energy system to mitigate against climate change. The microgrid is operated in the grid-connected mode and the islanded mode according to the conditions of the upstream power grid. The role of the energy storage system (ESS is especially important to maintain constant the frequency and voltage of an islanded microgrid. For this reason, various approaches for ESS control have been put forth. In this paper, a fuzzy PID controller is proposed to improve the frequency control performance of the ESS. This fuzzy PID controller consists of a fuzzy logic controller and a conventional PI controller, connected in series. The fuzzy logic controller has two input signals, and then the output signal of the fuzzy logic controller is the input signal of the conventional PI controller. For comparison of control performance, gains of each PI controller and fuzzy PID controller are tuned by the particle swam optimization (PSO algorithm. In the simulation study, the control performance of the fuzzy PID was also tested under various operating conditions using the PSCAD/EMTDC simulation platform.
L-fuzzy闭包系统和L-fuzzy弱邻域算子%L-fuzzy closure systems and L-fuzzy weak neighborhood operators
Institute of Scientific and Technical Information of China (English)
王新奇
2011-01-01
To further study L-fuzzy closure system.Another determination of L-fuzzy closure systems are studied with thehelp of idea of one-to-one correspondence and method of category.Let X be a set.L a Hutton algebra,we first introduce definitions of L-fuzzy weak neighborhood operators and their L-fuzzy continuous mappings.Then let L-FCSS be the category L-fuzzy closure system spaces and their L-fuzzy continuous mappings,L-FWNS be the category L-fuzzy weak neighborhood spaces and their continuous mappings,it mainly gives that L-FCSS is isomorphic to L-FWNS%进一步研究L-fuzzy闭包系统,运用一一对应的思想和范畴论的方法研究了确定L-fuzzy闭包系统的另一种方法.设X是集合,L是Hutton代数,首先介绍了L-fuzzy弱邻域算子和它们的L-fuzzy连续映射,然后设L-FCSS是L-fuzzy闭包系统空间和它们的L-fuzzy连续映射构成的范畴,L-FWNS是L-fuzzy弱邻域算子空间和它们的L-fuzzy连续映射构成的范畴,证明了L-FCSS和L-FWNS是同构的.
Directory of Open Access Journals (Sweden)
Mohsen Ebrahimian Baydokhty
2016-03-01
It would be useful to make use of the type 2 fuzzy in modeling of uncertainties in systems which are uncertain. In the present article, first, the simplified 4-block type-2 fuzzy has been used for modeling the fuzzy system. Then, fuzzy system regulations are reduced by 33% with the help of hierarchy fuzzy structure. The simplified type-2 fuzzy controller is optimized using the Cuckoo algorithm. Eventually, the performance of the proposed controller is compared to the Mamdani fuzzy controller in terms of the ISE, ITSE, and RMS criteria.
Analysis and synthesis for interval type-2 fuzzy-model-based systems
Li, Hongyi; Lam, Hak-Keung; Gao, Yabin
2016-01-01
This book develops a set of reference methods capable of modeling uncertainties existing in membership functions, and analyzing and synthesizing the interval type-2 fuzzy systems with desired performances. It also provides numerous simulation results for various examples, which fill certain gaps in this area of research and may serve as benchmark solutions for the readers. Interval type-2 T-S fuzzy models provide a convenient and flexible method for analysis and synthesis of complex nonlinear systems with uncertainties.
Analysis and interpretation of geodetic landslide monitoring data based on fuzzy systems
Directory of Open Access Journals (Sweden)
M. Haberler-Weber
2005-01-01
Full Text Available To place high precision geotechnical sensors exactly at the boundaries between blocks with different directions and rates of movement in a sliding area, it is important to detect these boundaries in a preceding step. An automated algorithm for the block detection based on fuzzy systems is presented. Combining objective geodetic indicators with fuzzy systems gives a powerful tool for the assessment of geodetic landslide monitoring data. The example of a landsliding area shows the applicability of the algorithm.
On the solution of a class of fuzzy system of linear equations
Indian Academy of Sciences (India)
Davod Khojasteh Salkuyeh
2015-04-01
In this paper, we consider the system of linear equations $A\\widetilde{x}=\\widetilde{b}$, where $A \\in \\mathbb{R}^{n \\times n}$ is a crisp H-matrix and \\widetilde{b} is a fuzzy -vector. We then investigate the existence and uniqueness of a fuzzy solution to this system. The results can also be used for the class of M-matrices and strictly diagonally dominant matrices. Finally, some numerical examples are given to illustrate the presented theoretical results.
Abdul Kareem; Mohammad Fazle Azeem
2012-01-01
This paper presents a novel fuzzy logic based Adaptive Super-twisting Sliding Mode Controller for the control of dynamic uncertain systems. The proposed controller combines the advantages of Second order Sliding Mode Control, Fuzzy Logic Control and Adaptive Control. The reaching conditions, stability and robustness of the system with the proposed controller are guaranteed. In addition, the proposed controller is well suited for simple design and implementation. The effectiveness ...
Maximum Power Point Tracking Using Adaptive Fuzzy Logic control for Photovoltaic System
Directory of Open Access Journals (Sweden)
Anass Ait Laachir
2015-01-01
Full Text Available This work presents an intelligent approach to the improvement and optimization of control performance of a photovoltaic system with maximum power point tracking based on fuzzy logic control. This control was compared with the conventional control based on Perturb &Observe algorithm. The results obtained in Matlab/Simulink under different conditions show a marked improvement in the performance of fuzzy control MPPT of the PV system.
Fuzzy Logic Based Control of Power of PEM Fuel Cell System for Residential Application
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Khaled MAMMAR
2009-07-01
Full Text Available This paper presents a dynamic model of Fuel cell system for residential power generation. The models proposed include a fuel cell stack model, reformer model and DC/AC inverter model. Furthermore a fuzzy logic (FLC controller is used to control active power of PEM fuel cell system. The controller modifies the hydrogen flow feedback from the terminal load. Simulation results confirmed the high performance capability of the fuzzy logic controller to control power generation.
Fuzzy Logic Based Control of Power of PEM Fuel Cell System for Residential Application
Khaled MAMMAR; CHAKER, Abdelkader
2009-01-01
This paper presents a dynamic model of Fuel cell system for residential power generation. The models proposed include a fuel cell stack model, reformer model and DC/AC inverter model. Furthermore a fuzzy logic (FLC) controller is used to control active power of PEM fuel cell system. The controller modifies the hydrogen flow feedback from the terminal load. Simulation results confirmed the high performance capability of the fuzzy logic controller to control power generation.
A Fuzzy-Neuro Scheme for Fault Diagnosis and Life Consumption of Rotordynamic Systems
1996-04-01
O6B DTIC Information For The Defense CommunrtY 000MWPP A FUZZY-NEURO SCHEME FOR FAULT DIAGNOSIS AND LIFE CONSUMPTION OF ROTORDYNAMIC SYSTEMS Michael J... rotordynamic , finite-etement modeling. A rotor demonstration rig is used as a proof of concept tool. The approach integrates rotor shaft vibration...measurements with detailed, rotordynamic , finite-element models through a fuzzy-neuro scheme which is specifically developed to respond to the rotor system
Finite-dimensional constrained fuzzy control for a class of nonlinear distributed process systems.
Wu, Huai-Ning; Li, Han-Xiong
2007-10-01
This correspondence studies the problem of finite-dimensional constrained fuzzy control for a class of systems described by nonlinear parabolic partial differential equations (PDEs). Initially, Galerkin's method is applied to the PDE system to derive a nonlinear ordinary differential equation (ODE) system that accurately describes the dynamics of the dominant (slow) modes of the PDE system. Subsequently, a systematic modeling procedure is given to construct exactly a Takagi-Sugeno (T-S) fuzzy model for the finite-dimensional ODE system under state constraints. Then, based on the T-S fuzzy model, a sufficient condition for the existence of a stabilizing fuzzy controller is derived, which guarantees that the state constraints are satisfied and provides an upper bound on the quadratic performance function for the finite-dimensional slow system. The resulting fuzzy controllers can also guarantee the exponential stability of the closed-loop PDE system. Moreover, a local optimization algorithm based on the linear matrix inequalities is proposed to compute the feedback gain matrices of a suboptimal fuzzy controller in the sense of minimizing the quadratic performance bound. Finally, the proposed design method is applied to the control of the temperature profile of a catalytic rod.
Prediction system of hydroponic plant growth and development using algorithm Fuzzy Mamdani method
Sudana, I. Made; Purnawirawan, Okta; Arief, Ulfa Mediaty
2017-03-01
Hydroponics is a method of farming without soil. One of the Hydroponic plants is Watercress (Nasturtium Officinale). The development and growth process of hydroponic Watercress was influenced by levels of nutrients, acidity and temperature. The independent variables can be used as input variable system to predict the value level of plants growth and development. The prediction system is using Fuzzy Algorithm Mamdani method. This system was built to implement the function of Fuzzy Inference System (Fuzzy Inference System/FIS) as a part of the Fuzzy Logic Toolbox (FLT) by using MATLAB R2007b. FIS is a computing system that works on the principle of fuzzy reasoning which is similar to humans' reasoning. Basically FIS consists of four units which are fuzzification unit, fuzzy logic reasoning unit, base knowledge unit and defuzzification unit. In addition to know the effect of independent variables on the plants growth and development that can be visualized with the function diagram of FIS output surface that is shaped three-dimensional, and statistical tests based on the data from the prediction system using multiple linear regression method, which includes multiple linear regression analysis, T test, F test, the coefficient of determination and donations predictor that are calculated using SPSS (Statistical Product and Service Solutions) software applications.
Supervisory Control of Fuzzy Discrete Event Systems Based on Agent
Institute of Scientific and Technical Information of China (English)
无
2006-01-01
FDES (fuzzy discrete event systems) can effectively represent a kind of complicated systems involving deterministic uncertainties and vagueness as well as human subjective observation and judgement from the view of discrete events, here the information system is divided into some independent intelligent entitative Agents. The concept of information processing state based on Agents was proposed. The processing state of Agent can be judged by some assistant observation parameters about the Agent and its environment around, and the transition among these states can be represented by FDES based on rules. In order to ensure the harmony of the Agents for information processing, its upstream and downstream buffers are considered in the modeling of the Agent system,and the supervisory controller based on FDES is constructed. The processing state of Agent can be adjusted by the supervisory controller to improve the stability of the system and the efficiency of resource utilization during the process according to the control policies. The result of its application was provided to illustrate the validity of the supervisory adjustment.
Solar-Based Fuzzy Intelligent Water Sprinkle System
Directory of Open Access Journals (Sweden)
Riza Muhida
2012-03-01
Full Text Available A solar-based intelligent water sprinkler system project that has been developed to ensure the effectiveness in watering the plant is improved by making the system automated. The control system consists of an electrical capacitance soil moisture sensor installed into the ground which is interfaced to a controller unit of Motorola 68HC11 Handy board microcontroller. The microcontroller was programmed based on the decision rules made using fuzzy logic approach on when to water the lawn. The whole system is powered up by the solar energy which is then interfaced to a particular type of irrigation timer for plant fertilizing schedule and rain detector through a simple design of rain dual-collector tipping bucket. The controller unit automatically disrupted voltage signals sent to the control valves whenever irrigation was not needed. Using this system we combined the logic implementation in the area of irrigation and weather sensing equipment, and more efficient water delivery can be made possible.
Energy Technology Data Exchange (ETDEWEB)
Djukanovic, M.B. [Inst. Nikola Tesla, Belgrade (Yugoslavia). Dept. of Power Systems; Calovic, M.S. [Univ. of Belgrade (Yugoslavia). Dept. of Electrical Engineering; Vesovic, B.V. [Inst. Mihajlo Pupin, Belgrade (Yugoslavia). Dept. of Automatic Control; Sobajic, D.J. [Electric Power Research Inst., Palo Alto, CA (United States)
1997-12-01
This paper presents an attempt of nonlinear, multivariable control of low-head hydropower plants, by using adaptive-network based fuzzy inference system (ANFIS). The new design technique enhances fuzzy controllers with self-learning capability for achieving prescribed control objectives in a near optimal manner. The controller has flexibility for accepting more sensory information, with the main goal to improve the generator unit transients, by adjusting the exciter input, the wicket gate and runner blade positions. The developed ANFIS controller whose control signals are adjusted by using incomplete on-line measurements, can offer better damping effects to generator oscillations over a wide range of operating conditions, than conventional controllers. Digital simulations of hydropower plant equipped with low-head Kaplan turbine are performed and the comparisons of conventional excitation-governor control, state-feedback optimal control and ANFIS based output feedback control are presented. To demonstrate the effectiveness of the proposed control scheme and the robustness of the acquired neuro-fuzzy controller, the controller has been implemented on a complex high-order non-linear hydrogenerator model.
Reliability modelling of repairable systems using Petri nets and fuzzy Lambda-Tau methodology
Energy Technology Data Exchange (ETDEWEB)
Knezevic, J.; Odoom, E.R
2001-07-01
A methodology is developed which uses Petri nets instead of the fault tree methodology and solves for reliability indices utilising fuzzy Lambda-Tau method. Fuzzy set theory is used for representing the failure rate and repair time instead of the classical (crisp) set theory because fuzzy numbers allow expert opinions, linguistic variables, operating conditions, uncertainty and imprecision in reliability information to be incorporated into the system model. Petri nets are used because unlike the fault tree methodology, the use of Petri nets allows efficient simultaneous generation of minimal cut and path sets.
An Evaluation of the Reliability of Complex Systems Using Shadowed Sets and Fuzzy Lifetime Data
Institute of Scientific and Technical Information of China (English)
Olgierd Hryniewicz
2006-01-01
In this paper, we consider the problem of the evaluation of system reliability using statistical data obtained from reliability tests of its elements, in which the lifetimes of elements are described using an exponential distribution. We assume that this lifetime data may be reported imprecisely and that this lack of precision may be described using fuzzy sets. As the direct application of the fuzzy sets methodology leads in this case to very complicated and time consuming calculations, we propose simple approximations of fuzzy numbers using shadowed sets introduced by Pedrycz (1998). The proposed methodology may be simply extended to the case of general lifetime probability distributions.
Tong, Shaocheng; Xu, Yinyin; Li, Yongming
2015-06-01
This paper is concerned with the problem of adaptive fuzzy decentralised output-feedback control for a class of uncertain stochastic nonlinear pure-feedback large-scale systems with completely unknown functions, the mismatched interconnections and without requiring the states being available for controller design. With the help of fuzzy logic systems approximating the unknown nonlinear functions, a fuzzy state observer is designed estimating the unmeasured states. Therefore, the nonlinear filtered signals are incorporated into the backstepping recursive design, and an adaptive fuzzy decentralised output-feedback control scheme is developed. It is proved that the filter system converges to a small neighbourhood of the origin based on appropriate choice of the design parameters. Simulation studies are included illustrating the effectiveness of the proposed approach.
The Temperature Fuzzy Control System of Barleythe Malt Drying Based on Microcontroller
Gao, Xiaoyang; Bi, Yang; Zhang, Lili; Chen, Jingjing; Yun, Jianmin
The control strategy of temperature and humidity in the beer barley malt drying chamber based on fuzzy logic control was implemented.Expounded in this paper was the selection of parameters for the structure of the regulatory device, as well as the essential design from control rules based on the existing experience. A temperature fuzzy controller was thus constructed using relevantfuzzy logic, and humidity control was achieved by relay, ensured the situation of the humidity to control the temperature. The temperature's fuzzy control and the humidity real-time control were all processed by single chip microcomputer with assembly program. The experimental results showed that the temperature control performance of this fuzzy regulatory system,especially in the ways of working stability and responding speed and so on,was better than normal used PID control. The cost of real-time system was inquite competitive position. It was demonstrated that the system have a promising prospect of extensive application.
Modelling of the automatic stabilization system of the aircraft course by a fuzzy logic method
Mamonova, T.; Syryamkin, V.; Vasilyeva, T.
2016-04-01
The problem of the present paper concerns the development of a fuzzy model of the system of an aircraft course stabilization. In this work modelling of the aircraft course stabilization system with the application of fuzzy logic is specified. Thus the authors have used the data taken for an ordinary passenger plane. As a result of the study the stabilization system models were realised in the environment of Matlab package Simulink on the basis of the PID-regulator and fuzzy logic. The authors of the paper have shown that the use of the method of artificial intelligence allows reducing the time of regulation to 1, which is 50 times faster than the time when standard receptions of the management theory are used. This fact demonstrates a positive influence of the use of fuzzy regulation.
Fuzzy Comprehensive Evaluation for Decision Making of Water Saving Irrigation System
Institute of Scientific and Technical Information of China (English)
LuoJin-yao; QiuYuan-feng
2003-01-01
A model of fuzzy comprehensive evaluation for water saving irrigation system (WSIS) decision making is proposed based on establishing an index system affected by six kinds of basic factors including qualitative and quantitative indexes. The object function of WSIS is set up by using the concept of fuzzy membership degree, it is to transform characteristic vector matrix into unify membership matrix and extending the least square method to the least of weighted distance square. The optimum weighted membership degree and the inferior weighted membership degree are used to solve the object function. This method effective solves the problem of classify for fuzzy attributive indexes and the problem of optimum for the set of different attributive indexes. A case study shows that the fuzzy comprehensive evaluation model is reasonable and effective in decision making for water saving irrigation system planning.
Han, Honggui; Wu, Xiao-Long; Qiao, Jun-Fei
2014-04-01
In this paper, a self-organizing fuzzy-neural-network with adaptive computation algorithm (SOFNN-ACA) is proposed for modeling a class of nonlinear systems. This SOFNN-ACA is constructed online via simultaneous structure and parameter learning processes. In structure learning, a set of fuzzy rules can be self-designed using an information-theoretic methodology. The fuzzy rules with high spiking intensities (SI) are divided into new ones. And the fuzzy rules with a small relative mutual information (RMI) value will be pruned in order to simplify the FNN structure. In parameter learning, the consequent part parameters are learned through the use of an ACA that incorporates an adaptive learning rate strategy into the learning process to accelerate the convergence speed. Then, the convergence of SOFNN-ACA is analyzed. Finally, the proposed SOFNN-ACA is used to model nonlinear systems. The modeling results demonstrate that this proposed SOFNN-ACA can model nonlinear systems effectively.
Fuzzy Comprehensive Evaluation for Decision Making of Water Saving Irrigation System
Institute of Scientific and Technical Information of China (English)
Luo Jin-yao; Qiu Yuan-feng
2003-01-01
A model of fuzzy comprehensive evaluation for water saving irrigation system (WSIS) decision making is proposed based on establishing an index system affected by six kinds of basic factors including qualitative and quantitative indexes. The object function of WSIS is set up by using the concept of fuzzy membership degree, it is to transform characteristic vector matrix into unify membership matrix and extending the least square method to the least of weighted dis tance square. The optimum weighted membership degree and the inferior weighted membership degree are used to solve the object function. This method effective solves the problem of classify for fuzzy attributive indexes and the problem of optimum for the set of different attributive indexes. A case study shows that the fuzzy comprehensive evaluation model is reasonable and effective in decision making for water saving irrigation system planning.
Study and simulation of a MPPT controller based on fuzzy logic controller for photovoltaic system
Energy Technology Data Exchange (ETDEWEB)
Belaidi, R.; Chikouche, A.; Fathi, M.; Mohand Kaci, G.; Smara, Z. [Unite de Developpement des Equipements Solaires (Algeria); Haddouche, A. [Universite Badji Mokhtar (Algeria)], E-mail: rachidi3434@yahoo.fr
2011-07-01
With the depletion of fossil fuels and the increasing concerns about the environment, renewable energies have become more and more attractive. Photovoltaic systems convert solar energy into electric energy through the use of photovoltaic cells. The aim of this paper is to compare the capacity of fuzzy logic and perturb and observe controllers in optimizing the control performance of photovoltaic systems. Simulations were performed using Matlab and Simulink and were analyzed to determine the effectiveness of both controllers and compare them. Results showed that the fuzzy controller has a better dynamic performance than the perturb and observe controller in terms of response time and damping characteristics. In addition, the fuzzy controller was found to better follow the maximum power point and to provide faster convergence and lower statistical error. This study demonstrated that the fuzzy controller gives a better performance than traditional controllers in optimizing the performance of photovoltaic systems.
Development of fuzzy control of a fuel cell generation system using FPGA
Institute of Scientific and Technical Information of China (English)
杨帆; 朱新坚; 李浩
2006-01-01
A fuzzy controller based on improved Generalized-Membership-Function(GMF) algorithm for a fuel cell generation system was introduced. Under the demands on control in application of the converter, a Field Programmable Gate Array (FPGA) realization method to manage the power flow was given. This control system based on the proposed modified GMF was proved to be a universal approximation system in theory. The fuzzy control technique was combined with Eletronic Design Automatic(EDA)technique and a paralleling fuzzy controller was implemented in FPGA. Paralleling fuzzy controller based on improved GMF algorithm was implemented on a Cyclone FPGA. The result of simulation based on QuartusⅡ confirmed the validity of the proposed method.
Energy Technology Data Exchange (ETDEWEB)
Kawamoto, S.; Takino, K. [Osaka Prefectural University, Osaka (Japan); Nojiri, K. [Kansai Electric Power Co. Inc., Osaka (Japan)
1996-11-20
In order to maintain the high reliability and security of electric power systems, the problem for stabilizing control is thought to be one of important subjects. Also, for the control of complex large scale nonlinear system like power system, decentralized control is preferable to centralized one. Therefore, the field of decentralized technology is much expected for the future power system. In this paper, a three-machine power model system is treated as an example, and first a decentralized system is constructed on a basis of the swing data by looking over the whole system at the largest generator bus. Next, the decentralized system is rewritten into a form of fuzzy system, and the stability theorem is applied to it. Then, feedback gains of the fuzzy control input can be determined under the guarantee of the stability, and the control input is given to the generator. Similarly, for the second generator, the decentralized system is obtained, and so on. Finally, it is shown that the decentralized control is constructed by using swing data based on three different faults, and is also available for another fault in the simulation. 10 refs., 10 figs., 1 tab.
On Stochastic Finite-Time Control of Discrete-Time Fuzzy Systems with Packet Dropout
Directory of Open Access Journals (Sweden)
Yingqi Zhang
2012-01-01
Full Text Available This paper is concerned with the stochastic finite-time stability and stochastic finite-time boundedness problems for one family of fuzzy discrete-time systems over networks with packet dropout, parametric uncertainties, and time-varying norm-bounded disturbance. Firstly, we present the dynamic model description studied, in which the discrete-time fuzzy T-S systems with packet loss can be described by one class of fuzzy Markovian jump systems. Then, the concepts of stochastic finite-time stability and stochastic finite-time boundedness and problem formulation are given. Based on Lyapunov function approach, sufficient conditions on stochastic finite-time stability and stochastic finite-time boundedness are established for the resulting closed-loop fuzzy discrete-time system with Markovian jumps, and state-feedback controllers are designed to ensure stochastic finite-time stability and stochastic finite-time boundedness of the class of fuzzy systems. The stochastic finite-time stability and stochastic finite-time boundedness criteria can be tackled in the form of linear matrix inequalities with a fixed parameter. As an auxiliary result, we also give sufficient conditions on the stochastic stability of the class of fuzzy T-S systems with packet loss. Finally, two illustrative examples are presented to show the validity of the developed methodology.
Designing a Fuzzy Expert System For Measuring E-Banking Service Quality
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Shohreh Nasri Nasrabadi
2015-06-01
Full Text Available With dramatic development of information technology and its widespread applications, increasing dependence on information technology and the increasing complexity of technologies and services which used in organizations, the management of these technologies and services is more difficult. Hence,wiith the development of IT-based banking services,including electronic banking services,the requirement methods which evaluate the quality of these services in the organization has been increased. Therefore, in this study, a comprehensive model for more accurate measurement of e-banking service quality through an extensive literature review and questions from experts in this field is presented using Fuzzy Delphi Technique. In this regard,after the identification of relevant dimensions and criteria, since the traditional scale can not accurately evaluate the e-banking service quality in the terms of the uncertainty , these dimensions and criteria are to be fuzzified,then the fuzzy expert system conceptual model is presented.Finally, a fuzzy expert system with 5 fuzzy module and a graphical user interface is provided in MATLAB.The fuzzy expert system indicate the final status of e-banking service quality and the main dimension.In this paper, the application of this system has been has been examined in the Sina bank. Finally by calculating expert system errors, optimum performance of designed fuzzy system has been approved which is evaluated by sina bank information.
Maintenance Policy for Multi-Component System with Fuzzy Lifetimes
Institute of Scientific and Technical Information of China (English)
赵瑞清; 高金伍
2003-01-01
The application of possibility theory to maintenance policies is proposed in this paper. The lifetime of a component is modeled as a fuzzy variable. Two types of replacement policies-block replacement and age replacement with fuzzy lifetimes are investigated. The theorems show that the long-run average fuzzy reward per unit time in both policies is just the expected cost per unit of time. In order to solve the proposed models, a hybrid intelligent algorithm is employed. Finally, numerical examples are provided for the sake of illustration.
Designing fuzzy inference system based on improved gradient descent method
Institute of Scientific and Technical Information of China (English)
Zhang Liquan; Shao Cheng
2006-01-01
The distribution of sampling data influences completeness of rule base so that extrapolating missing rules is very difficult. Based on data mining, a self-learning method is developed for identifying fuzzy model and extrapolating missing rules, by means of confidence measure and the improved gradient descent method. The proposed approach can not only identify fuzzy model, update its parameters and determine optimal output fuzzy sets simultaneously, but also resolve the uncontrollable problem led by the regions that data do not cover. The simulation results show the effectiveness and accuracy of the proposed approach with the classical truck backer-upper control problem verifying.
Uzoka, Faith-Michael Emeka; Obot, Okure; Barker, Ken; Osuji, J
2011-07-01
The task of medical diagnosis is a complex one, considering the level vagueness and uncertainty management, especially when the disease has multiple symptoms. A number of researchers have utilized the fuzzy-analytic hierarchy process (fuzzy-AHP) methodology in handling imprecise data in medical diagnosis and therapy. The fuzzy logic is able to handle vagueness and unstructuredness in decision making, while the AHP has the ability to carry out pairwise comparison of decision elements in order to determine their importance in the decision process. This study attempts to do a case comparison of the fuzzy and AHP methods in the development of medical diagnosis system, which involves basic symptoms elicitation and analysis. The results of the study indicate a non-statistically significant relative superiority of the fuzzy technology over the AHP technology. Data collected from 30 malaria patients were used to diagnose using AHP and fuzzy logic independent of one another. The results were compared and found to covary strongly. It was also discovered from the results of fuzzy logic diagnosis covary a little bit more strongly to the conventional diagnosis results than that of AHP.
Fuzzy neural network technique for system state forecasting.
Li, Dezhi; Wang, Wilson; Ismail, Fathy
2013-10-01
In many system state forecasting applications, the prediction is performed based on multiple datasets, each corresponding to a distinct system condition. The traditional methods dealing with multiple datasets (e.g., vector autoregressive moving average models and neural networks) have some shortcomings, such as limited modeling capability and opaque reasoning operations. To tackle these problems, a novel fuzzy neural network (FNN) is proposed in this paper to effectively extract information from multiple datasets, so as to improve forecasting accuracy. The proposed predictor consists of both autoregressive (AR) nodes modeling and nonlinear nodes modeling; AR models/nodes are used to capture the linear correlation of the datasets, and the nonlinear correlation of the datasets are modeled with nonlinear neuron nodes. A novel particle swarm technique [i.e., Laplace particle swarm (LPS) method] is proposed to facilitate parameters estimation of the predictor and improve modeling accuracy. The effectiveness of the developed FNN predictor and the associated LPS method is verified by a series of tests related to Mackey-Glass data forecast, exchange rate data prediction, and gear system prognosis. Test results show that the developed FNN predictor and the LPS method can capture the dynamics of multiple datasets effectively and track system characteristics accurately.
Direct Drive Electro-hydraulic Servo Control System Design with Self-Tuning Fuzzy PID Controller
Directory of Open Access Journals (Sweden)
Wang Yeqin
2013-06-01
Full Text Available According to the nonlinear and time-varying uncertainty characteristics of direct drive electro-hydraulic servo control system, a self-tuning fuzzy PID control method with speed change integral and differential ahead optimizing operator is put forward by combining fuzzy inference and traditional PID control in this paper.The rule of fuzzy logic is designed, the membership function of the fuzzy subsets is determined and lookup table method is used to correcte the PID parameters in real-time. Finally the simulation is conducted with the typical input signal, such as tracking step, sine etc. The simulation results show that，the self-tuning fuzzy PID control system can effectively improve the dynamic characteristic when the system is out of the range of the operating point compared with the traditional PID control system, there is obvious improvement in the indexes of rapidity, stability and accuracy, and fuzzy self-tuning PID Control is more robust, and more suitable for direct drive electro-hydraulic servo system.
Controller Design for Electric Power Steering System Using T-S Fuzzy Model Approach
Institute of Scientific and Technical Information of China (English)
Xin Li; Xue-Ping Zhao; Jie Chen
2009-01-01
Pressure ripples in electric power steering (EPS) systems can be caused by the phase lag between the driver's steering torque and steer angle, the nonlinear frictions, and the disturbances from road and sensor noise especially during high-frequency maneuvers. This paper investigates the use of the robust fuzzy control method for actively reducing pressure ripples for EPS systems. Remarkable progress on steering maneuverability is achieved. The EPS dynamics is described with an eight-order nonlinear state-space model and approximated by a Takagi-Sugeno (T-S) fuzzy model with time-varying delays and external disturbances. A stabilization approach is then presented for nonlinear time-delay systems through fuzzy state feedback controller in parallel distributed compensation (PDC) structure. The closed-loop stability conditions of EPS system with the fuzzy controller are parameterized in terms of the linear matrix inequality (LMI) problem. Simulations and experiments using the proposed robust fuzzy controller and traditional PID controller have been carried out for EPS systems. Both the simulation and experiment results show that the proposed fuzzy controller can reduce the torque ripples and allow us to have a good steering feeling and stable driving.
AN INTELLIGENT CONTROL SYSTEM BASED ON RECURRENT NEURAL FUZZY NETWORK AND ITS APPLICATION TO CSTR
Institute of Scientific and Technical Information of China (English)
JIA Li; YU Jinshou
2005-01-01
In this paper, an intelligent control system based on recurrent neural fuzzy network is presented for complex, uncertain and nonlinear processes, in which a recurrent neural fuzzy network is used as controller (RNFNC) to control a process adaptively and a recurrent neural network based on recursive predictive error algorithm (RNNM) is utilized to estimate the gradient information (ey)/(e)u for optimizing the parameters of controller.Compared with many neural fuzzy control systems, it uses recurrent neural network to realize the fuzzy controller. Moreover, recursive predictive error algorithm (RPE) is implemented to construct RNNM on line. Lastly, in order to evaluate the performance of theproposed control system, the presented control system is applied to continuously stirred tank reactor (CSTR). Simulation comparisons, based on control effect and output error,with general fuzzy controller and feed-forward neural fuzzy network controller (FNFNC),are conducted. In addition, the rates of convergence of RNNM respectively using RPE algorithm and gradient learning algorithm are also compared. The results show that the proposed control system is better for controlling uncertain and nonlinear processes.
A New Approach to Fault Diagnosis of Power Systems Using Fuzzy Reasoning Spiking Neural P Systems
Directory of Open Access Journals (Sweden)
Guojiang Xiong
2013-01-01
Full Text Available Fault diagnosis of power systems is an important task in power system operation. In this paper, fuzzy reasoning spiking neural P systems (FRSN P systems are implemented for fault diagnosis of power systems for the first time. As a graphical modeling tool, FRSN P systems are able to represent fuzzy knowledge and perform fuzzy reasoning well. When the cause-effect relationship between candidate faulted section and protective devices is represented by the FRSN P systems, the diagnostic conclusion can be drawn by means of a simple parallel matrix based reasoning algorithm. Three different power systems are used to demonstrate the feasibility and effectiveness of the proposed fault diagnosis approach. The simulations show that the developed FRSN P systems based diagnostic model has notable characteristics of easiness in implementation, rapidity in parallel reasoning, and capability in handling uncertainties. In addition, it is independent of the scale of power system and can be used as a reliable tool for fault diagnosis of power systems.
Small unmanned helicopter's attitude controller by an on-line adaptive fuzzy control system
Institute of Scientific and Technical Information of China (English)
GAO Tong-yue; RAO Jin-jun; GONG Zhen-bang; LUO Jun
2009-01-01
Since small unmanned helicopter flight attitude control process has strong time-varying characteristics and there are random disturbances, the conventional control methods with unchanged parameters are often unworkable. An on-line adaptive fuzzy control system (AFCS) was designed, in a way that does not depend on a process model of the plant or its approximation in the form of a Jacobian matrix. Neither is it necessary to know the desired response at each instant of time. AFCS implement a simultaneous on-line tuning of fuzzy rules and output scale of fuzzy control system. The two cascade controller design with an inner (attitude controller) and outer controller (navigation controller) of the small unmanned helicopter was proposed. At last, an attitude controller based on AFCS was implemented. The flight experiment showed that the proposed fuzzy logic controller provides quicker response, smaller overshoot, higher precision, robustness and adaptive ability. It satisfies the needs of autonomous flight.
Study of Fuzzy Neural Networks Model for System Condition Monitoring of AUV
Institute of Scientific and Technical Information of China (English)
WANG Yu-jia; ZHANG Ming-jun
2002-01-01
A structure equivalent model of fuzzy-neural networks for system condition monitoring is proposed, whose outputs are the condition or the degree of fault occurring in some parts of the system. This network is composed of six layers of neurons,which represent the membership functions, fuzzy rules and outputs respectively. The structure parameters and weights are obtained by processing off-line learning, and the fuzzy rules are derived from the experience. The results of the computer simulation for the autonomous underwater vehicle condition monitoring based on this fuzzy-neural networks show that the network is efficient and feasible in gaining the condition information or the degree of fault of the two main propellers.
Costal vulnerability systems-network using Fuzzy and Bayesian approaches
Taramelli, A.; Valentini, E.; Filipponi, F.; Nguyen Xuan, A.; Arosio, M.
2016-12-01
Marine drivers such as surge in the context of SLR, are threatening low-lying coastal plains. In order to deal with disturbances a deeper understanding of benefits deriving from ecosystem services assesment, management and planning (e.g. the role of dune ridges in surge mitigation and climate adaptation) can enhance the resilience of coastal systems. In this frame assessing the vulnerability is a key concern of many SOS (social, ecological, institutional) that deals with several challenges like the definition of Essential Variables (EVs) able to synthesize the required information, the assignment of different weight to be attributed to each considered variable, the selection of method for combining the relevant variables, etc.. To this end it is unclear how SLR, subsidence and erosion might affect coastal subsistence resources because of highly complex interactions and because of the subjective system of weighting many variables and their interaction within the systems. In this contribution, making the best use of many EO products, in situ data and modelling, we propose a multidimensional surge vulnerability assessment that aims at combining together geophysical and socioeconomic variable on the base of different approaches: 1) Fuzzy Logic; 2) Bayesian approach. The final goal is providing insight in understanding how to quantify regulating ecosystem services.
Fuzzy Controller based Neutral Current Harmonic Suppression in Distribution System
Directory of Open Access Journals (Sweden)
T.Guna Sekar
2013-10-01
Full Text Available Recent surveys of three-phase four-wire electric systems, buildings and industrial plants with computers and non-linear loads shows the excessive currents in the neutral conductor. This is mainly due to unbalancing system and non-linear loads. Third order harmonics are much dominant in the neutral conductor due to the presence of zero sequence components. In response to this concern, this paper presents a concept of series active filter scheme to suppress the neutral current harmonics to reduce the burden of the secondary of the distribution transformer. In this scheme, the series active filteris connected in series with the neutral conductor to eliminate the zero sequence components in the neutral conductor. In this paper, Fuzzy based controller is used to extract the harmonic component in the neutral conductor. The proposed method improves the overall performance of the system and eliminates the burden of the neutral conductor. To validate the proposed simulation results, a scale-down prototype experimental model is developed.
Fuzzy Aspect Based Opinion Classification System for Mining Tourist Reviews
Directory of Open Access Journals (Sweden)
Muhammad Afzaal
2016-01-01
Full Text Available Due to the large amount of opinions available on the websites, tourists are often overwhelmed with information and find it extremely difficult to use the available information to make a decision about the tourist places to visit. A number of opinion mining methods have been proposed in the past to identify and classify an opinion into positive or negative. Recently, aspect based opinion mining has been introduced which targets the various aspects present in the opinion text. A number of existing aspect based opinion classification methods are available in the literature but very limited research work has targeted the automatic aspect identification and extraction of implicit, infrequent, and coreferential aspects. Aspect based classification suffers from the presence of irrelevant sentences in a typical user review. Such sentences make the data noisy and degrade the classification accuracy of the machine learning algorithms. This paper presents a fuzzy aspect based opinion classification system which efficiently extracts aspects from user opinions and perform near to accurate classification. We conducted experiments on real world datasets to evaluate the effectiveness of our proposed system. Experimental results prove that the proposed system not only is effective in aspect extraction but also improves the classification accuracy.
Abou, Seraphin C
2012-03-01
In this paper, a new interpretation of intuitionistic fuzzy sets in the advanced framework of the Dempster-Shafer theory of evidence is extended to monitor safety-critical systems' performance. Not only is the proposed approach more effective, but it also takes into account the fuzzy rules that deal with imperfect knowledge/information and, therefore, is different from the classical Takagi-Sugeno fuzzy system, which assumes that the rule (the knowledge) is perfect. We provide an analytical solution to the practical and important problem of the conceptual probabilistic approach for formal ship safety assessment using the fuzzy set theory that involves uncertainties associated with the reliability input data. Thus, the overall safety of the ship engine is investigated as an object of risk analysis using the fuzzy mapping structure, which considers uncertainty and partial truth in the input-output mapping. The proposed method integrates direct evidence of the frame of discernment and is demonstrated through references to examples where fuzzy set models are informative. These simple applications illustrate how to assess the conflict of sensor information fusion for a sufficient cooling power system of vessels under extreme operation conditions. It was found that propulsion engine safety systems are not only a function of many environmental and operation profiles but are also dynamic and complex.
Hybrid Neuro-Fuzzy Systems for Software Development Effort Estimation
Directory of Open Access Journals (Sweden)
Rama Sree P
2012-12-01
Full Text Available The major prevailing challenges for Software Projects are Software Estimations like cost estimation, effort estimation, quality estimation and risk analysis. Though there are several algorithmiccost estimation models in practice, each model has its own pros and cons for estimation. There is still a need to find a model that gives accurate estimates. This paper is an attempt to experiment different types of Neuro-Fuzzy Models. Using the types of Neuro-Fuzzy Models for software effort prediction is a relatively unexplored area. Two case studies are used for this purpose. The first is based on NASA-93dataset and the other is based on Maxwell-62 dataset. The case studies were analyzed using six different criterions like Variance Accounted For (VAF, Mean Absolute Relative Error (MARE, VarianceAbsolute Relative Error (VARE, Mean Balance Relative Error (Mean BRE, Mean Magnitude Relative Error (MMRE and Prediction. From the results and from reasoning, it is concluded that Type BCompensationNeuro-Fuzzy Model with more fuzzy rules is best suitable for cases in which the datapoints are more linear. Type J Neuro-Fuzzy Model with more fuzzy rules is best suitable for cases in which the datapoints are not linear.
Extending the functional equivalence of radial basis function networks and fuzzy inference systems.
Hunt, K J; Haas, R; Murray-Smith, R
1996-01-01
We establish the functional equivalence of a generalized class of Gaussian radial basis function (RBFs) networks and the full Takagi-Sugeno model (1983) of fuzzy inference. This generalizes an existing result which applies to the standard Gaussian RBF network and a restricted form of the Takagi-Sugeno fuzzy system. The more general framework allows the removal of some of the restrictive conditions of the previous result.
Ant Colony System for a Fuzzy Adjacent Multiple-Level Warehouse Layout Problem
Institute of Scientific and Technical Information of China (English)
ZHANG Qiang; YU Ying-zi; LAI K K
2006-01-01
A warehouse layout problem where the warehouse has more than one level and both the distance from the cell to the receive/exit bay and demand of item types are fuzzy variables is proposed. The problem is to find a layout with the minimum transportation cost subject to adjacency and other constraints. A fuzzy expected value model is given and an ant colony system is designed to solve the problem. Computational results indicate the efficiency and effectiveness of the method.
Auto-control of pumping operations in sewerage systems by rule-based fuzzy neural networks
Chiang, Y.-M.; Chang, L.-C.; Tsai, M.-J.; Wang, Y. -F.; Chang, F.-J.
2011-01-01
Pumping stations play an important role in flood mitigation in metropolitan areas. The existing sewerage systems, however, are facing a great challenge of fast rising peak flow resulting from urbanization and climate change. It is imperative to construct an efficient and accurate operating prediction model for pumping stations to simulate the drainage mechanism for discharging the rainwater in advance. In this study, we propose two rule-based fuzzy neural networks, adaptive neuro-fuzzy infere...
Cheng, Jun; Park, Ju H; Wang, Hailing
2016-11-01
This paper addresses the problem of event-triggered H∞ control for a class of T-S fuzzy nonlinear systems. An improved event-triggered scheme (ETS) characterized by discrete sampling is proposed, where the time-derivative of the membership function is not required. To get conservative conditions, the deviation bound of asynchronous normalized membership functions is considered. By utilizing the non-quadratic fuzzy line-integral Lyapunov functions and a free-matrix-based integral inequality, novel criteria for stabilization analysis of T-S fuzzy nonlinear systems are established. Finally, a truck-trailer system is provided to show the effectiveness of the proposed theories.
FUZZY INFERENCE BASED LEAK ESTIMATION IN WATER PIPELINES SYSTEM
Directory of Open Access Journals (Sweden)
N. Lavanya
2015-01-01
Full Text Available Pipeline networks are the most widely used mode for transporting fluids and gases around the world. Leakage in this pipeline causes harmful effects when the flowing fluid/gas is hazardous. Hence the detection of leak becomes essential to avoid/minimize such undesirable effects. This paper presents the leak detection by spectral analysis methods in a laboratory pipeline system. Transient in the pressure signal in the pipeline is created by opening and closing the exit valve. These pressure variations are captured and power spectrum is obtained by using Fast Fourier Transform (FFT method and Filter Diagonalization Method (FDM. The leaks at various positions are simulated and located using these methods and the results are compared. In order to determine the quantity of leak a 2 × 1 fuzzy inference system is created using the upstream and downstream pressure as input and the leak size as the output. Thus a complete leak detection, localization and quantification are done by using only the pressure variations in the pipeline.
Annual Rainfall Forecasting by Using Mamdani Fuzzy Inference System
Fallah-Ghalhary, G.-A.; Habibi Nokhandan, M.; Mousavi Baygi, M.
2009-04-01
Long-term rainfall prediction is very important to countries thriving on agro-based economy. In general, climate and rainfall are highly non-linear phenomena in nature giving rise to what is known as "butterfly effect". The parameters that are required to predict the rainfall are enormous even for a short period. Soft computing is an innovative approach to construct computationally intelligent systems that are supposed to possess humanlike expertise within a specific domain, adapt themselves and learn to do better in changing environments, and explain how they make decisions. Unlike conventional artificial intelligence techniques the guiding principle of soft computing is to exploit tolerance for imprecision, uncertainty, robustness, partial truth to achieve tractability, and better rapport with reality. In this paper, 33 years of rainfall data analyzed in khorasan state, the northeastern part of Iran situated at latitude-longitude pairs (31°-38°N, 74°- 80°E). this research attempted to train Fuzzy Inference System (FIS) based prediction models with 33 years of rainfall data. For performance evaluation, the model predicted outputs were compared with the actual rainfall data. Simulation results reveal that soft computing techniques are promising and efficient. The test results using by FIS model showed that the RMSE was obtained 52 millimeter.
Adaptive Neuro-Fuzzy Inference System based control of six DOF robot manipulator
Directory of Open Access Journals (Sweden)
Srinivasan Alavandar
2008-01-01
Full Text Available The dynamics of robot manipulators are highly nonlinear with strong couplings existing between joints and are frequently subjected to structured and unstructured uncertainties. Fuzzy Logic Controller can very well describe the desired system behavior with simple “if-then” relations owing the designer to derive “if-then” rules manually by trial and error. On the other hand, Neural Networks perform function approximation of a system but cannot interpret the solution obtained neither check if its solution is plausible. The two approaches are complementary. Combining them, Neural Networks will allow learning capability while Fuzzy-Logic will bring knowledge representation (Neuro-Fuzzy. This paper presents the control of six degrees of freedom robot arm (PUMA Robot using Adaptive Neuro Fuzzy Inference System (ANFIS based PD plus I controller. Numerical simulation using the dynamic model of six DOF robot arm shows the effectiveness of the approach in trajectory tracking problems. Comparative evaluation with respect to PID, Fuzzy PD+I controls are presented to validate the controller design. The results presented emphasize that a satisfactory tracking precision could be achieved using ANFIS controller than PID and Fuzzy PD+I controllers
Adaptive neuro-fuzzy inference system for breath phase detection and breath cycle segmentation.
Palaniappan, Rajkumar; Sundaraj, Kenneth; Sundaraj, Sebastian
2017-07-01
The monitoring of the respiratory rate is vital in several medical conditions, including sleep apnea because patients with sleep apnea exhibit an irregular respiratory rate compared with controls. Therefore, monitoring the respiratory rate by detecting the different breath phases is crucial. This study aimed to segment the breath cycles from pulmonary acoustic signals using the newly developed adaptive neuro-fuzzy inference system (ANFIS) based on breath phase detection and to subsequently evaluate the performance of the system. The normalised averaged power spectral density for each segment was fuzzified, and a set of fuzzy rules was formulated. The ANFIS was developed to detect the breath phases and subsequently perform breath cycle segmentation. To evaluate the performance of the proposed method, the root mean square error (RMSE) and correlation coefficient values were calculated and analysed, and the proposed method was then validated using data collected at KIMS Hospital and the RALE standard dataset. The analysis of the correlation coefficient of the neuro-fuzzy model, which was performed to evaluate its performance, revealed a correlation strength of r = 0.9925, and the RMSE for the neuro-fuzzy model was found to equal 0.0069. The proposed neuro-fuzzy model performs better than the fuzzy inference system (FIS) in detecting the breath phases and segmenting the breath cycles and requires less rules than FIS. Copyright © 2017 Elsevier B.V. All rights reserved.
Application of fuzzy system theory in addressing the presence of uncertainties
Yusmye, A. Y. N.; Goh, B. Y.; Adnan, N. F.; Ariffin, A. K.
2015-02-01
In this paper, the combinations of fuzzy system theory with the finite element methods are present and discuss to deal with the uncertainties. The present of uncertainties is needed to avoid for prevent the failure of the material in engineering. There are three types of uncertainties, which are stochastic, epistemic and error uncertainties. In this paper, the epistemic uncertainties have been considered. For the epistemic uncertainty, it exists as a result of incomplete information and lack of knowledge or data. Fuzzy system theory is a non-probabilistic method, and this method is most appropriate to interpret the uncertainty compared to statistical approach when the deal with the lack of data. Fuzzy system theory contains a number of processes started from converting the crisp input to fuzzy input through fuzzification process and followed by the main process known as mapping process. The term mapping here means that the logical relationship between two or more entities. In this study, the fuzzy inputs are numerically integrated based on extension principle method. In the final stage, the defuzzification process is implemented. Defuzzification is an important process to allow the conversion of the fuzzy output to crisp outputs. Several illustrative examples are given and from the simulation, the result showed that propose the method produces more conservative results comparing with the conventional finite element method.
Application of fuzzy system theory in addressing the presence of uncertainties
Energy Technology Data Exchange (ETDEWEB)
Yusmye, A. Y. N. [Institute of Engineering Mathematics, Universiti Malaysia Perlis Kampus Pauh Putra, 02600, Arau, Perlis (Malaysia); Goh, B. Y.; Adnan, N. F.; Ariffin, A. K. [Department of Mechanical and Materials, Faculty of Engineering and Built Environment Universiti Kebangsaan Malaysia 43600 UKM Bangi, Selangor (Malaysia)
2015-02-03
In this paper, the combinations of fuzzy system theory with the finite element methods are present and discuss to deal with the uncertainties. The present of uncertainties is needed to avoid for prevent the failure of the material in engineering. There are three types of uncertainties, which are stochastic, epistemic and error uncertainties. In this paper, the epistemic uncertainties have been considered. For the epistemic uncertainty, it exists as a result of incomplete information and lack of knowledge or data. Fuzzy system theory is a non-probabilistic method, and this method is most appropriate to interpret the uncertainty compared to statistical approach when the deal with the lack of data. Fuzzy system theory contains a number of processes started from converting the crisp input to fuzzy input through fuzzification process and followed by the main process known as mapping process. The term mapping here means that the logical relationship between two or more entities. In this study, the fuzzy inputs are numerically integrated based on extension principle method. In the final stage, the defuzzification process is implemented. Defuzzification is an important process to allow the conversion of the fuzzy output to crisp outputs. Several illustrative examples are given and from the simulation, the result showed that propose the method produces more conservative results comparing with the conventional finite element method.
Santos, Sandra A; de Lima, Helano Póvoas; Massruhá, Silvia M F S; de Abreu, Urbano G P; Tomás, Walfrido M; Salis, Suzana M; Cardoso, Evaldo L; de Oliveira, Márcia Divina; Soares, Márcia Toffani S; Dos Santos, Antônio; de Oliveira, Luiz Orcírio F; Calheiros, Débora F; Crispim, Sandra M A; Soriano, Balbina M A; Amâncio, Christiane O G; Nunes, Alessandro Pacheco; Pellegrin, Luiz Alberto
2017-08-01
One of the most relevant issues in discussion worldwide nowadays is the concept of sustainability. However, sustainability assessment is a difficult task due to the complexity of factors involved in the natural world added to the human interference. In order to assess the sustainability of beef ranching in complex and uncertain tropical environment systems this paper describes a decision support system based on fuzzy rule-approach, the Sustainable Pantanal Ranch (SPR). This tool was built by a set of measurements and indicators integrated by fuzzy logic to evaluate the attributes of the three dimensions of sustainability. Indicators and decision rules, as well as scenario evaluations, were obtained from workshops involving multi-disciplinary team of experts. A Fuzzy Rule-Based System (FRBS) was developed to each attribute, dimension and general index. The essential parts of the FRBS are the knowledge database, rules and the inference engine. The FuzzyGen and WebFuzzy tools were developed to support the FRBS and both showed efficiency and low cost for digital applications. The results of each attribute, dimension and index were presented as radar graphs, showing the individual value (0-10) of each indicator. In the validation process using the WebFuzzy, different combinations of indicators were made for each attribute index to show the corresponding output, and which confirm the feasibility and usability of the tool. Copyright © 2017 Elsevier Ltd. All rights reserved.
Robust adaptive fuzzy neural tracking control for a class of unknown chaotic systems
Indian Academy of Sciences (India)
Abdurahman Kadir; Xing-Yuan Wang; Yu-Zhang Zhao
2011-06-01
In this paper, an adaptive fuzzy neural controller (AFNC) for a class of unknown chaotic systems is proposed. The proposed AFNC is comprised of a fuzzy neural controller and a robust controller. The fuzzy neural controller including a fuzzy neural network identiﬁer (FNNI) is the principal controller. The FNNI is used for online estimation of the controlled system dynamics by tuning the parameters of fuzzy neural network (FNN). The Gaussian function, a speciﬁc example of radial basis function, is adopted here as a membership function. So, the tuning parameters include the weighting factors in the consequent part and the means and variances of the Gaussian membership functions in the antecedent part of fuzzy implications. To tune the parameters online, the back-propagation (BP) algorithm is developed. The robust controller is used to guarantee the stability and to control the performance of the closed-loop adaptive system, which is achieved always. Finally, simulation results show that the AFNC can achieve favourable tracking performances.
A New Approach of Fuzzy Theory with Uncertainties in Geographic Information Systems
Directory of Open Access Journals (Sweden)
Mohammad Bazmara
2013-01-01
Full Text Available Until now, fuzzy logic has been extensively used to better analyze and design controllers for chemical processes. It has also been used for other applications like parameter estimation of nonlinear continuous-time systems but in general fuzzy logic has been intensively used for heuristics based system. Recently, fuzzy logic has been applied successfully in many areas where conventional model based approaches are difficult or not cost effective to implement. Mechanistic modeling of physical systems is often complicated by the presence of uncertainties. When models are used as purely predictive tools, uncertainty and variability lead to the need for assessment of the plausible range of model outcomes. A systematic uncertainty analysis provides insight into the level of confidence in model estimates, and can aid in assessing how various possible model estimates should be weighed. In this paper, generalized fuzzy α-cut is used to show the utility of fuzzy approach in uncertainty analysis of pollutant transport in ground water. Based on the concept of transformation method which is an extension of α-cuts, the approach shows superiority over conventional methods of uncertainty modeling. A 2-D groundwater transport model has been used to show the utility of this approach. Results are compared with commonly used probabilistic method and normal Fuzzy alpha-cut technique. In order to provide a basis for comparison between the two approaches, the shape of the membership functions used in the fuzzy methods are the same as the shape of the probability density function used in the Monte-Carlo method. The extended fuzzy α-cut technique presents a strong alternative to the conventional approach.
A Fuzzy Logic-Based Video Subtitle and Caption Coloring System
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Mohsen Davoudi
2012-01-01
Full Text Available An approach has been proposed for automatic adaptive subtitle coloring using fuzzy logic-based algorithm. This system changes the color of the video subtitle/caption to “pleasant” color according to color harmony and the visual perception of the image background colors. In the fuzzy analyzer unit, using RGB histograms of background image, the R, G, and B values for the color of the subtitle/caption are computed using fixed fuzzy IF-THEN rules fully driven from the color harmony theories to satisfy complementary color and subtitle-background color harmony conditions. A real-time hardware structure has been proposed for implementation of the front-end processing unit as well as the fuzzy analyzer unit.
Fuzzy Logic Control for Semi-Active Suspension System of Tracked Vehicle
Institute of Scientific and Technical Information of China (English)
管继富; 顾亮; 侯朝桢; 王国丽
2004-01-01
The model of half a tracked vehicle semi-active suspension is established. The fuzzy logic controller of the semi-active suspension system is constructed. The acceleration of driver's seat and its time derivative are used as the inputs of the fuzzy logic controller, and the fuzzy logic controller output determines the semi-active suspension controllable damping force. The fuzzy logic controller is to minimize the mean square root of acceleration of the driver's seat. The control forces of controllable dampers behind the first road wheel are obtained by time delay, and the delay times are determined by the vehicle speed and axles distances. The simulation results show that this control method can decrease the acceleration of driver's seat and the suspension travel of the first road wheel, the ride quality is improved obviously.
Directory of Open Access Journals (Sweden)
R. Ezzati
2014-09-01
Full Text Available We propose an approach for computing an approximate nonnegative symmetric solution of some fully fuzzy linear system of equations, where the components of the coefficient matrix and the right hand side vector are nonnegative fuzzy numbers, considering equality of the median intervals of the left and right hand sides of the system. We convert the m×n fully fuzzy linear system to two m×n real linear systems, one being related to the cores and the other being concerned with spreads of the solution. We propose an approach for solving the real systems using the modified Huang method of the Abaffy-Broyden-Spedicato (ABS class of algorithms. An appropriate constrained least squares problem is solved when the solution does not satisfy nonnegative fuzziness conditions, that is, when the obtained solution vector for the core system includes a negative component, or the solution of the spread system has at least one negative component, or there exists an index for which the component of the spread is greater than the corresponding component of the core. As a special case, we discuss fuzzy systems with the components of the coefficient matrix as real crisp numbers. We finally present two computational algorithms and illustrate their effectiveness by solving some randomly generated consistent as well as inconsistent systems.
Adaptive fuzzy-neural-network control for maglev transportation system.
Wai, Rong-Jong; Lee, Jeng-Dao
2008-01-01
A magnetic-levitation (maglev) transportation system including levitation and propulsion control is a subject of considerable scientific interest because of highly nonlinear and unstable behaviors. In this paper, the dynamic model of a maglev transportation system including levitated electromagnets and a propulsive linear induction motor (LIM) based on the concepts of mechanical geometry and motion dynamics is developed first. Then, a model-based sliding-mode control (SMC) strategy is introduced. In order to alleviate chattering phenomena caused by the inappropriate selection of uncertainty bound, a simple bound estimation algorithm is embedded in the SMC strategy to form an adaptive sliding-mode control (ASMC) scheme. However, this estimation algorithm is always a positive value so that tracking errors introduced by any uncertainty will cause the estimated bound increase even to infinity with time. Therefore, it further designs an adaptive fuzzy-neural-network control (AFNNC) scheme by imitating the SMC strategy for the maglev transportation system. In the model-free AFNNC, online learning algorithms are designed to cope with the problem of chattering phenomena caused by the sign action in SMC design, and to ensure the stability of the controlled system without the requirement of auxiliary compensated controllers despite the existence of uncertainties. The outputs of the AFNNC scheme can be directly supplied to the electromagnets and LIM without complicated control transformations for relaxing strict constrains in conventional model-based control methodologies. The effectiveness of the proposed control schemes for the maglev transportation system is verified by numerical simulations, and the superiority of the AFNNC scheme is indicated in comparison with the SMC and ASMC strategies.
STUDY ON FUZZY SELF-LEARNING CONTROL SYSTEM FOR SHIP STEERING
Institute of Scientific and Technical Information of China (English)
LIU Qing; WU Xiu-heng; ZOU Zao-jian
2004-01-01
Fuzzy control has shown success in some application areas and emerged as an alternative to some conventional control schemes. There are also some drawbacks in this approach, for example it is hard to justify the choice of fuzzy controller parameters and control rules, and control precision is low, and so on. Fuzzy control is developing towards self-learning and adaptive. The ship steering motion is a nonlinear, coupling, time-delay complicated system. How to control it effectively is the problem that many scholars are studying. In this paper, based on the repeated control of the robot, the self-learning arithmetic was worked out. The arithmetic was realized in fuzzy logic way and used in cargo steering. It is the first time for the arithmetic to be used in cargo steering. Our simulation results show that the arithmetic is effective and has several potential advantages over conventional fuzzy control.This work lays a foundation in modeling and analyzing the fuzzy learning control system.
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Jian Guo
2013-01-01
Full Text Available Information system (IS project selection is of critical importance to every organization in dynamic competing environment. The aim of this paper is to develop a hybrid multicriteria group decision making approach based on intuitionistic fuzzy theory for IS project selection. The decision makers’ assessment information can be expressed in the form of real numbers, interval-valued numbers, linguistic variables, and intuitionistic fuzzy numbers (IFNs. All these evaluation pieces of information can be transformed to the form of IFNs. Intuitionistic fuzzy weighted averaging (IFWA operator is utilized to aggregate individual opinions of decision makers into a group opinion. Intuitionistic fuzzy entropy is used to obtain the entropy weights of the criteria. TOPSIS method combined with intuitionistic fuzzy set is proposed to select appropriate IS project in group decision making environment. Finally, a numerical example for information system projects selection is given to illustrate application of hybrid multi-criteria group decision making (MCGDM method based on intuitionistic fuzzy theory and TOPSIS method.
Indirect adaptive control of nonlinear systems based on bilinear neuro-fuzzy approximation.
Boutalis, Yiannis; Christodoulou, Manolis; Theodoridis, Dimitrios
2013-10-01
In this paper, we investigate the indirect adaptive regulation problem of unknown affine in the control nonlinear systems. The proposed approach consists of choosing an appropriate system approximation model and a proper control law, which will regulate the system under the certainty equivalence principle. The main difference from other relevant works of the literature lies in the proposal of a potent approximation model that is bilinear with respect to the tunable parameters. To deploy the bilinear model, the components of the nonlinear plant are initially approximated by Fuzzy subsystems. Then, using appropriately defined fuzzy rule indicator functions, the initial dynamical fuzzy system is translated to a dynamical neuro-fuzzy model, where the indicator functions are replaced by High Order Neural Networks (HONNS), trained by sampled system data. The fuzzy output partitions of the initial fuzzy components are also estimated based on sampled data. This way, the parameters to be estimated are the weights of the HONNs and the centers of the output partitions, both arranged in matrices of appropriate dimensions and leading to a matrix to matrix bilinear parametric model. Based on the bilinear parametric model and the design of appropriate control law we use a Lyapunov stability analysis to obtain parameter adaptation laws and to regulate the states of the system. The weight updating laws guarantee that both the identification error and the system states reach zero exponentially fast, while keeping all signals in the closed loop bounded. Moreover, introducing a method of "concurrent" parameter hopping, the updating laws are modified so that the existence of the control signal is always assured. The main characteristic of the proposed approach is that the a priori experts information required by the identification scheme is extremely low, limited to the knowledge of the signs of the centers of the fuzzy output partitions. Therefore, the proposed scheme is not
Prediction of Earth rotation parameters by fuzzy inference systems
Akyilmaz, O.; Kutterer, H.
2004-09-01
The short-term prediction of Earth rotation parameters (ERP) (length-of-day and polar motion) is studied up to 10 days by means of ANFIS (adaptive network based fuzzy inference system). The prediction is then extended to 40 days into the future by using the formerly predicted values as input data. The ERP C04 time series with daily values from the International Earth Rotation Service (IERS) serve as the data base. Well-known effects in the ERP series, such as the impact of the tides of the solid Earth and the oceans or seasonal variations of the atmosphere, were removed a priori from the C04 series. The residual series were used for both training and validation of the network. Different network architectures are discussed and compared in order to optimize the network solution. The results of the prediction are analyzed and compared with those of other methods. Short-term ERP values predicted by ANFIS show root-mean-square errors which are equal to or even lower than those from the other considered methods. The presented method is easy to use.
Method study on fuzzy-PID adaptive control of electric-hydraulic hitch system
Li, Mingsheng; Wang, Liubu; Liu, Jian; Ye, Jin
2017-03-01
In this paper, fuzzy-PID adaptive control method is applied to the control of tractor electric-hydraulic hitch system. According to the characteristics of the system, a fuzzy-PID adaptive controller is designed and the electric-hydraulic hitch system model is established. Traction control and position control performance simulation are carried out with the common PID control method. A field test rig was set up to test the electric-hydraulic hitch system. The test results showed that, after the fuzzy-PID adaptive control is adopted, when the tillage depth steps from 0.1m to 0.3m, the system transition process time is 4s, without overshoot, and when the tractive force steps from 3000N to 7000N, the system transition process time is 5s, the system overshoot is 25%.
Analyses and Simulation of Fuzzy Logic Control for Suspension System of a Track Vehicle
Institute of Scientific and Technical Information of China (English)
YU Yang; WEI Xue-xia; ZHANG Yong-fa
2008-01-01
The vibration caused by terrible road excitation affects the ride quality and safety of track vehicles. The vibration control of suspension systems is a very important factor for modern track vehicles. A fuzzy logic control for suspension system of a track vehicle is presented. A mechanical model and a system of differential equations of motion taking account of the mass of loading wheel are established. Then the fuzzy logic control is applied to control the vibration of suspension system of track vehicles for sine signal and random road surfaces. Numerical simulation shows that the maximum acceleration of suspension system can be reduced to 44% of the original value for sine signal road surface, and the mean square root of acceleration of suspension system can be reduced to 21% for random road surface. Therefore, the proposed fuzzy logic control is an efficient method for the suspension systems of track vehicles.
Boutalis, Yiannis; Kottas, Theodore; Christodoulou, Manolis A
2014-01-01
Presenting current trends in the development and applications of intelligent systems in engineering, this monograph focuses on recent research results in system identification and control. The recurrent neurofuzzy and the fuzzy cognitive network (FCN) models are presented. Both models are suitable for partially-known or unknown complex time-varying systems. Neurofuzzy Adaptive Control contains rigorous proofs of its statements which result in concrete conclusions for the selection of the design parameters of the algorithms presented. The neurofuzzy model combines concepts from fuzzy systems and recurrent high-order neural networks to produce powerful system approximations that are used for adaptive control. The FCN model stems from fuzzy cognitive maps and uses the notion of “concepts” and their causal relationships to capture the behavior of complex systems. The book shows how, with the benefit of proper training algorithms, these models are potent system emulators suitable for use in engineering s...
Fuzzy logic based anaesthesia monitoring systems for the detection of absolute hypovolaemia.
Mansoor Baig, Mirza; Gholamhosseini, Hamid; Harrison, Michael J
2013-07-01
Anaesthesia monitoring involves critical diagnostic tasks carried out amongst lots of distractions. Computers are capable of handling large amounts of data at high speed and therefore decision support systems and expert systems are now capable of processing many signals simultaneously in real time. We have developed two fuzzy logic based anaesthesia monitoring systems; a real time smart anaesthesia alarm system (RT-SAAM) and fuzzy logic monitoring system-2 (FLMS-2), an updated version of FLMS for the detection of absolute hypovolaemia. This paper presents the design aspects of these two systems which employ fuzzy logic techniques to detect absolute hypovolaemia, and compares their performances in terms of usability and acceptability. The interpretation of these two systems of absolute hypovolaemia was compared with clinicians' assessments using Kappa analysis, RT-SAAM K=0.62, FLMS-2 K=0.75; an improvement in performance by FLMS-2.
DEFF Research Database (Denmark)
Hassan, Saima; Ahmadieh Khanesar, Mojtaba; Hajizadeh, Amin
2017-01-01
Learning of fuzzy parameters for system modeling using evolutionary algorithms is an interesting topic. In this paper, two optimal design and tuning of Interval type-2 fuzzy logic system are proposed using hybrid learning algorithms. The consequent parameters of the interval type-2 fuzzy logic....../D) in the second hybrid algorithm. Root mean square error and maximum absolute error as the two accuracy objective are utilized to find the Pareto-optimal solution with the MOPSO and MOEA/D respectively. The proposed hybrid multi-objective designs of the interval type-2 fuzzy logic system are utilized...
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Shruti Jain
2015-06-01
Full Text Available In this paper a well defined method for the design of JNK pathway for epidermal growth factor/ insulin using fuzzy system using operational transconductance amplifier was discussed. Fuzzy system includes fuzzification of the input variables, application of the fuzzy operator (AND or OR in the antecedent, implication from the antecedent to the consequent, aggregation of the consequents across the rules, and defuzzfication. Fuzzy system with various electrical parameters for different voltages of OTA with different membership function was found. Results with 3V were the best.
Operation of SCINDA Receiver at the University of Calcutta and Space Weather Studies
2015-02-18
15. SUBJECT TERMS Space weather , Ionospheric Irregularities, scintillation 16. SECURITY CLASSIFICATION OF: 17. LIMITATION OF ABSTRACT...using the SCINDA data. 15. SUBJECT TERMS Space weather , Ionospheric Irregularities, scintillation 16. SECURITY CLASSIFICATION OF: 17. LIMITATION OF... Space Weather Studies Principal Investigator: Dr. Ashik Paul Institute of Radio Physics and Electronics University of Calcutta ashik_paul
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Leandro Ferreira
2012-01-01
Full Text Available Cloacal temperature (CT of broiler chickens is an important parameter to classify its comfort status; therefore its prediction can be used as decision support to turn on acclimatization systems. The aim of this research was to develop and validate a system using the fuzzy set theory for CT prediction of broiler chickens. The fuzzy system was developed based on three input variables: air temperature (T, relative humidity (RH and air velocity (V. The output variable was the CT. The fuzzy inference system was performed via Mamdani's method which consisted in 48 rules. The defuzzification was done using center of gravity method. The fuzzy system was developed using MAPLE® 8. Experimental results, used for validation, showed that the average standard deviation between simulated and measured values of CT was 0.13°C. The proposed fuzzy system was found to satisfactorily predict CT based on climatic variables. Thus, it could be used as a decision support system on broiler chicken growth.A temperatura cloacal (TC de frangos de corte é um importante parâmetro para classificar a sua condição de conforto, portanto, a sua predição pode ser usada no suporte à decisão de acionamento de sistemas de climatização. Objetivou-se com a presente pesquisa desenvolver e validar um sistema, utilizando a teoria dos conjuntos fuzzy para predição da TC de frangos de corte. O sistema fuzzy foi desenvolvido com base em três variáveis de entrada: temperatura do ar (T, umidade relativa (UR e velocidade do ar (V, tendo, como variável de saída, a TC. A inferência fuzzy foi realizada por meio do método tipo Mamdani, que consistiu na elaboração de 48 regras e a defuzzificação por meio do método do Centro de Gravidade. O sistema fuzzy foi desenvolvido no ambiente computacional MAPLE® 8. Resultados experimentais, usados para a validação, mostraram que o desvio padrão médio entre os valores simulados e medidos de TC foi de 0,13°C. O sistema fuzzy
Research of robust adaptive trajectory linearization control based on T-S fuzzy system
Institute of Scientific and Technical Information of China (English)
Jiang Changsheng; Zhang Chunyu; Zhu Liang
2008-01-01
A robust adaptive trajectory linearization control (RATLC) algorithm for a class of nonlinear systems with uncertainty and disturbance based on the T-S fuzzy system is presented. The unknown disturbance and uncertainty are estimated by the T-S fuzzy system, and a robust adaptive control law is designed by the Lyapunov theory. Irrespective of whether the dimensions of the system and the rules of the fuzzy system are large or small, there is only one parameter adjusting on line. Uniformly ultimately boundedness of all signals of the composite closed-loop system are proved by theory analysis. Finally, a numerical example is studied based on the proposed method. The simulation results demonstrate the effectiveness and robustness of the control scheme.
Robust Adaptive Fuzzy Output Tracking Control for a Class of Twin-Roll Strip Casting Systems
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Yu-Jun Zhang
2017-01-01
Full Text Available This paper is concerned with the adaptive fuzzy control problem for a class of twin-roll strip casting systems. By using fuzzy logic systems (FLSs to approximate the compounded nonlinear functions, a novel robust output tracking controller with adaptation laws is designed based on the high gain observer. First, the nonlinear dynamic equations for the roll gap and the molten steel level are constructed, respectively. Then, the mean value theorem is employed to transform the nonaffine nonlinear systems to the corresponding affine nonlinear systems. Moreover, it is also proved that all the closed-loop signals are bounded and the systems output tracking errors can converge to the desired neighborhoods of the origin via the Lyapunov stability analysis. Finally, simulation results, based on semiexperimental system dynamic model and parameters, are worked out to show the effectiveness of the proposed adaptive fuzzy design method.
Control of suspended low-gravity simulation system based on self-adaptive fuzzy PID
Chen, Zhigang; Qu, Jiangang
2017-09-01
In this paper, an active suspended low-gravity simulation system is proposed to follow the vertical motion of the spacecraft. Firstly, working principle and mathematical model of the low-gravity simulation system are shown. In order to establish the balance process and suppress the strong position interference of the system, the idea of self-adaptive fuzzy PID control strategy is proposed. It combines the PID controller with a fuzzy controll strategy, the control system can be automatically adjusted by changing the proportional parameter, integral parameter and differential parameter of the controller in real-time. At last, we use the Simulink tools to verify the performance of the controller. The results show that the system can reach balanced state quickly without overshoot and oscillation by the method of the self-adaptive fuzzy PID, and follow the speed of 3m/s, while simulation degree of accuracy of system can reach to 95.9% or more.
Chattering free adaptive fuzzy terminal sliding mode control for second order nonlinear system
Institute of Scientific and Technical Information of China (English)
Jinkun LIU; Fuchun SUN
2006-01-01
A novel fuzzy terminal sliding mode control (FTSMC) scheme is proposed for position tracking of a class of second-order nonlinear uncertain system. In the proposed scheme, we integrate input-output linearization technique to cancel the nonlinearities. By using a function-augmented sliding hyperplane, it is guaranteed that the output tracking error converges to zero in finite time which can be set arbitrarily. The proposed scheme eliminates reaching phase problem, so that the closed-loop system always shows invariance property to parameter uncertainties. Fuzzy logic systems are used to approximate the unknown system functions and switch item. Robust adaptive law is proposed to reduce approximation errors between true nonlinear functions and fuzzy systems, thus chattering phenomenon can be eliminated. Stability of the proposed control scheme is proved and the scheme is applied to an inverted pendulum system. Simulation studies are provided to confirm performance and effectiveness of the proposed control approach.
Design of the Fuzzy Control Systems Based on Genetic Algorithm for Intelligent Robots
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Gyula Mester
2014-07-01
Full Text Available This paper gives the structure optimization of fuzzy control systems based on genetic algorithm in the MATLAB environment. The genetic algorithm is a powerful tool for structure optimization of the fuzzy controllers, therefore, in this paper, integration and synthesis of fuzzy logic and genetic algorithm has been proposed. The genetic algorithms are applied for fuzzy rules set, scaling factors and membership functions optimization. The fuzzy control structure initial consist of the 3 membership functions and 9 rules and after the optimization it is enough for the 4 DOF SCARA Robot control to compensate for structured and unstructured uncertainty. Fuzzy controller with the generalized bell membership functions can provide better dynamic performance of the robot then with the triangular membership functions. The proposed joint-space controller is computationally simple and had adaptability to a sudden change in the dynamics of the robot. Results of the computer simulation applied to the 4 DOF SCARA Robot show the validity of the proposed method.
HYBRID SYSTEM BASED FUZZY-PID CONTROL SCHEMES FOR UNPREDICTABLE PROCESS
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M.K. Tan
2011-07-01
Full Text Available In general, the primary aim of polymerization industry is to enhance the process operation in order to obtain high quality and purity product. However, a sudden and large amount of heat will be released rapidly during the mixing process of two reactants, i.e. phenol and formalin due to its exothermic behavior. The unpredictable heat will cause deviation of process temperature and hence affect the quality of the product. Therefore, it is vital to control the process temperature during the polymerization. In the modern industry, fuzzy logic is commonly used to auto-tune PID controller to control the process temperature. However, this method needs an experienced operator to fine tune the fuzzy membership function and universe of discourse via trial and error approach. Hence, the setting of fuzzy inference system might not be accurate due to the human errors. Besides that, control of the process can be challenging due to the rapid changes in the plant parameters which will increase the process complexity. This paper proposes an optimization scheme using hybrid of Q-learning (QL and genetic algorithm (GA to optimize the fuzzy membership function in order to allow the conventional fuzzy-PID controller to control the process temperature more effectively. The performances of the proposed optimization scheme are compared with the existing fuzzy-PID scheme. The results show that the proposed optimization scheme is able to control the process temperature more effectively even if disturbance is introduced.
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Jing Lu
2014-11-01
Full Text Available We propose a weather prediction model in this article based on neural network and fuzzy inference system (NFIS-WPM, and then apply it to predict daily fuzzy precipitation given meteorological premises for testing. The model consists of two parts: the first part is the “fuzzy rule-based neural network”, which simulates sequential relations among fuzzy sets using artificial neural network; and the second part is the “neural fuzzy inference system”, which is based on the first part, but could learn new fuzzy rules from the previous ones according to the algorithm we proposed. NFIS-WPM (High Pro and NFIS-WPM (Ave are improved versions of this model. It is well known that the need for accurate weather prediction is apparent when considering the benefits. However, the excessive pursuit of accuracy in weather prediction makes some of the “accurate” prediction results meaningless and the numerical prediction model is often complex and time-consuming. By adapting this novel model to a precipitation prediction problem, we make the predicted outcomes of precipitation more accurate and the prediction methods simpler than by using the complex numerical forecasting model that would occupy large computation resources, be time-consuming and which has a low predictive accuracy rate. Accordingly, we achieve more accurate predictive precipitation results than by using traditional artificial neural networks that have low predictive accuracy.
Moment Method Based on Fuzzy Reliability Sensitivity Analysis for a Degradable Structural System
Institute of Scientific and Technical Information of China (English)
Song Jun; Lu Zhenzhou
2008-01-01
For a degradable structural system with fuzzy failure region, a moment method based on fuzzy reliability sensitivity algorithm is presented. According to the value assignment of porformance function, the integral region for calculating the fuzzy failure probability is first split into a series of subregions in which the membership function values of the performance function within the fuzzy failure region can be approximated by a set of constants. The fuzzy failure probability is then transformed into a sum of products oftbe random failure probabilities and the approximate constants of the membership function in the subregions. Furthermore, the fuzzy reliability sensitivity analysis is transformed into a series of random reliability sensitivity analysis, and the random reliability sensitivity can be obtained by the constructed moment method. The primary advantages of the presented method include higher efficiency for implicit performance function with low and medium dimensionality and wide applicability to multiple failure modes and nonnormal basic random variables. The limitation is that the required computation effort grows exponentially with the increase of dimensionality of the basic random vari-able; hence, it is not suitable for high dimensionality problem. Compared with the available methods, the presented one is pretty com-petitive in the case that the dimensionality is lower than 10. The presented examples are used to verify the advantages and indicate the limitations.
Carlsson, Christer; Fullér, Robert
2004-01-01
Fuzzy Logic in Management demonstrates that difficult problems and changes in the management environment can be more easily handled by bringing fuzzy logic into the practice of management. This explicit theme is developed through the book as follows: Chapter 1, "Management and Intelligent Support Technologies", is a short survey of management leadership and what can be gained from support technologies. Chapter 2, "Fuzzy Sets and Fuzzy Logic", provides a short introduction to fuzzy sets, fuzzy relations, the extension principle, fuzzy implications and linguistic variables. Chapter 3, "Group Decision Support Systems", deals with group decision making, and discusses methods for supporting the consensus reaching processes. Chapter 4, "Fuzzy Real Options for Strategic Planning", summarizes research where the fuzzy real options theory was implemented as a series of models. These models were thoroughly tested on a number of real life investments, and validated in 2001. Chapter 5, "Soft Computing Methods for Reducing...
Dzung Nguyen, Sy; Kim, Wanho; Park, Jhinha; Choi, Seung-Bok
2017-04-01
Vibration control systems using smart dampers (SmDs) such as magnetorheological and electrorheological dampers (MRD and ERD), which are classified as the integrated structure-SmD control systems (ISSmDCSs), have been actively researched and widely used. This work proposes a new controller for a class of ISSmDCSs in which high accuracy of SmD models as well as increment of control ability to deal with uncertainty and time delay are to be expected. In order to achieve this goal, two formualtion steps are required; a non-parametric SmD model based on an adaptive neuro-fuzzy inference system (ANFIS) and a novel fuzzy sliding mode controller (FSMC) which can weaken the model error of the ISSmDCSs and hence provide enhanced vibration control performances. As for the formulation of the proposed controller, first, an ANFIS controller is desgned to identify SmDs using the improved control algorithm named improved establishing neuro-fuzzy system (establishing neuro-fuzzy system). Second, a new control law for the FSMC is designed via Lyapunov stability analysis. An application to a semi-active MRD vehicle suspension system is then undertaken to illustrate and evaluate the effectiveness of the proposed control method. It is demonstrated through an experimental realization that the FSMC proposed in this work shows superior vibration control performance of the vehicle suspension compared to other surveyed controller which have similar structures to the FSMC, such as fuzzy logic and sliding mode control.
Fuzzy coordinator compensation for balancing control of cart-seesaw system
Lin, J.; Guo, S.-Y.; Chang, Julian
2011-12-01
In contrast with fully controllable systems, a super articulated mechanical system (SAMS) is a controlled underactuated mechanical system in which the dimensions of the configuration space exceed the dimensions of the control input space. The control of the cart-seesaw system is especially difficult since it is an underactuated mechanism (three degrees of freedom and only two inputs). This research develops a balancing approach for a novel SAMS model, called the cart-seesaw system, using fuzzy logic and fuzzy coordinator compensation to drive the sliding carts and keep the seesaw angle close to zero in the equilibrium state. Experimental results indicate that utilizing the proposed control methodology significantly enhances the performance. Moreover, the presentation of the fuzzy balancing controller is not considerably affected by changes in the environmental parameters, which demonstrates the effectiveness of the fuzzy controller in minimizing the seesaw tilt angle in the time domain, although the system is caused by unpredicted loading variation. Moreover, the experimental results indicate the usefulness and robustness of the proposed fuzzy control methodology. Furthermore, the proposed software/hardware platform can be beneficial for standardizing laboratory equipment and developing amusement apparatus.
DEVELOPMENT OF THE CROSS-COUPLING PHENOMENA OF MIMO FLIGHT SYSTEM USING FUZZY LOGIC CONTROLLER
Directory of Open Access Journals (Sweden)
MUNA H. SALEH
2010-03-01
Full Text Available This paper describes the performance of a simplified dynamic controller with fuzzy logic controllers. The six degree-of-freedom simulation study focuses on the results with and without fuzzy logic controller. One area of interest is the performance of a simulated the cross coupling effect. The controller uses explicit models to produce the desired commands. In this paper the effect of the cross-coupling between channels on the overall performance of the flight system has been considered. Two fuzzy controllers have been added to the system to improve its performance. This paper presents the development and simulation of a modified system is presented using MatLab Simulink. Also it focuses on the use of fuzzy logic controller in model-based control of multiple-input, multiple-output systems. Here, we address the question of how the overall performance of the system is affected when both fuzzy logic controllers are applied at the same time. Simulation and experimental results of a flight system , as an illustrative example, are presented.
Realworld maximum power point tracking simulation of PV system based on Fuzzy Logic control
Othman, Ahmed M.; El-arini, Mahdi M. M.; Ghitas, Ahmed; Fathy, Ahmed
2012-12-01
In the recent years, the solar energy becomes one of the most important alternative sources of electric energy, so it is important to improve the efficiency and reliability of the photovoltaic (PV) systems. Maximum power point tracking (MPPT) plays an important role in photovoltaic power systems because it maximize the power output from a PV system for a given set of conditions, and therefore maximize their array efficiency. This paper presents a maximum power point tracker (MPPT) using Fuzzy Logic theory for a PV system. The work is focused on the well known Perturb and Observe (P&O) algorithm and is compared to a designed fuzzy logic controller (FLC). The simulation work dealing with MPPT controller; a DC/DC Ćuk converter feeding a load is achieved. The results showed that the proposed Fuzzy Logic MPPT in the PV system is valid.
Adaptive Fuzzy Tracking Control for a Class of MIMO Nonlinear Systems in Nonstrict-Feedback Form.
Chen, Bing; Lin, Chong; Liu, Xiaoping; Liu, Kefu
2015-12-01
This paper focuses on the problem of fuzzy adaptive control for a class of multiinput and multioutput (MIMO) nonlinear systems in nonstrict-feedback form, which contains the strict-feedback form as a special case. By the condition of variable partition, a new fuzzy adaptive backstepping is proposed for such a class of nonlinear MIMO systems. The suggested fuzzy adaptive controller guarantees that the proposed control scheme can guarantee that all the signals in the closed-loop system are semi-globally uniformly ultimately bounded and the tracking errors eventually converge to a small neighborhood around the origin. The main advantage of this paper is that a control approach is systematically derived for nonlinear systems with strong interconnected terms which are the functions of all states of the whole system. Simulation results further illustrate the effectiveness of the suggested approach.
Khazaee, Mostafa; Markazi, Amir H D; Omidi, Ehsan
2015-11-01
In this paper, a new Adaptive Fuzzy Predictive Sliding Mode Control (AFP-SMC) is presented for nonlinear systems with uncertain dynamics and unknown input delay. The control unit consists of a fuzzy inference system to approximate the ideal linearization control, together with a switching strategy to compensate for the estimation errors. Also, an adaptive fuzzy predictor is used to estimate the future values of the system states to compensate for the time delay. The adaptation laws are used to tune the controller and predictor parameters, which guarantee the stability based on a Lyapunov-Krasovskii functional. To evaluate the method effectiveness, the simulation and experiment on an overhead crane system are presented. According to the obtained results, AFP-SMC can effectively control the uncertain nonlinear systems, subject to input delays of known bound.
An Overview of the Fuzzy Axiomatic Systems and Characterizations Proposed at Ghent University
Directory of Open Access Journals (Sweden)
Etienne E. Kerre
2016-06-01
Full Text Available During the past 40 years of fuzzy research at the Fuzziness and Uncertainty Modeling research unit of Ghent University several axiomatic systems and characterizations have been introduced. In this paper we highlight some of them. The main purpose of this paper consists of an invitation to continue research on these first attempts to axiomatize important concepts and systems in fuzzy set theory. Currently, these attempts are spread over many journals; with this paper they are now collected in a neat overview. In the literature, many axiom systems have been introduced, but as far as we know the axiomatic system of Huntington concerning a Boolean algebra has been the only one where the axioms have been proven independent. Another line of further research could be with respect to the simplification of these systems, in discovering redundancies between the axioms.
Adaptive Fuzzy Robust Control for a Class of Nonlinear Systems via Small Gain Theorem
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Xingjian Wang
2013-01-01
Full Text Available Practical nonlinear systems can usually be represented by partly linearizable models with unknown nonlinearities and external disturbances. Based on this consideration, we propose a novel adaptive fuzzy robust control (AFRC algorithm for such systems. The AFRC effectively combines techniques of adaptive control and fuzzy control, and it improves the performance by retaining the advantages of both methods. The linearizable part will be linearly parameterized with unknown but constant parameters, and the discontinuous-projection-based adaptive control law is used to compensate these parts. The Takagi-Sugeno fuzzy logic systems are used to approximate unknown nonlinearities. Robust control law ensures the robustness of closed-loop control system. A systematic design procedure of the AFRC algorithm by combining the backstepping technique and small-gain approach is presented. Then the closed-loop stability is studied by using small gain theorem, and the result indicates that the closed-loop system is semiglobally uniformly ultimately bounded.
Hybrid fuzzy charged system search algorithm based state estimation in distribution networks
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Sachidananda Prasad
2017-06-01
Full Text Available This paper proposes a new hybrid charged system search (CSS algorithm based state estimation in radial distribution networks in fuzzy framework. The objective of the optimization problem is to minimize the weighted square of the difference between the measured and the estimated quantity. The proposed method of state estimation considers bus voltage magnitude and phase angle as state variable along with some equality and inequality constraints for state estimation in distribution networks. A rule based fuzzy inference system has been designed to control the parameters of the CSS algorithm to achieve better balance between the exploration and exploitation capability of the algorithm. The efficiency of the proposed fuzzy adaptive charged system search (FACSS algorithm has been tested on standard IEEE 33-bus system and Indian 85-bus practical radial distribution system. The obtained results have been compared with the conventional CSS algorithm, weighted least square (WLS algorithm and particle swarm optimization (PSO for feasibility of the algorithm.
Realworld maximum power point tracking simulation of PV system based on Fuzzy Logic control
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Ahmed M. Othman
2012-12-01
Full Text Available In the recent years, the solar energy becomes one of the most important alternative sources of electric energy, so it is important to improve the efficiency and reliability of the photovoltaic (PV systems. Maximum power point tracking (MPPT plays an important role in photovoltaic power systems because it maximize the power output from a PV system for a given set of conditions, and therefore maximize their array efficiency. This paper presents a maximum power point tracker (MPPT using Fuzzy Logic theory for a PV system. The work is focused on the well known Perturb and Observe (P&O algorithm and is compared to a designed fuzzy logic controller (FLC. The simulation work dealing with MPPT controller; a DC/DC Ćuk converter feeding a load is achieved. The results showed that the proposed Fuzzy Logic MPPT in the PV system is valid.
A Fuzzy Approach of the Optimal Analysis Based of Failure States in Manufacturing Systems
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E. Minca
2012-12-01
Full Text Available This article proposes an algorithm for prognosis in optimal analysis of manufacturing systems. Uncertain knowledge of such task requires for specific reasoning and adaptive model base of fuzzy logic analyzes. The proposed method performs the interfaces between the results provided by the fuzzy supervision model and the algorithm witch identify the real state of the monitored system. The supervisory system sends failure signals described in a fuzzy approach. These ones represent inputs values in the system of failure optimal analysis which identifies the current degradation states by recurrent identification cycle. The proposed algorithm has also predictive component capable to determine the possible evolution of the system state towards a critical state of failure.
Energy Technology Data Exchange (ETDEWEB)
Zheng Yongai, E-mail: zhengyongai@163.co [Department of Computer, Yangzhou University, Yangzhou, 225009 (China); Nian Yibei [School of Energy and Power Engineering, Yangzhou University, Yangzhou, 225009 (China); Wang Dejin [Department of Computer, Yangzhou University, Yangzhou, 225009 (China)
2010-12-01
In this Letter, a kind of novel model, called the generalized Takagi-Sugeno (T-S) fuzzy model, is first developed by extending the conventional T-S fuzzy model. Then, a simple but efficient method to control fractional order chaotic systems is proposed using the generalized T-S fuzzy model and adaptive adjustment mechanism (AAM). Sufficient conditions are derived to guarantee chaos control from the stability criterion of linear fractional order systems. The proposed approach offers a systematic design procedure for stabilizing a large class of fractional order chaotic systems from the literature about chaos research. The effectiveness of the approach is tested on fractional order Roessler system and fractional order Lorenz system.
Adaptive fuzzy backstepping control for a class of switched nonlinear systems with actuator faults
Hou, Yingxue; Tong, Shaocheng; Li, Yongming
2016-11-01
This paper investigates the problem of fault-tolerant control (FTC) for a class of switched nonlinear systems. These systems are under arbitrary switchings and are subject to both lock-in-place and loss-of-effectiveness actuator faults. In the control design, fuzzy logic systems are used to identify the unknown switched nonlinear systems. Under the framework of the backstepping control design, FTC, fuzzy adaptive control and common Lyapunov function stability theory, an adaptive fuzzy control approach is developed. It is proved that the proposed control approach can guarantee that all the signals in the closed-loop switched system are semi-globally uniformly ultimately bounded (SGUUB) and the tracking error remains an adjustable neighbourhood of the origin. Two simulation examples are provided to illustrate the effectiveness of the proposed approach.