Uncertainty analysis of flexible rotors considering fuzzy parameters and fuzzy-random parameters
Directory of Open Access Journals (Sweden)
Fabian Andres Lara-Molina
Full Text Available Abstract The components of flexible rotors are subjected to uncertainties. The main sources of uncertainties include the variation of mechanical properties. This contribution aims at analyzing the dynamics of flexible rotors under uncertain parameters modeled as fuzzy and fuzzy random variables. The uncertainty analysis encompasses the modeling of uncertain parameters and the numerical simulation of the corresponding flexible rotor model by using an approach based on fuzzy dynamic analysis. The numerical simulation is accomplished by mapping the fuzzy parameters of the deterministic flexible rotor model. Thereby, the flexible rotor is modeled by using both the Fuzzy Finite Element Method and the Fuzzy Stochastic Finite Element Method. Numerical simulations illustrate the methodology conveyed in terms of orbits and frequency response functions subject to uncertain parameters.
Fuzzy random variables — I. definitions and theorems
Kwakernaak, H.
1978-01-01
Fuzziness is discussed in the context of multivalued logic, and a corresponding view of fuzzy sets is given. Fuzzy random variables are introduced as random variables whose values are not real but fuzzy numbers, and subsequently redefined as a particular kind of fuzzy set. Expectations of fuzzy
Fuzziness and randomness in an optimization framework
International Nuclear Information System (INIS)
Luhandjula, M.K.
1994-03-01
This paper presents a semi-infinite approach for linear programming in the presence of fuzzy random variable coefficients. As a byproduct a way for dealing with optimization problems including both fuzzy and random data is obtained. Numerical examples are provided for the sake of illustration. (author). 13 refs
Directory of Open Access Journals (Sweden)
Kai Kang
2018-01-01
Full Text Available There is a growing concern that business enterprises focus primarily on their economic activities and ignore the impact of these activities on the environment and the society. This paper investigates a novel sustainable inventory-allocation planning model with carbon emissions and defective item disposal over multiple periods under a fuzzy random environment. In this paper, a carbon credit price and a carbon cap are proposed to demonstrate the effect of carbon emissions’ costs on the inventory-allocation network costs. The percentage of poor quality products from manufacturers that need to be rejected is assumed to be fuzzy random. Because of the complexity of the model, dynamic programming-based particle swarm optimization with multiple social learning structures, a DP-based GLNPSO, and a fuzzy random simulation are proposed to solve the model. A case is then given to demonstrate the efficiency and effectiveness of the proposed model and the DP-based GLNPSO algorithm. The results found that total costs across the inventory-allocation network varied with changes in the carbon cap and that carbon emissions’ reductions could be utilized to gain greater profits.
Fuzzy control of pressurizer dynamic process
International Nuclear Information System (INIS)
Ming Zhedong; Zhao Fuyu
2006-01-01
Considering the characteristics of pressurizer dynamic process, the fuzzy control system that takes the advantages of both fuzzy controller and PID controller is designed for the dynamic process in pressurizer. The simulation results illustrate this type of composite control system is with better qualities than those of single fuzzy controller and single PID controller. (authors)
Dhruba Das; Hemanta K. Baruah
2015-01-01
In this article, based on Zadeh’s extension principle we have apply the parametric programming approach to construct the membership functions of the performance measures when the interarrival time and the service time are fuzzy numbers based on the Baruah’s Randomness- Fuzziness Consistency Principle. The Randomness-Fuzziness Consistency Principle leads to defining a normal law of fuzziness using two different laws of randomness. In this article, two fuzzy queues FM...
International Nuclear Information System (INIS)
Bennett, D.L.; Brene, N.; Nielsen, H.B.
1986-06-01
The goal of random dynamics is the derivation of the laws of Nature as we know them (standard model) from inessential assumptions. The inessential assumptions made here are expressed as sets of general models at extremely high energies: gauge glass and spacetime foam. Both sets of models lead tentatively to the standard model. (orig.)
Fuzzy randomness uncertainty in civil engineering and computational mechanics
Möller, Bernd
2004-01-01
This book, for the first time, provides a coherent, overall concept for taking account of uncertainty in the analysis, the safety assessment, and the design of structures. The reader is introduced to the problem of uncertainty modeling and familiarized with particular uncertainty models. For simultaneously considering stochastic and non-stochastic uncertainty the superordinated uncertainty model fuzzy randomness, which contains real valued random variables as well as fuzzy variables as special cases, is presented. For this purpose basic mathematical knowledge concerning the fuzzy set theory and the theory of fuzzy random variables is imparted. The body of the book comprises the appropriate quantification of uncertain structural parameters, the fuzzy and fuzzy probabilistic structural analysis, the fuzzy probabilistic safety assessment, and the fuzzy cluster structural design. The completely new algorithms are described in detail and illustrated by way of demonstrative examples.
Simulation of fuzzy dynamical systems using the LU-representation of fuzzy numbers
International Nuclear Information System (INIS)
Stefanini, Luciano; Sorini, Laerte; Guerra, Maria Letizia
2006-01-01
We suggest the use of the parametric LU-representation of the fuzzy numbers, introduced in Gear and Sintofene [Gear Ml, Sintofene L. Approximate fuzzy arithmetic operations using monotonic interpolations. Fuzzy Sets Syst 2005;150:5-33] and improved in Sintofene et al. [Stefanini L, Sorini L, Guerra ML. Parametric representations of fuzzy numbers and applications. Working Paper Series EMS, 95, University of Urbino, 2004], in the simulation of fuzzy dynamical systems or fuzzy iterated maps. We show the computational advantages of the LU-representation in extending some well known standard maps to the fuzzy context, allowing the simulation by the Zadeh's extension principle in the general case of fuzzy parameters
Directory of Open Access Journals (Sweden)
Dhruba Das
2015-04-01
Full Text Available In this article, based on Zadeh’s extension principle we have apply the parametric programming approach to construct the membership functions of the performance measures when the interarrival time and the service time are fuzzy numbers based on the Baruah’s Randomness- Fuzziness Consistency Principle. The Randomness-Fuzziness Consistency Principle leads to defining a normal law of fuzziness using two different laws of randomness. In this article, two fuzzy queues FM/M/1 and M/FM/1 has been studied and constructed their membership functions of the system characteristics based on the aforesaid principle. The former represents a queue with fuzzy exponential arrivals and exponential service rate while the latter represents a queue with exponential arrival rate and fuzzy exponential service rate.
Tahriri, Farzad; Dawal, Siti Zawiah Md; Taha, Zahari
2014-01-01
A new multiobjective dynamic fuzzy genetic algorithm is applied to solve a fuzzy mixed-model assembly line sequencing problem in which the primary goals are to minimize the total make-span and minimize the setup number simultaneously. Trapezoidal fuzzy numbers are implemented for variables such as operation and travelling time in order to generate results with higher accuracy and representative of real-case data. An improved genetic algorithm called fuzzy adaptive genetic algorithm (FAGA) is ...
Z Number Based Fuzzy Inference System for Dynamic Plant Control
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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.
Tahriri, Farzad; Dawal, Siti Zawiah Md; Taha, Zahari
2014-01-01
A new multiobjective dynamic fuzzy genetic algorithm is applied to solve a fuzzy mixed-model assembly line sequencing problem in which the primary goals are to minimize the total make-span and minimize the setup number simultaneously. Trapezoidal fuzzy numbers are implemented for variables such as operation and travelling time in order to generate results with higher accuracy and representative of real-case data. An improved genetic algorithm called fuzzy adaptive genetic algorithm (FAGA) is proposed in order to solve this optimization model. In establishing the FAGA, five dynamic fuzzy parameter controllers are devised in which fuzzy expert experience controller (FEEC) is integrated with automatic learning dynamic fuzzy controller (ALDFC) technique. The enhanced algorithm dynamically adjusts the population size, number of generations, tournament candidate, crossover rate, and mutation rate compared with using fixed control parameters. The main idea is to improve the performance and effectiveness of existing GAs by dynamic adjustment and control of the five parameters. Verification and validation of the dynamic fuzzy GA are carried out by developing test-beds and testing using a multiobjective fuzzy mixed production assembly line sequencing optimization problem. The simulation results highlight that the performance and efficacy of the proposed novel optimization algorithm are more efficient than the performance of the standard genetic algorithm in mixed assembly line sequencing model.
Directory of Open Access Journals (Sweden)
Farzad Tahriri
2014-01-01
Full Text Available A new multiobjective dynamic fuzzy genetic algorithm is applied to solve a fuzzy mixed-model assembly line sequencing problem in which the primary goals are to minimize the total make-span and minimize the setup number simultaneously. Trapezoidal fuzzy numbers are implemented for variables such as operation and travelling time in order to generate results with higher accuracy and representative of real-case data. An improved genetic algorithm called fuzzy adaptive genetic algorithm (FAGA is proposed in order to solve this optimization model. In establishing the FAGA, five dynamic fuzzy parameter controllers are devised in which fuzzy expert experience controller (FEEC is integrated with automatic learning dynamic fuzzy controller (ALDFC technique. The enhanced algorithm dynamically adjusts the population size, number of generations, tournament candidate, crossover rate, and mutation rate compared with using fixed control parameters. The main idea is to improve the performance and effectiveness of existing GAs by dynamic adjustment and control of the five parameters. Verification and validation of the dynamic fuzzy GA are carried out by developing test-beds and testing using a multiobjective fuzzy mixed production assembly line sequencing optimization problem. The simulation results highlight that the performance and efficacy of the proposed novel optimization algorithm are more efficient than the performance of the standard genetic algorithm in mixed assembly line sequencing model.
Tahriri, Farzad; Dawal, Siti Zawiah Md; Taha, Zahari
2014-01-01
A new multiobjective dynamic fuzzy genetic algorithm is applied to solve a fuzzy mixed-model assembly line sequencing problem in which the primary goals are to minimize the total make-span and minimize the setup number simultaneously. Trapezoidal fuzzy numbers are implemented for variables such as operation and travelling time in order to generate results with higher accuracy and representative of real-case data. An improved genetic algorithm called fuzzy adaptive genetic algorithm (FAGA) is proposed in order to solve this optimization model. In establishing the FAGA, five dynamic fuzzy parameter controllers are devised in which fuzzy expert experience controller (FEEC) is integrated with automatic learning dynamic fuzzy controller (ALDFC) technique. The enhanced algorithm dynamically adjusts the population size, number of generations, tournament candidate, crossover rate, and mutation rate compared with using fixed control parameters. The main idea is to improve the performance and effectiveness of existing GAs by dynamic adjustment and control of the five parameters. Verification and validation of the dynamic fuzzy GA are carried out by developing test-beds and testing using a multiobjective fuzzy mixed production assembly line sequencing optimization problem. The simulation results highlight that the performance and efficacy of the proposed novel optimization algorithm are more efficient than the performance of the standard genetic algorithm in mixed assembly line sequencing model. PMID:24982962
Method of dynamic fuzzy symptom vector in intelligent diagnosis
International Nuclear Information System (INIS)
Sun Hongyan; Jiang Xuefeng
2010-01-01
Aiming at the requirement of diagnostic symptom real-time updating brought from diagnostic knowledge accumulation and great gap in unit and value of diagnostic symptom in multi parameters intelligent diagnosis, the method of dynamic fuzzy symptom vector is proposed. The concept of dynamic fuzzy symptom vector is defined. Ontology is used to specify the vector elements, and the vector transmission method based on ontology is built. The changing law of symptom value is analyzed and fuzzy normalization method based on fuzzy membership functions is built. An instance proved method of dynamic fussy symptom vector is efficient to solve the problems of symptom updating and unify of symptom value and unit. (authors)
Fuzzy conditional random fields for temporal data mining
Nurma Yulita, Intan; Setiawan Abdullah, Atje
2017-10-01
Temporal data mining is one of the interesting problems in computer science and its application has been performed in a wide variety of fields. The difference between the temporal data mining and data mining is the use of variable time. Therefore, the method used must be capable of processing variables of time. Compared with other methods, conditional random field has advantages in the processing variables of time. The method is a directed graph models that has been widely applied for segmenting and labelling sequence data that appears in various domains. In this study, we proposed use of Fuzzy Logic to be applied in Conditional Random Fields to overcome the problems of uncertainty. The experiment is compared Fuzzy Conditional Random Fields, Conditional Random Fields, and Hidden Markov Models. The result showed that accuracy of Fuzzy Conditional Random Fields is the best.
Fuzzy Random Walkers with Second Order Bounds: An Asymmetric Analysis
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Georgios Drakopoulos
2017-03-01
Full Text Available Edge-fuzzy graphs constitute an essential modeling paradigm across a broad spectrum of domains ranging from artificial intelligence to computational neuroscience and social network analysis. Under this model, fundamental graph properties such as edge length and graph diameter become stochastic and as such they are consequently expressed in probabilistic terms. Thus, algorithms for fuzzy graph analysis must rely on non-deterministic design principles. One such principle is Random Walker, which is based on a virtual entity and selects either edges or, like in this case, vertices of a fuzzy graph to visit. This allows the estimation of global graph properties through a long sequence of local decisions, making it a viable strategy candidate for graph processing software relying on native graph databases such as Neo4j. As a concrete example, Chebyshev Walktrap, a heuristic fuzzy community discovery algorithm relying on second order statistics and on the teleportation of the Random Walker, is proposed and its performance, expressed in terms of community coherence and number of vertex visits, is compared to the previously proposed algorithms of Markov Walktrap, Fuzzy Walktrap, and Fuzzy Newman–Girvan. In order to facilitate this comparison, a metric based on the asymmetric metrics of Tversky index and Kullback–Leibler divergence is used.
Dynamic Classifier Aggregation using Interaction-Sensitive Fuzzy Measures
Czech Academy of Sciences Publication Activity Database
Štefka, D.; Holeňa, Martin
2015-01-01
Roč. 270, 1 July (2015), s. 25-52 ISSN 0165-0114 R&D Projects: GA ČR GA13-17187S Institutional support: RVO:67985807 Keywords : Fuzzy integral * Fuzzy measure * Dynamic classifier aggregation Subject RIV: IN - Informatics, Computer Science Impact factor: 2.098, year: 2015
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...
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.
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.
Fuzzy Dark Matter from Infrared Confining Dynamics
Davoudiasl, Hooman; Murphy, Christopher W.
2017-04-01
A very light boson of mass O (10-22) eV may potentially be a viable dark matter (DM) candidate, which can avoid phenomenological problems associated with cold DM. Such "fuzzy DM (FDM)" may naturally be an axion with a decay constant fa˜1 016- 1 018 GeV and a mass ma˜μ2/fa with μ ˜1 02 eV . Here, we propose a concrete model, where μ arises as a dynamical scale from infrared confining dynamics, analogous to QCD. Our model is an alternative to the usual approach of generating μ through string theoretic instanton effects. We outline the features of this scenario that result from various cosmological constraints. We find that those constraints are suggestive of a period of mild of inflation, perhaps from a strong first order phase transition, that reheats the standard model (SM) sector only. A typical prediction of our scenario, broadly speaking, is a larger effective number of neutrinos compared to the SM value Neff≈3 , as inferred from precision measurements of the cosmic microwave background. Some of the new degrees of freedom may be identified as "sterile neutrinos," which may be required to explain certain neutrino oscillation anomalies. Hence, aspects of our scenario could be testable in terrestrial experiments, which is a novelty of our FDM model.
Fuzzy Geometry of Commutative Spaces and Quantum Dynamics
International Nuclear Information System (INIS)
Mayburov, S.N.
2016-01-01
Fuzzy topology and geometry considered as the possible mathematical framework for novel quantum-mechanical formalism. In such formalism the states of massive particle m correspond to the elements of fuzzy manifold called fuzzy points. Due to the manifold weak topology, m space coordinate x acquires principal uncertainty σ x and described by the positive, normalized density w(r-vector , t) in 3-dimensional case. It’s shown that the evolution of m state on such 3-dimensional manifold corresponds to Shroedinger dynamics of massive quantum particle
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.
Adding dynamic rules to self-organizing fuzzy systems
Buhusi, Catalin V.
1992-01-01
This paper develops a Dynamic Self-Organizing Fuzzy System (DSOFS) capable of adding, removing, and/or adapting the fuzzy rules and the fuzzy reference sets. The DSOFS background consists of a self-organizing neural structure with neuron relocation features which will develop a map of the input-output behavior. The relocation algorithm extends the topological ordering concept. Fuzzy rules (neurons) are dynamically added or released while the neural structure learns the pattern. The DSOFS advantages are the automatic synthesis and the possibility of parallel implementation. A high adaptation speed and a reduced number of neurons is needed in order to keep errors under some limits. The computer simulation results are presented in a nonlinear systems modelling application.
AN APPROACH TO REMOVE THE EFFECT OF RANDOM INITIALIZATION FROM FUZZY C-MEANS CLUSTERING TECHNIQUE
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Samarjit Das
2014-01-01
Full Text Available Out of the different available fuzzy clustering techniques Bezdek’s Fuzzy C-Means clustering technique is among the most popular ones. Due to the random initialization of the membership values the performance of Fuzzy C-Means clustering technique varies significantly in its different executions. We have tried to remove the effect of random initialization from Fuzzy CMeans clustering technique by using the Subtractive clustering technique of Chiu as a preprocessor to it. We have also provided a comparison of the performance of our method with those of Fuzzy C-Means clustering technique and Subtractive clustering technique.
Directory of Open Access Journals (Sweden)
Xiaonan Wang
2018-02-01
Full Text Available Naïve Geography, intelligent geographical information systems (GIS, and spatial data mining especially from social media all rely on natural-language spatial relations (NLSR terms to incorporate commonsense spatial knowledge into conventional GIS and to enhance the semantic interoperability of spatial information in social media data. Yet, the inherent fuzziness of NLSR terms makes them challenging to interpret. This study proposes to interpret the fuzzy semantics of NLSR terms using the fuzzy random forest (FRF algorithm. Based on a large number of fuzzy samples acquired by transforming a set of crisp samples with the random forest algorithm, two FRF models with different membership assembling strategies are trained to obtain the fuzzy interpretation of three line-region geometric representations using 69 NLSR terms. Experimental results demonstrate that the two FRF models achieve good accuracy in interpreting line-region geometric representations using fuzzy NLSR terms. In addition, fuzzy classification of FRF can interpret the fuzzy semantics of NLSR terms more fully than their crisp counterparts.
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...
Estimation of the Randomized Complete Block Design Parameters with Fuzzy Goal Programming
Kula, Kamile; Apaydin, Ayşen
2011-01-01
Since goal programming was introduced by Charnes, Cooper and Ferguson (1955), goal programming has been widely studied and applied in various areas. Parameter estimation is quite important in many areas. Recently, many researches have been studied in fuzzy estimation. In this study, fuzzy goal programming was proposed by Hannan (1981) adapted to estimation of randomized complete block design parameters. Suggested fuzzy goal programming is used for estimation of randomized complete block desig...
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.
Dynamic Trajectory Extraction from Stereo Vision Using Fuzzy Clustering
Onishi, Masaki; Yoda, Ikushi
In recent years, many human tracking researches have been proposed in order to analyze human dynamic trajectory. These researches are general technology applicable to various fields, such as customer purchase analysis in a shopping environment and safety control in a (railroad) crossing. In this paper, we present a new approach for tracking human positions by stereo image. We use the framework of two-stepped clustering with k-means method and fuzzy clustering to detect human regions. In the initial clustering, k-means method makes middle clusters from objective features extracted by stereo vision at high speed. In the last clustering, c-means fuzzy method cluster middle clusters based on attributes into human regions. Our proposed method can be correctly clustered by expressing ambiguity using fuzzy clustering, even when many people are close to each other. The validity of our technique was evaluated with the experiment of trajectories extraction of doctors and nurses in an emergency room of a hospital.
H∞ Tracking Control of Fuzzy Dynamic Output for Nonlinear Networked System with Packet Dropouts
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Yang Wang
2018-01-01
Full Text Available The tracking control of H∞ dynamic output feedback is proposed for the fuzzy networked systems of the same category, in which each system is discrete-time nonlinear and is missing measurable data. In other words, the loss of data packet occurs randomly in both the uplink and the downlink. The independent variables that are called the Bernoulli random variables are considered to design the loss of data packets. The method of parallel distributed compensation (PDC in terms of the T-S fuzzy model is applied to investigate the dynamic controller of tracking control on the systems. Then, it is presented that the analytical H∞ performance of the output error between the reference model and the fuzzy model for the closed-loop system containing dynamic output feedback controller is proven. Furthermore, the achieved sufficient conditions in terms of LMIs ensure that the closed-loop system is stochastically stable in the H∞ sense. Finally, a numerical system is offered to show the effectiveness of the established technique.
Directory of Open Access Journals (Sweden)
Hideki Katagiri
2017-10-01
Full Text Available This paper considers linear programming problems (LPPs where the objective functions involve discrete fuzzy random variables (fuzzy set-valued discrete random variables. New decision making models, which are useful in fuzzy stochastic environments, are proposed based on both possibility theory and probability theory. In multi-objective cases, Pareto optimal solutions of the proposed models are newly defined. Computational algorithms for obtaining the Pareto optimal solutions of the proposed models are provided. It is shown that problems involving discrete fuzzy random variables can be transformed into deterministic nonlinear mathematical programming problems which can be solved through a conventional mathematical programming solver under practically reasonable assumptions. A numerical example of agriculture production problems is given to demonstrate the applicability of the proposed models to real-world problems in fuzzy stochastic environments.
High dimensional model representation method for fuzzy structural dynamics
Adhikari, S.; Chowdhury, R.; Friswell, M. I.
2011-03-01
Uncertainty propagation in multi-parameter complex structures possess significant computational challenges. This paper investigates the possibility of using the High Dimensional Model Representation (HDMR) approach when uncertain system parameters are modeled using fuzzy variables. In particular, the application of HDMR is proposed for fuzzy finite element analysis of linear dynamical systems. The HDMR expansion is an efficient formulation for high-dimensional mapping in complex systems if the higher order variable correlations are weak, thereby permitting the input-output relationship behavior to be captured by the terms of low-order. The computational effort to determine the expansion functions using the α-cut method scales polynomically with the number of variables rather than exponentially. This logic is based on the fundamental assumption underlying the HDMR representation that only low-order correlations among the input variables are likely to have significant impacts upon the outputs for most high-dimensional complex systems. The proposed method is first illustrated for multi-parameter nonlinear mathematical test functions with fuzzy variables. The method is then integrated with a commercial finite element software (ADINA). Modal analysis of a simplified aircraft wing with fuzzy parameters has been used to illustrate the generality of the proposed approach. In the numerical examples, triangular membership functions have been used and the results have been validated against direct Monte Carlo simulations. It is shown that using the proposed HDMR approach, the number of finite element function calls can be reduced without significantly compromising the accuracy.
Uncertain structural dynamics of aircraft panels and fuzzy structures analysis
Sparrow, Victor W.; Buehrle, Ralph D.
2002-11-01
Aircraft fuselage panels, seemingly simple structures, are actually complex because of the uncertainty of the attachments of the frame stiffeners and longitudinal stringers. It is clearly important to understand the dynamics of these panels because of the subsequent radiation into the passenger cabin, even when complete information is not available for all portions of the finite-element model. Over the last few years a fuzzy structures analysis (FSA) approach has been undertaken at Penn State and NASA Langley to quantify the uncertainty in modeling aircraft panels. A new MSC.Nastran [MSC.Software Corp. (Santa Ana, CA)] Direct Matrix Abstraction Program (DMAP) code was written and tested [AIAA paper 2001-1320, 42nd AIAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics, and Materials Conf., Seattle, WA, 16 April 2001] and was applied to simple fuselage panel models [J. Acoust. Soc. Am. 109, 2410(A) (2001)]. Recently the work has focused on understanding the dynamics of a realistic aluminum fuselage panel, typical of today's aircraft construction. This presentation will provide an overview of the research and recent results will be given for the fuselage panel. Comparison between experiments and the FSA results will be shown for different fuzzy input parameters. [Work supported by NASA Research Cooperative Agreement NCC-1-382.
Robust Takagi-Sugeno Fuzzy Dynamic Regulator for Trajectory Tracking of a Pendulum-Cart System
Directory of Open Access Journals (Sweden)
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.
Classification of mammographic masses using generalized dynamic fuzzy neural networks
International Nuclear Information System (INIS)
Lim, Wei Keat; Er, Meng Joo
2004-01-01
In this article, computer-aided classification of mammographic masses using generalized dynamic fuzzy neural networks (GDFNN) is presented. The texture parameters, derived from first-order gradient distribution and gray-level co-occurrence matrices, were computed from the regions of interest. A total of 343 images containing 180 benign masses and 163 malignant masses from the Digital Database for Screening Mammography were analyzed. A fast approach of automatically generating fuzzy rules from training samples was implemented to classify tumors. This work is novel in that it alleviates the problem of requiring a designer to examine all the input-output relationships of a training database in order to obtain the most appropriate structure for the classifier in a conventional computer-aided diagnosis. In this approach, not only the connection weights can be adjusted, but also the structure can be self-adaptive during the learning process. By virtue of the automatic generation of the classifier by the GDFNN learning algorithm, the area under the receiver-operating characteristic curve, A z , attains 0.868±0.020, which corresponds to a true-positive fraction of 95.0% at a false positive fraction of 52.8%. The corresponding accuracy is 70.0%, the positive predictive value is 62.0%, and the negative predictive value is 91.4%
Visualizing dynamical neural assemblies with a fuzzy synchronization clustering analysis.
Zhou, Shu; Wu, Yan; Dos Santos, Claudia C
2009-12-01
Phase synchrony has been proposed as a possible communication mechanism between cerebral regions. The participation index method (PIM) may be used to investigate integrating structures within an oscillatory network, based on the eigenvalue decomposition of matrix of bivariate synchronization indices. However, eigenvector orthogonality between clusters may result in categorization difficulties for hub oscillators and pseudoclustering phenomenon. Here, we propose a method of fuzzy synchronization clustering analysis (FSCA) to avoid the constraint of orthogonality by combining the fuzzy c-means algorithm with the phase-locking value. Following mathematical derivation, we cross-validated the FSCA and the PIM using the same multichannel phase time series of event-related EEG from a subject performing a working memory task. Both clustering methods produced consistent findings for the qualitatively salient configuration of the original network-illustrated here by a visualization technique. In contrast to PIM, use of common virtual oscillatory centroids enabled the FSCA to reveal multiple dynamical neural assemblies as well as the unitary phase information within each assembly.
Mansouri, Mohammad; Teshnehlab, Mohammad; Aliyari Shoorehdeli, Mahdi
2015-05-01
In this paper, a novel adaptive hierarchical fuzzy control system based on the variable structure control is developed for a class of SISO canonical nonlinear systems in the presence of bounded disturbances. It is assumed that nonlinear functions of the systems be completely unknown. Switching surfaces are incorporated into the hierarchical fuzzy control scheme to ensure the system stability. A fuzzy soft switching system decides the operation area of the hierarchical fuzzy control and variable structure control systems. All the nonlinearly appeared parameters of conclusion parts of fuzzy blocks located in different layers of the hierarchical fuzzy control system are adjusted through adaptation laws deduced from the defined Lyapunov function. The proposed hierarchical fuzzy control system reduces the number of rules and consequently the number of tunable parameters with respect to the ordinary fuzzy control system. Global boundedness of the overall adaptive system and the desired precision are achieved using the proposed adaptive control system. In this study, an adaptive hierarchical fuzzy system is used for two objectives; it can be as a function approximator or a control system based on an intelligent-classic approach. Three theorems are proven to investigate the stability of the nonlinear dynamic systems. The important point about the proposed theorems is that they can be applied not only to hierarchical fuzzy controllers with different structures of hierarchical fuzzy controller, but also to ordinary fuzzy controllers. Therefore, the proposed algorithm is more general. To show the effectiveness of the proposed method four systems (two mechanical, one mathematical and one chaotic) are considered in simulations. Simulation results demonstrate the validity, efficiency and feasibility of the proposed approach to control of nonlinear dynamic systems. Copyright © 2014 ISA. Published by Elsevier Ltd. All rights reserved.
Fan, Xiaozheng; Wang, Yan; Hu, Manfeng
2016-01-01
In this paper, the fuzzy [Formula: see text] output-feedback control problem is investigated for a class of discrete-time T-S fuzzy systems with channel fadings, sector nonlinearities, randomly occurring interval delays (ROIDs) and randomly occurring nonlinearities (RONs). A series of variables of the randomly occurring phenomena obeying the Bernoulli distribution is used to govern ROIDs and RONs. Meanwhile, the measurement outputs are subject to the sector nonlinearities ( i.e. the sensor saturations) and we assume the system output is [Formula: see text], [Formula: see text]. The L th-order Rice model is utilized to describe the phenomenon of channel fadings by setting different values of the channel coefficients. The aim of this work is to deal with the problem of designing a full-order dynamic fuzzy [Formula: see text] output-feedback controller such that the fuzzy closed-loop system is exponentially mean-square stable and the [Formula: see text] performance constraint is satisfied, by means of a combination of Lyapunov stability theory and stochastic analysis along with LMI methods. The proposed fuzzy controller parameters are derived by solving a convex optimization problem via the semidefinite programming technique. Finally, a numerical simulation is given to illustrate the feasibility and effectiveness of the proposed design technique.
Lü, Hui; Shangguan, Wen-Bin; Yu, Dejie
2017-09-01
Automotive brake systems are always subjected to various types of uncertainties and two types of random-fuzzy uncertainties may exist in the brakes. In this paper, a unified approach is proposed for squeal instability analysis of disc brakes with two types of random-fuzzy uncertainties. In the proposed approach, two uncertainty analysis models with mixed variables are introduced to model the random-fuzzy uncertainties. The first one is the random and fuzzy model, in which random variables and fuzzy variables exist simultaneously and independently. The second one is the fuzzy random model, in which uncertain parameters are all treated as random variables while their distribution parameters are expressed as fuzzy numbers. Firstly, the fuzziness is discretized by using α-cut technique and the two uncertainty analysis models are simplified into random-interval models. Afterwards, by temporarily neglecting interval uncertainties, the random-interval models are degraded into random models, in which the expectations, variances, reliability indexes and reliability probabilities of system stability functions are calculated. And then, by reconsidering the interval uncertainties, the bounds of the expectations, variances, reliability indexes and reliability probabilities are computed based on Taylor series expansion. Finally, by recomposing the analysis results at each α-cut level, the fuzzy reliability indexes and probabilities can be obtained, by which the brake squeal instability can be evaluated. The proposed approach gives a general framework to deal with both types of random-fuzzy uncertainties that may exist in the brakes and its effectiveness is demonstrated by numerical examples. It will be a valuable supplement to the systematic study of brake squeal considering uncertainty.
Fuzzy ensemble clustering based on random projections for DNA microarray data analysis.
Avogadri, Roberto; Valentini, Giorgio
2009-01-01
Two major problems related the unsupervised analysis of gene expression data are represented by the accuracy and reliability of the discovered clusters, and by the biological fact that the boundaries between classes of patients or classes of functionally related genes are sometimes not clearly defined. The main goal of this work consists in the exploration of new strategies and in the development of new clustering methods to improve the accuracy and robustness of clustering results, taking into account the uncertainty underlying the assignment of examples to clusters in the context of gene expression data analysis. We propose a fuzzy ensemble clustering approach both to improve the accuracy of clustering results and to take into account the inherent fuzziness of biological and bio-medical gene expression data. We applied random projections that obey the Johnson-Lindenstrauss lemma to obtain several instances of lower dimensional gene expression data from the original high-dimensional ones, approximately preserving the information and the metric structure of the original data. Then we adopt a double fuzzy approach to obtain a consensus ensemble clustering, by first applying a fuzzy k-means algorithm to the different instances of the projected low-dimensional data and then by using a fuzzy t-norm to combine the multiple clusterings. Several variants of the fuzzy ensemble clustering algorithms are proposed, according to different techniques to combine the base clusterings and to obtain the final consensus clustering. We applied our proposed fuzzy ensemble methods to the gene expression analysis of leukemia, lymphoma, adenocarcinoma and melanoma patients, and we compared the results with other state of the art ensemble methods. Results show that in some cases, taking into account the natural fuzziness of the data, we can improve the discovery of classes of patients defined at bio-molecular level. The reduction of the dimension of the data, achieved through random
Sub-optimal control of fuzzy linear dynamical systems under granular differentiability concept.
Mazandarani, Mehran; Pariz, Naser
2018-03-16
This paper deals with sub-optimal control of a fuzzy linear dynamical system. The aim is to keep the state variables of the fuzzy linear dynamical system close to zero in an optimal manner. In the fuzzy dynamical system, the fuzzy derivative is considered as the granular derivative; and all the coefficients and initial conditions can be uncertain. The criterion for assessing the optimality is regarded as a granular integral whose integrand is a quadratic function of the state variables and control inputs. Using the relative-distance-measure (RDM) fuzzy interval arithmetic and calculus of variations, the optimal control law is presented as the fuzzy state variables feedback. Since the optimal feedback gains are obtained as fuzzy functions, they need to be defuzzified. This will result in the sub-optimal control law. This paper also sheds light on the restrictions imposed by the approaches which are based on fuzzy standard interval arithmetic (FSIA), and use strongly generalized Hukuhara and generalized Hukuhara differentiability concepts for obtaining the optimal control law. The granular eigenvalues notion is also defined. Using an RLC circuit mathematical model, it is shown that, due to their unnatural behavior in the modeling phenomenon, the FSIA-based approaches may obtain some eigenvalues sets that might be different from the inherent eigenvalues set of the fuzzy dynamical system. This is, however, not the case with the approach proposed in this study. The notions of granular controllability and granular stabilizability of the fuzzy linear dynamical system are also presented in this paper. Moreover, a sub-optimal control for regulating a Boeing 747 in longitudinal direction with uncertain initial conditions and parameters is gained. In addition, an uncertain suspension system of one of the four wheels of a bus is regulated using the sub-optimal control introduced in this paper. Copyright © 2018 ISA. Published by Elsevier Ltd. All rights reserved.
Selection of optimal variant route based on dynamic fuzzy GRA
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Jalil Heidary Dahooie
2018-09-01
Full Text Available Given the high costs of construction and maintenance, an optimum design methodology is one of the most important steps towards the development of transportation infrastructure, especially freeways. However, the effects of different variables on the decision-making process to find an optimal variant have caused the choice to become a very difficult and professional task for decision makers. So, the current paper aims to determine the optimal variant route for Isfahan-Shiraz freeway through MADM approaches. First, evaluation indices for an optimal route variant are derived through literature review and expert panel assessment. Then, a dynamic fuzzy GRA method is used for weightings and optimal route selection. Bases on the results, the road longevity, views of NGOs and route integration are identified as the highest-weighted criteria in route variant prioritization. Further, Route 3 is defined as the priority for the optimal variant for Isfahan–Shiraz freeway, which is the main basis in practice.
Couso, Inés; Sánchez, Luciano
2014-01-01
This short book provides a unified view of the history and theory of random sets and fuzzy random variables, with special emphasis on its use for representing higher-order non-statistical uncertainty about statistical experiments. The authors lay bare the existence of two streams of works using the same mathematical ground, but differing form their use of sets, according to whether they represent objects of interest naturally taking the form of sets, or imprecise knowledge about such objects. Random (fuzzy) sets can be used in many fields ranging from mathematical morphology, economics, artificial intelligence, information processing and statistics per se, especially in areas where the outcomes of random experiments cannot be observed with full precision. This book also emphasizes the link between random sets and fuzzy sets with some techniques related to the theory of imprecise probabilities. This small book is intended for graduate and doctoral students in mathematics or engineering, but also provides an i...
Fuzzy Dynamic Discrimination Algorithms for Distributed Knowledge Management Systems
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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.
Survival analysis following dynamic randomization
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Xiaolong Luo
2016-08-01
Full Text Available In this paper, we propose a method to analyze survival data from a clinical trial that utilizes a dynamic randomization for subject enrollment. The method directly accounts for dynamic subject randomization process using a marked point process (MPP. Its corresponding martingale process is used to formulate an equation for estimating the treatment effect size and for hypothesis testing. We perform simulation analyses to evaluate the outcomes of the proposed method as well as the conventional log rank method and re-randomized testing procedure.
Wai, Rong-Jong; Yang, Zhi-Wei
2008-10-01
This paper focuses on the development of adaptive fuzzy neural network control (AFNNC), including indirect and direct frameworks for an n-link robot manipulator, to achieve high-precision position tracking. In general, it is difficult to adopt a model-based design to achieve this control objective due to the uncertainties in practical applications, such as friction forces, external disturbances, and parameter variations. In order to cope with this problem, an indirect AFNNC (IAFNNC) scheme and a direct AFNNC (DAFNNC) strategy are investigated without the requirement of prior system information. In these model-free control topologies, a continuous-time Takagi-Sugeno (T-S) dynamic fuzzy model with online learning ability is constructed to represent the system dynamics of an n-link robot manipulator. In the IAFNNC, an FNN estimator is designed to tune the nonlinear dynamic function vector in fuzzy local models, and then, the estimative vector is used to indirectly develop a stable IAFNNC law. In the DAFNNC, an FNN controller is directly designed to imitate a predetermined model-based stabilizing control law, and then, the stable control performance can be achieved by only using joint position information. All the IAFNNC and DAFNNC laws and the corresponding adaptive tuning algorithms for FNN weights are established in the sense of Lyapunov stability analyses to ensure the stable control performance. Numerical simulations and experimental results of a two-link robot manipulator actuated by dc servomotors are given to verify the effectiveness and robustness of the proposed methodologies. In addition, the superiority of the proposed control schemes is indicated in comparison with proportional-differential control, fuzzy-model-based control, T-S-type FNN control, and robust neural fuzzy network control systems.
Analysis of construction dynamic plan using fuzzy critical path method
Kurij Kazimir V.; Milajić Aleksandar V.; Beljaković Dejan D.
2014-01-01
Critical Path Method (CPM) technique has become widely recognized as valuable tool for the planning and scheduling large construction projects. The aim of this paper is to present an analytical method for finding the Critical Path in the precedence network diagram where the duration of each activity is represented by a trapezoidal fuzzy number. This Fuzzy Critical Path Method (FCPM) uses a defuzzification formula for trapezoidal fuzzy number and applies it on the total float (slack time) for ...
Analysis of construction dynamic plan using fuzzy critical path method
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Kurij Kazimir V.
2014-01-01
Full Text Available Critical Path Method (CPM technique has become widely recognized as valuable tool for the planning and scheduling large construction projects. The aim of this paper is to present an analytical method for finding the Critical Path in the precedence network diagram where the duration of each activity is represented by a trapezoidal fuzzy number. This Fuzzy Critical Path Method (FCPM uses a defuzzification formula for trapezoidal fuzzy number and applies it on the total float (slack time for each activity in the fuzzy precedence network to find the critical path. The method presented in this paper is very effective in determining the critical activities and finding the critical paths.
Flatness-based adaptive fuzzy control of chaotic finance dynamics
Rigatos, G.; Siano, P.; Loia, V.; Tommasetti, A.; Troisi, O.
2017-11-01
A flatness-based adaptive fuzzy control is applied to the problem of stabilization of the dynamics of a chaotic finance system, describing interaction between the interest rate, the investment demand and the price exponent. By proving that the system is differentially flat and by applying differential flatness diffeomorphisms, its transformation to the linear canonical (Brunovsky) is performed. For the latter description of the system, the design of a stabilizing state feedback controller becomes possible. A first problem in the design of such a controller is that the dynamic model of the finance system is unknown and thus it has to be identified with the use neurofuzzy approximators. The estimated dynamics provided by the approximators is used in the computation of the control input, thus establishing an indirect adaptive control scheme. The learning rate of the approximators is chosen from the requirement the system's Lyapunov function to have always a negative first-order derivative. Another problem that has to be dealt with is that the control loop is implemented only with the use of output feedback. To estimate the non-measurable state vector elements of the finance system, a state observer is implemented in the control loop. The computation of the feedback control signal requires the solution of two algebraic Riccati equations at each iteration of the control algorithm. Lyapunov stability analysis demonstrates first that an H-infinity tracking performance criterion is satisfied. This signifies elevated robustness against modelling errors and external perturbations. Moreover, the global asymptotic stability is proven for the control loop.
Consolidity analysis for fully fuzzy functions, matrices, probability and statistics
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Walaa Ibrahim Gabr
2015-03-01
Full Text Available The paper presents a comprehensive review of the know-how for developing the systems consolidity theory for modeling, analysis, optimization and design in fully fuzzy environment. The solving of systems consolidity theory included its development for handling new functions of different dimensionalities, fuzzy analytic geometry, fuzzy vector analysis, functions of fuzzy complex variables, ordinary differentiation of fuzzy functions and partial fraction of fuzzy polynomials. On the other hand, the handling of fuzzy matrices covered determinants of fuzzy matrices, the eigenvalues of fuzzy matrices, and solving least-squares fuzzy linear equations. The approach demonstrated to be also applicable in a systematic way in handling new fuzzy probabilistic and statistical problems. This included extending the conventional probabilistic and statistical analysis for handling fuzzy random data. Application also covered the consolidity of fuzzy optimization problems. Various numerical examples solved have demonstrated that the new consolidity concept is highly effective in solving in a compact form the propagation of fuzziness in linear, nonlinear, multivariable and dynamic problems with different types of complexities. Finally, it is demonstrated that the implementation of the suggested fuzzy mathematics can be easily embedded within normal mathematics through building special fuzzy functions library inside the computational Matlab Toolbox or using other similar software languages.
Fractional random walk lattice dynamics
Michelitsch, T. M.; Collet, B. A.; Riascos, A. P.; Nowakowski, A. F.; Nicolleau, F. C. G. A.
2017-02-01
We analyze time-discrete and time-continuous ‘fractional’ random walks on undirected regular networks with special focus on cubic periodic lattices in n = 1, 2, 3,.. dimensions. The fractional random walk dynamics is governed by a master equation involving fractional powers of Laplacian matrices {{L}\\fracα{2}}} where α =2 recovers the normal walk. First we demonstrate that the interval 0expressions for the transition matrix of the fractional random walk and closely related the average return probabilities. We further obtain the fundamental matrix {{Z}(α )} , and the mean relaxation time (Kemeny constant) for the fractional random walk. The representation for the fundamental matrix {{Z}(α )} relates fractional random walks with normal random walks. We show that the matrix elements of the transition matrix of the fractional random walk exihibit for large cubic n-dimensional lattices a power law decay of an n-dimensional infinite space Riesz fractional derivative type indicating emergence of Lévy flights. As a further footprint of Lévy flights in the n-dimensional space, the transition matrix and return probabilities of the fractional random walk are dominated for large times t by slowly relaxing long-wave modes leading to a characteristic {{t}-\\frac{n{α}} -decay. It can be concluded that, due to long range moves of fractional random walk, a small world property is emerging increasing the efficiency to explore the lattice when instead of a normal random walk a fractional random walk is chosen.
IMPROVING MARKOV RANDOM FIELD BASED SUPER RESOLUTION MAPPING THROUGH FUZZY PARAMETER INTEGRATION
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D. R . Welikanna
2012-07-01
Full Text Available The objective of this study was to improve the Markov Random Field (MRF based Super Resolution Mapping (SRM technique to account for the vague land-cover interpretations (class mixture and the intermediate conditions in an urban area. The algorithm has been improved to integrate the fuzzy mean and fuzzy covariance measurements, to a MRF based SRM scheme to optimize the classification results. The technique was tested on a WORLDVIEW-2 data set, acquired over a highway construction area, in Colombo, Sri Lanka. Based on the visual interpretation of the image, three major land-cover types of this area were identified for the study; those were vegetation, soil and exposed grass and impervious surface with low medium and high albedo. The membership values for each pixel were determined from training samples through Spectral Angle Mapper (SAM technique. The compulsory fuzzy mean and the covariance measurements were derived using these membership grades, and subsequently was applied in MRF based SRM technique. The primary reference data was generated using Maximum Likelihood Classification (MLC performed on the same data which was resampled to 1m resolution. The scale factor was set to be (S = 2, to generate SRM of 1m resolution. The smoothening parameter (λ which balances the prior and likelihood energy terms were tested in the range from 0.3 to 0.9. SRM were generated using fuzzy MRF and the conventional MRF models respectively. Results suggest that the fuzzy integrated model has improved the results with an overall accuracy of 85.60% and kappa value of 0.78 between the optimal results and the reference data, while in the conventional case it was 77.81% of overall accuracy with kappa being 0.65. Among the two MRF models, fuzzy parameter integrated model shows the highest agreement with class fractions from the reference image with a smallest average _MAE (MAE, Mean Absolute Error of 0.03.
Kwong, C K; Fung, K Y; Jiang, Huimin; Chan, K Y; Siu, Kin Wai Michael
2013-01-01
Affective design is an important aspect of product development to achieve a competitive edge in the marketplace. A neural-fuzzy network approach has been attempted recently to model customer satisfaction for affective design and it has been proved to be an effective one to deal with the fuzziness and non-linearity of the modeling as well as generate explicit customer satisfaction models. However, such an approach to modeling customer satisfaction has two limitations. First, it is not suitable for the modeling problems which involve a large number of inputs. Second, it cannot adapt to new data sets, given that its structure is fixed once it has been developed. In this paper, a modified dynamic evolving neural-fuzzy approach is proposed to address the above mentioned limitations. A case study on the affective design of mobile phones was conducted to illustrate the effectiveness of the proposed methodology. Validation tests were conducted and the test results indicated that: (1) the conventional Adaptive Neuro-Fuzzy Inference System (ANFIS) failed to run due to a large number of inputs; (2) the proposed dynamic neural-fuzzy model outperforms the subtractive clustering-based ANFIS model and fuzzy c-means clustering-based ANFIS model in terms of their modeling accuracy and computational effort.
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C. K. Kwong
2013-01-01
Full Text Available Affective design is an important aspect of product development to achieve a competitive edge in the marketplace. A neural-fuzzy network approach has been attempted recently to model customer satisfaction for affective design and it has been proved to be an effective one to deal with the fuzziness and non-linearity of the modeling as well as generate explicit customer satisfaction models. However, such an approach to modeling customer satisfaction has two limitations. First, it is not suitable for the modeling problems which involve a large number of inputs. Second, it cannot adapt to new data sets, given that its structure is fixed once it has been developed. In this paper, a modified dynamic evolving neural-fuzzy approach is proposed to address the above mentioned limitations. A case study on the affective design of mobile phones was conducted to illustrate the effectiveness of the proposed methodology. Validation tests were conducted and the test results indicated that: (1 the conventional Adaptive Neuro-Fuzzy Inference System (ANFIS failed to run due to a large number of inputs; (2 the proposed dynamic neural-fuzzy model outperforms the subtractive clustering-based ANFIS model and fuzzy c-means clustering-based ANFIS model in terms of their modeling accuracy and computational effort.
On A Takagi-Sugeno Fuzzy Controller With Non-Homogenous Dynamics
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Radu-Emil PRECUP
2001-12-01
Full Text Available The paper proposes a Takagi-Sugeno fuzzy controller with non-homogenous controller dynamics with respect to the two input channels, that means, in the linear case, different transfer functions with respect to the reference input and to the controlled output. The considered controller is dedicated to a class of third-order integral-type plants, specific to the field of electrical drives, which can be characterised in their simplified linearised forms by standard models. For these models even conventional linear control structures give satisfaction. There is proposed a development method for the fuzzy controller, based on the fact that fuzzy controllers can be, in some certain conditions, well approximated by linear controllers and, so, the Extended Symmetrical Optimum (ESO method and the Modified Structure of ESO Method are applicable in this situation. The fuzzy controller and its corresponding development method are validated by an application example that can correspond to the speed control of an electrical drive.
Fast Threshold Image Segmentation Based on 2D Fuzzy Fisher and Random Local Optimized QPSO.
Chunming Zhang; Yongchun Xie; Da Liu; Li Wang
2017-03-01
In the paper, a real-time segmentation method that separates the target signal from the navigation image is proposed. In the approaching docking stage, the navigation image is composed of target and non-target signal, which are separately bright spot and space vehicle itself. Since the non-target signals is the main part of the navigation image, the traditional entropy-related criterions and Ostu-related criterions will bring inadequate segmentation, while the mere 2D Fisher criterion will causes over-segmentation, all the methods show their shortages in dealing with this kind of case. To guarantee a precise image segmentation, a revised 2D fuzzy Fisher is proposed in the paper to make a trade-off between positioning target regions and retaining target fuzzy boundaries. First, to reduce redundant computations in finding the threshold pair, a 2D fuzzy Fisher criterion-based integral image is established by way of simplifying the corresponding fuzzy domains. Then, to quicken the convergence, a random orthogonal component is added in its quasi-optimum particle to enhance its local searching capacity in each iteration. Experimental results show its competence of quick segmentation.
Fuzzy Modelling for Human Dynamics Based on Online Social Networks.
Cuenca-Jara, Jesus; Terroso-Saenz, Fernando; Valdes-Vela, Mercedes; Skarmeta, Antonio F
2017-08-24
Human mobility mining has attracted a lot of attention in the research community due to its multiple implications in the provisioning of innovative services for large metropolises. In this scope, Online Social Networks (OSN) have arisen as a promising source of location data to come up with new mobility models. However, the human nature of this data makes it rather noisy and inaccurate. In order to deal with such limitations, the present work introduces a framework for human mobility mining based on fuzzy logic. Firstly, a fuzzy clustering algorithm extracts the most active OSN areas at different time periods. Next, such clusters are the building blocks to compose mobility patterns. Furthermore, a location prediction service based on a fuzzy rule classifier has been developed on top of the framework. Finally, both the framework and the predictor has been tested with a Twitter and Flickr dataset in two large cities.
El-Nagar, Ahmad M
2018-01-01
In this study, a novel structure of a recurrent interval type-2 Takagi-Sugeno-Kang (TSK) fuzzy neural network (FNN) is introduced for nonlinear dynamic and time-varying systems identification. It combines the type-2 fuzzy sets (T2FSs) and a recurrent FNN to avoid the data uncertainties. The fuzzy firing strengths in the proposed structure are returned to the network input as internal variables. The interval type-2 fuzzy sets (IT2FSs) is used to describe the antecedent part for each rule while the consequent part is a TSK-type, which is a linear function of the internal variables and the external inputs with interval weights. All the type-2 fuzzy rules for the proposed RIT2TSKFNN are learned on-line based on structure and parameter learning, which are performed using the type-2 fuzzy clustering. The antecedent and consequent parameters of the proposed RIT2TSKFNN are updated based on the Lyapunov function to achieve network stability. The obtained results indicate that our proposed network has a small root mean square error (RMSE) and a small integral of square error (ISE) with a small number of rules and a small computation time compared with other type-2 FNNs. Copyright © 2017 ISA. Published by Elsevier Ltd. All rights reserved.
Modelling of dynamics through fuzzy enhanced high level petri net
Indian Academy of Sciences (India)
R. Narasimhan (Krishtel eMaging) 1461 1996 Oct 15 13:05:22
Peterson. 1981; Desrochers et al ... The various extensions of PNs include predicate/transition nets (P/T nets) (Generich &. Lautenbach 1979) ..... PCTLC is the set of control places; PTNLC is the set of transition places; PFZ is the set of fuzzy places.
Lin, Yang-Yin; Chang, Jyh-Yeong; Lin, Chin-Teng
2013-02-01
This paper presents a novel recurrent fuzzy neural network, called an interactively recurrent self-evolving fuzzy neural network (IRSFNN), for prediction and identification of dynamic systems. The recurrent structure in an IRSFNN is formed as an external loops and internal feedback by feeding the rule firing strength of each rule to others rules and itself. The consequent part in the IRSFNN is composed of a Takagi-Sugeno-Kang (TSK) or functional-link-based type. The proposed IRSFNN employs a functional link neural network (FLNN) to the consequent part of fuzzy rules for promoting the mapping ability. Unlike a TSK-type fuzzy neural network, the FLNN in the consequent part is a nonlinear function of input variables. An IRSFNNs learning starts with an empty rule base and all of the rules are generated and learned online through a simultaneous structure and parameter learning. An on-line clustering algorithm is effective in generating fuzzy rules. The consequent update parameters are derived by a variable-dimensional Kalman filter algorithm. The premise and recurrent parameters are learned through a gradient descent algorithm. We test the IRSFNN for the prediction and identification of dynamic plants and compare it to other well-known recurrent FNNs. The proposed model obtains enhanced performance results.
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Emer Bernal
2017-01-01
Full Text Available In this paper we are presenting a method using fuzzy logic for dynamic parameter adaptation in the imperialist competitive algorithm, which is usually known by its acronym ICA. The ICA algorithm was initially studied in its original form to find out how it works and what parameters have more effect upon its results. Based on this study, several designs of fuzzy systems for dynamic adjustment of the ICA parameters are proposed. The experiments were performed on the basis of solving complex optimization problems, particularly applied to benchmark mathematical functions. A comparison of the original imperialist competitive algorithm and our proposed fuzzy imperialist competitive algorithm was performed. In addition, the fuzzy ICA was compared with another metaheuristic using a statistical test to measure the advantage of the proposed fuzzy approach for dynamic parameter adaptation.
Fuzzy norm method for evaluating random vibration of airborne platform from limited PSD data
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Wang Zhongyu
2014-12-01
Full Text Available For random vibration of airborne platform, the accurate evaluation is a key indicator to ensure normal operation of airborne equipment in flight. However, only limited power spectral density (PSD data can be obtained at the stage of flight test. Thus, those conventional evaluation methods cannot be employed when the distribution characteristics and priori information are unknown. In this paper, the fuzzy norm method (FNM is proposed which combines the advantages of fuzzy theory and norm theory. The proposed method can deeply dig system information from limited data, which probability distribution is not taken into account. Firstly, the FNM is employed to evaluate variable interval and expanded uncertainty from limited PSD data, and the performance of FNM is demonstrated by confidence level, reliability and computing accuracy of expanded uncertainty. In addition, the optimal fuzzy parameters are discussed to meet the requirements of aviation standards and metrological practice. Finally, computer simulation is used to prove the adaptability of FNM. Compared with statistical methods, FNM has superiority for evaluating expanded uncertainty from limited data. The results show that the reliability of calculation and evaluation is superior to 95%.
Interactive and fuzzy search: a dynamic way to explore MEDLINE.
Wang, Jiannan; Cetindil, Inci; Ji, Shengyue; Li, Chen; Xie, Xiaohui; Li, Guoliang; Feng, Jianhua
2010-09-15
The MEDLINE database, consisting of over 19 million publication records, is the primary source of information for biomedicine and health questions. Although the database itself has been growing rapidly, the search paradigm of MEDLINE has remained largely unchanged. Here, we propose a new system for exploring the entire MEDLINE collection, represented by two unique features: (i) interactive: providing instant feedback to users' query letter by letter, and (ii) fuzzy: allowing approximate search. We develop novel index structures and search algorithms to make such a search model possible. We also develop incremental-update techniques to keep the data up to date. Interactive and fuzzy searching algorithms for exploring MEDLINE are implemented in a system called iPubMed, freely accessible over the web at http://ipubmed.ics.uci.edu/ and http://tastier.cs.tsinghua.edu.cn/ipubmed.
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Oscar Castillo
2013-01-01
Full Text Available Ant Colony Optimization (ACO is a population-based constructive meta-heuristic that exploits a form of past performance memory inspired by the foraging behaviour of real ants. The behaviour of the ACO algorithm is highly dependent on the values defined for its parameters. Adaptation and parameter control are recurring themes in the field of bio-inspired algorithms. The present paper explores a new approach to diversity control in ACO. The central idea is to avoid or slow down full convergence through the dynamic variation of certain parameters. The performance of different variants of the ACO algorithm was observed to choose one as the basis for the proposed approach. A convergence fuzzy logic controller with the objective of maintaining diversity at some level to avoid premature convergence was created. Encouraging results have been obtained on its application to the design of fuzzy controllers. In particular, the optimization of membership functions for a unicycle mobile robot trajectory control is presented with the proposed method.
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Karam Hwang
2015-01-01
Full Text Available An autonomous coil alignment system (ACAS using fuzzy steering control is proposed for vehicles with dynamic wireless charging. The misalignment between the power receiver coil and power transmitter coil is determined based on the voltage difference between two coils installed on the front-left/front-right of the power receiver coil and is corrected through autonomous steering using fuzzy control. The fuzzy control is chosen over other control methods for implementation in ACAS due to the nonlinear characteristic between voltage difference and lateral misalignment distance, as well as the imprecise and constantly varying voltage readings from sensors. The operational validity and feasibility of the ACAS are verified through simulation, where the vehicle equipped with ACAS is able to align with the power transmitter in the road majority of the time during operation, which also implies achieving better wireless power delivery.
Uncertainty analysis for dynamic properties of MEMS resonator supported by fuzzy arithmetics
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A Martowicz
2016-04-01
Full Text Available In the paper the application of uncertainty analysis performed formicroelectromechanical resonator is presented. Main objective ofundertaken analysis is to assess the propagation of considered uncertaintiesin the variation of chosen dynamic characteristics of Finite Element model ofmicroresonator. Many different model parameters have been assumed tobe uncertain: geometry and material properties. Apart from total uncertaintypropagation, sensitivity analysis has been carried out to study separateinfluences of all input uncertain characteristics. Uncertainty analysis has beenperformed by means of fuzzy arithmetics in which alpha-cut strategy hasbeen applied to assemble output fuzzy number. Monte Carlo Simulation andGenetic Algorithms have been employed to calculate intervals connectedwith each alpha-cut of searched fuzzy number. Elaborated model ofmicroresonator has taken into account in a simplified way the presence ofsurrounding air and constant electrostatic field.
Adaptive Fuzzy Computed-Torque Control for Robot Manipulator with Uncertain Dynamics
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Yuan Chen
2012-12-01
Full Text Available To overcome the disadvantages of conventional computed-torque control and fuzzy control, and to exploit their attractive features, this paper proposes two types of adaptive control scheme combining conventional computed-torque control and different fuzzy compensators for the robust tracking control of robotic manipulators with structured and unstructured uncertainties. Fuzzy compensators based on feed-forward and feed-back are developed to compensate these uncertain dynamics. On the basis of Lyapunov stability theory, a tracking error limit is derived for the closed-loop control system and the convergence and stability of the control schemes are proved. Comparisons of their performances with conventional computed-torque controllers under the condition of these uncertainties are carried out. The validity of the two types of adaptive control scheme is shown by numerical simulations of a three-link rotary robot manipulator.
A genetic-based neuro-fuzzy approach for modeling and control of dynamical systems.
Farag, W A; Quintana, V H; Lambert-Torres, G
1998-01-01
Linguistic modeling of complex irregular systems constitutes the heart of many control and decision making systems, and fuzzy logic represents one of the most effective algorithms to build such linguistic models. In this paper, a linguistic (qualitative) modeling approach is proposed. The approach combines the merits of the fuzzy logic theory, neural networks, and genetic algorithms (GA's). The proposed model is presented in a fuzzy-neural network (FNN) form which can handle both quantitative (numerical) and qualitative (linguistic) knowledge. The learning algorithm of an FNN is composed of three phases. The first phase is used to find the initial membership functions of the fuzzy model. In the second phase, a new algorithm is developed and used to extract the linguistic-fuzzy rules. In the third phase, a multiresolutional dynamic genetic algorithm (MRD-GA) is proposed and used for optimized tuning of membership functions of the proposed model. Two well-known benchmarks are used to evaluate the performance of the proposed modeling approach, and compare it with other modeling approaches.
Entanglement dynamics in random media
Menezes, G.; Svaiter, N. F.; Zarro, C. A. D.
2017-12-01
We study how the entanglement dynamics between two-level atoms is impacted by random fluctuations of the light cone. In our model the two-atom system is envisaged as an open system coupled with an electromagnetic field in the vacuum state. We employ the quantum master equation in the Born-Markov approximation in order to describe the completely positive time evolution of the atomic system. We restrict our investigations to the situation in which the atoms are coupled individually to two spatially separated cavities, one of which displays the emergence of light-cone fluctuations. In such a disordered cavity, we assume that the coefficients of the Klein-Gordon equation are random functions of the spatial coordinates. The disordered medium is modeled by a centered, stationary, and Gaussian process. We demonstrate that disorder has the effect of slowing down the entanglement decay. We conjecture that in a strong-disorder environment the mean life of entangled states can be enhanced in such a way as to almost completely suppress quantum nonlocal decoherence.
International Nuclear Information System (INIS)
Hu, Yan; Wen, Jing-ya; Li, Xiao-li; Wang, Da-zhou; Li, Yu
2013-01-01
Highlights: • Using interval mathematics to describe spatial and temporal variability and parameter uncertainty. • Using fuzzy theory to quantify variability of environmental guideline values. • Using probabilistic approach to integrate interval concentrations and fuzzy environmental guideline. • Establishment of dynamic multimedia environmental integrated risk assessment framework. -- Abstract: A dynamic multimedia fuzzy-stochastic integrated environmental risk assessment approach was developed for contaminated sites management. The contaminant concentrations were simulated by a validated interval dynamic multimedia fugacity model, and different guideline values for the same contaminant were represented as a fuzzy environmental guideline. Then, the probability of violating environmental guideline (Pv) can be determined by comparison between the modeled concentrations and the fuzzy environmental guideline, and the constructed relationship between the Pvs and environmental risk levels was used to assess the environmental risk level. The developed approach was applied to assess the integrated environmental risk at a case study site in China, simulated from 1985 to 2020. Four scenarios were analyzed, including “residential land” and “industrial land” environmental guidelines under “strict” and “loose” strictness. It was found that PAH concentrations will increase steadily over time, with soil found to be the dominant sink. Source emission in soil was the leading input and atmospheric sedimentation was the dominant transfer process. The integrated environmental risks primarily resulted from petroleum spills and coke ovens, while the soil environmental risks came from coal combustion. The developed approach offers an effective tool for quantifying variability and uncertainty in the dynamic multimedia integrated environmental risk assessment and the contaminated site management
Fuzzy Logic Navigation and Obstacle Avoidance by a Mobile Robot in an Unknown Dynamic Environment
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Mohammed Faisal
2013-01-01
Full Text Available Mobile robot navigation has remained an open problem over the last two decades. Mobile robots are required to navigate in unknown and dynamic environments, and in recent years the use of mobile robots in material handling has considerably increased. Usually workers push carts around warehouses and manually handle orders which is not very cost-effective. To this end, a potential method to control a swarm of mobile robots in a warehouse with static and dynamic obstacles is to use the wireless control approach. Further, to be able to control different types of mobile robots in the warehouse, the fuzzy logic control approach has been chosen. Therefore, in this paper, an on-line navigation technique for a wheeled mobile robot (WMR in an unknown dynamic environment using fuzzy logic techniques is investigated. In this paper, we aim to use the robot in application in a warehouse. Experimental results show the effectiveness of the proposed algorithm.
Fuzzy Constrained Predictive Optimal Control of High Speed Train with Actuator Dynamics
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Xi Wang
2016-01-01
Full Text Available We investigate the problem of fuzzy constrained predictive optimal control of high speed train considering the effect of actuator dynamics. The dynamics feature of the high speed train is modeled as a cascade of cars connected by flexible couplers, and the formulation is mathematically transformed into a Takagi-Sugeno (T-S fuzzy model. The goal of this study is to design a state feedback control law at each decision step to enhance safety, comfort, and energy efficiency of high speed train subject to safety constraints on the control input. Based on Lyapunov stability theory, the problem of optimizing an upper bound on the cruise control cost function subject to input constraints is reduced to a convex optimization problem involving linear matrix inequalities (LMIs. Furthermore, we analyze the influences of second-order actuator dynamics on the fuzzy constrained predictive controller, which shows risk of potentially deteriorating the overall system. Employing backstepping method, an actuator compensator is proposed to accommodate for the influence of the actuator dynamics. The experimental results show that with the proposed approach high speed train can track the desired speed, the relative coupler displacement between the neighbouring cars is stable at the equilibrium state, and the influence of actuator dynamics is reduced, which demonstrate the validity and effectiveness of the proposed approaches.
Dynamic computing random access memory
International Nuclear Information System (INIS)
Traversa, F L; Bonani, F; Pershin, Y V; Di Ventra, M
2014-01-01
The present von Neumann computing paradigm involves a significant amount of information transfer between a central processing unit and memory, with concomitant limitations in the actual execution speed. However, it has been recently argued that a different form of computation, dubbed memcomputing (Di Ventra and Pershin 2013 Nat. Phys. 9 200–2) and inspired by the operation of our brain, can resolve the intrinsic limitations of present day architectures by allowing for computing and storing of information on the same physical platform. Here we show a simple and practical realization of memcomputing that utilizes easy-to-build memcapacitive systems. We name this architecture dynamic computing random access memory (DCRAM). We show that DCRAM provides massively-parallel and polymorphic digital logic, namely it allows for different logic operations with the same architecture, by varying only the control signals. In addition, by taking into account realistic parameters, its energy expenditures can be as low as a few fJ per operation. DCRAM is fully compatible with CMOS technology, can be realized with current fabrication facilities, and therefore can really serve as an alternative to the present computing technology. (paper)
Combined fuzzy logic and random walker algorithm for PET image tumor delineation.
Soufi, Motahare; Kamali-Asl, Alireza; Geramifar, Parham; Abdoli, Mehrsima; Rahmim, Arman
2016-02-01
The random walk (RW) technique serves as a powerful tool for PET tumor delineation, which typically involves significant noise and/or blurring. One challenging step is hard decision-making in pixel labeling. Fuzzy logic techniques have achieved increasing application in edge detection. We aimed to combine the advantages of fuzzy edge detection with the RW technique to improve PET tumor delineation. A fuzzy inference system was designed for tumor edge detection from RW probabilities. Three clinical PET/computed tomography datasets containing 12 liver, 13 lung, and 18 abdomen tumors were analyzed, with manual expert tumor contouring as ground truth. The standard RW and proposed combined method were compared quantitatively using the dice similarity coefficient, the Hausdorff distance, and the mean standard uptake value. The dice similarity coefficient of the proposed method versus standard RW showed significant mean improvements of 21.0±7.2, 12.3±5.8, and 18.4%±6.1% for liver, lung, and abdominal tumors, respectively, whereas the mean improvements in the Hausdorff distance were 3.6±1.4, 1.3±0.4, 1.8±0.8 mm, and the mean improvements in SUVmean error were 15.5±6.3, 11.7±8.6, and 14.1±6.8% (all P's<0.001). For all tumor sizes, the proposed method outperformed the RW algorithm. Furthermore, tumor edge analysis demonstrated further enhancement of the performance of the algorithm, relative to the RW method, with decreasing edge gradients. The proposed technique improves PET lesion delineation at different tumor sites. It depicts greater effectiveness in tumors with smaller size and/or low edge gradients, wherein most PET segmentation algorithms encounter serious challenges. Favorable execution time and accurate performance of the algorithm make it a great tool for clinical applications.
Parkinson's disease Assessment using Fuzzy Expert System and Nonlinear Dynamics
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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.
Perturbative dynamics of fuzzy spheres at large N
International Nuclear Information System (INIS)
Azuma, Takehiro; Nagao, Keiichi; Nishimura, Jun
2005-01-01
We clarify some peculiar aspects of the perturbative expansion around a classical fuzzy-sphere solution in matrix models with a cubic term. While the effective action in the large-N limit is saturated at the one-loop level, we find that the 'one-loop dominance' does not hold for generic observables due to one-particle reducible diagrams. However, we may exploit the one-loop dominance for the effective action and obtain various observables to all orders from one-loop calculation by simply shifting the center of expansion to the 'quantum solution', which extremizes the effective action. We confirm the validity of this method by comparison with the direct two-loop calculation and with Monte Carlo results in the 3d Yang-Mills-Chern-Simons matrix model. From the all order result we find that the perturbative expansion has a finite radius of convergence
Study on 2D random medium inversion algorithm based on Fuzzy C-means Clustering theory
Xu, Z.; Zhu, P.; Gu, Y.; Yang, X.; Jiang, J.
2015-12-01
Abstract: In seismic exploration for metal deposits, the traditional seismic inversion method based on layered homogeneous medium theory seems difficult to inverse small scale inhomogeneity and spatial variation of the actual medium. The reason is that physical properties of actual medium are more likely random distribution rather than layered. Thus, it is necessary to investigate a random medium inversion algorithm. The velocity of 2D random medium can be described as a function of five parameters: the background velocity (V0), the standard deviation of velocity (σ), the horizontal and vertical autocorrelation lengths (A and B), and the autocorrelation angle (θ). In this study, we propose an inversion algorithm for random medium based on the Fuzzy C-means Clustering (FCM) theory, whose basic idea is that FCM is used to control the inversion process to move forward to the direction we desired by clustering the estimated parameters into groups. Our method can be divided into three steps: firstly, the three parameters (A, B, θ) are estimated from 2D post-stack seismic data using the non-stationary random medium parameter estimation method, and then the estimated parameters are clustered to different groups according to FCM; secondly, the initial random medium model is constructed with clustered groups and the rest two parameters (V0 and σ) obtained from the well logging data; at last, inversion of the random medium are conducted to obtain velocity, impedance and random medium parameters using the Conjugate Gradient Method. The inversion experiments of synthetic seismic data show that the velocity models inverted by our algorithm are close to the real velocity distribution and the boundary of different media can be distinguished clearly.Key words: random medium, inversion, FCM, parameter estimation
Taheri, Mehdi; Sheikholeslam, Farid; Najafi, Majddedin; Zekri, Maryam
2017-07-01
In this paper, consensus problem is considered for second order multi-agent systems with unknown nonlinear dynamics under undirected graphs. A novel distributed control strategy is suggested for leaderless systems based on adaptive fuzzy wavelet networks. Adaptive fuzzy wavelet networks are employed to compensate for the effect of unknown nonlinear dynamics. Moreover, the proposed method is developed for leader following systems and leader following systems with state time delays. Lyapunov functions are applied to prove uniformly ultimately bounded stability of closed loop systems and to obtain adaptive laws. Three simulation examples are presented to illustrate the effectiveness of the proposed control algorithms. Copyright © 2017 ISA. Published by Elsevier Ltd. All rights reserved.
Optimal Fuzzy and Dynamics Design of Ecological Sandwich Panel Vessel Roofs
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Heikki Martikka
2011-01-01
Full Text Available In this study the basic engineering principles, goals, and constraints are all combined with fuzzy methodology and applied to optimally design sandwich panel circular plate roofs for large vessels loaded statically and dynamically. These panels are made up of two stiff, strong veneer skins separated by vertical and peripheral stiffener plates. Advantages are high strength, lightweight, and sustainability. In the present approach, first the goals and constraints of the end user are identified and expressed as decision variables which are formulated using the engineering variables for materials, geometry, and function. Then same consistent fuzzy satisfaction functions are formed over the desired ranges to suit the customer's desires. The risk of extreme dynamic loadings exciting resonance is studied by natural frequency and mode analysis by FEM and analytical models. The results show the most critical locations and give guidelines for innovative remedies of the concept before detailed FEM analyses to finalize the design.
Hasanien, Hany M.; Al-Ammar, Essam A.
2012-11-01
Doubly fed induction generator (DFIG) based wind farm is today the most widely used concept. This paper presents dynamic response enhancement of DFIG based wind farm under remote fault conditions using the fuzzy logic controller. The goal of the work is to improve the dynamic response of DFIG based wind farm during and after the clearance of fault using the proposed controller. The stability of wind farm during and after the clearance of fault is investigated. The effectiveness of the fuzzy logic controller is then compared with that of a PI controller. The validity of the controllers in restoring the wind farms normal operation after the clearance of fault is illustrated by the simulation results which are carried out using MATLAB/SIMULINK. Simulation results are analyzed under different fault conditions.
Power System Stabilizer Driven by an Adaptive Fuzzy Set for Better Dynamic Performance
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H. F. Soliman
2006-01-01
Full Text Available This paper presents a novel application of a fuzzy logic controller (FLC driven by an adaptive fuzzy set (AFS for a power system stabilizer (PSS.The proposed FLC, driven by AFS, is compared with a classical FLC, driven by a fixed fuzzy set (FFS. Both FLC algorithms use the speed error and its rate of change as input vectors. A single generator equipped with FLC-PSS and connected to an infinite bus bar through double transmission lines is considered. Both FLCs, using AFS and FFS, are simulated and tested when the system is subjected to different step changes in the reference value. The simulation results of the proposed FLC, using the adaptive fuzzy set, give a better dynamic response of the overall system by improving the damping coefficient and decreasing the rise time and settling time compared with classical FLC using FFS. The proposed FLC using AFS also reduces the computational time of the FLC as the number of rules is reduced.
Hybrid Generalised Additive Type-2 Fuzzy-Wavelet-Neural Network in Dynamic Data Mining
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Bodyanskiy Yevgeniy
2015-12-01
Full Text Available In the paper, a new hybrid system of computational intelligence is proposed. This system combines the advantages of neuro-fuzzy system of Takagi-Sugeno-Kang, type-2 fuzzy logic, wavelet neural networks and generalised additive models of Hastie-Tibshirani. The proposed system has universal approximation properties and learning capability based on the experimental data sets which pertain to the neural networks and neuro-fuzzy systems; interpretability and transparency of the obtained results due to the soft computing systems and, first of all, due to type-2 fuzzy systems; possibility of effective description of local signal and process features due to the application of systems based on wavelet transform; simplicity and speed of learning process due to generalised additive models. The proposed system can be used for solving a wide class of dynamic data mining tasks, which are connected with non-stationary, nonlinear stochastic and chaotic signals. Such a system is sufficiently simple in numerical implementation and is characterised by a high speed of learning and information processing.
Automatic Lung Tumor Segmentation on PET/CT Images Using Fuzzy Markov Random Field Model
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Yu Guo
2014-01-01
Full Text Available The combination of positron emission tomography (PET and CT images provides complementary functional and anatomical information of human tissues and it has been used for better tumor volume definition of lung cancer. This paper proposed a robust method for automatic lung tumor segmentation on PET/CT images. The new method is based on fuzzy Markov random field (MRF model. The combination of PET and CT image information is achieved by using a proper joint posterior probability distribution of observed features in the fuzzy MRF model which performs better than the commonly used Gaussian joint distribution. In this study, the PET and CT simulation images of 7 non-small cell lung cancer (NSCLC patients were used to evaluate the proposed method. Tumor segmentations with the proposed method and manual method by an experienced radiation oncologist on the fused images were performed, respectively. Segmentation results obtained with the two methods were similar and Dice’s similarity coefficient (DSC was 0.85 ± 0.013. It has been shown that effective and automatic segmentations can be achieved with this method for lung tumors which locate near other organs with similar intensities in PET and CT images, such as when the tumors extend into chest wall or mediastinum.
Dynamic Allan Variance Analysis Method with Time-Variant Window Length Based on Fuzzy Control
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Shanshan Gu
2015-01-01
Full Text Available To solve the problem that dynamic Allan variance (DAVAR with fixed length of window cannot meet the identification accuracy requirement of fiber optic gyro (FOG signal over all time domains, a dynamic Allan variance analysis method with time-variant window length based on fuzzy control is proposed. According to the characteristic of FOG signal, a fuzzy controller with the inputs of the first and second derivatives of FOG signal is designed to estimate the window length of the DAVAR. Then the Allan variances of the signals during the time-variant window are simulated to obtain the DAVAR of the FOG signal to describe the dynamic characteristic of the time-varying FOG signal. Additionally, a performance evaluation index of the algorithm based on radar chart is proposed. Experiment results show that, compared with different fixed window lengths DAVAR methods, the change of FOG signal with time can be identified effectively and the evaluation index of performance can be enhanced by 30% at least by the DAVAR method with time-variant window length based on fuzzy control.
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Dipak Kumar Jana
2013-01-01
Full Text Available An inventory model for deteriorating item is considered in a random planning horizon under inflation and time value money. The model is described in two different environments: random and fuzzy random. The proposed model allows stock-dependent consumption rate and shortages with partial backlogging. In the fuzzy stochastic model, possibility chance constraints are used for defuzzification of imprecise expected total profit. Finally, genetic algorithm (GA and fuzzy simulation-based genetic algorithm (FSGA are used to make decisions for the above inventory models. The models are illustrated with some numerical data. Sensitivity analysis on expected profit function is also presented. Scope and Purpose. The traditional inventory model considers the ideal case in which depletion of inventory is caused by a constant demand rate. However, to keep sales higher, the inventory level would need to remain high. Of course, this would also result in higher holding or procurement cost. Also, in many real situations, during a longer-shortage period some of the customers may refuse the management. For instance, for fashionable commodities and high-tech products with short product life cycle, the willingness for a customer to wait for backlogging is diminishing with the length of the waiting time. Most of the classical inventory models did not take into account the effects of inflation and time value of money. But in the past, the economic situation of most of the countries has changed to such an extent due to large-scale inflation and consequent sharp decline in the purchasing power of money. So, it has not been possible to ignore the effects of inflation and time value of money any more. The purpose of this paper is to maximize the expected profit in the random planning horizon.
Pan, Indranil; Das, Saptarshi; Gupta, Amitava
2011-01-01
An optimal PID and an optimal fuzzy PID have been tuned by minimizing the Integral of Time multiplied Absolute Error (ITAE) and squared controller output for a networked control system (NCS). The tuning is attempted for a higher order and a time delay system using two stochastic algorithms viz. the Genetic Algorithm (GA) and two variants of Particle Swarm Optimization (PSO) and the closed loop performances are compared. The paper shows that random variation in network delay can be handled efficiently with fuzzy logic based PID controllers over conventional PID controllers. Copyright © 2010 ISA. Published by Elsevier Ltd. All rights reserved.
Fuzzy logic applied to the modeling of water dynamics in an Oxisol in northeastern Brazil
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Antônio Cláudio Marques Afonso
2014-04-01
Full Text Available Modeling of water movement in non-saturated soil usually requires a large number of parameters and variables, such as initial soil water content, saturated water content and saturated hydraulic conductivity, which can be assessed relatively easily. Dimensional flow of water in the soil is usually modeled by a nonlinear partial differential equation, known as the Richards equation. Since this equation cannot be solved analytically in certain cases, one way to approach its solution is by numerical algorithms. The success of numerical models in describing the dynamics of water in the soil is closely related to the accuracy with which the water-physical parameters are determined. That has been a big challenge in the use of numerical models because these parameters are generally difficult to determine since they present great spatial variability in the soil. Therefore, it is necessary to develop and use methods that properly incorporate the uncertainties inherent to water displacement in soils. In this paper, a model based on fuzzy logic is used as an alternative to describe water flow in the vadose zone. This fuzzy model was developed to simulate the displacement of water in a non-vegetated crop soil during the period called the emergency phase. The principle of this model consists of a Mamdani fuzzy rule-based system in which the rules are based on the moisture content of adjacent soil layers. The performances of the results modeled by the fuzzy system were evaluated by the evolution of moisture profiles over time as compared to those obtained in the field. The results obtained through use of the fuzzy model provided satisfactory reproduction of soil moisture profiles.
Fuzzy c-Means and Cluster Ensemble with Random Projection for Big Data Clustering
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Mao Ye
2016-01-01
Full Text Available Because of its positive effects on dealing with the curse of dimensionality in big data, random projection for dimensionality reduction has become a popular method recently. In this paper, an academic analysis of influences of random projection on the variability of data set and the dependence of dimensions has been proposed. Together with the theoretical analysis, a new fuzzy c-means (FCM clustering algorithm with random projection has been presented. Empirical results verify that the new algorithm not only preserves the accuracy of original FCM clustering, but also is more efficient than original clustering and clustering with singular value decomposition. At the same time, a new cluster ensemble approach based on FCM clustering with random projection is also proposed. The new aggregation method can efficiently compute the spectral embedding of data with cluster centers based representation which scales linearly with data size. Experimental results reveal the efficiency, effectiveness, and robustness of our algorithm compared to the state-of-the-art methods.
Dynamic Optimization for IPS2 Resource Allocation Based on Improved Fuzzy Multiple Linear Regression
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Maokuan Zheng
2017-01-01
Full Text Available The study mainly focuses on resource allocation optimization for industrial product-service systems (IPS2. The development of IPS2 leads to sustainable economy by introducing cooperative mechanisms apart from commodity transaction. The randomness and fluctuation of service requests from customers lead to the volatility of IPS2 resource utilization ratio. Three basic rules for resource allocation optimization are put forward to improve system operation efficiency and cut unnecessary costs. An approach based on fuzzy multiple linear regression (FMLR is developed, which integrates the strength and concision of multiple linear regression in data fitting and factor analysis and the merit of fuzzy theory in dealing with uncertain or vague problems, which helps reduce those costs caused by unnecessary resource transfer. The iteration mechanism is introduced in the FMLR algorithm to improve forecasting accuracy. A case study of human resource allocation optimization in construction machinery industry is implemented to test and verify the proposed model.
Lledó, Luis D; Badesa, Francisco J; Almonacid, Miguel; Cano-Izquierdo, José M; Sabater-Navarro, José M; Fernández, Eduardo; Garcia-Aracil, Nicolás
2015-01-01
This paper presents the application of an Adaptive Resonance Theory (ART) based on neural networks combined with Fuzzy Logic systems to classify physiological reactions of subjects performing robot-assisted rehabilitation therapies. First, the theoretical background of a neuro-fuzzy classifier called S-dFasArt is presented. Then, the methodology and experimental protocols to perform a robot-assisted neurorehabilitation task are described. Our results show that the combination of the dynamic nature of S-dFasArt classifier with a supervisory module are very robust and suggest that this methodology could be very useful to take into account emotional states in robot-assisted environments and help to enhance and better understand human-robot interactions.
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Luis D Lledó
Full Text Available This paper presents the application of an Adaptive Resonance Theory (ART based on neural networks combined with Fuzzy Logic systems to classify physiological reactions of subjects performing robot-assisted rehabilitation therapies. First, the theoretical background of a neuro-fuzzy classifier called S-dFasArt is presented. Then, the methodology and experimental protocols to perform a robot-assisted neurorehabilitation task are described. Our results show that the combination of the dynamic nature of S-dFasArt classifier with a supervisory module are very robust and suggest that this methodology could be very useful to take into account emotional states in robot-assisted environments and help to enhance and better understand human-robot interactions.
A Dynamic Interval-Valued Intuitionistic Fuzzy Sets Applied to Pattern Recognition
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Zhenhua Zhang
2013-01-01
Full Text Available We present dynamic interval-valued intuitionistic fuzzy sets (DIVIFS, which can improve the recognition accuracy when they are applied to pattern recognition. By analyzing the degree of hesitancy, we propose some DIVIFS models from intuitionistic fuzzy sets (IFS and interval-valued IFS (IVIFS. And then we present a novel ranking condition on the distance of IFS and IVIFS and introduce some distance measures of DIVIFS satisfying the ranking condition. Finally, a pattern recognition example applied to medical diagnosis decision making is given to demonstrate the application of DIVIFS and its distances. The simulation results show that the DIVIFS method is more comprehensive and flexible than the IFS method and the IVIFS method.
Fuzzy Boundary and Fuzzy Semiboundary
M. Athar; B. Ahmad
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...
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Mohammad Ali Fariborzi Araghi
2016-02-01
Full Text Available In this paper, a reliable scheme is proposed to solve fuzzy differential equations by fuzzy Runge-Kutta method of order $m$. For this purpose, the stochastic arithmetic and CESTAC method are applied to validate the results. In order to implement the C++ codes, the CADNA library is used. In this case, the optimal step size is found. The examples illustrate the efficiency and importance of using the stochastic arithmetic in place of the floating-point arithmetic.
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.
International Nuclear Information System (INIS)
Acton, P.D.; Pilowsky, L.S.; Kung, H.F.; Ell, P.J.
1999-01-01
The segmentation of medical images is one of the most important steps in the analysis and quantification of imaging data. However, partial volume artefacts make accurate tissue boundary definition difficult, particularly for images with lower resolution commonly used in nuclear medicine. In single-photon emission tomography (SPET) neuroreceptor studies, areas of specific binding are usually delineated by manually drawing regions of interest (ROIs), a time-consuming and subjective process. This paper applies the technique of fuzzy c-means clustering (FCM) to automatically segment dynamic neuroreceptor SPET images. Fuzzy clustering was tested using a realistic, computer-generated, dynamic SPET phantom derived from segmenting an MR image of an anthropomorphic brain phantom. Also, the utility of applying FCM to real clinical data was assessed by comparison against conventional ROI analysis of iodine-123 iodobenzamide (IBZM) binding to dopamine D 2 /D 3 receptors in the brains of humans. In addition, a further test of the methodology was assessed by applying FCM segmentation to [ 123 I]IDAM images (5-iodo-2-[[2-2-[(dimethylamino)methyl]phenyl]thio] benzyl alcohol) of serotonin transporters in non-human primates. In the simulated dynamic SPET phantom, over a wide range of counts and ratios of specific binding to background, FCM correlated very strongly with the true counts (correlation coefficient r 2 >0.99, P 123 I]IBZM data comparable with manual ROI analysis, with the binding ratios derived from both methods significantly correlated (r 2 =0.83, P<0.0001). Fuzzy clustering is a powerful tool for the automatic, unsupervised segmentation of dynamic neuroreceptor SPET images. Where other automated techniques fail completely, and manual ROI definition would be highly subjective, FCM is capable of segmenting noisy images in a robust and repeatable manner. (orig.)
Fuzzy Logic Controlled DC-DC Converter Based Dynamic Voltage Restorer
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Mustafa İnci
2015-12-01
Full Text Available This paper presents fuzzy logic controlled dc-dc boost converter based Dynamic Voltage Restorer (DVR to compensate severe voltage sag problems in an electrical system. DVR absorbs real power from battery to compensate voltage sags in the system. This condition causes reduction in voltage magnitude of dc-link capacitor. Additionally, DVR requires large dc capacitors to compensate long and severe voltage sags in the system. In this study, dc-dc boost converter is connected to DVR for keeping dc link voltage constant. For this propose, a control algorithm based on Fuzzy Logic (FL control is developed for dc-dc boost converter. The main contribution of this study is that Fuzzy Logic (FL is firstly used to generate reference signal for PWM signals of dc-dc converter applied in DVR. FL is a very flexible controller which keeps the dc link voltage constant during voltage sag. The performance results of proposed study are verified with PSCAD/EMDTC.
Fuzzy Counter Propagation Neural Network Control for a Class of Nonlinear Dynamical Systems
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Vandana Sakhre
2015-01-01
Full Text Available Fuzzy Counter Propagation Neural Network (FCPN controller design is developed, for a class of nonlinear dynamical systems. In this process, the weight connecting between the instar and outstar, that is, input-hidden and hidden-output layer, respectively, is adjusted by using Fuzzy Competitive Learning (FCL. FCL paradigm adopts the principle of learning, which is used to calculate Best Matched Node (BMN which is proposed. This strategy offers a robust control of nonlinear dynamical systems. FCPN is compared with the existing network like Dynamic Network (DN and Back Propagation Network (BPN on the basis of Mean Absolute Error (MAE, Mean Square Error (MSE, Best Fit Rate (BFR, and so forth. It envisages that the proposed FCPN gives better results than DN and BPN. The effectiveness of the proposed FCPN algorithms is demonstrated through simulations of four nonlinear dynamical systems and multiple input and single output (MISO and a single input and single output (SISO gas furnace Box-Jenkins time series data.
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Anish Pandey
2017-02-01
Full Text Available This article introduces a singleton type-1 fuzzy logic system (T1-SFLS controller and Fuzzy-WDO hybrid for the autonomous mobile robot navigation and collision avoidance in an unknown static and dynamic environment. The WDO (Wind Driven Optimization algorithm is used to optimize and tune the input/output membership function parameters of the fuzzy controller. The WDO algorithm is working based on the atmospheric motion of infinitesimal small air parcels navigates over an N-dimensional search domain. The performance of this proposed technique has compared through many computer simulations and real-time experiments by using Khepera-III mobile robot. As compared to the T1-SFLS controller the Fuzzy-WDO algorithm is found good agreement for mobile robot navigation.
Fuzzy CMAC With incremental Bayesian Ying-Yang learning and dynamic rule construction.
Nguyen, M N
2010-04-01
Inspired by the philosophy of ancient Chinese Taoism, Xu's Bayesian ying-yang (BYY) learning technique performs clustering by harmonizing the training data (yang) with the solution (ying). In our previous work, the BYY learning technique was applied to a fuzzy cerebellar model articulation controller (FCMAC) to find the optimal fuzzy sets; however, this is not suitable for time series data analysis. To address this problem, we propose an incremental BYY learning technique in this paper, with the idea of sliding window and rule structure dynamic algorithms. Three contributions are made as a result of this research. First, an online expectation-maximization algorithm incorporated with the sliding window is proposed for the fuzzification phase. Second, the memory requirement is greatly reduced since the entire data set no longer needs to be obtained during the prediction process. Third, the rule structure dynamic algorithm with dynamically initializing, recruiting, and pruning rules relieves the "curse of dimensionality" problem that is inherent in the FCMAC. Because of these features, the experimental results of the benchmark data sets of currency exchange rates and Mackey-Glass show that the proposed model is more suitable for real-time streaming data analysis.
Characterizations of N-Ary Fuzzy Set Operations Which Induce Homomorphic Random Set Operations
1982-01-01
ordinary set operation * being continuous iff \\, is independent. Then the characterization for 0 in Theorem 5 continuous willi respect to topologies (V. 9...946-9!, 1. 113J Manes. E.G.. Review of *Fuzzy Switching and Auto- mata: Theory and Applications’. SIAM Review. 23. 12) Dubois, D. and 11. Prade . Fuzzy
Molecular Dynamics of "Fuzzy" Transcriptional Activator-Coactivator Interactions.
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Natalie S Scholes
2016-05-01
Full Text Available Transcriptional activation domains (ADs are generally thought to be intrinsically unstructured, but capable of adopting limited secondary structure upon interaction with a coactivator surface. The indeterminate nature of this interface made it hitherto difficult to study structure/function relationships of such contacts. Here we used atomistic accelerated molecular dynamics (aMD simulations to study the conformational changes of the GCN4 AD and variants thereof, either free in solution, or bound to the GAL11 coactivator surface. We show that the AD-coactivator interactions are highly dynamic while obeying distinct rules. The data provide insights into the constant and variable aspects of orientation of ADs relative to the coactivator, changes in secondary structure and energetic contributions stabilizing the various conformers at different time points. We also demonstrate that a prediction of α-helical propensity correlates directly with the experimentally measured transactivation potential of a large set of mutagenized ADs. The link between α-helical propensity and the stimulatory activity of ADs has fundamental practical and theoretical implications concerning the recruitment of ADs to coactivators.
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Jahedul Islam Chowdhury
2018-04-01
Full Text Available The organic Rankine cycle (ORC-based waste heat recovery (WHR system operating under a supercritical condition has a higher potential of thermal efficiency and work output than a traditional subcritical cycle. However, the operation of supercritical cycles is more challenging due to the high pressure in the system and transient behavior of waste heat sources from industrial and automotive engines that affect the performance of the system and the evaporator, which is the most crucial component of the ORC. To take the transient behavior into account, the dynamic model of the evaporator using renowned finite volume (FV technique is developed in this paper. Although the FV model can capture the transient effects accurately, the model has a limitation for real-time control applications due to its time-intensive computation. To capture the transient effects and reduce the simulation time, a novel fuzzy-based nonlinear dynamic evaporator model is also developed and presented in this paper. The results show that the fuzzy-based model was able to capture the transient effects at a data fitness of over 90%, while it has potential to complete the simulation 700 times faster than the FV model. By integrating with other subcomponent models of the system, such as pump, expander, and condenser, the predicted system output and pressure have a mean average percentage error of 3.11% and 0.001%, respectively. These results suggest that the developed fuzzy-based evaporator and the overall ORC-WHR system can be used for transient simulations and to develop control strategies for real-time applications.
Brain Dynamics in Predicting Driving Fatigue Using a Recurrent Self-Evolving Fuzzy Neural Network.
Liu, Yu-Ting; Lin, Yang-Yin; Wu, Shang-Lin; Chuang, Chun-Hsiang; Lin, Chin-Teng
2016-02-01
This paper proposes a generalized prediction system called a recurrent self-evolving fuzzy neural network (RSEFNN) that employs an on-line gradient descent learning rule to address the electroencephalography (EEG) regression problem in brain dynamics for driving fatigue. The cognitive states of drivers significantly affect driving safety; in particular, fatigue driving, or drowsy driving, endangers both the individual and the public. For this reason, the development of brain-computer interfaces (BCIs) that can identify drowsy driving states is a crucial and urgent topic of study. Many EEG-based BCIs have been developed as artificial auxiliary systems for use in various practical applications because of the benefits of measuring EEG signals. In the literature, the efficacy of EEG-based BCIs in recognition tasks has been limited by low resolutions. The system proposed in this paper represents the first attempt to use the recurrent fuzzy neural network (RFNN) architecture to increase adaptability in realistic EEG applications to overcome this bottleneck. This paper further analyzes brain dynamics in a simulated car driving task in a virtual-reality environment. The proposed RSEFNN model is evaluated using the generalized cross-subject approach, and the results indicate that the RSEFNN is superior to competing models regardless of the use of recurrent or nonrecurrent structures.
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Keyvan Kamandanipour
2013-01-01
Full Text Available Recovery of used products has steadily become interesting issue for research due to economic reasons and growing environmental or legislative concern. This paper presents a closed-loop logistics network design based on reverse logistics models. A mixed integer linear programming model is implemented to integrate logistics network design in order to prevent the sub-optimality caused by the separate design of the forward and reverse networks. The study presents a single product and multi-stage logistics network problem for the new and return products not only to determine subsets of logistics centers to be opened, but also to determine transportation strategy, which satisfies demand imposed by facilities and minimizes fixed opening and total shipping costs. Since the deterministic estimation of some parameters such as demand and rate of return of used products in closed loop logistics models is impractical, an uncertain programming is proposed. In this case, we assume there are several economic conditions with predefined probabilities calculated from historical data. Then by means of expert's opinion, a fuzzy variable is offered as customer's demand under each economic condition. In addition, demand and rate of return of products for each customer zone is presented by fuzzy-random variables, similarly. Therefore, a fuzzy-random programming is used and a priority-based genetic algorithm is proposed to solve large-scale problems.
Fuzzy cognitive maps outmatch loop analysis in dynamic modeling of ecological systems
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Alessandro Ferrarini
2011-04-01
Full Text Available Modeling natural systems is challenging due to their complexity in terms of variables, interactions, and dynamics. Much of this complexity is rooted in the existence of multiple ways through which acting variables affect each other. Besides the simple direct effects, numerous indirect effects emerge in ecological systems. Through an illustrative example, I exemplify here several advantages of fuzzy cognitive maps (FCM over loop analysis (LA in dynamic modeling of ecological systems. In addition to being able to incorporate information about the magnitude of variables interactions, FCM can make predictions about multiple simultaneous perturbations. Furthermore, FCM allow for the simulation of different magnitude of initial perturbations to the system. Last, FCM estimate the amount of variable increase/decrease, not just the likely direction of change. Hence, even if LA is still much more used than FCM in the scientific literature, FCM can be considered fitter than LA in modeling ecological systems.
Dynamical generation of fuzzy extra dimensions, dimensional reduction and symmetry breaking
Aschieri, Paolo; Grammatikopoulos, Theodoros; Steinacker, Harold; Zoupanos, George
2006-09-01
We present a renormalizable 4-dimensional SU(Script N) gauge theory with a suitable multiplet of scalar fields, which dynamically develops extra dimensions in the form of a fuzzy sphere S2N. We explicitly find the tower of massive Kaluza-Klein modes consistent with an interpretation as gauge theory on M4 × S2, the scalars being interpreted as gauge fields on S2. The gauge group is broken dynamically, and the low-energy content of the model is determined. Depending on the parameters of the model the low-energy gauge group can be SU(n), or broken further to SU(n1) × SU(n2) × U(1), with mass scale determined by the size of the extra dimension.
Quantum chaotic dynamics and random polynomials
International Nuclear Information System (INIS)
Bogomolny, E.; Bohigas, O.; Leboeuf, P.
1995-11-01
The distribution of roots of polynomials of high degree with random coefficients is investigated which, among others, appear naturally in the context of 'quantum chaotic dynamics'. It is shown that under quite general conditions their roots tend to concentrate near the unit circle in the complex plane. In order to further increase this tendency, the particular case of self-inverse random polynomials is studied, and it is shown that for them a finite portion of all roots lies exactly on the unit circle. Correlation functions of these roots are also computed analytically, and compared to the correlations of eigenvalues of random matrices. The problem of ergodicity of chaotic wavefunctions is also considered. Special attention is devoted to the role of symmetries in the distribution of roots of random polynomials. (author)
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GEMAN, O.
2014-02-01
Full Text Available Neurological diseases like Alzheimer, epilepsy, Parkinson's disease, multiple sclerosis and other dementias influence the lives of patients, their families and society. Parkinson's disease (PD is a neurodegenerative disease that occurs due to loss of dopamine, a neurotransmitter and slow destruction of neurons. Brain area affected by progressive destruction of neurons is responsible for controlling movements, and patients with PD reveal rigid and uncontrollable gestures, postural instability, small handwriting and tremor. Commercial activity-promoting gaming systems such as the Nintendo Wii and Xbox Kinect can be used as tools for tremor, gait or other biomedical signals acquisitions. They also can aid for rehabilitation in clinical settings. This paper emphasizes the use of intelligent optical sensors or accelerometers in biomedical signal acquisition, and of the specific nonlinear dynamics parameters or fuzzy logic in Parkinson's disease tremor analysis. Nowadays, there is no screening test for early detection of PD. So, we investigated a method to predict PD, based on the image processing of the handwriting belonging to a candidate of PD. For classification and discrimination between healthy people and PD people we used Artificial Neural Networks (Radial Basis Function - RBF and Multilayer Perceptron - MLP and an Adaptive Neuro-Fuzzy Classifier (ANFC. In general, the results may be expressed as a prognostic (risk degree to contact PD.
Integrating System Dynamic and Fuzzy Logic for Economic Assessment of BOT Projects
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Farnad Nasirzadeh
2016-03-01
Full Text Available The selection of the most appropriate project for the investment is one of the most important decisions that should be made by the private investors. This problem is of vital importance in BOT projects, where the total investment as well as the investor's profit should be recovered by the project's income. There are several approaches proposed by the researchers to determine the best economical project in comparison to other projects. The previous researches, however, faced with some major defects. As an example, the effects of various factors affecting the project evaluation process as well as the existing risks and uncertainties are not taken into account. In this research, economic assessment of BOT projects is performed by integrating system dynamic simulation approach and fuzzy logic. For this purpose, first the project's NPV is modeled considering all the influencing factors qualitatively. The relationships that exist between different factors are then determined and the quantitative model is built. Using the developed model, the value of NPV is simulated considering the effects of all the influencing factors and the existing uncertainties. Finally, the value of project's NPV is determined as a triangular fuzzy number. Finally, the best alternative project is selected by comparing the simulated values of NPV. To evaluate the capabilities and performance of the proposed model, the project economical evaluation is performed for two highway projects and the best project is selected.
Alomari, Abdullah; Phillips, William; Aslam, Nauman; Comeau, Frank
2017-08-18
Mobile anchor path planning techniques have provided as an alternative option for node localization in wireless sensor networks (WSNs). In such context, path planning is a movement pattern where a mobile anchor node's movement is designed in order to achieve a maximum localization ratio possible with a minimum error rate. Typically, the mobility path planning is designed in advance, which is applicable when the mobile anchor has sufficient sources of energy and time. However, when the mobility movement is restricted or limited, a dynamic path planning design is needed. This paper proposes a novel distributed range-free movement mechanism for mobility-assisted localization in WSNs when the mobile anchor's movement is limited. The designed movement is formed in real-time pattern using a fuzzy-logic approach based on the information received from the network and the nodes' deployment. Our proposed model, Fuzzy-Logic based Path Planning for mobile anchor-assisted Localization in WSNs (FLPPL), offers superior results in several metrics including both localization accuracy and localization ratio in comparison to other similar works.
International Nuclear Information System (INIS)
Ardehali, M.M.; Saboori, M.; Teshnelab, M.
2004-01-01
Energy efficiency enhancement is achieved by utilizing control algorithms that reduce overshoots and undershoots as well as unnecessary fluctuations in the amount of energy input to energy consuming systems during transient operation periods. It is hypothesized that application of control methodologies with characteristics that change with time and according to the system dynamics, identified as dynamic energy efficiency measures (DEEM), achieves the desired enhancement. The objective of this study is to simulate and analyze the effects of fuzzy logic based tuning of proportional integral derivative (F-PID) and proportional sum derivative (F-PSD) controllers for a heating and cooling energy system while accounting for the dynamics of the major system components. The procedure to achieve the objective includes utilization of fuzzy logic rules to determine the PID and PSD controllers gain coefficients so that the control laws for regulating the heat exchangers heating or cooling energy inputs are determined in each time step of the operation period. The performances of the F-PID and F-PSD controllers are measured by means of two cost functions that are based on quadratic forms of the energy input and deviation from a set point temperature. It is found that application of the F-PID control algorithm, as a DEEM, results in lower costs for energy input and deviation from a set point temperature by 24% and 17% as compared to a PID and 13% and 8% as compared to a PSD, respectively. It is also shown that the F-PSD performance is better than that of the F-PID controller
Random matrix theories and chaotic dynamics
International Nuclear Information System (INIS)
Bohigas, O.
1991-01-01
A review of some of the main ideas, assumptions and results of the Wigner-Dyson type random matrix theories (RMT) which are relevant in the general context of 'Chaos and Quantum Physics' is presented. RMT are providing interesting and unexpected clues to connect classical dynamics with quantum phenomena. It is this aspect which will be emphasised and, concerning the main body of RMT, the author will restrict himself to a minimum. However, emphasis will be put on some generalizations of the 'canonical' random matrix ensembles that increase their flexibility, rendering the incorporation of relevant physical constraints possible. (R.P.) 112 refs., 35 figs., 5 tabs
Agreement dynamics on directed random graphs
Lipowski, Adam; Lipowska, Dorota; Ferreira, António Luis
2017-06-01
We examine some agreement-dynamics models that are placed on directed random graphs. In such systems, a fraction of sites \\exp(-z) , where z is the average degree, become permanently fixed or flickering. In the voter model, which has no surface tension, such zealots or flickers freely spread their opinions and that makes the system disordered. For models with a surface tension, like the Ising model or the Naming Game model, their role is limited, and such systems are ordered at large z. However, when z decreases, the density of zealots or flickers increases, and below a certain threshold (z∼ 1.9-2.0 ) the system becomes disordered. On undirected random graphs, agreement dynamics is very different and ordering appears as soon the graph is above the percolation threshold at z = 1.
Han, Seong-Ik; Lee, Jang-Myung
2014-01-01
This paper proposes a backstepping control system that uses a tracking error constraint and recurrent fuzzy neural networks (RFNNs) to achieve a prescribed tracking performance for a strict-feedback nonlinear dynamic system. A new constraint variable was defined to generate the virtual control that forces the tracking error to fall within prescribed boundaries. An adaptive RFNN was also used to obtain the required improvement on the approximation performances in order to avoid calculating the explosive number of terms generated by the recursive steps of traditional backstepping control. The boundedness and convergence of the closed-loop system was confirmed based on the Lyapunov stability theory. The prescribed performance of the proposed control scheme was validated by using it to control the prescribed error of a nonlinear system and a robot manipulator. © 2013 ISA. Published by Elsevier Ltd. All rights reserved.
Dynamic random walks theory and applications
Guillotin-Plantard, Nadine
2006-01-01
The aim of this book is to report on the progress realized in probability theory in the field of dynamic random walks and to present applications in computer science, mathematical physics and finance. Each chapter contains didactical material as well as more advanced technical sections. Few appendices will help refreshing memories (if necessary!).· New probabilistic model, new results in probability theory· Original applications in computer science· Applications in mathematical physics· Applications in finance
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Mackey, Lester [Department of Statistics, Stanford University,Stanford, CA 94305 (United States); Nachman, Benjamin [Department of Physics, Stanford University,Stanford, CA 94305 (United States); SLAC National Accelerator Laboratory, Stanford University,2575 Sand Hill Rd, Menlo Park, CA 94025 (United States); Schwartzman, Ariel [SLAC National Accelerator Laboratory, Stanford University,2575 Sand Hill Rd, Menlo Park, CA 94025 (United States); Stansbury, Conrad [Department of Physics, Stanford University,Stanford, CA 94305 (United States)
2016-06-01
Collimated streams of particles produced in high energy physics experiments are organized using clustering algorithms to form jets. To construct jets, the experimental collaborations based at the Large Hadron Collider (LHC) primarily use agglomerative hierarchical clustering schemes known as sequential recombination. We propose a new class of algorithms for clustering jets that use infrared and collinear safe mixture models. These new algorithms, known as fuzzy jets, are clustered using maximum likelihood techniques and can dynamically determine various properties of jets like their size. We show that the fuzzy jet size adds additional information to conventional jet tagging variables in boosted topologies. Furthermore, we study the impact of pileup and show that with some slight modifications to the algorithm, fuzzy jets can be stable up to high pileup interaction multiplicities.
Extremal dynamics in random replicator ecosystems
Energy Technology Data Exchange (ETDEWEB)
Kärenlampi, Petri P., E-mail: petri.karenlampi@uef.fi
2015-10-02
The seminal numerical experiment by Bak and Sneppen (BS) is repeated, along with computations with replicator models, including a greater amount of features. Both types of models do self-organize, and do obey power-law scaling for the size distribution of activity cycles. However species extinction within the replicator models interferes with the BS self-organized critical (SOC) activity. Speciation–extinction dynamics ruins any stationary state which might contain a steady size distribution of activity cycles. The BS-type activity appears as a dissimilar phenomenon in comparison to speciation–extinction dynamics in the replicator system. No criticality is found from the speciation–extinction dynamics. Neither are speciations and extinctions in real biological macroevolution known to contain any diverging distributions, or self-organization towards any critical state. Consequently, biological macroevolution probably is not a self-organized critical phenomenon. - Highlights: • Extremal Dynamics organizes random replicator ecosystems to two phases in fitness space. • Replicator systems show power-law scaling of activity. • Species extinction interferes with Bak–Sneppen type mutation activity. • Speciation–extinction dynamics does not show any critical phase transition. • Biological macroevolution probably is not a self-organized critical phenomenon.
Directory of Open Access Journals (Sweden)
Yanzi Wang
2016-01-01
Full Text Available Over the last few years; issues regarding the use of hybrid energy storage systems (HESSs in hybrid electric vehicles have been highlighted by the industry and in academic fields. This paper proposes a fuzzy-logic power management strategy based on Markov random prediction for an active parallel battery-UC HESS. The proposed power management strategy; the inputs for which are the vehicle speed; the current electric power demand and the predicted electric power demand; is used to distribute the electrical power between the battery bank and the UC bank. In this way; the battery bank power is limited to a certain range; and the peak and average charge/discharge power of the battery bank and overall loss incurred by the whole HESS are also reduced. Simulations and scaled-down experimental platforms are constructed to verify the proposed power management strategy. The simulations and experimental results demonstrate the advantages; feasibility and effectiveness of the fuzzy-logic power management strategy based on Markov random prediction.
Pricing for a basket of LCDS under fuzzy environments.
Wu, Liang; Liu, Jie-Fang; Wang, Jun-Tao; Zhuang, Ya-Ming
2016-01-01
This paper looks at both the prepayment risks of housing mortgage loan credit default swaps (LCDS) as well as the fuzziness and hesitation of investors as regards prepayments by borrowers. It further discusses the first default pricing of a basket of LCDS in a fuzzy environment by using stochastic analysis and triangular intuition-based fuzzy set theory. Through the 'fuzzification' of the sensitivity coefficient in the prepayment intensity, this paper describes the dynamic features of mortgage housing values using the One-factor copula function and concludes with a formula for 'fuzzy' pricing the first default of a basket of LCDS. Using analog simulation to analyze the sensitivity of hesitation, we derive a model that considers what the LCDS fair premium is in a fuzzy environment, including a pure random environment. In addition, the model also shows that a suitable pricing range will give investors more flexible choices and make the predictions of the model closer to real market values.
Quantum Entanglement Growth under Random Unitary Dynamics
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Adam Nahum
2017-07-01
Full Text Available Characterizing how entanglement grows with time in a many-body system, for example, after a quantum quench, is a key problem in nonequilibrium quantum physics. We study this problem for the case of random unitary dynamics, representing either Hamiltonian evolution with time-dependent noise or evolution by a random quantum circuit. Our results reveal a universal structure behind noisy entanglement growth, and also provide simple new heuristics for the “entanglement tsunami” in Hamiltonian systems without noise. In 1D, we show that noise causes the entanglement entropy across a cut to grow according to the celebrated Kardar-Parisi-Zhang (KPZ equation. The mean entanglement grows linearly in time, while fluctuations grow like (time^{1/3} and are spatially correlated over a distance ∝(time^{2/3}. We derive KPZ universal behavior in three complementary ways, by mapping random entanglement growth to (i a stochastic model of a growing surface, (ii a “minimal cut” picture, reminiscent of the Ryu-Takayanagi formula in holography, and (iii a hydrodynamic problem involving the dynamical spreading of operators. We demonstrate KPZ universality in 1D numerically using simulations of random unitary circuits. Importantly, the leading-order time dependence of the entropy is deterministic even in the presence of noise, allowing us to propose a simple coarse grained minimal cut picture for the entanglement growth of generic Hamiltonians, even without noise, in arbitrary dimensionality. We clarify the meaning of the “velocity” of entanglement growth in the 1D entanglement tsunami. We show that in higher dimensions, noisy entanglement evolution maps to the well-studied problem of pinning of a membrane or domain wall by disorder.
Li, Dongjie; Fu, Yu; Yang, Liu
2017-08-01
For further research on the microparticles trajectory in the process of micromanipulation, the paper modeled on the coupling dynamic of three-degree-of-freedom micromanipulator which is based on piezoelectric ceramic. In the micromanipulation, the transformation of certain movement direction can generate a corresponding change in the coupling in three-degree-of-freedom micromanipulator movement, the fuzzy PID method was adopted by the control system of this study, and the modeling analysis was performed on the control system. After completing the above modeling, the simulation model is built by the MATLAB Simulink software. The simulation output results are basically in accordance with the actual trajectory, which achieve the successful research purposes of coupling dynamics model for three-degree-of-freedom micromanipulator and application of fuzzy PID method.
Edalati, L.; Khaki Sedigh, A.; Aliyari Shooredeli, M.; Moarefianpour, A.
2018-02-01
This paper deals with the design of adaptive fuzzy dynamic surface control for uncertain strict-feedback nonlinear systems with asymmetric time-varying output constraints in the presence of input saturation. To approximate the unknown nonlinear functions and overcome the problem of explosion of complexity, a Fuzzy logic system is combined with the dynamic surface control in the backstepping design technique. To ensure the output constraints satisfaction, an asymmetric time-varying Barrier Lyapunov Function (BLF) is used. Moreover, by applying the minimal learning parameter technique, the number of the online parameters update for each subsystem is reduced to 2. Hence, the semi-globally uniformly ultimately boundedness (SGUUB) of all the closed-loop signals with appropriate tracking error convergence is guaranteed. The effectiveness of the proposed control is demonstrated by two simulation examples.
Huang, Ming-zhi; Wan, Jin-quan; Ma, Yong-wen; Li, Wei-jiang; Sun, Xiao-fei; Wan, Yan
2010-03-01
In this paper a software sensor based on a fuzzy neural network approach was proposed for real-time estimation of nutrient concentrations. In order to improve the network performance, fuzzy subtractive clustering was used to identify model architecture, extract and optimize fuzzy rule of the model. A split network structure was applied separately for anaerobic and aerobic conditions was employed with dynamic modeling methods such as autoregressive with exogenous inputs and multi-way principal component analysis (MPCA). The proposed methodology was applied to a bench-scale anoxic/oxic process for biological nitrogen removal. The simulative results indicate that the learning ability and generalization of the model performed well and also worked well for normal batch operations corresponding to three data points inside the confidence limit determined by MPCA. Real-time estimation of NO(3)(-), NH(4)(+) and PO(4)(3-) concentration based on fuzzy neural network analysis were successfully carried out with the simple on-line information regarding the anoxic/oxic system. Copyright (c) 2009 Elsevier Ltd. All rights reserved.
Wai, Rong-Jong; Muthusamy, Rajkumar
2013-02-01
This paper presents the design and analysis of an intelligent control system that inherits the robust properties of sliding-mode control (SMC) for an n-link robot manipulator, including actuator dynamics in order to achieve a high-precision position tracking with a firm robustness. First, the coupled higher order dynamic model of an n-link robot manipulator is briefy introduced. Then, a conventional SMC scheme is developed for the joint position tracking of robot manipulators. Moreover, a fuzzy-neural-network inherited SMC (FNNISMC) scheme is proposed to relax the requirement of detailed system information and deal with chattering control efforts in the SMC system. In the FNNISMC strategy, the FNN framework is designed to mimic the SMC law, and adaptive tuning algorithms for network parameters are derived in the sense of projection algorithm and Lyapunov stability theorem to ensure the network convergence as well as stable control performance. Numerical simulations and experimental results of a two-link robot manipulator actuated by DC servo motors are provided to justify the claims of the proposed FNNISMC system, and the superiority of the proposed FNNISMC scheme is also evaluated by quantitative comparison with previous intelligent control schemes.
Berenji, Hamid R.
1992-01-01
Fuzzy logic and neural networks provide new methods for designing control systems. Fuzzy logic controllers do not require a complete analytical model of a dynamic system and can provide knowledge-based heuristic controllers for ill-defined and complex systems. Neural networks can be used for learning control. In this chapter, we discuss hybrid methods using fuzzy logic and neural networks which can start with an approximate control knowledge base and refine it through reinforcement learning.
Amador-Angulo, Leticia; Mendoza, Olivia; Castro, Juan R.; Rodríguez-Díaz, Antonio; Melin, Patricia; Castillo, Oscar
2016-01-01
A hybrid approach composed by different types of fuzzy systems, such as the Type-1 Fuzzy Logic System (T1FLS), Interval Type-2 Fuzzy Logic System (IT2FLS) and Generalized Type-2 Fuzzy Logic System (GT2FLS) for the dynamic adaptation of the alpha and beta parameters of a Bee Colony Optimization (BCO) algorithm is presented. The objective of the work is to focus on the BCO technique to find the optimal distribution of the membership functions in the design of fuzzy controllers. We use BCO specifically for tuning membership functions of the fuzzy controller for trajectory stability in an autonomous mobile robot. We add two types of perturbations in the model for the Generalized Type-2 Fuzzy Logic System to better analyze its behavior under uncertainty and this shows better results when compared to the original BCO. We implemented various performance indices; ITAE, IAE, ISE, ITSE, RMSE and MSE to measure the performance of the controller. The experimental results show better performances using GT2FLS then by IT2FLS and T1FLS in the dynamic adaptation the parameters for the BCO algorithm. PMID:27618062
Opinion dynamics on an adaptive random network
Benczik, I. J.; Benczik, S. Z.; Schmittmann, B.; Zia, R. K. P.
2009-04-01
We revisit the classical model for voter dynamics in a two-party system with two basic modifications. In contrast to the original voter model studied in regular lattices, we implement the opinion formation process in a random network of agents in which interactions are no longer restricted by geographical distance. In addition, we incorporate the rapidly changing nature of the interpersonal relations in the model. At each time step, agents can update their relationships. This update is determined by their own opinion, and by their preference to make connections with individuals sharing the same opinion, or rather with opponents. In this way, the network is built in an adaptive manner, in the sense that its structure is correlated and evolves with the dynamics of the agents. The simplicity of the model allows us to examine several issues analytically. We establish criteria to determine whether consensus or polarization will be the outcome of the dynamics and on what time scales these states will be reached. In finite systems consensus is typical, while in infinite systems a disordered metastable state can emerge and persist for infinitely long time before consensus is reached.
Directory of Open Access Journals (Sweden)
Zhi-Ren Tsai
2013-01-01
Full Text Available A tracking problem, time-delay, uncertainty and stability analysis of a predictive control system are considered. The predictive control design is based on the input and output of neural plant model (NPM, and a recursive fuzzy predictive tracker has scaling factors which limit the value zone of measured data and cause the tuned parameters to converge to obtain a robust control performance. To improve the further control performance, the proposed random-local-optimization design (RLO for a model/controller uses offline initialization to obtain a near global optimal model/controller. Other issues are the considerations of modeling error, input-delay, sampling distortion, cost, greater flexibility, and highly reliable digital products of the model-based controller for the continuous-time (CT nonlinear system. They are solved by a recommended two-stage control design with the first-stage (offline RLO and second-stage (online adaptive steps. A theorizing method is then put forward to replace the sensitivity calculation, which reduces the calculation of Jacobin matrices of the back-propagation (BP method. Finally, the feedforward input of reference signals helps the digital fuzzy controller improve the control performance, and the technique works to control the CT systems precisely.
Jahanian, Hesamoddin; Hossein-Zadeh, Gholam-Ali; Soltanian-Zadeh, Hamid; Ardekani, Babak A
2004-06-01
Despite its potential advantages for fMRI analysis, fuzzy C-means (FCM) clustering suffers from limitations such as the need for a priori knowledge of the number of clusters, and unknown statistical significance and instability of the results. We propose a randomization-based method to control the false-positive rate and estimate statistical significance of the FCM results. Using this novel approach, we develop an fMRI activation detection method. The ability of the method in controlling the false-positive rate is shown by analysis of false positives in activation maps of resting-state fMRI data. Controlling the false-positive rate in FCM allows comparison of different fuzzy clustering methods, using different feature spaces, to other fMRI detection methods. In this article, using simulation and real fMRI data, we compare a novel feature space that takes the variability of the hemodynamic response function into account (HRF-based feature space) to the conventional cross-correlation analysis and FCM using the cross-correlation feature space. In both cases, the HRF-based feature space provides a greater sensitivity compared to the cross-correlation feature space and conventional cross-correlation analysis. Application of the proposed method to finger-tapping fMRI data, using HRF-based feature space, detected activation in sub-cortical regions, whereas both of the FCM with cross-correlation feature space and the conventional cross-correlation method failed to detect them.
Nagar, Lokesh; Dutta, Pankaj; Jain, Karuna
2014-05-01
In the present day business scenario, instant changes in market demand, different source of materials and manufacturing technologies force many companies to change their supply chain planning in order to tackle the real-world uncertainty. The purpose of this paper is to develop a multi-objective two-stage stochastic programming supply chain model that incorporates imprecise production rate and supplier capacity under scenario dependent fuzzy random demand associated with new product supply chains. The objectives are to maximise the supply chain profit, achieve desired service level and minimise financial risk. The proposed model allows simultaneous determination of optimum supply chain design, procurement and production quantities across the different plants, and trade-offs between inventory and transportation modes for both inbound and outbound logistics. Analogous to chance constraints, we have used the possibility measure to quantify the demand uncertainties and the model is solved using fuzzy linear programming approach. An illustration is presented to demonstrate the effectiveness of the proposed model. Sensitivity analysis is performed for maximisation of the supply chain profit with respect to different confidence level of service, risk and possibility measure. It is found that when one considers the service level and risk as robustness measure the variability in profit reduces.
Directory of Open Access Journals (Sweden)
Mario Collotta
2014-07-01
Full Text Available Heating, ventilating and air-conditioning (HVAC systems are typical non-linear time-variable multivariate systems with disturbances and uncertainties. In this paper, an approach based on a combined neuro-fuzzy model for dynamic and automatic regulation of indoor temperature is proposed. The proposed artificial neural network performs indoor temperatures forecasts that are used to feed a fuzzy logic control unit in order to manage the on/off switching of the HVAC system and the regulation of the inlet air speed. Moreover, the used neural network is optimized by the analytical calculation of the embedding parameters, and the goodness of this approach is tested through MATLAB. The fuzzy controller is driven by the indoor temperature forecasted by the neural network module and is able to adjust the membership functions dynamically, since thermal comfort is a very subjective factor and may vary even in the same subject. The paper shows some experimental results, through a real implementation in an embedded prototyping board, of the proposed approach in terms of the evolution of the inlet air speed injected by the fan coils, the indoor air temperature forecasted by the neural network model and the adjusting of the membership functions after receiving user feedback.
Implementation of a Small Type DC Microgrid Based on Fuzzy Control and Dynamic Programming
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Chin-Hsing Cheng
2016-09-01
Full Text Available A DC microgrid (DC-MG is a novel power system that uses DC distribution in order to provide high quality power. The study system is made by a photovoltaic array (PV, a wind generator (WG, a fuel cell (FC, and an energy storage system (ESS to establish a small type DC microgrid, with the bus being established by DC/DC converters with fuzzy controllers. An overall power dispatch was designed for the proposed system to distribute the power flows among the different energy sources and the storage unit in the system in order to satisfy the load requirements throughout an entire 24-h period. The structure of a power supervisor based on an optimal power dispatch algorithm is here proposed. Optimization was performed using dynamic programming (DP. In this paper, a system configuration of a DC microgrid is analyzed in different scenarios to show the efficacy of the control for all devices for the variable weather conditions with different DC loads. Thus, the voltage level and the power flow of the system are shown for different load conditions.
International Nuclear Information System (INIS)
Song, Jeong Hoon
2013-01-01
In this study, four integrated dynamics control (IDC) systems abbreviated as IDCB, IDCS, IDCF, and IDCR are developed, evaluated and compared. IDC systems were integrated with brake and steer control systems to enhance lateral stability and handling performance. To construct the IDC systems, a vehicle model with fourteen degrees of freedom, a fuzzy logic controller, and a sliding mode ABS controller were used. They were tested with various steering inputs when excessive full brake pressure or no brake pressure was applied on dry asphalt, wet asphalt, a snow-covered paved road, and a split-µ road. The results showed that an IDC-equipped vehicle improved lateral stability and controllability in every driving condition compared to an ABS-equipped vehicle. Under all road conditions, IDC controllers enabled the yaw rate to follow the reference yaw rate almost perfectly and reduced the body slip angle. On a split-µ road, IDCB, IDCS, IDCF, and IDCR vehicles drove straight ahead with only very small deviations.
Blind Source Separation and Dynamic Fuzzy Neural Network for Fault Diagnosis in Machines
International Nuclear Information System (INIS)
Huang, Haifeng; Ouyang, Huajiang; Gao, Hongli
2015-01-01
Many assessment and detection methods are used to diagnose faults in machines. High accuracy in fault detection and diagnosis can be achieved by using numerical methods with noise-resistant properties. However, to some extent, noise always exists in measured data on real machines, which affects the identification results, especially in the diagnosis of early- stage faults. In view of this situation, a damage assessment method based on blind source separation and dynamic fuzzy neural network (DFNN) is presented to diagnose the early-stage machinery faults in this paper. In the processing of measurement signals, blind source separation is adopted to reduce noise. Then sensitive features of these faults are obtained by extracting low dimensional manifold characteristics from the signals. The model for fault diagnosis is established based on DFNN. Furthermore, on-line computation is accelerated by means of compressed sensing. Numerical vibration signals of ball screw fault modes are processed on the model for mechanical fault diagnosis and the results are in good agreement with the actual condition even at the early stage of fault development. This detection method is very useful in practice and feasible for early-stage fault diagnosis. (paper)
Zhao, Yongli; Li, Xin; Li, Huadong; Wang, Xinbo; Zhang, Jie; Huang, Shanguo
2013-01-28
Based on a distributed method of bit-error-rate (BER) monitoring, a novel multi-link faults restoration algorithm is proposed for dynamic optical networks. The concept of fuzzy fault set (FFS) is first introduced for multi-link faults localization, which includes all possible optical equipment or fiber links with a membership describing the possibility of faults. Such a set is characterized by a membership function which assigns each object a grade of membership ranging from zero to one. OSPF protocol extension is designed for the BER information flooding in the network. The BER information can be correlated to link faults through FFS. Based on the BER information and FFS, multi-link faults localization mechanism and restoration algorithm are implemented and experimentally demonstrated on a GMPLS enabled optical network testbed with 40 wavelengths in each fiber link. Experimental results show that the novel localization mechanism has better performance compared with the extended limited perimeter vector matching (LVM) protocol and the restoration algorithm can improve the restoration success rate under multi-link faults scenario.
Zhao, Jingjing; Yu, Lean; Li, Lian
2017-05-01
Select an appropriate technology in an emergency response is a very important issue with various kinds of chemical contingency spills frequently taking place. Due to the complexity, fuzziness and uncertainties of the chemical contingency spills, the theory of GRA method, dynamic analysis combined with fuzzy set theory will be appropriately applied to selection and evaluation of emergency treatment technology. Finally, a emergency phenol spill accidence occurred in highway is provided to illustrate the applicability and feasibility of the proposed methods.
International Nuclear Information System (INIS)
Milligan, M. R.; Factor, T.
2001-01-01
This paper illustrates a method for choosing the optimal mix of wind capacity at several geographically dispersed locations. The method is based on a dynamic fuzzy search algorithm that can be applied to different optimization targets. We illustrate the method using two objective functions for the optimization: maximum economic benefit and maximum reliability. We also illustrate the sensitivity of the fuzzy economic benefit solutions to small perturbations of the capacity selections at each wind site. We find that small changes in site capacity and/or location have small effects on the economic benefit provided by wind power plants. We use electric load and generator data from Iowa, along with high-quality wind-speed data collected by the Iowa Wind Energy Institute
Directory of Open Access Journals (Sweden)
Jiahang Yuan
2017-01-01
Full Text Available In consideration of the interaction among attributes and the influence of decision makers’ risk attitude, this paper proposes an intuitionistic trapezoidal fuzzy aggregation operator based on Choquet integral and prospect theory. With respect to a multiattribute group decision-making problem, the prospect value functions of intuitionistic trapezoidal fuzzy numbers are aggregated by the proposed operator; then a grey relation-projection pursuit dynamic cluster method is developed to obtain the ranking of alternatives; the firefly algorithm is used to optimize the objective function of projection for obtaining the best projection direction of grey correlation projection values, and the grey correlation projection values are evaluated, which are applied to classify, rank, and prefer the alternatives. Finally, an illustrative example is taken in the present study to make the proposed method comprehensible.
Energy Technology Data Exchange (ETDEWEB)
Milligan, M. R., National Renewable Energy Laboratory; Factor, T., Iowa Wind Energy Institute
2001-09-21
This paper illustrates a method for choosing the optimal mix of wind capacity at several geographically dispersed locations. The method is based on a dynamic fuzzy search algorithm that can be applied to different optimization targets. We illustrate the method using two objective functions for the optimization: maximum economic benefit and maximum reliability. We also illustrate the sensitivity of the fuzzy economic benefit solutions to small perturbations of the capacity selections at each wind site. We find that small changes in site capacity and/or location have small effects on the economic benefit provided by wind power plants. We use electric load and generator data from Iowa, along with high-quality wind-speed data collected by the Iowa Wind Energy Institute.
Virtual reality simulation of fuzzy-logic control during underwater dynamic positioning
Thekkedan, Midhin Das; Chin, Cheng Siong; Woo, Wai Lok
2015-03-01
In this paper, graphical-user-interface (GUI) software for simulation and fuzzy-logic control of a remotely operated vehicle (ROV) using MATLAB™ GUI Designing Environment is proposed. The proposed ROV's GUI platform allows the controller such as fuzzy-logic control systems design to be compared with other controllers such as proportional-integral-derivative (PID) and sliding-mode controller (SMC) systematically and interactively. External disturbance such as sea current can be added to improve the modelling in actual underwater environment. The simulated results showed the position responses of the fuzzy-logic control exhibit reasonable performance under the sea current disturbance.
Oladipupo Bello; Yskandar Hamam; Karim Djouani
2014-01-01
In this paper, a fuzzy model predictive control (FMPC) strategy is proposed to regulate the output variables of a coagulation chemical dosing unit. A multiple-input, multiple-output (MIMO) process model in form of a linearised Takagi–Sugeno (T–S) fuzzy model is derived. The process model is obtained through subtractive clustering from the plant's data set. The MIMO model is described by a set of coupled multiple-input, single-output models (MISO). In the controller design, the T–S fuzzy model...
Glassy dynamics in randomly pinned particle systems
Phan, Anh; Schweizer, Kenneth
We generalize the force-level, microscopic Elastically Collective Nonlinear Langevin Equation theory of activated relaxation in bulk hard sphere and thermal liquids to address the role of internal quenched disorder. So-called neutral confinement is considered where a subset of particles are randomly pinned and there is no change of equilibrium pair structure. As the pinned fraction grows, the cage scale dynamical constraints are intensified, resulting in the mobile particles becoming more localized, a larger glassy shear modulus, and an enhanced cage scale barrier. However, based on an approximate analysis of how quenched disorder modifies collective elastic field fluctuations, random pinning is predicted to effectively screen or localize the strain field associated with the longer range elastic component of the activation barrier, leading to an overall reduction of it with pinning fraction. The different response of the cage and elastic barriers to quenched disorder results in subtle predictions for how the alpha relaxation time varies with pinning fraction and system volume fraction. A semi-quantitative comparison with recent simulations of a pinned-mobile water model are consistent with the theory. Predictions are made for thermal molecular liquids.
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Fabrizio Maturo
2016-06-01
Full Text Available In practical applications relating to business and management sciences, there are many variables that, for their own nature, are better described by a pair of ordered values (i.e. financial data. By summarizing this measurement with a single value, there is a loss of information; thus, in these situations, data are better described by interval values rather than by single values. Interval arithmetic studies and analyzes this type of imprecision; however, if the intervals has no sharp boundaries, fuzzy set theory is the most suitable instrument. Moreover, fuzzy regression models are able to overcome some typical limitation of classical regression because they do not need the same strong assumptions. In this paper, we present a review of the main methods introduced in the literature on this topic and introduce some recent developments regarding the concept of randomness in fuzzy regression.
On potential kernels associated with random dynamical systems
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Mohamed Hmissi
2015-01-01
In particular, we provide a constructive method for global Lyapunov functions for gradient-like random dynamical systems. This result generalizes an analogous theorem known for deterministic dynamical systems.
C. K. Kwong; K. Y. Fung; Huimin Jiang; K. Y. Chan; Kin Wai Michael Siu
2013-01-01
Affective design is an important aspect of product development to achieve a competitive edge in the marketplace. A neural-fuzzy network approach has been attempted recently to model customer satisfaction for affective design and it has been proved to be an effective one to deal with the fuzziness and non-linearity of the modeling as well as generate explicit customer satisfaction models. However, such an approach to modeling customer satisfaction has two limitations. First, it is not suitable...
Liu, Yi-Ting
2018-01-01
This paper proposes an enhanced ant colony optimization with dynamic mutation and ad hoc initialization, ACODM-I, for improving the accuracy of Takagi-Sugeno-Kang- (TSK-) type fuzzy systems design. Instead of the generic initialization usually used in most population-based algorithms, ACODM-I proposes an ad hoc application-specific initialization for generating the initial ant solutions to improve the accuracy of fuzzy system design. The generated initial ant solutions are iteratively improved by a new approach incorporating the dynamic mutation into the existing continuous ACO (ACOR). The introduced dynamic mutation balances the exploration ability and convergence rate by providing more diverse search directions in the early stage of optimization process. Application examples of two zero-order TSK-type fuzzy systems for dynamic plant tracking control and one first-order TSK-type fuzzy system for the prediction of the chaotic time series have been simulated to validate the proposed algorithm. Performance comparisons with ACOR and different advanced algorithms or neural-fuzzy models verify the superiority of the proposed algorithm. The effects on the design accuracy and convergence rate yielded by the proposed initialization and introduced dynamic mutation have also been discussed and verified in the simulations. PMID:29568311
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Chi-Chung Chen
2018-01-01
Full Text Available This paper proposes an enhanced ant colony optimization with dynamic mutation and ad hoc initialization, ACODM-I, for improving the accuracy of Takagi-Sugeno-Kang- (TSK- type fuzzy systems design. Instead of the generic initialization usually used in most population-based algorithms, ACODM-I proposes an ad hoc application-specific initialization for generating the initial ant solutions to improve the accuracy of fuzzy system design. The generated initial ant solutions are iteratively improved by a new approach incorporating the dynamic mutation into the existing continuous ACO (ACOR. The introduced dynamic mutation balances the exploration ability and convergence rate by providing more diverse search directions in the early stage of optimization process. Application examples of two zero-order TSK-type fuzzy systems for dynamic plant tracking control and one first-order TSK-type fuzzy system for the prediction of the chaotic time series have been simulated to validate the proposed algorithm. Performance comparisons with ACOR and different advanced algorithms or neural-fuzzy models verify the superiority of the proposed algorithm. The effects on the design accuracy and convergence rate yielded by the proposed initialization and introduced dynamic mutation have also been discussed and verified in the simulations.
Fuzzy stochastic multiobjective programming
Sakawa, Masatoshi; Katagiri, Hideki
2011-01-01
With a stress on interactive decision-making, this work breaks new ground by covering both the random nature of events related to environments, and the fuzziness of human judgements. The text runs from mathematical preliminaries to future research directions.
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Mohammad Ali Keramati
2012-12-01
Full Text Available Due to different effects of ICT on varied aspects of performing the duties in organizations, governments have been intending to use ICT in the recent years very dramatically. The significant issue to which we should pay attention is the using of ICT without directing attention towards the mutual effects of different ICT domains shall be resulted in malfunction and inefficiency of organizations in carrying out their tasks. Therefore, the present research tried to develop a systematic structure in ICT domain and analyze the various ICT domains in order to identify the penetrating and penetrated factors (cause and effect. In doing so, at the present research firstly by the usage of other researchers' results and achievements, it was attempted to specify the different ICT domains including objectives, obstacles, drivers, team dynamics and organizational support and then another elements so-called ICT effectiveness was added in order to study the effect of above-mentioned factors on ICT effectiveness. Then, standard fuzzy DEMATEL technique questionnaire was distributed among 35 persons of experts working in ICT and IT fields to gather required information and data. After gathering required data and information, they were analyzed through DEMATEL techniques in fuzzy states, respectively. The results obtained from the DEMATEL technique in fuzzy state reveal that in ICT domain, the objectives were determined as the most penetrating elements into other elements of ICT domain and the drivers were the most penetrable element in ICT domain too.
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Sanping Rao
2013-01-01
Full Text Available This paper is an attempt to develop quantitative domain theory over frames. Firstly, we propose the notion of a fuzzy basis, and several equivalent characterizations of fuzzy bases are obtained. Furthermore, the concept of a fuzzy algebraic domain is introduced, and a relationship between fuzzy algebraic domains and fuzzy domains is discussed from the viewpoint of fuzzy basis. We finally give an application of fuzzy bases, where the image of a fuzzy domain can be preserved under some special kinds of fuzzy Galois connections.
International Nuclear Information System (INIS)
Jahedi, G.; Ardehali, M.M.
2011-01-01
The simplicity in coding the heuristic judgment of experienced operator by means of fuzzy logic can be exploited for enhancement of energy efficiency. Fuzzy logic has been used as an effective tool for scheduling conventional PID controllers gain coefficients (F-PID). However, to search for the most desirable fuzzy system characteristics that allow for best performance of the energy system with minimum energy input, optimization techniques such as genetic algorithm (GA) could be utilized and the control methodology is identified as GA-based F-PID (GA-F-PID). The objective of this study is to examine the performance of PID, F-PID, and GA-F-PID controllers for enhancement of energy efficiency of a dynamic energy system. The performance evaluation of the controllers is accomplished by means of two cost functions that are based on the quadratic forms of the energy input and deviation from a setpoint temperature, referred to as energy and comfort costs, respectively. The GA-F-PID controller is examined in two different forms, namely, global form and local form. For the global form, all possible combinations of fuzzy system characteristics in the search domain are explored by GA for finding the fittest chromosome for all discrete time intervals during the entire operation period. For the local form, however, GA is used in each discrete time interval to find the fittest chromosome for implementation. The results show that the global form GA-F-PID and local form GA-F-PID control methodologies, in comparison with PID controller, achieve higher energy efficiency by lowering energy costs by 51.2%, and 67.8%, respectively. Similarly, the comfort costs for deviation from setpoint are enhanced by 54.4%, and 62.4%, respectively. It is determined that GA-F-PID performs better in local from than global form.
Dynamics of the Random Field Ising Model
Xu, Jian
The Random Field Ising Model (RFIM) is a general tool to study disordered systems. Crackling noise is generated when disordered systems are driven by external forces, spanning a broad range of sizes. Systems with different microscopic structures such as disordered mag- nets and Earth's crust have been studied under the RFIM. In this thesis, we investigated the domain dynamics and critical behavior in two dipole-coupled Ising ferromagnets Nd2Fe14B and LiHoxY 1-xF4. With Tc well above room temperature, Nd2Fe14B has shown reversible disorder when exposed to an external transverse field and crosses between two universality classes in the strong and weak disorder limits. Besides tunable disorder, LiHoxY1-xF4 has shown quantum tunneling effects arising from quantum fluctuations, providing another mechanism for domain reversal. Universality within and beyond power law dependence on avalanche size and energy were studied in LiHo0.65Y0.35 F4.
Dynamics and bifurcations of random circle diffeomorphisms
Zmarrou, H.; Homburg, A.J.
2008-01-01
We discuss iterates of random circle diffeomorphisms with identically distributed noise, where the noise is bounded and absolutely continuous. Using arguments of B. Deroin, V.A. Kleptsyn and A. Navas, we provide precise conditions under which random attracting fixed points or random attracting
Directory of Open Access Journals (Sweden)
Oladipupo Bello
2014-09-01
Full Text Available In this paper, a fuzzy model predictive control (FMPC strategy is proposed to regulate the output variables of a coagulation chemical dosing unit. A multiple-input, multiple-output (MIMO process model in form of a linearised Takagi–Sugeno (T–S fuzzy model is derived. The process model is obtained through subtractive clustering from the plant's data set. The MIMO model is described by a set of coupled multiple-input, single-output models (MISO. In the controller design, the T–S fuzzy model is applied in combination with the nonlinear model predictive control (MPC algorithm. The results show that the proposed controller has good set-point tracking when compared with nonlinear MPC and adequate disturbance rejection ability required for efficient coagulation control and process optimisation in water treatment operations.
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Yueling Wang
2013-01-01
Full Text Available A unique fuzzy self-tuning disturbance decoupling controller (FSDDC is designed for a serial-parallel hybrid humanoid arm (HHA to implement the throwing trajectory-tracking mission. Firstly, the dynamic model of the HHA is established and the input signal of the throwing process is obtained by studying the throwing process of human's arm. Secondly, the FSDDC, incorporating the disturbance decoupling controller (DDC and the fuzzy logic controller (FLC, is designed to ensure trajectory tracking of the HHA in the presence of uncertainties and disturbances. With the FSDDC method, the HHA system can be decoupled by actively estimating and rejecting the effects of both the internal plant dynamics and external disturbances. The self-tuning parameters are adapted online to improve the performance of the FSDDC; thus, it does not require detailed system parameters of the presented FSDDC. Finally, the controller introduced is compared with a PD controller which is commonly used for the robot manipulators control in industry. The effectiveness of the designed FSDDC is illustrated by simulations.
Abdulbaqi, Hayder Saad; Jafri, Mohd Zubir Mat; Omar, Ahmad Fairuz; Mustafa, Iskandar Shahrim Bin; Abood, Loay Kadom
2015-04-01
Brain tumors, are an abnormal growth of tissues in the brain. They may arise in people of any age. They must be detected early, diagnosed accurately, monitored carefully, and treated effectively in order to optimize patient outcomes regarding both survival and quality of life. Manual segmentation of brain tumors from CT scan images is a challenging and time consuming task. Size and location accurate detection of brain tumor plays a vital role in the successful diagnosis and treatment of tumors. Brain tumor detection is considered a challenging mission in medical image processing. The aim of this paper is to introduce a scheme for tumor detection in CT scan images using two different techniques Hidden Markov Random Fields (HMRF) and Fuzzy C-means (FCM). The proposed method has been developed in this research in order to construct hybrid method between (HMRF) and threshold. These methods have been applied on 4 different patient data sets. The result of comparison among these methods shows that the proposed method gives good results for brain tissue detection, and is more robust and effective compared with (FCM) techniques.
Fuzzy Stochastic Optimization Theory, Models and Applications
Wang, Shuming
2012-01-01
Covering in detail both theoretical and practical perspectives, this book is a self-contained and systematic depiction of current fuzzy stochastic optimization that deploys the fuzzy random variable as a core mathematical tool to model the integrated fuzzy random uncertainty. It proceeds in an orderly fashion from the requisite theoretical aspects of the fuzzy random variable to fuzzy stochastic optimization models and their real-life case studies. The volume reflects the fact that randomness and fuzziness (or vagueness) are two major sources of uncertainty in the real world, with significant implications in a number of settings. In industrial engineering, management and economics, the chances are high that decision makers will be confronted with information that is simultaneously probabilistically uncertain and fuzzily imprecise, and optimization in the form of a decision must be made in an environment that is doubly uncertain, characterized by a co-occurrence of randomness and fuzziness. This book begins...
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Laith Jasim Saud
2017-07-01
Full Text Available This work is concerned with designing two types of controllers, a PID and a Fuzzy PID, to be used for flying and stabilizing a quadcopter. The designed controllers have been tuned, tested, and compared using two performance indices which are the Integral Square Error (ISE and the Integral Absolute Error (IAE, and also some response characteristics like the rise time, overshoot, settling time, and the steady state error. To try and test the controllers, a quadcopter mathematical model has been developed. The model concentrated on the rotational dynamics of the quadcopter, i.e. the roll, pitch, and yaw variables. The work has been simulated with “MATLAB”. To make testing the simulated model and the controllers more realistic, the testing signals have been applied by a user through a joystick interfaced to the computer. The results obtained indicated a general superiority in performance for the Fuzzy PID controller over the PID controller used in this work. This conclusion is based by the following figures:lesser ISA for the roll, pitch, and yaw consequently, lesser IAE for the roll, pitch, and yaw consequently, lesser rise time and settling time for the roll and pitch consequently, and lesser settling time for the yaw. Moreover, the FPID gave zero overshoot versus and in the PID case for the roll, pitch, and yaw consequently. Both controllers gave zero steady state error with close rise times for the yaw. This superiority of the FPID controller is gained as the fuzzy part of it continuously and online adapts the parameters of the PID part.
Fuzzy Set Field and Fuzzy Metric
Gebray, Gebru; Reddy, B. Krishna
2014-01-01
The notation of fuzzy set field is introduced. A fuzzy metric is redefined on fuzzy set field and on arbitrary fuzzy set in a field. The metric redefined is between fuzzy points and constitutes both fuzziness and crisp property of vector. In addition, a fuzzy magnitude of a fuzzy point in a field is defined.
Dynamics of excitable nodes on random graphs
Indian Academy of Sciences (India)
emergence of different structural features as well as the level of dynamical activity supported on the network. Keywords ... dynamics of discrete excitable nodes and the rules of interaction between them are dis- cussed. ... if there is a connection between nodes i and j, the element Aij of the adjacency matrix A is. 1, else it is 0.
Theodoridis, Dimitrios; Boutalis, Yiannis; Christodoulou, Manolis
2010-04-01
The indirect adaptive regulation of unknown nonlinear dynamical systems with multiple inputs and states (MIMS) under the presence of dynamic and parameter uncertainties, is considered in this paper. The method is based on a new neuro-fuzzy dynamical systems description, which uses the fuzzy partitioning of an underlying fuzzy systems outputs and high order neural networks (HONN's) associated with the centers of these partitions. Every high order neural network approximates a group of fuzzy rules associated with each center. The indirect regulation is achieved by first identifying the system around the current operation point, and then using its parameters to device the control law. Weight updating laws for the involved HONN's are provided, which guarantee that, under the presence of both parameter and dynamic uncertainties, both the identification error and the system states reach zero, while keeping all signals in the closed loop bounded. The control signal is constructed to be valid for both square and non square systems by using a pseudoinverse, in Moore-Penrose sense. The existence of the control signal is always assured by employing a novel method of parameter hopping instead of the conventional projection method. The applicability is tested on well known benchmarks.
Gao, Qing; Liu, Jinguo; Tian, Tongtong; Li, Yangmin
2017-09-01
Space robots can perform some tasks in harsh environment as assistants of astronauts or substitutions of astronauts. Taking the limited working time and the arduous task of the astronauts in the space station into account, an astronaut assistant robot (AAR-2) applied in the space station is proposed and designed in this paper. The AAR-2 is achieved with some improvements on the basis of AAR-1 which was designed before. It can exploit its position and attitude sensors and control system to free flight or hover in the space cabin. And it also has a definite environmental awareness and artificial intelligence to complete some specified tasks under the control of astronauts or autonomously. In this paper, it mainly analyzes and controls the 6-DOF motion of the AAR-2. Firstly, the system configuration of AAR-2 is specifically described, and the movement principles are analyzed. Secondly, according to the physical model of the AAR-2, the Newton - Euler equation is applied in the preparation of space dynamics model of 6-DOF motion. Then, according to the mathematical model's characteristics which are nonlinear and strong coupling, a dual closed loop position and attitude controller based on fuzzy sliding mode control is proposed and designed. Finally, simulation experiments are appropriate to provide for AAR-2 control system by using Matlab/Simulink. From the simulation results it can be observed that the designed fuzzy sliding mode controller can control the 6-DOF motion of AAR-2 quickly and precisely.
Entanglement dynamics in critical random quantum Ising chain with perturbations
Energy Technology Data Exchange (ETDEWEB)
Huang, Yichen, E-mail: ychuang@caltech.edu
2017-05-15
We simulate the entanglement dynamics in a critical random quantum Ising chain with generic perturbations using the time-evolving block decimation algorithm. Starting from a product state, we observe super-logarithmic growth of entanglement entropy with time. The numerical result is consistent with the analytical prediction of Vosk and Altman using a real-space renormalization group technique. - Highlights: • We study the dynamical quantum phase transition between many-body localized phases. • We simulate the dynamics of a very long random spin chain with matrix product states. • We observe numerically super-logarithmic growth of entanglement entropy with time.
Fuzzy-like multiple objective multistage decision making
Xu, Jiuping
2014-01-01
Decision has inspired reflection of many thinkers since the ancient times. With the rapid development of science and society, appropriate dynamic decision making has been playing an increasingly important role in many areas of human activity including engineering, management, economy and others. In most real-world problems, decision makers usually have to make decisions sequentially at different points in time and space, at different levels for a component or a system, while facing multiple and conflicting objectives and a hybrid uncertain environment where fuzziness and randomness co-exist in a decision making process. This leads to the development of fuzzy-like multiple objective multistage decision making. This book provides a thorough understanding of the concepts of dynamic optimization from a modern perspective and presents the state-of-the-art methodology for modeling, analyzing and solving the most typical multiple objective multistage decision making practical application problems under fuzzy-like un...
Zhai, Ding; An, Liwei; Dong, Jiuxiang; Zhang, Qingling
2017-10-16
This paper studies the robust stabilization problem for a class of uncertain nonlinear systems with unstable zero dynamics. The considered zero dynamic is not assumed to be input-to-state practically stable and contains nonlinear uncertainties and mismatched external disturbances. A new robust adaptive fuzzy control method is developed by combining H∞ theory with backstepping technique. First, an ideal C¹ virtual control function is designed, which can guarantee the zero dynamic asymptotically stable with a suboptimal H∞ performance. Then, based on some non-negative functions and backstepping design, the actual controller is constructed for the overall system, which ensures that the tracking error for the ideal virtual control signal converges to a priori accuracy regardless of external disturbances. In this design, an auxiliary signal is introduced to overcome the difficulties from the unavailable virtual reference signal. By exploiting the implicit function theorem, the proposed design technique is directly applied to a special case, where the zero dynamic is partially linear. A two inverted pendulums is used to illustrate the application and effectiveness of the proposed design method.
Dynamic Repeated Random Dictatorship and Gender Discrimination
Dittrich, Dennis Alexis Valin; Büchner, Susanne; Kulesz, Micaela Maria
2014-01-01
To reduce the cognitive experimenter demand effect we embed a dictator game in a more complex decision environment, a dynamic household savings decision problem, thus rendering the dictator decision to share some endowment less salient. We then use this game in a laboratory experiment to investigate gender specific allocation behaviour and discrimination. We observe that dictators treat females nicer than males independent of their own gender. Participants are not aware of their discriminatin...
A Comparative Study of Neural Networks and Fuzzy Systems in Modeling of a Nonlinear Dynamic System
Directory of Open Access Journals (Sweden)
Metin Demirtas
2011-07-01
Full Text Available The aim of this paper is to compare the neural networks and fuzzy modeling approaches on a nonlinear system. We have taken Permanent Magnet Brushless Direct Current (PMBDC motor data and have generated models using both approaches. The predictive performance of both methods was compared on the data set for model configurations. The paper describes the results of these tests and discusses the effects of changing model parameters on predictive and practical performance. Modeling sensitivity was used to compare for two methods.
Effect of diabetic neuropathy severity classified by a fuzzy model in muscle dynamics during gait.
Watari, Ricky; Sartor, Cristina D; Picon, Andreja P; Butugan, Marco K; Amorim, Cesar F; Ortega, Neli R S; Sacco, Isabel C N
2014-02-08
Electromyography (EMG) alterations during gait, supposedly caused by diabetic sensorimotor polyneuropathy, are subtle and still inconsistent, due to difficulties in defining homogeneous experimental groups with a clear definition of disease stages. Since evaluating these patients involve many uncertainties, the use of a fuzzy model could enable a better discrimination among different stages of diabetic polyneuropathy and lead to a clarification of when changes in muscle activation start occurring. The aim of this study was to investigate EMG patterns during gait in diabetic individuals with different stages of DSP severity, classified by a fuzzy system. 147 subjects were divided into a control group (n = 30) and four diabetic groups: absent (n = 43), mild (n = 30), moderate (n = 16), and severe (n = 28) neuropathy, classified by a fuzzy model. The EMG activity of the vastus lateralis, tibialis anterior, and gastrocnemius medialis were measured during gait. Temporal and relative magnitude variables were compared among groups using ANOVA tests. Muscle activity changes are present even before an established neural involvement, with delay in vastus lateralis peak and lower tibialis anterior relative magnitude. These alterations suggest an impaired ankle shock absorption mechanism, with compensation at the knee. This condition seems to be more pronounced in higher degrees of neuropathy, as there is an increased vastus lateralis activity in the mild and severe neuropathy groups. Tibialis anterior onset at terminal stance was anticipated in all diabetic groups; at higher degrees of neuropathy, the gastrocnemius medialis exhibited activity reduction and peak delay. EMG alterations in the vastus lateralis and tibialis anterior occur even in the absence of diabetic neuropathy and in mild neuropathic subjects, seemingly causing changes in the shock absorption mechanisms at the heel strike. These changes increase with the onset of neural impairments, and the gastrocnemius
Effect of diabetic neuropathy severity classified by a fuzzy model in muscle dynamics during gait
2014-01-01
Background Electromyography (EMG) alterations during gait, supposedly caused by diabetic sensorimotor polyneuropathy, are subtle and still inconsistent, due to difficulties in defining homogeneous experimental groups with a clear definition of disease stages. Since evaluating these patients involve many uncertainties, the use of a fuzzy model could enable a better discrimination among different stages of diabetic polyneuropathy and lead to a clarification of when changes in muscle activation start occurring. The aim of this study was to investigate EMG patterns during gait in diabetic individuals with different stages of DSP severity, classified by a fuzzy system. Methods 147 subjects were divided into a control group (n = 30) and four diabetic groups: absent (n = 43), mild (n = 30), moderate (n = 16), and severe (n = 28) neuropathy, classified by a fuzzy model. The EMG activity of the vastus lateralis, tibialis anterior, and gastrocnemius medialis were measured during gait. Temporal and relative magnitude variables were compared among groups using ANOVA tests. Results Muscle activity changes are present even before an established neural involvement, with delay in vastus lateralis peak and lower tibialis anterior relative magnitude. These alterations suggest an impaired ankle shock absorption mechanism, with compensation at the knee. This condition seems to be more pronounced in higher degrees of neuropathy, as there is an increased vastus lateralis activity in the mild and severe neuropathy groups. Tibialis anterior onset at terminal stance was anticipated in all diabetic groups; at higher degrees of neuropathy, the gastrocnemius medialis exhibited activity reduction and peak delay. Conclusion EMG alterations in the vastus lateralis and tibialis anterior occur even in the absence of diabetic neuropathy and in mild neuropathic subjects, seemingly causing changes in the shock absorption mechanisms at the heel strike. These changes increase with the onset of neural
Giabbanelli, Philippe J; Crutzen, Rik
2014-12-12
Controlling bias is key to successful randomized controlled trials for behaviour change. Bias can be generated at multiple points during a study, for example, when participants are allocated to different groups. Several methods of allocations exist to randomly distribute participants over the groups such that their prognostic factors (e.g., socio-demographic variables) are similar, in an effort to keep participants' outcomes comparable at baseline. Since it is challenging to create such groups when all prognostic factors are taken together, these factors are often balanced in isolation or only the ones deemed most relevant are balanced. However, the complex interactions among prognostic factors may lead to a poor estimate of behaviour, causing unbalanced groups at baseline, which may introduce accidental bias. We present a novel computational approach for allocating participants to different groups. Our approach automatically uses participants' experiences to model (the interactions among) their prognostic factors and infer how their behaviour is expected to change under a given intervention. Participants are then allocated based on their inferred behaviour rather than on selected prognostic factors. In order to assess the potential of our approach, we collected two datasets regarding the behaviour of participants (n = 430 and n = 187). The potential of the approach on larger sample sizes was examined using synthetic data. All three datasets highlighted that our approach could lead to groups with similar expected behavioural changes. The computational approach proposed here can complement existing statistical approaches when behaviours involve numerous complex relationships, and quantitative data is not readily available to model these relationships. The software implementing our approach and commonly used alternatives is provided at no charge to assist practitioners in the design of their own studies and to compare participants' allocations.
A stochastic dynamic programming model for stream water quality ...
Indian Academy of Sciences (India)
This paper deals with development of a seasonal fraction-removal policy model for waste load allocation in streams addressing uncertainties due to randomness and fuzziness. A stochastic dynamic programming (SDP) model is developed to arrive at the steady-state seasonal fraction-removal policy. A fuzzy decision model ...
Smets, P
1995-01-01
We start by describing the nature of imperfect data, and giving an overview of the various models that have been proposed. Fuzzy sets theory is shown to be an extension of classical set theory, and as such has a proeminent role or modelling imperfect data. The mathematic of fuzzy sets theory is detailled, in particular the role of the triangular norms. The use of fuzzy sets theory in fuzzy logic and possibility theory,the nature of the generalized modus ponens and of the implication operator for approximate reasoning are analysed. The use of fuzzy logic is detailled for application oriented towards process control and database problems.
Rahonis, George
The theory of fuzzy recognizable languages over bounded distributive lattices is presented as a paradigm of recognizable formal power series. Due to the idempotency properties of bounded distributive lattices, the equality of fuzzy recognizable languages is decidable, the determinization of multi-valued automata is effective, and a pumping lemma exists. Fuzzy recognizable languages over finite and infinite words are expressively equivalent to sentences of the multi-valued monadic second-order logic. Fuzzy recognizability over bounded ℓ-monoids and residuated lattices is briefly reported. The chapter concludes with two applications of fuzzy recognizable languages to real world problems in medicine.
Fuzzy Cores and Fuzzy Balancedness
van Gulick, G.; Norde, H.W.
2011-01-01
We study the relation between the fuzzy core and balancedness for fuzzy games. For regular games, this relation has been studied by Bondareva (1963) and Shapley (1967). First, we gain insight in this relation when we analyse situations where the fuzzy game is continuous. Our main result shows that
Random walk in dynamically disordered chains: Poisson white noise disorder
International Nuclear Information System (INIS)
Hernandez-Garcia, E.; Pesquera, L.; Rodriguez, M.A.; San Miguel, M.
1989-01-01
Exact solutions are given for a variety of models of random walks in a chain with time-dependent disorder. Dynamic disorder is modeled by white Poisson noise. Models with site-independent (global) and site-dependent (local) disorder are considered. Results are described in terms of an affective random walk in a nondisordered medium. In the cases of global disorder the effective random walk contains multistep transitions, so that the continuous limit is not a diffusion process. In the cases of local disorder the effective process is equivalent to usual random walk in the absence of disorder but with slower diffusion. Difficulties associated with the continuous-limit representation of random walk in a disordered chain are discussed. In particular, the authors consider explicit cases in which taking the continuous limit and averaging over disorder sources do not commute
Intuitionistic fuzzy proximity spaces
Eun Pyo Lee; Seok Jong Lee
2004-01-01
We introduce the concept of the intuitionistic fuzzy proximity as a generalization of fuzzy proximity, and investigate its properties. Also we investigate the relationship among intuitionistic fuzzy proximity and fuzzy proximity, and intuitionistic fuzzy topology.
Gossips and prejudices: ergodic randomized dynamics in social networks
Frasca, Paolo; Ravazzi, Chiara; Tempo, Roberto; Ishii, Hideaki
In this paper we study a new model of opinion dynamics in social networks, which has two main features. First, agents asynchronously interact in pairs, and these pairs are chosen according to a random process: following recent literature, we refer to this communication model as “gossiping‿. Second,
Entanglement dynamics in critical random quantum Ising chain with perturbations
Huang, Yichen
2017-05-01
We simulate the entanglement dynamics in a critical random quantum Ising chain with generic perturbations using the time-evolving block decimation algorithm. Starting from a product state, we observe super-logarithmic growth of entanglement entropy with time. The numerical result is consistent with the analytical prediction of Vosk and Altman using a real-space renormalization group technique.
Designing PID-Fuzzy Controller for Pendubot System
Directory of Open Access Journals (Sweden)
Ho Trong Nguyen
2017-12-01
Full Text Available In the paper, authors analize dynamic equation of a pendubot system. Familiar kinds of controller – PID, fuzzy controllers – are concerned. Then, a structure of PID-FUZZY is presented. The comparison of three kinds of controllers – PID, fuzzy and PID-FUZZY shows the better response of system under PID-FUZZY controller. Then, the experiments on the real model also prove the better stabilization of the hybrid controller which is combined between linear and intelligent controller.
Dynamical continuous time random Lévy flights
Liu, Jian; Chen, Xiaosong
2016-03-01
The Lévy flights' diffusive behavior is studied within the framework of the dynamical continuous time random walk (DCTRW) method, while the nonlinear friction is introduced in each step. Through the DCTRW method, Lévy random walker in each step flies by obeying the Newton's Second Law while the nonlinear friction f(v) = - γ0v - γ2v3 being considered instead of Stokes friction. It is shown that after introducing the nonlinear friction, the superdiffusive Lévy flights converges, behaves localization phenomenon with long time limit, but for the Lévy index μ = 2 case, it is still Brownian motion.
Relational Demonic Fuzzy Refinement
Directory of Open Access Journals (Sweden)
Fairouz Tchier
2014-01-01
Full Text Available We use relational algebra to define a refinement fuzzy order called demonic fuzzy refinement and also the associated fuzzy operators which are fuzzy demonic join (⊔fuz, fuzzy demonic meet (⊓fuz, and fuzzy demonic composition (□fuz. Our definitions and properties are illustrated by some examples using mathematica software (fuzzy logic.
Fuzzy Riesz subspaces, fuzzy ideals, fuzzy bands and fuzzy band projections
Hong, Liang
2015-01-01
Fuzzy ordered linear spaces, Riesz spaces, fuzzy Archimedean spaces and $\\sigma$-complete fuzzy Riesz spaces were defined and studied in several works. Following the efforts along this line, we define fuzzy Riesz subspaces, fuzzy ideals, fuzzy bands and fuzzy band projections and establish their fundamental properties.
Effectiveness of Securities with Fuzzy Probabilistic Return
Directory of Open Access Journals (Sweden)
Krzysztof Piasecki
2011-01-01
Full Text Available The generalized fuzzy present value of a security is defined here as fuzzy valued utility of cash flow. The generalized fuzzy present value cannot depend on the value of future cash flow. There exists such a generalized fuzzy present value which is not a fuzzy present value in the sense given by some authors. If the present value is a fuzzy number and the future value is a random one, then the return rate is given as a probabilistic fuzzy subset on a real line. This kind of return rate is called a fuzzy probabilistic return. The main goal of this paper is to derive the family of effective securities with fuzzy probabilistic return. Achieving this goal requires the study of the basic parameters characterizing fuzzy probabilistic return. Therefore, fuzzy expected value and variance are determined for this case of return. These results are a starting point for constructing a three-dimensional image. The set of effective securities is introduced as the Pareto optimal set determined by the maximization of the expected return rate and minimization of the variance. Finally, the set of effective securities is distinguished as a fuzzy set. These results are obtained without the assumption that the distribution of future values is Gaussian. (original abstract
Li, Bing; Yang, Guishan; Wan, Rongrong; Hörmann, Georg
2017-08-01
Comprehensively evaluating water quality with a single method alone is challenging because water quality evaluation involves complex, uncertain, and fuzzy processes. Moreover, water quality evaluation is limited by finite water quality monitoring that can only represent water quality conditions at certain time points. Thus, the present study proposed a dynamic fuzzy matter-element model (D-FME) to comprehensively and continuously evaluate water quality status. D-FME was first constructed by introducing functional data analysis (FDA) theory into a fuzzy matter-element model and then validated using monthly water quality data for the Poyang Lake outlet (Hukou) from 2011 to 2012. Results showed that the finite water quality indicators were represented as dynamic functional curves despite missing values and irregular sampling time. The water quality rank feature curve was integrated by the D-FME model and revealed comprehensive and continuous variations in water quality. The water quality in Hukou showed remarkable seasonal variations, with the best water quality in summer and worst water quality in winter. These trends were significantly correlated with water level fluctuations (R = -0.71, p quality of the Poyang Lake outlet. The proposed D-FME model can obtain scientific and intuitive results. Moreover, the D-FME model is not restricted to water quality evaluation and can be readily applied to other areas with similar problems.
Directory of Open Access Journals (Sweden)
Izadbakhsh Maziar
2014-12-01
Full Text Available The microgrid (MG technology integrates distributed generations, energy storage elements and loads. In this paper, dynamic performance enhancement of an MG consisting of wind turbine was investigated using permanent magnet synchronous generation (PMSG, photovoltaic (PV, microturbine generation (MTG systems and flywheel under different circumstances. In order to maximize the output of solar arrays, maximum power point tracking (MPPT technique was used by an adaptive neuro-fuzzy inference system (ANFIS; also, control of turbine output power in high speed winds was achieved using pitch angle control technic by fuzzy logic. For tracking the maximum point, the proposed ANFIS was trained by the optimum values. The simulation results showed that the ANFIS controller of grid-connected mode could easily meet the load demand with less fluctuation around the maximum power point. Moreover, pitch angle controller, which was based on fuzzy logic with wind speed and active power as the inputs, could have faster responses, thereby leading to flatter power curves, enhancement of the dynamic performance of wind turbine and prevention of both frazzle and mechanical damages to PMSG. The thorough wind power generation system, PV system, MTG, flywheel and power electronic converter interface were proposed by using Mat-lab/Simulink.
Neutron detection using soft errors in dynamic Random Access Memories
International Nuclear Information System (INIS)
Darambara, D.G.; Spyrou, N.M.
1994-01-01
The purpose of this paper is to present results from experiments that have been performed to show the memory cycle time dependence of the soft errors produced by the interaction of alpha particles with dynamic random access memory devices, with a view to using these as position sensitive detectors. Furthermore, a preliminary feasibility study being carried out indicates the use of dynamic RAMs as neutron detectors by the utilization of (n, α) capture reactions in a Li converter placed on the top of the active area of the memory chip. ((orig.))
Directory of Open Access Journals (Sweden)
Miguel Ramirez-Gonzalez
2015-01-01
Full Text Available In this paper, the effect of fuzzy logic-based robust power system stabilizers on the improvement of the dynamics of a large-scale power system is investigated. The study is particularly focused on the Mexican Interconnected System and on adding damping to two critical inter-area system oscillation modes: The north-south mode and the western-peninsular mode. The fuzzy power system stabilizers (FPSSs applied here are based on a significantly reduced rule base, small number of tuning parameters, and simple control algorithm and architecture, which makes their design and implementation easier and suitable for practical applications. Non-linear time-domain simulations for a set of test cases and results from Prony Analysis verify the robustness of the designed FPSSs, as compared to conventional PSSs.
Directory of Open Access Journals (Sweden)
Luis Daniel Lledó
2015-03-01
Full Text Available This paper presents an application formed by a classification method based on the architecture of ART neural network (Adaptive Resonance Theory and the Fuzzy Set Theory to classify physiological reactions in order to automatically and dynamically adapt a robot-assisted rehabilitation therapy to the patient needs, using a three-dimensional task in a virtual reality system. Firstly, the mathematical and structural model of the neuro-fuzzy classification method is described together with the signal and training data acquisition. Then, the virtual designed task with physics behavior and its development procedure are explained. Finally, the general architecture of the experimentation for the auto-adaptive therapy is presented using the classification method with the virtual reality exercise.
Small-world networks of fuzzy chaotic oscillators
Bucolo, M; Fortuna, L
2003-01-01
Small-world topology has been used to build lattices of nonlinear fuzzy systems. Chaotic units, ruled by linguistic description and with specified Lyapunov exponent, have been realized and connected using linear diffusion coefficient. The dynamic features of the networks versus the number of systems connected have been investigated to underline phenomena like spatiotemporal chaos and complete regularization. The synchronization characteristics in case of sparse long-term connections and the performances comparison with regular and random network configurations are shown.
Fuzzy statistical decision-making theory and applications
Kabak, Özgür
2016-01-01
This book offers a comprehensive reference guide to fuzzy statistics and fuzzy decision-making techniques. It provides readers with all the necessary tools for making statistical inference in the case of incomplete information or insufficient data, where classical statistics cannot be applied. The respective chapters, written by prominent researchers, explain a wealth of both basic and advanced concepts including: fuzzy probability distributions, fuzzy frequency distributions, fuzzy Bayesian inference, fuzzy mean, mode and median, fuzzy dispersion, fuzzy p-value, and many others. To foster a better understanding, all the chapters include relevant numerical examples or case studies. Taken together, they form an excellent reference guide for researchers, lecturers and postgraduate students pursuing research on fuzzy statistics. Moreover, by extending all the main aspects of classical statistical decision-making to its fuzzy counterpart, the book presents a dynamic snapshot of the field that is expected to stimu...
Uniform attractors for non-autonomous random dynamical systems
Cui, Hongyong; Langa, José A.
2017-07-01
This paper is devoted to establishing a (random) uniform attractor theory for non-autonomous random dynamical systems (NRDS). The uniform attractor is defined as the minimal compact uniformly pullback attracting random set. Nevertheless, the uniform pullback attraction in fact implies a uniform forward attraction in probability, and implies also an almost uniform pullback attraction for discrete time-sequences. Though no invariance is required by definition, the uniform attractor can have a negative semi-invariance under certain conditions. Several existence criteria for uniform attractors are given, and the relationship between uniform and cocycle attractors is carefully studied. To overcome the measurability difficulty, the symbol space is required to be Polish which is shown fulfilled by the hulls of Llocp (R ;Lr) functions, p , r > 1. Moreover, uniform attractors for continuous NRDS are shown determined by uniformly attracting deterministic compact sets. Finally, the uniform attractor for a stochastic reaction-diffusion equation with translation-bounded external forcing are studied as applications.
DEFF Research Database (Denmark)
Anker, Thomas Boysen; Kappel, Klemens; Eadie, Douglas
2012-01-01
This article clarifies the commonplace assumption that brands make promises by developing definitions of brand promise delivery. Distinguishing between clear and fuzzy brand promises, we develop definitions of what it is for a brand to deliver on fuzzy functional, symbolic, and experiential...
DEFF Research Database (Denmark)
Berks, G.; Keyserlingk, Diedrich Graf von; Jantzen, Jan
2000-01-01
and clustering are the basic concerns in medicine. Classification depends on definitions of the classes and their required degree of participant of the elements in the cases' symptoms. In medicine imprecise conditions are the rule and therefore fuzzy methods are much more suitable than crisp ones. Fuzzy c...
Effects of random noise in a dynamical model of love
International Nuclear Information System (INIS)
Xu Yong; Gu Rencai; Zhang Huiqing
2011-01-01
Highlights: → We model the complexity and unpredictability of psychology as Gaussian white noise. → The stochastic system of love is considered including bifurcation and chaos. → We show that noise can both suppress and induce chaos in dynamical models of love. - Abstract: This paper aims to investigate the stochastic model of love and the effects of random noise. We first revisit the deterministic model of love and some basic properties are presented such as: symmetry, dissipation, fixed points (equilibrium), chaotic behaviors and chaotic attractors. Then we construct a stochastic love-triangle model with parametric random excitation due to the complexity and unpredictability of the psychological system, where the randomness is modeled as the standard Gaussian noise. Stochastic dynamics under different three cases of 'Romeo's romantic style', are examined and two kinds of bifurcations versus the noise intensity parameter are observed by the criteria of changes of top Lyapunov exponent and shape of stationary probability density function (PDF) respectively. The phase portraits and time history are carried out to verify the proposed results, and the good agreement can be found. And also the dual roles of the random noise, namely suppressing and inducing chaos are revealed.
Effects of random noise in a dynamical model of love
Energy Technology Data Exchange (ETDEWEB)
Xu Yong, E-mail: hsux3@nwpu.edu.cn [Department of Applied Mathematics, Northwestern Polytechnical University, Xi' an 710072 (China); Gu Rencai; Zhang Huiqing [Department of Applied Mathematics, Northwestern Polytechnical University, Xi' an 710072 (China)
2011-07-15
Highlights: > We model the complexity and unpredictability of psychology as Gaussian white noise. > The stochastic system of love is considered including bifurcation and chaos. > We show that noise can both suppress and induce chaos in dynamical models of love. - Abstract: This paper aims to investigate the stochastic model of love and the effects of random noise. We first revisit the deterministic model of love and some basic properties are presented such as: symmetry, dissipation, fixed points (equilibrium), chaotic behaviors and chaotic attractors. Then we construct a stochastic love-triangle model with parametric random excitation due to the complexity and unpredictability of the psychological system, where the randomness is modeled as the standard Gaussian noise. Stochastic dynamics under different three cases of 'Romeo's romantic style', are examined and two kinds of bifurcations versus the noise intensity parameter are observed by the criteria of changes of top Lyapunov exponent and shape of stationary probability density function (PDF) respectively. The phase portraits and time history are carried out to verify the proposed results, and the good agreement can be found. And also the dual roles of the random noise, namely suppressing and inducing chaos are revealed.
Theory of activated glassy dynamics in randomly pinned fluids
Phan, Anh D.; Schweizer, Kenneth S.
2018-02-01
We generalize the force-level, microscopic, Nonlinear Langevin Equation (NLE) theory and its elastically collective generalization [elastically collective nonlinear Langevin equation (ECNLE) theory] of activated dynamics in bulk spherical particle liquids to address the influence of random particle pinning on structural relaxation. The simplest neutral confinement model is analyzed for hard spheres where there is no change of the equilibrium pair structure upon particle pinning. As the pinned fraction grows, cage scale dynamical constraints are intensified in a manner that increases with density. This results in the mobile particles becoming more transiently localized, with increases of the jump distance, cage scale barrier, and NLE theory mean hopping time; subtle changes of the dynamic shear modulus are predicted. The results are contrasted with recent simulations. Similarities in relaxation behavior are identified in the dynamic precursor regime, including a roughly exponential, or weakly supra-exponential, growth of the alpha time with pinning fraction and a reduction of dynamic fragility. However, the increase of the alpha time with pinning predicted by the local NLE theory is too small and severely so at very high volume fractions. The strong deviations are argued to be due to the longer range collective elasticity aspect of the problem which is expected to be modified by random pinning in a complex manner. A qualitative physical scenario is offered for how the three distinct aspects that quantify the elastic barrier may change with pinning. ECNLE theory calculations of the alpha time are then presented based on the simplest effective-medium-like treatment for how random pinning modifies the elastic barrier. The results appear to be consistent with most, but not all, trends seen in recent simulations. Key open problems are discussed with regard to both theory and simulation.
Directory of Open Access Journals (Sweden)
Baranov A. O.
2016-06-01
Full Text Available The purpose of this study is to adapt methods of fuzzy sets to analyze the effectiveness of multistage investment projects. The problem solved by the study is as follows. Some innovative projects are characterized by the lack of profitability in the early stages of implementation and high risk associated with high uncertainty of assessment of expected future cash flows generated by the project. In this situation, the use of standard methods of analysis of economic efficiency of investment projects in high-tech industries, does not provide a comprehensive assessment of the appropriateness of investing, as well as to quantify the accuracy of the dynamics of the projected figures. All this requires the development of theory and methods of analysis of economic efficiency of innovation. Application of real options, as well as the fuzzy sets is, in our view, the direction of improving these methods. The fuzzy random pairs approach is developed in order to study fuzzy set properties of random pointwise set mappings. The articles proposes generalization of the fuzzy random pairs approach for research of stochastic processes. The generalization is initiated by an approach to exploration of uncertainty in research project supported with an RFBR grant no. 15-06-06914, which is based on application of the Geske model modification. Mathematical description of the generalization is carried out for an example of a real venture-backed investment project aimed at organization of methyl chloride to ethylene processing. The generalization essence is in the following: 1 time variable t in a random process ξ ( t is replaced with a random value u , distributed uniformly within a segment [0; T ], which turns the process ξ ( t into a bidimensional random value V = u ,ξ( 0;( u , defined on [ T ]× R ; 2 the random value V value is translated into a random pointwise set mapping using the interval translation; 3 in order to translate the random pointwise set mapping
Fuzzy cores and fuzzy balancedness
van Gulick, G.; Norde, H.W.
2013-01-01
We study the relation between the fuzzy core and balancedness for fuzzy games. For regular games, this relation has been studied by Bondareva (Problemy Kibernet 10:119–139, 1963) and Shapley (Naval Res Logist Q 14: 453–460, 1967). First, we gain insight in this relation when we analyse situations
Dynamic compensatory pattern matching in a fuzzy rule-based control system
Sun, Chuen-Tsai
1991-01-01
A dynamic compensatory matching procedure is suggested as a method to generate an aggregated measure for evaluating the appropriateness of rules for control systems. It is a dynamic weighted matching technique which takes into account incomplete information under real-time requirements. The initial weights of importance of variables are generated with a generalized neural network architecture and a gradient descent algorithm. An intuitive compensatory scheme based on correlations among input variables of training data is adopted so that the system is coherent to a noisy environment.
2010-04-01
... Semiconductor Chips Having Synchronous Dynamic Random Access Memory Controllers and Products Containing Same... synchronous dynamic random access memory controllers and products containing same by reason of infringement of... semiconductor chips having synchronous dynamic random access memory controllers and products containing same...
Probabilistic Quadratic Programming Problems with Some Fuzzy Parameters
Directory of Open Access Journals (Sweden)
S. K. Barik
2012-01-01
making problem by using some specified random variables and fuzzy numbers. In the present paper, randomness is characterized by Weibull random variables and fuzziness is characterized by triangular and trapezoidal fuzzy number. A defuzzification method has been introduced for finding the crisp values of the fuzzy numbers using the proportional probability density function associated with the membership functions of these fuzzy numbers. An equivalent deterministic crisp model has been established in order to solve the proposed model. Finally, a numerical example is presented to illustrate the solution procedure.
Dynamic Optimization for IPS2 Resource Allocation Based on Improved Fuzzy Multiple Linear Regression
Maokuan Zheng; Xinguo Ming; Guoming Li
2017-01-01
The study mainly focuses on resource allocation optimization for industrial product-service systems (IPS2). The development of IPS2 leads to sustainable economy by introducing cooperative mechanisms apart from commodity transaction. The randomness and fluctuation of service requests from customers lead to the volatility of IPS2 resource utilization ratio. Three basic rules for resource allocation optimization are put forward to improve system operation efficiency and cut unnecessary costs. An...
Macroscopic damping model for structural dynamics with random polycrystalline configurations
Yang, Yantao; Cui, Junzhi; Yu, Yifan; Xiang, Meizhen
2017-12-01
In this paper the macroscopic damping model for dynamical behavior of the structures with random polycrystalline configurations at micro-nano scales is established. First, the global motion equation of a crystal is decomposed into a set of motion equations with independent single degree of freedom (SDOF) along normal discrete modes, and then damping behavior is introduced into each SDOF motion. Through the interpolation of discrete modes, the continuous representation of damping effects for the crystal is obtained. Second, from energy conservation law the expression of the damping coefficient is derived, and the approximate formula of damping coefficient is given. Next, the continuous damping coefficient for polycrystalline cluster is expressed, the continuous dynamical equation with damping term is obtained, and then the concrete damping coefficients for a polycrystalline Cu sample are shown. Finally, by using statistical two-scale homogenization method, the macroscopic homogenized dynamical equation containing damping term for the structures with random polycrystalline configurations at micro-nano scales is set up.
Nguyen, Hung T.; Kreinovich, Vladik
2014-01-01
To help computers make better decisions, it is desirable to describe all our knowledge in computer-understandable terms. This is easy for knowledge described in terms on numerical values: we simply store the corresponding numbers in the computer. This is also easy for knowledge about precise (well-defined) properties which are either true or false for each object: we simply store the corresponding “true” and “false” values in the computer. The challenge is how to store information about imprecise properties. In this paper, we overview different ways to fully store the expert information about imprecise properties. We show that in the simplest case, when the only source of imprecision is disagreement between different experts, a natural way to store all the expert information is to use random sets; we also show how fuzzy sets naturally appear in such random-set representation. We then show how the random-set representation can be extended to the general (“fuzzy”) case when, in addition to disagreements, experts are also unsure whether some objects satisfy certain properties or not. PMID:25386045
AL-Hawary, Talal Ali
2012-01-01
The aim of this paper is to discuss properties of fuzzy regular-flats, fuzzy C- flats, fuzzy alternative-sets and fuzzy i-flats. Moreover, we characterize some peculiar fuzzy matroids via these notions. Finally, we provide a decomposition of fuzzy strong maps.
Directory of Open Access Journals (Sweden)
Halis Aygün
2008-01-01
Full Text Available We introduce definitions of fuzzy inverse compactness, fuzzy inverse countable compactness, and fuzzy inverse Lindelöfness on arbitrary -fuzzy sets in -fuzzy topological spaces. We prove that the proposed definitions are good extensions of the corresponding concepts in ordinary topology and obtain different characterizations of fuzzy inverse compactness.
Network Randomization and Dynamic Defense for Critical Infrastructure Systems
Energy Technology Data Exchange (ETDEWEB)
Chavez, Adrian R. [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Martin, Mitchell Tyler [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Hamlet, Jason [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Stout, William M.S. [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Lee, Erik [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)
2015-04-01
Critical Infrastructure control systems continue to foster predictable communication paths, static configurations, and unpatched systems that allow easy access to our nation's most critical assets. This makes them attractive targets for cyber intrusion. We seek to address these attack vectors by automatically randomizing network settings, randomizing applications on the end devices themselves, and dynamically defending these systems against active attacks. Applying these protective measures will convert control systems into moving targets that proactively defend themselves against attack. Sandia National Laboratories has led this effort by gathering operational and technical requirements from Tennessee Valley Authority (TVA) and performing research and development to create a proof-of-concept solution. Our proof-of-concept has been tested in a laboratory environment with over 300 nodes. The vision of this project is to enhance control system security by converting existing control systems into moving targets and building these security measures into future systems while meeting the unique constraints that control systems face.
Dynamics of generalized Gaussian polymeric structures in random layered flows.
Katyal, Divya; Kant, Rama
2015-04-01
We develop a formalism for the dynamics of a flexible branched polymer with arbitrary topology in the presence of random flows. This is achieved by employing the generalized Gaussian structure (GGS) approach and the Matheron-de Marsily model for the random layered flow. The expression for the average square displacement (ASD) of the center of mass of the GGS is obtained in such flow. The averaging is done over both the thermal noise and the external random flow. Although the formalism is valid for branched polymers with various complex topologies, we mainly focus here on the dynamics of the flexible star and dendrimer. We analyze the effect of the topology (the number and length of branches for stars and the number of generations for dendrimers) on the dynamics under the influence of external flow, which is characterized by their root-mean-square velocity, persistence flow length, and flow exponent α. Our analysis shows two anomalous power-law regimes, viz., subdiffusive (intermediate-time polymer stretching and flow-induced diffusion) and superdiffusive (long-time flow-induced diffusion). The influence of the topology of the GGS is unraveled in the intermediate-time regime, while the long-time regime is only weakly dependent on the topology of the polymer. With the decrease in the value of α, the magnitude of the ASD decreases, while the temporal exponent of the ASD increases in both the time regimes. Also there is an increase in both the magnitude of the ASD and the crossover time (from the subdiffusive to the superdiffusive regime) with an increase in the total mass of the polymeric structure.
Complex networks: when random walk dynamics equals synchronization
International Nuclear Information System (INIS)
Kriener, Birgit; Anand, Lishma; Timme, Marc
2012-01-01
Synchrony prevalently emerges from the interactions of coupled dynamical units. For simple systems such as networks of phase oscillators, the asymptotic synchronization process is assumed to be equivalent to a Markov process that models standard diffusion or random walks on the same network topology. In this paper, we analytically derive the conditions for such equivalence for networks of pulse-coupled oscillators, which serve as models for neurons and pacemaker cells interacting by exchanging electric pulses or fireflies interacting via light flashes. We find that the pulse synchronization process is less simple, but there are classes of, e.g., network topologies that ensure equivalence. In particular, local dynamical operators are required to be doubly stochastic. These results provide a natural link between stochastic processes and deterministic synchronization on networks. Tools for analyzing diffusion (or, more generally, Markov processes) may now be transferred to pin down features of synchronization in networks of pulse-coupled units such as neural circuits. (paper)
Dynamic Simulation of Random Packing of Polydispersive Fine Particles
Ferraz, Carlos Handrey Araujo; Marques, Samuel Apolinário
2018-02-01
In this paper, we perform molecular dynamic (MD) simulations to study the two-dimensional packing process of both monosized and random size particles with radii ranging from 1.0 to 7.0 μm. The initial positions as well as the radii of five thousand fine particles were defined inside a rectangular box by using a random number generator. Both the translational and rotational movements of each particle were considered in the simulations. In order to deal with interacting fine particles, we take into account both the contact forces and the long-range dispersive forces. We account for normal and static/sliding tangential friction forces between particles and between particle and wall by means of a linear model approach, while the long-range dispersive forces are computed by using a Lennard-Jones-like potential. The packing processes were studied assuming different long-range interaction strengths. We carry out statistical calculations of the different quantities studied such as packing density, mean coordination number, kinetic energy, and radial distribution function as the system evolves over time. We find that the long-range dispersive forces can strongly influence the packing process dynamics as they might form large particle clusters, depending on the intensity of the long-range interaction strength.
International Nuclear Information System (INIS)
Phillips, Carolyn L.; Anderson, Joshua A.; Glotzer, Sharon C.
2011-01-01
Highlights: → Molecular Dynamics codes implemented on GPUs have achieved two-order of magnitude computational accelerations. → Brownian Dynamics and Dissipative Particle Dynamics simulations require a large number of random numbers per time step. → We introduce a method for generating small batches of pseudorandom numbers distributed over many threads of calculations. → With this method, Dissipative Particle Dynamics is implemented on a GPU device without requiring thread-to-thread communication. - Abstract: Brownian Dynamics (BD), also known as Langevin Dynamics, and Dissipative Particle Dynamics (DPD) are implicit solvent methods commonly used in models of soft matter and biomolecular systems. The interaction of the numerous solvent particles with larger particles is coarse-grained as a Langevin thermostat is applied to individual particles or to particle pairs. The Langevin thermostat requires a pseudo-random number generator (PRNG) to generate the stochastic force applied to each particle or pair of neighboring particles during each time step in the integration of Newton's equations of motion. In a Single-Instruction-Multiple-Thread (SIMT) GPU parallel computing environment, small batches of random numbers must be generated over thousands of threads and millions of kernel calls. In this communication we introduce a one-PRNG-per-kernel-call-per-thread scheme, in which a micro-stream of pseudorandom numbers is generated in each thread and kernel call. These high quality, statistically robust micro-streams require no global memory for state storage, are more computationally efficient than other PRNG schemes in memory-bound kernels, and uniquely enable the DPD simulation method without requiring communication between threads.
A numerical algorithm of fuzzy reliability
International Nuclear Information System (INIS)
Jiang Qimi; Chen, C.-H.
2003-01-01
In this paper, a computational model of fuzzy reliability focusing on solving the engineering problems with random general stress-fuzzy general strength is presented. The mathematical basis of this computational model is that the fuzzy probability can be computed with the computational method of conventional probability by use of a mathematical transition. Based on this computational model, a numerical algorithm is given which can be applied to compute the fuzzy reliability of mechanical components, sensors, electronic units, etc. This establishes a basis for the reliability analysis of systems consisting of components with fuzzy reliability. As an example, a case study about the fuzzy reliability analysis of a kind of sensor used in railway systems is provided to verify the logic of this algorithm. The computation results show that this algorithm fits the engineering experience
Temporal dynamics of recurrent airway symptoms and cellular random walk.
Suki, Béla; Frey, Urs
2003-11-01
Asthma is a complex chronic inflammatory disease of the small airways that has dramatically increased in prevalence in industrialized countries during the last decades. Risk factors for adult asthma have been related to the complex array of gene-environment interactions and exposure of the immune system to allergens in early childhood. In genetically predisposed subjects, continuous exposure to environmental agents such as allergens or infections can lead to recurrent airway symptoms characterized by recurrent episodes of airway inflammation and bronchoconstriction with clinical symptoms of cough, dyspnea, or wheezing. In this study, we report that the longterm temporal dynamics of recurrent airway symptoms in a population of unselected infants display a complex intermittent pattern and that the distribution of interepisode intervals follows a power law. We interpret the data by using a model of the dynamics of attack episodes in which an attack is triggered by an avalanche of airway constrictions. We map the dynamics of this model to the known problem of a random walk in the presence of an absorbing boundary in which the walker corresponds to the fluctuations in contractile state of airway smooth muscle cells. These findings may provide new insight into the mechanisms of otherwise unexplained symptom episodes.
Dias, Sofia B.; Diniz, José A.; Hadjileontiadis, Leontios J.
2014-01-01
The combination of the process of pedagogical planning within the Blended (b-) learning environment with the users' quality of interaction ("QoI") with the Learning Management System (LMS) is explored here. The required "QoI" (both for professors and students) is estimated by adopting a fuzzy logic-based modeling approach,…
Fatouros, Dimitrios G; Nielsen, Flemming Seier; Douroumis, Dionysios; Hadjileontiadis, Leontios J; Mullertz, Anette
2008-08-01
The aim of the current study was to evaluate the potential of the dynamic lipolysis model to simulate the absorption of a poorly soluble model drug compound, probucol, from three lipid-based formulations and to predict the in vitro-in vivo correlation (IVIVC) using neuro-fuzzy networks. An oil solution and two self-micro and nano-emulsifying drug delivery systems were tested in the lipolysis model. The release of probucol to the aqueous (micellar) phase was monitored during the progress of lipolysis. These release profiles compared with plasma profiles obtained in a previous bioavailability study conducted in mini-pigs at the same conditions. The release rate and extent of release from the oil formulation were found to be significantly lower than from SMEDDS and SNEDDS. The rank order of probucol released (SMEDDS approximately SNEDDS > oil formulation) was similar to the rank order of bioavailability from the in vivo study. The employed neuro-fuzzy model (AFM-IVIVC) achieved significantly high prediction ability for different data formations (correlation greater than 0.91 and prediction error close to zero), without employing complex configurations. These preliminary results suggest that the dynamic lipolysis model combined with the AFM-IVIVC can be a useful tool in the prediction of the in vivo behavior of lipid-based formulations.
van Lith, Pascal; van Lith, P.F.; Betlem, Bernardus H.L.; Roffel, B.
2002-01-01
Hybrid fuzzy-first principles models can be a good alternative if a complete physical model is difficult to derive. These hybrid models consist of a framework of dynamic mass and energy balances, supplemented by fuzzy submodels describing additional equations, such as mass transformation and
Lith, Pascal F. van; Betlem, Ben H.L.; Roffel, Brian
2002-01-01
Hybrid fuzzy-first principles models can be a good alternative if a complete physical model is difficult to derive. These hybrid models consist of a framework of dynamic mass and energy balances, supplemented by fuzzy submodels describing additional equations, such as mass transformation and
Nguyen, Hung T
2005-01-01
THE CONCEPT OF FUZZINESS Examples Mathematical modeling Some operations on fuzzy sets Fuzziness as uncertainty Exercises SOME ALGEBRA OF FUZZY SETS Boolean algebras and lattices Equivalence relations and partitions Composing mappings Isomorphisms and homomorphisms Alpha-cuts Images of alpha-level sets Exercises FUZZY QUANTITIES Fuzzy quantities Fuzzy numbers Fuzzy intervals Exercises LOGICAL ASPECTS OF FUZZY SETS Classical two-valued logic A three-valued logic Fuzzy logic Fuzzy and Lukasiewi
Intuitionistic fuzzy metric spaces
International Nuclear Information System (INIS)
Park, Jin Han
2004-01-01
Using the idea of intuitionistic fuzzy set due to Atanassov [Intuitionistic fuzzy sets. in: V. Sgurev (Ed.), VII ITKR's Session, Sofia June, 1983; Fuzzy Sets Syst. 20 (1986) 87], we define the notion of intuitionistic fuzzy metric spaces as a natural generalization of fuzzy metric spaces due to George and Veeramani [Fuzzy Sets Syst. 64 (1994) 395] and prove some known results of metric spaces including Baire's theorem and the Uniform limit theorem for intuitionistic fuzzy metric spaces
Vortex-Core Reversal Dynamics: Towards Vortex Random Access Memory
Kim, Sang-Koog
2011-03-01
An energy-efficient, ultrahigh-density, ultrafast, and nonvolatile solid-state universal memory is a long-held dream in the field of information-storage technology. The magnetic random access memory (MRAM) along with a spin-transfer-torque switching mechanism is a strong candidate-means of realizing that dream, given its nonvolatility, infinite endurance, and fast random access. Magnetic vortices in patterned soft magnetic dots promise ground-breaking applications in information-storage devices, owing to the very stable twofold ground states of either their upward or downward core magnetization orientation and plausible core switching by in-plane alternating magnetic fields or spin-polarized currents. However, two technologically most important but very challenging issues --- low-power recording and reliable selection of each memory cell with already existing cross-point architectures --- have not yet been resolved for the basic operations in information storage, that is, writing (recording) and readout. Here, we experimentally demonstrate a magnetic vortex random access memory (VRAM) in the basic cross-point architecture. This unique VRAM offers reliable cell selection and low-power-consumption control of switching of out-of-plane core magnetizations using specially designed rotating magnetic fields generated by two orthogonal and unipolar Gaussian-pulse currents along with optimized pulse width and time delay. Our achievement of a new device based on a new material, that is, a medium composed of patterned vortex-state disks, together with the new physics on ultrafast vortex-core switching dynamics, can stimulate further fruitful research on MRAMs that are based on vortex-state dot arrays.
Intuitionistic Fuzzy Hyperhomomorphism and Intuitionistic Fuzzy Normal Subhypergroups
Abdulmula, Karema S; Salleh, Abdul Razak
2012-01-01
The purpose of this paper is to introduce some basic concepts of intuitionistic fuzzy hyperalgebra. We continue our study of intuitionistic fuzzy hypergroups, by generalising the concept of fuzzy homomorphism and fuzzy normal subgroup based on fuzzy spaces to intuitionistic fuzzy hyperhomomorphism based on intuitionstic fuzzy spaces. We will introduce the notion of an intuitionistic fuzzy quotient hypergroup induced by an intuitionistic fuzzy normal subhypergroup under intuitionistic fuzzy hy...
Fuzzy model-based observers for fault detection in CSTR.
Ballesteros-Moncada, Hazael; Herrera-López, Enrique J; Anzurez-Marín, Juan
2015-11-01
Under the vast variety of fuzzy model-based observers reported in the literature, what would be the properone to be used for fault detection in a class of chemical reactor? In this study four fuzzy model-based observers for sensor fault detection of a Continuous Stirred Tank Reactor were designed and compared. The designs include (i) a Luenberger fuzzy observer, (ii) a Luenberger fuzzy observer with sliding modes, (iii) a Walcott-Zak fuzzy observer, and (iv) an Utkin fuzzy observer. A negative, an oscillating fault signal, and a bounded random noise signal with a maximum value of ±0.4 were used to evaluate and compare the performance of the fuzzy observers. The Utkin fuzzy observer showed the best performance under the tested conditions. Copyright © 2015 ISA. Published by Elsevier Ltd. All rights reserved.
Human factors and fuzzy set theory for safety analysis
International Nuclear Information System (INIS)
Nishiwaki, Y.
1987-01-01
Human reliability and performance is affected by many factors: medical, physiological and psychological, etc. The uncertainty involved in human factors may not necessarily be probabilistic, but fuzzy. Therefore, it is important to develop a theory by which both the non-probabilistic uncertainties, or fuzziness, of human factors and the probabilistic properties of machines can be treated consistently. In reality, randomness and fuzziness are sometimes mixed. From the mathematical point of view, probabilistic measures may be considered a special case of fuzzy measures. Therefore, fuzzy set theory seems to be an effective tool for analysing man-machine systems. The concept 'failure possibility' based on fuzzy sets is suggested as an approach to safety analysis and fault diagnosis of a large complex system. Fuzzy measures and fuzzy integrals are introduced and their possible applications are also discussed. (author)
Mohammadi Nasrabadi, Ali; Hosseinpour, Mohammad Hossein; Ebrahimnejad, Sadoullah
2013-05-01
In competitive markets, market segmentation is a critical point of business, and it can be used as a generic strategy. In each segment, strategies lead companies to their targets; thus, segment selection and the application of the appropriate strategies over time are very important to achieve successful business. This paper aims to model a strategy-aligned fuzzy approach to market segment evaluation and selection. A modular decision support system (DSS) is developed to select an optimum segment with its appropriate strategies. The suggested DSS has two main modules. The first one is SPACE matrix which indicates the risk of each segment. Also, it determines the long-term strategies. The second module finds the most preferred segment-strategies over time. Dynamic network process is applied to prioritize segment-strategies according to five competitive force factors. There is vagueness in pairwise comparisons, and this vagueness has been modeled using fuzzy concepts. To clarify, an example is illustrated by a case study in Iran's coffee market. The results show that success possibility of segments could be different, and choosing the best ones could help companies to be sure in developing their business. Moreover, changing the priority of strategies over time indicates the importance of long-term planning. This fact has been supported by a case study on strategic priority difference in short- and long-term consideration.
International Nuclear Information System (INIS)
Watanabe, K.
1990-01-01
Studies have been made on fuzzy diagnosis using inverse problem solutions of the fuzzy relational equation of ao R=b, where a is the failure vector, R the fuzzy relation matrix and b the sympton vector. Four phases of analyses were carried out in this study. First, fault tree analysis was undertaken to investigate what kind of causes produce fall of water level in a steam drum of ATR (Advanced Thermal Reactor), which is heavy-water-moderated boiling-water-cooled pressure-tube-type reactor. Next, simulation for 100 seconds was executed to determine how plant parameters respond to an occurrence of a transient induced by the cause. Third, the simulation data was analysed utilizing an autoregressive model. From this analysis, a total of 36 coherency functions up to 0.5 Hz in each transient were computed among nine important and detectable plant parameters, that is neutron flux, flow rate of coolant, steam and feed water, water level in the steam drum, pressure and opening area of control valve in a steam pipe, feed water temperature and electrical power. Last, the inverse problem of the fuzzy relational equation was solved. Relation matrices were adjusted from 0.00 to 1.00, after nine membership functions following the Gussian distribution for the symptom vector were estimated from correlation values of the coherency functions
Phillips, Carolyn L.; Anderson, Joshua A.; Glotzer, Sharon C.
2011-08-01
Brownian Dynamics (BD), also known as Langevin Dynamics, and Dissipative Particle Dynamics (DPD) are implicit solvent methods commonly used in models of soft matter and biomolecular systems. The interaction of the numerous solvent particles with larger particles is coarse-grained as a Langevin thermostat is applied to individual particles or to particle pairs. The Langevin thermostat requires a pseudo-random number generator (PRNG) to generate the stochastic force applied to each particle or pair of neighboring particles during each time step in the integration of Newton's equations of motion. In a Single-Instruction-Multiple-Thread (SIMT) GPU parallel computing environment, small batches of random numbers must be generated over thousands of threads and millions of kernel calls. In this communication we introduce a one-PRNG-per-kernel-call-per-thread scheme, in which a micro-stream of pseudorandom numbers is generated in each thread and kernel call. These high quality, statistically robust micro-streams require no global memory for state storage, are more computationally efficient than other PRNG schemes in memory-bound kernels, and uniquely enable the DPD simulation method without requiring communication between threads.
Postmodern Fuzzy System Theory: A Deconstruction Approach Based on Kabbalah
Directory of Open Access Journals (Sweden)
Gabriel Burstein
2014-11-01
Full Text Available Modern general system theory proposed a holistic integrative approach based on input-state-output dynamics as opposed to the traditional reductionist detail based approach. Information complexity and uncertainty required a fuzzy system theory, based on fuzzy sets and fuzzy logic. While successful in dealing with analysis, synthesis and control of technical engineering systems, general system theory and fuzzy system theory could not fully deal with humanistic and human-like intelligent systems which combine technical engineering components with human or human-like components characterized by their cognitive, emotional/motivational and behavioral/action levels of operation. Such humanistic systems are essential in artificial intelligence, cognitive and behavioral science applications, organization management and social systems, man-machine systems or human factor systems, behavioral knowledge based economics and finance applications. We are introducing here a “postmodern fuzzy system theory” for controlled state dynamics and output fuzzy systems and fuzzy rule based systems using our earlier postmodern fuzzy set theory and a Kabbalah possible worlds model of modal logic and semantics type. In order to create a postmodern fuzzy system theory, we “deconstruct” a fuzzy system in order to incorporate in it the cognitive, emotional and behavioral actions and expressions levels characteristic for humanistic systems. Kabbalah offers a structural, fractal and hierarchic model for integrating cognition, emotions and behavior. We obtain a canonic deconstruction for a fuzzy system into its cognitive, emotional and behavioral fuzzy subsystems.
A computationally efficient fuzzy control s
Directory of Open Access Journals (Sweden)
Abdel Badie Sharkawy
2013-12-01
Full Text Available This paper develops a decentralized fuzzy control scheme for MIMO nonlinear second order systems with application to robot manipulators via a combination of genetic algorithms (GAs and fuzzy systems. The controller for each degree of freedom (DOF consists of a feedforward fuzzy torque computing system and a feedback fuzzy PD system. The feedforward fuzzy system is trained and optimized off-line using GAs, whereas not only the parameters but also the structure of the fuzzy system is optimized. The feedback fuzzy PD system, on the other hand, is used to keep the closed-loop stable. The rule base consists of only four rules per each DOF. Furthermore, the fuzzy feedback system is decentralized and simplified leading to a computationally efficient control scheme. The proposed control scheme has the following advantages: (1 it needs no exact dynamics of the system and the computation is time-saving because of the simple structure of the fuzzy systems and (2 the controller is robust against various parameters and payload uncertainties. The computational complexity of the proposed control scheme has been analyzed and compared with previous works. Computer simulations show that this controller is effective in achieving the control goals.
Relational Demonic Fuzzy Refinement
Tchier, Fairouz
2014-01-01
We use relational algebra to define a refinement fuzzy order called demonic fuzzy refinement and also the associated fuzzy operators which are fuzzy demonic join $({\\bigsqcup }_{\\mathrm{\\text{f}}\\mathrm{\\text{u}}\\mathrm{\\text{z}}})$ , fuzzy demonic meet $({\\sqcap }_{\\mathrm{\\text{f}}\\mathrm{\\text{u}}\\mathrm{\\text{z}}})$ , and fuzzy demonic composition $({\\square }_{\\mathrm{\\text{f}}\\mathrm{\\text{u}}\\mathrm{\\text{z}}})$ . Our definitions and properties are illustrated by some examples using ma...
Intuitionistic Fuzzy Cycles and Intuitionistic Fuzzy Trees
Alshehri, N. O.
2014-01-01
Connectivity has an important role in neural networks, computer network, and clustering. In the design of a network, it is important to analyze connections by the levels. The structural properties of intuitionistic fuzzy graphs provide a tool that allows for the solution of operations research problems. In this paper, we introduce various types of intuitionistic fuzzy bridges, intuitionistic fuzzy cut vertices, intuitionistic fuzzy cycles, and intuitionistic fuzzy trees in intuitionistic fuzzy graphs and investigate some of their interesting properties. Most of these various types are defined in terms of levels. We also describe comparison of these types. PMID:24701155
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.
Das, Saptarshi; Pan, Indranil; Das, Shantanu
2013-01-01
Nonlinear state space modeling of a nuclear reactor has been done for the purpose of controlling its global power in load following mode. The nonlinear state space model has been linearized at different percentage of reactor powers and a novel fractional order (FO) fuzzy proportional integral derivative (PID) controller is designed using real coded Genetic Algorithm (GA) to control the reactor power level at various operating conditions. The effectiveness of using the fuzzy FOPID controller o...
Neutron detection using soft errors in dynamic random access memories
International Nuclear Information System (INIS)
Darambara, D.G.; Spyrou, N.M.
1992-01-01
The fact that energetic alpha particles have been observed to be capable of inducing single-event upsets in integrated circuit memories has become a topic of considerable interest in the past few years. One recognized difficulty with dynamic random access memory devices (dRAMs) is that the alpha-particle 'contamination' present within the dRAM encapsulating material interact sufficiently as to corrupt stored data. The authors essentially utilized the fact that these corruptions may be induced in dRAMs by the interaction of charged particles with the chip of the dRAM itself as a basis of a hardware system for neutron detection with a view to applications in neutron imaging and elemental analysis. The design incorporates a bank of dRAMs on which the particles are incident. Initially, these particles were alpha particles from an appropriate alpha-emitting source employed to assess system parameters. The sensitivity of the device to logic state upsets by ionizing radiation is a function of design and technology parameters, inducing storage node area, node capacitance, operating voltage, minority carrier lifetime, electric fields pattern in the bulk silicon, and specific device geometry. The soft error rate of the device in a given package depends on the flux of alphas, the energy spectrum, the distribution of incident angles, the target area, the total stored charge, the collection efficiency, the cell geometry, the supply voltage, the cycle and refreshing time, and the noise margin
Gomaa Haroun, A H; Li, Yin-Ya
2017-11-01
In the fast developing world nowadays, load frequency control (LFC) is considered to be a most significant role for providing the power supply with good quality in the power system. To deliver a reliable power, LFC system requires highly competent and intelligent control technique. Hence, in this article, a novel hybrid fuzzy logic intelligent proportional-integral-derivative (FLiPID) controller has been proposed for LFC of interconnected multi-area power systems. A four-area interconnected thermal power system incorporated with physical constraints and boiler dynamics is considered and the adjustable parameters of the FLiPID controller are optimized using particle swarm optimization (PSO) scheme employing an integral square error (ISE) criterion. The proposed method has been established to enhance the power system performances as well as to reduce the oscillations of uncertainties due to variations in the system parameters and load perturbations. The supremacy of the suggested method is demonstrated by comparing the simulation results with some recently reported heuristic methods such as fuzzy logic proportional-integral (FLPI) and intelligent proportional-integral-derivative (PID) controllers for the same electrical power system. the investigations showed that the FLiPID controller provides a better dynamic performance and outperform compared to the other approaches in terms of the settling time, and minimum undershoots of the frequency as well as tie-line power flow deviations following a perturbation, in addition to perform appropriate settlement of integral absolute error (IAE). Finally, the sensitivity analysis of the plant is inspected by varying the system parameters and operating load conditions from their nominal values. It is observed that the suggested controller based optimization algorithm is robust and perform satisfactorily with the variations in operating load condition, system parameters and load pattern. Copyright © 2017 ISA. Published by
fuzzy control technique fuzzy control technique applied to modified
African Journals Online (AJOL)
eobe
Keywords: Keywords: fuzzy control, malaria, drug effectiveness, mosquitoes, equilibrium state, dynamic equation. 1. INTRODUCTION. INTRODUCTION. INTRODUCTION. Malaria is a vector borne infectious disease that has affected the human race since earliest times and an estimated 40% of the world's population lives in.
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.
Fluid Dynamics Appearing during Simulated Microgravity Using Random Positioning Machines.
Wuest, Simon L; Stern, Philip; Casartelli, Ernesto; Egli, Marcel
2017-01-01
Random Positioning Machines (RPMs) are widely used as tools to simulate microgravity on ground. They consist of two gimbal mounted frames, which constantly rotate biological samples around two perpendicular axes and thus distribute the Earth's gravity vector in all directions over time. In recent years, the RPM is increasingly becoming appreciated as a laboratory instrument also in non-space-related research. For instance, it can be applied for the formation of scaffold-free spheroid cell clusters. The kinematic rotation of the RPM, however, does not only distribute the gravity vector in such a way that it averages to zero, but it also introduces local forces to the cell culture. These forces can be described by rigid body analysis. Although RPMs are commonly used in laboratories, the fluid motion in the cell culture flasks on the RPM and the possible effects of such on cells have not been examined until today; thus, such aspects have been widely neglected. In this study, we used a numerical approach to describe the fluid dynamic characteristic occurring inside a cell culture flask turning on an operating RPM. The simulations showed that the fluid motion within the cell culture flask never reached a steady state or neared a steady state condition. The fluid velocity depends on the rotational velocity of the RPM and is in the order of a few centimeters per second. The highest shear stresses are found along the flask walls; depending of the rotational velocity, they can reach up to a few 100 mPa. The shear stresses in the "bulk volume," however, are always smaller, and their magnitude is in the order of 10 mPa. In conclusion, RPMs are highly appreciated as reliable tools in microgravity research. They have even started to become useful instruments in new research fields of mechanobiology. Depending on the experiment, the fluid dynamic on the RPM cannot be neglected and needs to be taken into consideration. The results presented in this study elucidate the fluid
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....
Random matrix approach to the dynamics of stock inventory variations
Zhou, Wei-Xing; Mu, Guo-Hua; Kertész, János
2012-09-01
It is well accepted that investors can be classified into groups owing to distinct trading strategies, which forms the basic assumption of many agent-based models for financial markets when agents are not zero-intelligent. However, empirical tests of these assumptions are still very rare due to the lack of order flow data. Here we adopt the order flow data of Chinese stocks to tackle this problem by investigating the dynamics of inventory variations for individual and institutional investors that contain rich information about the trading behavior of investors and have a crucial influence on price fluctuations. We find that the distributions of cross-correlation coefficient Cij have power-law forms in the bulk that are followed by exponential tails, and there are more positive coefficients than negative ones. In addition, it is more likely that two individuals or two institutions have a stronger inventory variation correlation than one individual and one institution. We find that the largest and the second largest eigenvalues (λ1 and λ2) of the correlation matrix cannot be explained by random matrix theory and the projections of investors' inventory variations on the first eigenvector u(λ1) are linearly correlated with stock returns, where individual investors play a dominating role. The investors are classified into three categories based on the cross-correlation coefficients CV R between inventory variations and stock returns. A strong Granger causality is unveiled from stock returns to inventory variations, which means that a large proportion of individuals hold the reversing trading strategy and a small part of individuals hold the trending strategy. Our empirical findings have scientific significance in the understanding of investors' trading behavior and in the construction of agent-based models for emerging stock markets.
Random matrix approach to the dynamics of stock inventory variations
International Nuclear Information System (INIS)
Zhou Weixing; Mu Guohua; Kertész, János
2012-01-01
It is well accepted that investors can be classified into groups owing to distinct trading strategies, which forms the basic assumption of many agent-based models for financial markets when agents are not zero-intelligent. However, empirical tests of these assumptions are still very rare due to the lack of order flow data. Here we adopt the order flow data of Chinese stocks to tackle this problem by investigating the dynamics of inventory variations for individual and institutional investors that contain rich information about the trading behavior of investors and have a crucial influence on price fluctuations. We find that the distributions of cross-correlation coefficient C ij have power-law forms in the bulk that are followed by exponential tails, and there are more positive coefficients than negative ones. In addition, it is more likely that two individuals or two institutions have a stronger inventory variation correlation than one individual and one institution. We find that the largest and the second largest eigenvalues (λ 1 and λ 2 ) of the correlation matrix cannot be explained by random matrix theory and the projections of investors' inventory variations on the first eigenvector u(λ 1 ) are linearly correlated with stock returns, where individual investors play a dominating role. The investors are classified into three categories based on the cross-correlation coefficients C VR between inventory variations and stock returns. A strong Granger causality is unveiled from stock returns to inventory variations, which means that a large proportion of individuals hold the reversing trading strategy and a small part of individuals hold the trending strategy. Our empirical findings have scientific significance in the understanding of investors' trading behavior and in the construction of agent-based models for emerging stock markets. (paper)
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.
Flows in networks under fuzzy conditions
Bozhenyuk, Alexander Vitalievich; Kacprzyk, Janusz; Rozenberg, Igor Naymovich
2017-01-01
This book offers a comprehensive introduction to fuzzy methods for solving flow tasks in both transportation and networks. It analyzes the problems of minimum cost and maximum flow finding with fuzzy nonzero lower flow bounds, and describes solutions to minimum cost flow finding in a network with fuzzy arc capacities and transmission costs. After a concise introduction to flow theory and tasks, the book analyzes two important problems. The first is related to determining the maximum volume for cargo transportation in the presence of uncertain network parameters, such as environmental changes, measurement errors and repair work on the roads. These parameters are represented here as fuzzy triangular, trapezoidal numbers and intervals. The second problem concerns static and dynamic flow finding in networks under fuzzy conditions, and an effective method that takes into account the network’s transit parameters is presented here. All in all, the book provides readers with a practical reference guide to state-of-...
Alkouri, Abd Ulazeez M.; Salleh, Abdul Razak
2014-09-01
In this paper we combine two definitions, namely fuzzy soft multiset and complex fuzzy set to construct the definition of a complex fuzzy soft multiset and study its properties. In other words, we study the extension of a fuzzy soft multiset from real numbers to complex numbers. We also introduce its basic operations, namely complement, union and intersection. Some examples are given.
Some Additions to the Fuzzy Convergent and Fuzzy Bounded Sequence Spaces of Fuzzy Numbers
Şengönül, M.; Zararsız, Z.
2011-01-01
Some properties of the fuzzy convergence and fuzzy boundedness of a sequence of fuzzy numbers were studied in Choi (1996). In this paper, we have consider, some important problems on these spaces and shown that these spaces are fuzzy complete module spaces. Also, the fuzzy α-, fuzzy β-, and fuzzy γ-duals of the fuzzy module spaces of fuzzy numbers have been computeded, and some matrix transformations are given.
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
International Nuclear Information System (INIS)
Bahmani-Firouzi, Bahman; Farjah, Ebrahim; Azizipanah-Abarghooee, Rasoul
2013-01-01
Renewable energy resources such as wind power plants are playing an ever-increasing role in power generation. This paper extends the dynamic economic emission dispatch problem by incorporating wind power plant. This problem is a multi-objective optimization approach in which total electrical power generation costs and combustion emissions are simultaneously minimized over a short-term time span. A stochastic approach based on scenarios is suggested to model the uncertainty associated with hourly load and wind power forecasts. A roulette wheel technique on the basis of probability distribution functions of load and wind power is implemented to generate scenarios. As a result, the stochastic nature of the suggested problem is emancipated by decomposing it into a set of equivalent deterministic problem. An improved multi-objective particle swarm optimization algorithm is applied to obtain the best expected solutions for the proposed stochastic programming framework. To enhance the overall performance and effectiveness of the particle swarm optimization, a fuzzy adaptive technique, θ-search and self-adaptive learning strategy for velocity updating are used to tune the inertia weight factor and to escape from local optima, respectively. The suggested algorithm goes through the search space in the polar coordinates instead of the Cartesian one; whereby the feasible space is more compact. In order to evaluate the efficiency and feasibility of the suggested framework, it is applied to two test systems with small and large scale characteristics. - Highlights: ► Formulates multi-objective DEED problem under a stochastic programming framework. ► Considers uncertainties related to forecasted values of load demand and wind power. ► Proposes an interactive fuzzy satisfying method based on the novel FSALPSO. ► Presents a new self-adaptive learning strategy to improve original PSO algorithm
International Nuclear Information System (INIS)
Schildt, G.H.
1996-01-01
After an introduction into safety terms a fuzzy controller for safety related process control will be presented, especially for applications in the field of NPPs. One can show that the size of necessary rules is relatively small. Thus, there exists a real chance for verification and validation of software due to the fact that the whole software can be structured into standard fuzzy software (like fuzzyfication, inference algorithms, and defuzzyfication), real-time operating system software, and the contents of the rule base. Furthermore, there is an excellent advantage due to real-time behaviour, because program execution time can be much more planned than for conventional PID-controller software. Additionally, up to now special know-how does exist to prove stability of fuzzy controller. Hardware design has been done due to fundamental principles of safety technique like watch dog function, dynamization principle, and quiescent current principle
International Nuclear Information System (INIS)
Schildt, G.H.
1997-01-01
A fuzzy controller for safety related process control is presented for applications in the field of NPPs. The size of necessary rules is relatively small. Thus, there exists a real chance for verification and validation of software due to the fact that the whole software can be structured into standard fuzzy software (like fuzzyfication, inference algorithms, and defuzzyfication), real-time operating system software, and the contents of the rule base. Furthermore, there is an excellent advantage fuel to real-time behaviour, because program execution time is much more predictable than for conventional PID-controller software. Additionally, up to now special know-how does exist to prove stability of fuzzy controller. Hardware design has been done due to fundamental principles of safety technique like watch dog function, dynamization principles, and quiescent current principle. (author). 3 refs, 5 figs
Fuzzy logic of Aristotelian forms
Energy Technology Data Exchange (ETDEWEB)
Perlovsky, L.I. [Nichols Research Corp., Lexington, MA (United States)
1996-12-31
Model-based approaches to pattern recognition and machine vision have been proposed to overcome the exorbitant training requirements of earlier computational paradigms. However, uncertainties in data were found to lead to a combinatorial explosion of the computational complexity. This issue is related here to the roles of a priori knowledge vs. adaptive learning. What is the a-priori knowledge representation that supports learning? I introduce Modeling Field Theory (MFT), a model-based neural network whose adaptive learning is based on a priori models. These models combine deterministic, fuzzy, and statistical aspects to account for a priori knowledge, its fuzzy nature, and data uncertainties. In the process of learning, a priori fuzzy concepts converge to crisp or probabilistic concepts. The MFT is a convergent dynamical system of only linear computational complexity. Fuzzy logic turns out to be essential for reducing the combinatorial complexity to linear one. I will discuss the relationship of the new computational paradigm to two theories due to Aristotle: theory of Forms and logic. While theory of Forms argued that the mind cannot be based on ready-made a priori concepts, Aristotelian logic operated with just such concepts. I discuss an interpretation of MFT suggesting that its fuzzy logic, combining a-priority and adaptivity, implements Aristotelian theory of Forms (theory of mind). Thus, 2300 years after Aristotle, a logic is developed suitable for his theory of mind.
Directory of Open Access Journals (Sweden)
S. Nazmul
2014-03-01
Full Text Available Notions of Lowen type fuzzy soft topological space are introduced and some of their properties are established in the present paper. Besides this, a combined structure of a fuzzy soft topological space and a fuzzy soft group, which is termed here as fuzzy soft topological group is introduced. Homomorphic images and preimages are also examined. Finally, some definitions and results on fuzzy soft set are studied.
Coupland, Simon
2006-01-01
There has recently been a significant increase in academic interest in the field oftype-2 fuzzy sets and systems. Type-2 fuzzy systems offer the ability to model and reason with uncertain concepts. When faced with uncertainties type-2 fuzzy systems should, theoretically, give an increase in performance over type-l fuzzy systems. However, the computational complexity of generalised type-2 fuzzy systems is significantly higher than type-l systems. A direct consequence of this is that, prior to ...
DEFF Research Database (Denmark)
Justesen, Kristian Kjær; Andreasen, Søren Juhl; Shaker, Hamid Reza
2013-01-01
an empirical approach. Fin efficiency models for the cooling effect of the air are also developed using empirical methods. A fuel cell model is also implemented based on a standard model which is adapted to fit the measured performance of the H3-350 module. All the individual parts of the model are verified...... hydrogen, which is difficult and energy consuming to store and transport. The models include thermal equilibrium models of the individual components of the system. Models of the heating and cooling of the gas flows between components are also modeled and Adaptive Neuro-Fuzzy Inference System models...... of the reforming process are implemented. Models of the cooling flow of the blowers for the fuel cell and the burner which supplies process heat for the reformer are made. The two blowers have a common exhaust, which means that the two blowers influence each other’s output. The models take this into account using...
Hierarchization process by possibilistic fuzzy clustering of fuzzy rules
Salgado, Paulo; Cunha, Manuela; Pavão, João; Igrejas, Getúlio
2010-01-01
This paper presents a possibilistic fuzzy clustering algorithm that is applied to a multidimensional fuzzy set or fuzzy rules. This method can be used to decompose the fuzzy system into an hierarchical structure. The methodology presented leads to a fuzzy partition of the fuzzy rules, one for each cluster, which corresponds to a new set of fuzzy sub-systems. This technique is tested to organize the fuzzy model into a new and more comprehensive structure.
Fuzzy image processing and applications with Matlab
Chaira, Tamalika
2009-01-01
In contrast to classical image analysis methods that employ ""crisp"" mathematics, fuzzy set techniques provide an elegant foundation and a set of rich methodologies for diverse image-processing tasks. However, a solid understanding of fuzzy processing requires a firm grasp of essential principles and background knowledge.Fuzzy Image Processing and Applications with MATLAB® presents the integral science and essential mathematics behind this exciting and dynamic branch of image processing, which is becoming increasingly important to applications in areas such as remote sensing, medical imaging,
Intuitionistic supra fuzzy topological spaces
International Nuclear Information System (INIS)
Abbas, S.E.
2004-01-01
In this paper, We introduce an intuitionistic supra fuzzy closure space and investigate the relationship between intuitionistic supra fuzzy topological spaces and intuitionistic supra fuzzy closure spaces. Moreover, we can obtain intuitionistic supra fuzzy topological space induced by an intuitionistic fuzzy bitopological space. We study the relationship between intuitionistic supra fuzzy closure space and the intuitionistic supra fuzzy topological space induced by an intuitionistic fuzzy bitopological space
Xu, Zeshui
2014-01-01
This book provides the readers with a thorough and systematic introduction to hesitant fuzzy theory. It presents the most recent research results and advanced methods in the field. These includes: hesitant fuzzy aggregation techniques, hesitant fuzzy preference relations, hesitant fuzzy measures, hesitant fuzzy clustering algorithms and hesitant fuzzy multi-attribute decision making methods. Since its introduction by Torra and Narukawa in 2009, hesitant fuzzy sets have become more and more popular and have been used for a wide range of applications, from decision-making problems to cluster analysis, from medical diagnosis to personnel appraisal and information retrieval. This book offers a comprehensive report on the state-of-the-art in hesitant fuzzy sets theory and applications, aiming at becoming a reference guide for both researchers and practitioners in the area of fuzzy mathematics and other applied research fields (e.g. operations research, information science, management science and engineering) chara...
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...
2011-01-13
... DEPARTMENT OF COMMERCE International Trade Administration [C-580-851] Dynamic Random Access Memory...: Import Administration, International Trade Administration, Department of Commerce. SUMMARY: On September..., uncut die, and cut die. Processed wafers fabricated in the ROK, but assembled into finished...
2012-05-07
... INTERNATIONAL TRADE COMMISSION [Investigation No. 337-TA-661] Certain Semiconductor Chips Having Synchronous Dynamic Random Access Memory Controllers and Products Containing Same; Determination Rescinding the Exclusion Order and Cease and Desist Orders AGENCY: U.S. International Trade Commission. ACTION...
2010-03-25
... Access Memory Semiconductors and Products Containing Same, Including Memory Modules; Notice of... semiconductors and products containing same, including memory modules, by reason of infringement of certain... importation of certain dynamic random access memory semiconductors or products containing the same, including...
DYNAMIC STRAIN MAPPING AND REAL-TIME DAMAGE STATE ESTIMATION UNDER BIAXIAL RANDOM FATIGUE LOADING
National Aeronautics and Space Administration — DYNAMIC STRAIN MAPPING AND REAL-TIME DAMAGE STATE ESTIMATION UNDER BIAXIAL RANDOM FATIGUE LOADING SUBHASISH MOHANTY*, ADITI CHATTOPADHYAY, JOHN N. RAJADAS, AND CLYDE...
Anaesthesia monitoring using fuzzy logic.
Baig, Mirza Mansoor; Gholamhosseini, Hamid; Kouzani, Abbas; Harrison, Michael J
2011-10-01
Humans have a limited ability to accurately and continuously analyse large amount of data. In recent times, there has been a rapid growth in patient monitoring and medical data analysis using smart monitoring systems. Fuzzy logic-based expert systems, which can mimic human thought processes in complex circumstances, have indicated potential to improve clinicians' performance and accurately execute repetitive tasks to which humans are ill-suited. The main goal of this study is to develop a clinically useful diagnostic alarm system based on fuzzy logic for detecting critical events during anaesthesia administration. The proposed diagnostic alarm system called fuzzy logic monitoring system (FLMS) is presented. New diagnostic rules and membership functions (MFs) are developed. In addition, fuzzy inference system (FIS), adaptive neuro fuzzy inference system (ANFIS), and clustering techniques are explored for developing the FLMS' diagnostic modules. The performance of FLMS which is based on fuzzy logic expert diagnostic systems is validated through a series of off-line tests. The training and testing data set are selected randomly from 30 sets of patients' data. The accuracy of diagnoses generated by the FLMS was validated by comparing the diagnostic information with the one provided by an anaesthetist for each patient. Kappa-analysis was used for measuring the level of agreement between the anaesthetist's and FLMS's diagnoses. When detecting hypovolaemia, a substantial level of agreement was observed between FLMS and the human expert (the anaesthetist) during surgical procedures. The diagnostic alarm system FLMS demonstrated that evidence-based expert diagnostic systems can diagnose hypovolaemia, with a substantial degree of accuracy, in anaesthetized patients and could be useful in delivering decision support to anaesthetists.
International Nuclear Information System (INIS)
Abbas, S.E.
2004-01-01
In this paper, fuzzy SP-irresolute, fuzzy SP-irresolute open and fuzzy SP-irresolute closed functions between fuzzy topological spaces in Sostak sense are defined. Their properties and the relationships between these functions and other functions introduced previously are investigated. Next fuzzy SP-connectedness is introduced and studied with the help of r-fuzzy strongly preopen sets
FUZZY-GENETIC CONTROL OF QUADROTOR UNMANNED AERIAL VEHICLES
Directory of Open Access Journals (Sweden)
Attila Nemes
2016-03-01
Full Text Available This article presents a novel fuzzy identification method for dynamic modelling of quadrotor unmanned aerial vehicles. The method is based on a special parameterization of the antecedent part of fuzzy systems that results in fuzzy-partitions for antecedents. This antecedent parameter representation method of fuzzy rules ensures upholding of predefined linguistic value ordering and ensures that fuzzy-partitions remain intact throughout an unconstrained hybrid evolutionary and gradient descent based optimization process. In the equations of motion the first order derivative component is calculated based on Christoffel symbols, the derivatives of fuzzy systems are used for modelling the Coriolis effects, gyroscopic and centrifugal terms. The non-linear parameters are subjected to an initial global evolutionary optimization scheme and fine tuning with gradient descent based local search. Simulation results of the proposed new quadrotor dynamic model identification method are promising.
Fuzzy intelligent system for the operation of fossil power plants
Energy Technology Data Exchange (ETDEWEB)
Arroyo-Figueroa, G. [Unidad de Sistemas Informatics, Morelos (Mexico). Instituto de Investigaciones Electricas; Sucar, L.E. [ITESM Campus, Morelos (Mexico). Departamento de Computacion; Villavicencio, A. [Unidad de Supervision de Procesos, Morelos (Mexico). Instituto de Investigaciones Electricas
2000-08-01
In artificial intelligence applications in large-scale industry, such as fossil fuel power plants, the knowledge about the process comes from an expert's experience, and is generally expressed in a vague and fuzzy way, using ill-defined linguistic terms. This paper presents a fuzzy intelligent system to assist an operator of fossil power plants. The approach is characterized as a fuzzy diagnostic and fuzzy control system. The fuzzy diagnostic system is based on a novel representation for dealing with uncertainty and time, called as fuzzy temporal network (FTN). An FTN is a formal and systematic structure, used to model temporal linguistic sentences about the occurrence of an event. The fuzzy controller was designed for the regulation of the steam temperature of a steam generator. The fuzzy rules were obtained by observing the dynamic characteristics of the steam temperature response. The results show that the fuzzy controller has a better performance than advanced model-based controller, either an dynamic matrix control (DMC) or a conventional PID controller. The main benefits are the reduction of the overshoot and the tighter regulation of the superheater and reheater steam temperatures. The intelligent system has shown that fuzzy logic techniques can play an important role in power-plant operation and control tasks. The scheme not only makes the problem formulation more flexible but, if applied correctly, can improve the computational efficiency. This makes it practical for many applications in complex fields where the real-time tasks are important. (author)
Stochastic reservoir operation under drought with fuzzy objectives
International Nuclear Information System (INIS)
Parent, E.; Duckstein, L.
1993-01-01
Biojective reservoir operation under drought conditions is investigated using stochastic dynamic programming. As both objectives (irrigation water supply, water quality) can only be defined imprecisely, a fuzzy set approach is used to encode the decision maker (DM)'s preferences. The nature driven components are modeled by means of classical stage-state system analysis. The state is three dimensional (inflow memory, drought irrigation index, reservoir level); the decision vector elements are release and irrigation allocation. Stochasticity stems from the random nature of inflows and irrigation demands. The transition function includes a lag one inflow Markov model and mass balance equations. The human driven component is designed as a confluence of fuzzy objectives and constraints after Bellman and Zadeh. Fuzzy numbers are assessed to represent the DM's objectives by two different techniques, the direct one and indirect pairwise comparison. The real case study of the Neste river system in southwestern France is used to illustrate the approach; the result are compared to a classical sequential decision theoretical model derived earlier from the viewpoints of ease of modeling, computational efforts, plausibility and robustness of results
Czech Academy of Sciences Publication Activity Database
Běhounek, Libor; Cintula, Petr
2005-01-01
Roč. 154, - (2005), s. 34-55 ISSN 0165-0114 R&D Projects: GA AV ČR IAA1030004; GA MŠk OC 274.001 Grant - others:COST(EU) Action 274 TARSKI Institutional research plan: CEZ:AV0Z10300504 Keywords : formal fuzzy logic * fuzzy set * foundations of fuzzy mathematics * LPi logic * higher-order fuzzy logic * fuzzy type theory * multi-sorted fuzzy logic Subject RIV: BA - General Mathematics Impact factor: 1.039, year: 2005
Gesture Recognition using Latent-Dynamic based Conditional Random Fields and Scalar Features
Yulita, I. N.; Fanany, M. I.; Arymurthy, A. M.
2017-02-01
The need for segmentation and labeling of sequence data appears in several fields. The use of the conditional models such as Conditional Random Fields is widely used to solve this problem. In the pattern recognition, Conditional Random Fields specify the possibilities of a sequence label. This method constructs its full label sequence to be a probabilistic graphical model based on its observation. However, Conditional Random Fields can not capture the internal structure so that Latent-based Dynamic Conditional Random Fields is developed without leaving external dynamics of inter-label. This study proposes the use of Latent-Dynamic Conditional Random Fields for Gesture Recognition and comparison between both methods. Besides, this study also proposes the use of a scalar features to gesture recognition. The results show that performance of Latent-dynamic based Conditional Random Fields is not better than the Conditional Random Fields, and scalar features are effective for both methods are in gesture recognition. Therefore, it recommends implementing Conditional Random Fields and scalar features in gesture recognition for better performance
Kolokoltsov, Vassili N.
2007-01-01
Functional limit theorem for continuous-time random walks (CTRW) are found in general case of dependent waiting times and jump sizes that are also position dependent. The limiting anomalous diffusion is described in terms of fractional dynamics. Probabilistic interpretation of generalized fractional evolution is given in terms of the random time change (subordination) by means of hitting times processes.
Random operators disorder effects on quantum spectra and dynamics
Aizenman, Michael
2015-01-01
This book provides an introduction to the mathematical theory of disorder effects on quantum spectra and dynamics. Topics covered range from the basic theory of spectra and dynamics of self-adjoint operators through Anderson localization-presented here via the fractional moment method, up to recent results on resonant delocalization. The subject's multifaceted presentation is organized into seventeen chapters, each focused on either a specific mathematical topic or on a demonstration of the theory's relevance to physics, e.g., its implications for the quantum Hall effect. The mathematical chapters include general relations of quantum spectra and dynamics, ergodicity and its implications, methods for establishing spectral and dynamical localization regimes, applications and properties of the Green function, its relation to the eigenfunction correlator, fractional moments of Herglotz-Pick functions, the phase diagram for tree graph operators, resonant delocalization, the spectral statistics conjecture, and rela...
Why fuzzy controllers should be fuzzy
International Nuclear Information System (INIS)
Nowe, A.
1996-01-01
Fuzzy controllers are usually looked at as crisp valued mappings especially when artificial intelligence learning techniques are used to build up the controller. By doing so the semantics of a fuzzy conclusion being a fuzzy restriction on the viable control actions is non-existing. In this paper the authors criticise from an approximation point of view using a fuzzy controller to express a crisp mapping does not seem the right way to go. Secondly it is illustrated that interesting information is contained in a fuzzy conclusion when indeed this conclusion is considered as a fuzzy restriction. This information turns out to be very valuable when viability problems are concerned, i.e. problems where the objective is to keep a system within predefined boundaries
Fuzzy Model-based Pitch Stabilization and Wing Vibration Suppression of Flexible Wing Aircraft.
Ayoubi, Mohammad A.; Swei, Sean Shan-Min; Nguyen, Nhan T.
2014-01-01
This paper presents a fuzzy nonlinear controller to regulate the longitudinal dynamics of an aircraft and suppress the bending and torsional vibrations of its flexible wings. The fuzzy controller utilizes full-state feedback with input constraint. First, the Takagi-Sugeno fuzzy linear model is developed which approximates the coupled aeroelastic aircraft model. Then, based on the fuzzy linear model, a fuzzy controller is developed to utilize a full-state feedback and stabilize the system while it satisfies the control input constraint. Linear matrix inequality (LMI) techniques are employed to solve the fuzzy control problem. Finally, the performance of the proposed controller is demonstrated on the NASA Generic Transport Model (GTM).
Construction of Fuzzy Ontologies from Fuzzy UML Models
Directory of Open Access Journals (Sweden)
Fu Zhang
2013-05-01
Full Text Available The success and proliferation of the Semantic Web depends heavily on construction of Web ontologies. However, classical ontology construction approaches are not sufficient for handling imprecise and uncertain information that is commonly found in many application domains. Therefore, great efforts on construction of fuzzy ontologies have been made in recent years. In this paper, we propose a formal approach and develop an automated tool for constructing fuzzy ontologies from fuzzy UML models. , we propose formalization methods of fuzzy UML models and fuzzy ontologies, where fuzzy UML models and fuzzy ontologies can be represented and interpreted by their respective formal definitions and semantic interpretation methods. , we propose an approach for constructing fuzzy ontologies from fuzzy UML models, i.e., transforming fuzzy UML models (including the structure and instance information of fuzzy UML models into fuzzy ontologies. , following the proposed approach, we implement a prototype transformation tool called that can construct fuzzy ontologies from fuzzy UML models. Constructing fuzzy ontologies from fuzzy UML models will facilitate the development of Web ontologies. , in order to show that the constructed fuzzy ontologies may be useful for reasoning on fuzzy UML models, we investigate how to reason on fuzzy UML models based on the constructed fuzzy ontologies, and it turns out that the reasoning tasks of fuzzy UML models can be checked by means of the reasoning mechanism of fuzzy ontologies.
Fuzzy Neuroidal Nets and Recurrent Fuzzy Computations
Czech Academy of Sciences Publication Activity Database
Wiedermann, Jiří
2001-01-01
Roč. 11, č. 6 (2001), s. 675-686 ISSN 1210-0552. [SOFSEM 2001 Workshop on Soft Computing. Piešťany, 29.11.2001-30.11.2001] R&D Projects: GA ČR GA201/00/1489; GA AV ČR KSK1019101 Institutional research plan: AV0Z1030915 Keywords : fuzzy computing * fuzzy neural nets * fuzzy Turing machines * non-uniform computational complexity Subject RIV: BA - General Mathematics
'Dicty dynamics': Dictyostelium motility as persistent random motion
DEFF Research Database (Denmark)
Li, Liang; Cox, Edward C; Flyvbjerg, Henrik
2011-01-01
to the amoeba's direction of motion. This motion propels the amoeba with a random periodic left–right waddle in a direction that has a long persistence time. The model fully accounts for the statistics of the experimental trajectories, including velocity power spectra and auto-correlations, non...
Single-cluster dynamics for the random-cluster model
Deng, Y.; Qian, X.; Blöte, H.W.J.
2009-01-01
We formulate a single-cluster Monte Carlo algorithm for the simulation of the random-cluster model. This algorithm is a generalization of the Wolff single-cluster method for the q-state Potts model to noninteger values q>1. Its results for static quantities are in a satisfactory agreement with those
Segmental front line dynamics of randomly pinned ferroelastic domain walls
Puchberger, S.; Soprunyuk, V.; Schranz, W.; Carpenter, M. A.
2018-01-01
Dynamic mechanical analysis (DMA) measurements as a function of temperature, frequency, and dynamic force amplitude are used to perform a detailed study of the domain wall motion in LaAlO3. In previous DMA measurements Harrison et al. [Phys. Rev. B 69, 144101 (2004), 10.1103/PhysRevB.69.144101] found evidence for dynamic phase transitions of ferroelastic domain walls in LaAlO3. In the present work we focus on the creep-to-relaxation region of domain wall motion using two complementary methods. We determine, in addition to dynamic susceptibility data, waiting time distributions of strain jerks during slowly increasing stress. These strain jerks, which result from self-similar avalanches close to the depinning threshold, follow a power-law behavior with an energy exponent ɛ =1.7 ±0.1 . Also, the distribution of waiting times between events follows a power law N (tw) ∝tw-(n +1 ) with an exponent n =0.9 , which transforms to a power law of susceptibility S (ω ) ∝ω-n . The present dynamic susceptibility data can be well fitted with a power law, with the same exponent (n =0.9 ) up to a characteristic frequency ω ≈ω* , where a crossover from stochastic DW motion to the pinned regime is well described using the scaling function of Fedorenko et al. [Phys. Rev. B 70, 224104 (2004), 10.1103/PhysRevB.70.224104].
Jahangoshai Rezaee, Mustafa; Jozmaleki, Mehrdad; Valipour, Mahsa
2018-01-01
One of the main features to invest in stock exchange companies is their financial performance. On the other hand, conventional evaluation methods such as data envelopment analysis are not only a retrospective process, but are also a process, which are incomplete and ineffective approaches to evaluate the companies in the future. To remove this problem, it is required to plan an expert system for evaluating organizations when the online data are received from stock exchange market. This paper deals with an approach for predicting the online financial performance of companies when data are received in different time's intervals. The proposed approach is based on integrating fuzzy C-means (FCM), data envelopment analysis (DEA) and artificial neural network (ANN). The classical FCM method is unable to update the number of clusters and their members when the data are changed or the new data are received. Hence, this method is developed in order to make dynamic features for the number of clusters and clusters members in classical FCM. Then, DEA is used to evaluate DMUs by using financial ratios to provide targets in neural network. Finally, the designed network is trained and prepared for predicting companies' future performance. The data on Tehran Stock Market companies for six consecutive years (2007-2012) are used to show the abilities of the proposed approach.
Arockiarani; J. Martina Jency
2017-01-01
The focus of this paper is to present the concept of fuzzy neutrosophic relations. Further we study the composition of fuzzy neutrosophic relations with the choice of t-norms and tconorms and characterize their properties.
Fuzzy logic controller optimization
Sepe, Jr., Raymond B; Miller, John Michael
2004-03-23
A method is provided for optimizing a rotating induction machine system fuzzy logic controller. The fuzzy logic controller has at least one input and at least one output. Each input accepts a machine system operating parameter. Each output produces at least one machine system control parameter. The fuzzy logic controller generates each output based on at least one input and on fuzzy logic decision parameters. Optimization begins by obtaining a set of data relating each control parameter to at least one operating parameter for each machine operating region. A model is constructed for each machine operating region based on the machine operating region data obtained. The fuzzy logic controller is simulated with at least one created model in a feedback loop from a fuzzy logic output to a fuzzy logic input. Fuzzy logic decision parameters are optimized based on the simulation.
Dynamic analysis of a pumped-storage hydropower plant with random power load
Zhang, Hao; Chen, Diyi; Xu, Beibei; Patelli, Edoardo; Tolo, Silvia
2018-02-01
This paper analyzes the dynamic response of a pumped-storage hydropower plant in generating mode. Considering the elastic water column effects in the penstock, a linearized reduced order dynamic model of the pumped-storage hydropower plant is used in this paper. As the power load is always random, a set of random generator electric power output is introduced to research the dynamic behaviors of the pumped-storage hydropower plant. Then, the influences of the PI gains on the dynamic characteristics of the pumped-storage hydropower plant with the random power load are analyzed. In addition, the effects of initial power load and PI parameters on the stability of the pumped-storage hydropower plant are studied in depth. All of the above results will provide theoretical guidance for the study and analysis of the pumped-storage hydropower plant.
Proceedings of the Second Joint Technology Workshop on Neural Networks and Fuzzy Logic, volume 2
Lea, Robert N. (Editor); Villarreal, James A. (Editor)
1991-01-01
Documented here are papers presented at the Neural Networks and Fuzzy Logic Workshop sponsored by NASA and the University of Texas, Houston. Topics addressed included adaptive systems, learning algorithms, network architectures, vision, robotics, neurobiological connections, speech recognition and synthesis, fuzzy set theory and application, control and dynamics processing, space applications, fuzzy logic and neural network computers, approximate reasoning, and multiobject decision making.
Cheap diagnosis using structural modelling and fuzzy-logic based detection
DEFF Research Database (Denmark)
Izadi-Zamanabadi, Roozbeh; Blanke, Mogens; Katebi, Serajeddin
2003-01-01
relations for linear or non-linear dynamic behaviour, and combine this with fuzzy output observer design to provide an effective diagnostic approach. An adaptive neuro-fuzzy inference method is used. A fuzzy adaptive threshold is employed to cope with practical uncertainty. The methods are demonstrated...... using measurements on a ship propulsion system subject to simulated faults....
Czech Academy of Sciences Publication Activity Database
Mesiar, Radko
2005-01-01
Roč. 28, č. 156 (2005), s. 365-370 ISSN 0165-0114 R&D Projects: GA ČR(CZ) GA402/04/1026 Institutional research plan: CEZ:AV0Z10750506 Keywords : fuzzy measures * fuzzy integral * regular fuzzy integral Subject RIV: BA - General Mathematics Impact factor: 1.039, year: 2005
DEFF Research Database (Denmark)
Rodríguez, J. Tinguaro; Franco de los Ríos, Camilo; Gómez, Daniel
2015-01-01
In this paper we want to stress the relevance of paired fuzzy sets, as already proposed in previous works of the authors, as a family of fuzzy sets that offers a unifying view for different models based upon the opposition of two fuzzy sets, simply allowing the existence of different types...
Howard, Ayanna
2005-01-01
The Fuzzy Logic Engine is a software package that enables users to embed fuzzy-logic modules into their application programs. Fuzzy logic is useful as a means of formulating human expert knowledge and translating it into software to solve problems. Fuzzy logic provides flexibility for modeling relationships between input and output information and is distinguished by its robustness with respect to noise and variations in system parameters. In addition, linguistic fuzzy sets and conditional statements allow systems to make decisions based on imprecise and incomplete information. The user of the Fuzzy Logic Engine need not be an expert in fuzzy logic: it suffices to have a basic understanding of how linguistic rules can be applied to the user's problem. The Fuzzy Logic Engine is divided into two modules: (1) a graphical-interface software tool for creating linguistic fuzzy sets and conditional statements and (2) a fuzzy-logic software library for embedding fuzzy processing capability into current application programs. The graphical- interface tool was developed using the Tcl/Tk programming language. The fuzzy-logic software library was written in the C programming language.
Intuitionistic Fuzzy Subbialgebras and Duality
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Wenjuan Chen
2014-01-01
Full Text Available We investigate connections between bialgebras and Atanassov’s intuitionistic fuzzy sets. Firstly we define an intuitionistic fuzzy subbialgebra of a bialgebra with an intuitionistic fuzzy subalgebra structure and also with an intuitionistic fuzzy subcoalgebra structure. Secondly we investigate the related properties of intuitionistic fuzzy subbialgebras. Finally we prove that the dual of an intuitionistic fuzzy strong subbialgebra is an intuitionistic fuzzy strong subbialgebra.
Synthesis of nonlinear control strategies from fuzzy logic control algorithms
Langari, Reza
1993-01-01
Fuzzy control has been recognized as an alternative to conventional control techniques in situations where the plant model is not sufficiently well known to warrant the application of conventional control techniques. Precisely what fuzzy control does and how it does what it does is not quite clear, however. This important issue is discussed and in particular it is shown how a given fuzzy control scheme can resolve into a nonlinear control law and that in those situations the success of fuzzy control hinges on its ability to compensate for nonlinearities in plant dynamics.
Some weakly mappings on intuitionistic fuzzy topological spaces
Zhen-Guo Xu; Fu-Gui Shi
2008-01-01
In this paper, we shall introduce concepts of fuzzy semiopen set, fuzzy semiclosed set, fuzzy semiinterior, fuzzy semiclosure on intuitionistic fuzzy topological space and fuzzy open (fuzzy closed) mapping, fuzzy irresolute mapping, fuzzy irresolute open (closed) mapping, fuzzy semicontinuous mapping and fuzzy semiopen (semiclosed) mapping between two intuitionistic fuzzy topological spaces. Moreover, we shall discuss their some properties.
A new approach for automatic control modeling, analysis and design in fully fuzzy environment
Gabr, Walaa Ibrahim
2015-01-01
The paper presents a new approach for the modeling, analysis and design of automatic control systems in fully fuzzy environment based on the normalized fuzzy matrices. The approach is also suitable for determining the propagation of fuzziness in automatic control and dynamical systems where all system coefficients are expressed as fuzzy parameters. A new consolidity chart is suggested based on the recently newly developed system consolidity index for testing the susceptibility of the system t...
Optimality Conditions for Fuzzy Number Quadratic Programming with Fuzzy Coefficients
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Xue-Gang Zhou
2014-01-01
Full Text Available The purpose of the present paper is to investigate optimality conditions and duality theory in fuzzy number quadratic programming (FNQP in which the objective function is fuzzy quadratic function with fuzzy number coefficients and the constraint set is fuzzy linear functions with fuzzy number coefficients. Firstly, the equivalent quadratic programming of FNQP is presented by utilizing a linear ranking function and the dual of fuzzy number quadratic programming primal problems is introduced. Secondly, we present optimality conditions for fuzzy number quadratic programming. We then prove several duality results for fuzzy number quadratic programming problems with fuzzy coefficients.
Countable Fuzzy Topological Space and Countable Fuzzy Topological Vector Space
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Apu Kumar Saha
2015-06-01
Full Text Available This paper deals with countable fuzzy topological spaces, a generalization of the notion of fuzzy topological spaces. A collection of fuzzy sets F on a universe X forms a countable fuzzy topology if in the definition of a fuzzy topology, the condition of arbitrary supremum is relaxed to countable supremum. In this generalized fuzzy structure, the continuity of fuzzy functions and some other related properties are studied. Also the class of countable fuzzy topological vector spaces as a generalization of the class of fuzzy topological vector spaces has been introduced and investigated.
Spectrum aware fuzzy clustering algorithm for cognative radio ...
African Journals Online (AJOL)
This paper proposes a SAFCA for a self-organized CH selection within a CRSN. The algorithm caters CR and WSN constraints by exploiting the dynamic spectrum access and fuzzy inference technique for an energy efficient CRSN. It utilizes channel availability and fuzzy parameters of residual energy, communication cost ...
Design of a stable fuzzy controller for an articulated vehicle.
Tanaka, K; Kosaki, T
1997-01-01
This paper presents a backward movement control of an articulated vehicle via a model-based fuzzy control technique. A nonlinear dynamic model of the articulated vehicle is represented by a Takagi-Sugeno fuzzy model. The concept of parallel distributed compensation is employed to design a fuzzy controller from the Takagi-Sugeno fuzzy model of the articulated vehicle. Stability of the designed fuzzy control system is guaranteed via Lyapunov approach. The stability conditions are characterized in terms of linear matrix inequalities since the stability analysis is reduced to a problem of finding a common Lyapunov function for a set of Lyapunov inequalities. Simulation results and experimental results show that the designed fuzzy controller effectively achieves the backward movement control of the articulated vehicle.
Mathematics of Fuzzy Sets and Fuzzy Logic
Bede, Barnabas
2013-01-01
This book presents a mathematically-based introduction into the fascinating topic of Fuzzy Sets and Fuzzy Logic and might be used as textbook at both undergraduate and graduate levels and also as reference guide for mathematician, scientists or engineers who would like to get an insight into Fuzzy Logic. Fuzzy Sets have been introduced by Lotfi Zadeh in 1965 and since then, they have been used in many applications. As a consequence, there is a vast literature on the practical applications of fuzzy sets, while theory has a more modest coverage. The main purpose of the present book is to reduce this gap by providing a theoretical introduction into Fuzzy Sets based on Mathematical Analysis and Approximation Theory. Well-known applications, as for example fuzzy control, are also discussed in this book and placed on new ground, a theoretical foundation. Moreover, a few advanced chapters and several new results are included. These comprise, among others, a new systematic and constructive approach for fuzzy infer...
On generalized fuzzy strongly semiclosed sets in fuzzy topological spaces
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Oya Bedre Ozbakir
2002-01-01
semiclosed, generalized fuzzy almost-strongly semiclosed, generalized fuzzy strongly closed, and generalized fuzzy almost-strongly closed sets. In the light of these definitions, we also define some generalizations of fuzzy continuous functions and discuss the relations between these new classes of functions and other fuzzy continuous functions.
How we pass from fuzzy $po$-semigroups to fuzzy $po$-$\\Gamma$-semigroups
Kehayopulu, Niovi
2014-01-01
The results on fuzzy ordered semigroups (or on fuzzy semigroups) can be transferred to fuzzy ordered gamma (or to fuzzy gamma) semigroups. We show the way we pass from fuzzy ordered semigroups to fuzzy ordered gamma semigroups.
Dynamics of quasiparticles in a nonstationary random field
International Nuclear Information System (INIS)
Bratus, E.N.; Gredeskul, S.A.; Pastur, L.A.; Shumeiko, V.S.
1989-01-01
The problem of nonlinear absorption of a stochastic acoustic signal in superconductors is reduced to an investigation of the states of the one-dimensional Dirac equation in a coordinate system moving with constant velocity and with a random potential. In the present paper a study is made of the properties of the problem of scattering by a random potential that determine the rate of dissipation of the acoustic energy and also of the localized properties of solutions in the case of an infinitely extended signal. If the projection of the Fermi velocity of an electron onto the direction of propagation of the signal is less than the velocity of sound, then all states in the field of an infinitely extended signal are localized (there is a purely point spectrum), and the mean coefficient of transmission of an electron through the region occupied by the sound is exponentially small for a sufficiently long signal. In the opposite case all states are delocalized (the spectrum is absolutely continuous), and on scattering reflection is replaced by partial transformation, for which the mean coefficient of disbalance is exponentially small for a sufficiently long signal
Recurrent fuzzy ranking methods
Hajjari, Tayebeh
2012-11-01
With the increasing development of fuzzy set theory in various scientific fields and the need to compare fuzzy numbers in different areas. Therefore, Ranking of fuzzy numbers plays a very important role in linguistic decision-making, engineering, business and some other fuzzy application systems. Several strategies have been proposed for ranking of fuzzy numbers. Each of these techniques has been shown to produce non-intuitive results in certain case. In this paper, we reviewed some recent ranking methods, which will be useful for the researchers who are interested in this area.
Myravyova, E. A.; Sharipov, M. I.; Radakina, D. S.
2017-10-01
During writing this work, the fuzzy controller with a double base of rules was studied, which was applied for the synthesis of the automated control system. A method for fuzzy controller adaptation has been developed. The adaptation allows the fuzzy controller to automatically compensate for parametric interferences that occur at the control object. Specifically, the fuzzy controller controlled the outlet steam temperature in the boiler unit BKZ-75-39 GMA. The software code was written in the programming support environment Unity Pro XL designed for fuzzy controller adaptation.
Evaluating software architecture using fuzzy formal models
Directory of Open Access Journals (Sweden)
Payman Behbahaninejad
2012-04-01
Full Text Available Unified Modeling Language (UML has been recognized as one of the most popular techniques to describe static and dynamic aspects of software systems. One of the primary issues in designing software packages is the existence of uncertainty associated with such models. Fuzzy-UML to describe software architecture has both static and dynamic perspective, simultaneously. The evaluation of software architecture design phase initiates always help us find some additional requirements, which helps reduce cost of design. In this paper, we use a fuzzy data model to describe the static aspects of software architecture and the fuzzy sequence diagram to illustrate the dynamic aspects of software architecture. We also transform these diagrams into Petri Nets and evaluate reliability of the architecture. The web-based hotel reservation system for further explanation has been studied.
Chen, Zhijia; Zhu, Yuanchang; Di, Yanqiang; Feng, Shaochong
2015-01-01
In IaaS (infrastructure as a service) cloud environment, users are provisioned with virtual machines (VMs). To allocate resources for users dynamically and effectively, accurate resource demands predicting is essential. For this purpose, this paper proposes a self-adaptive prediction method using ensemble model and subtractive-fuzzy clustering based fuzzy neural network (ESFCFNN). We analyze the characters of user preferences and demands. Then the architecture of the prediction model is constructed. We adopt some base predictors to compose the ensemble model. Then the structure and learning algorithm of fuzzy neural network is researched. To obtain the number of fuzzy rules and the initial value of the premise and consequent parameters, this paper proposes the fuzzy c-means combined with subtractive clustering algorithm, that is, the subtractive-fuzzy clustering. Finally, we adopt different criteria to evaluate the proposed method. The experiment results show that the method is accurate and effective in predicting the resource demands.
Directory of Open Access Journals (Sweden)
Zhijia Chen
2015-01-01
Full Text Available In IaaS (infrastructure as a service cloud environment, users are provisioned with virtual machines (VMs. To allocate resources for users dynamically and effectively, accurate resource demands predicting is essential. For this purpose, this paper proposes a self-adaptive prediction method using ensemble model and subtractive-fuzzy clustering based fuzzy neural network (ESFCFNN. We analyze the characters of user preferences and demands. Then the architecture of the prediction model is constructed. We adopt some base predictors to compose the ensemble model. Then the structure and learning algorithm of fuzzy neural network is researched. To obtain the number of fuzzy rules and the initial value of the premise and consequent parameters, this paper proposes the fuzzy c-means combined with subtractive clustering algorithm, that is, the subtractive-fuzzy clustering. Finally, we adopt different criteria to evaluate the proposed method. The experiment results show that the method is accurate and effective in predicting the resource demands.
Chen, Zhijia; Zhu, Yuanchang; Di, Yanqiang; Feng, Shaochong
2015-01-01
In IaaS (infrastructure as a service) cloud environment, users are provisioned with virtual machines (VMs). To allocate resources for users dynamically and effectively, accurate resource demands predicting is essential. For this purpose, this paper proposes a self-adaptive prediction method using ensemble model and subtractive-fuzzy clustering based fuzzy neural network (ESFCFNN). We analyze the characters of user preferences and demands. Then the architecture of the prediction model is constructed. We adopt some base predictors to compose the ensemble model. Then the structure and learning algorithm of fuzzy neural network is researched. To obtain the number of fuzzy rules and the initial value of the premise and consequent parameters, this paper proposes the fuzzy c-means combined with subtractive clustering algorithm, that is, the subtractive-fuzzy clustering. Finally, we adopt different criteria to evaluate the proposed method. The experiment results show that the method is accurate and effective in predicting the resource demands. PMID:25691896
B Gibilisco, Michael; E Albert, Karen; N Mordeson, John; J Wierman, Mark; D Clark, Terry
2014-01-01
This book offers a comprehensive analysis of the social choice literature and shows, by applying fuzzy sets, how the use of fuzzy preferences, rather than that of strict ones, may affect the social choice theorems. To do this, the book explores the presupposition of rationality within the fuzzy framework and shows that the two conditions for rationality, completeness and transitivity, do exist with fuzzy preferences. Specifically, this book examines: the conditions under which a maximal set exists; the Arrow’s theorem; the Gibbard-Satterthwaite theorem; and the median voter theorem. After showing that a non-empty maximal set does exists for fuzzy preference relations, this book goes on to demonstrating the existence of a fuzzy aggregation rule satisfying all five Arrowian conditions, including non-dictatorship. While the Gibbard-Satterthwaite theorem only considers individual fuzzy preferences, this work shows that both individuals and groups can choose alternatives to various degrees, resulting in a so...
Special functions in Fuzzy Analysis
Angel Garrido
2006-01-01
In the treatment of Fuzzy Logic an useful tool appears: the membership function, with the information about the degree of completion of a condition which defines the respective Fuzzy Set or Fuzzy Relation. With their introduction, it is possible to prove some results on the foundations of Fuzzy Logic and open new ways in Fuzzy Analysis.
Special functions in Fuzzy Analysis
Directory of Open Access Journals (Sweden)
Angel Garrido
2006-01-01
Full Text Available In the treatment of Fuzzy Logic an useful tool appears: the membership function, with the information about the degree of completion of a condition which defines the respective Fuzzy Set or Fuzzy Relation. With their introduction, it is possible to prove some results on the foundations of Fuzzy Logic and open new ways in Fuzzy Analysis.
Approximate Reasoning with Fuzzy Booleans
van den Broek, P.M.; Noppen, J.A.R.
This paper introduces, in analogy to the concept of fuzzy numbers, the concept of fuzzy booleans, and examines approximate reasoning with the compositional rule of inference using fuzzy booleans. It is shown that each set of fuzzy rules is equivalent to a set of fuzzy rules with singleton crisp
Spectral and Dynamical Properties of Random Models with Nonlocal and Singular Interactions
Hislop, P D; Krishna, M G
2002-01-01
We give a spectral and dynamical description of certain models of random Schr\\"odinger operators on $L^2 ( \\R^d)$ for which a modified version of the small moment method of Aizenman and Molchanov \\cite{[AizenmanMolchanov]} can be applied. One family of models includes includes \\Schr\\ operators with random, nonlocal interactions constructed from a wavelet basis. The second family includes \\Schr\\ operators with random singular interactions randomly located on sublattices of $\\Z^d$, for $d = 1 , 2, 3$. We prove that these models are amenable to Aizenman-Molchanov-type analysis of the Green's function, thereby eliminating the use of multiscale analysis. The basic technical result is an estimate on the expectation of small moments of the Green's function. Among our results, we prove a good Wegner estimate and the H\\"older continuity of the integrated density of states, and spectral and dynamical localization at negative energies.
Stability and dynamical properties of material flow systems on random networks
Anand, K.; Galla, T.
2009-04-01
The theory of complex networks and of disordered systems is used to study the stability and dynamical properties of a simple model of material flow networks defined on random graphs. In particular we address instabilities that are characteristic of flow networks in economic, ecological and biological systems. Based on results from random matrix theory, we work out the phase diagram of such systems defined on extensively connected random graphs, and study in detail how the choice of control policies and the network structure affects stability. We also present results for more complex topologies of the underlying graph, focussing on finitely connected Erdös-Réyni graphs, Small-World Networks and Barabási-Albert scale-free networks. Results indicate that variability of input-output matrix elements, and random structures of the underlying graph tend to make the system less stable, while fast price dynamics or strong responsiveness to stock accumulation promote stability.
All-time dynamics of continuous-time random walks on complex networks
Teimouri, Hamid; Kolomeisky, Anatoly B.
2012-01-01
The concept of continuous-time random walks (CTRW) is a generalization of ordinary random walk models, and it is a powerful tool for investigating a broad spectrum of phenomena in natural, engineering, social and economic sciences. Recently, several theoretical approaches have been developed that allowed to analyze explicitly dynamics of CTRW at all times, which is critically important for understanding mechanisms of underlying phenomena. However, theoretical analysis has been done mostly for...
A Comparison of Three Random Number Generators for Aircraft Dynamic Modeling Applications
Grauer, Jared A.
2017-01-01
Three random number generators, which produce Gaussian white noise sequences, were compared to assess their suitability in aircraft dynamic modeling applications. The first generator considered was the MATLAB (registered) implementation of the Mersenne-Twister algorithm. The second generator was a website called Random.org, which processes atmospheric noise measured using radios to create the random numbers. The third generator was based on synthesis of the Fourier series, where the random number sequences are constructed from prescribed amplitude and phase spectra. A total of 200 sequences, each having 601 random numbers, for each generator were collected and analyzed in terms of the mean, variance, normality, autocorrelation, and power spectral density. These sequences were then applied to two problems in aircraft dynamic modeling, namely estimating stability and control derivatives from simulated onboard sensor data, and simulating flight in atmospheric turbulence. In general, each random number generator had good performance and is well-suited for aircraft dynamic modeling applications. Specific strengths and weaknesses of each generator are discussed. For Monte Carlo simulation, the Fourier synthesis method is recommended because it most accurately and consistently approximated Gaussian white noise and can be implemented with reasonable computational effort.
Dynamic superhydrophobic behavior in scalable random textured polymeric surfaces
Moreira, David; Park, Sung-hoon; Lee, Sangeui; Verma, Neil; Bandaru, Prabhakar R.
2016-03-01
Superhydrophobic (SH) surfaces, created from hydrophobic materials with micro- or nano- roughness, trap air pockets in the interstices of the roughness, leading, in fluid flow conditions, to shear-free regions with finite interfacial fluid velocity and reduced resistance to flow. Significant attention has been given to SH conditions on ordered, periodic surfaces. However, in practical terms, random surfaces are more applicable due to their relative ease of fabrication. We investigate SH behavior on a novel durable polymeric rough surface created through a scalable roll-coating process with varying micro-scale roughness through velocity and pressure drop measurements. We introduce a new method to construct the velocity profile over SH surfaces with significant roughness in microchannels. Slip length was measured as a function of differing roughness and interstitial air conditions, with roughness and air fraction parameters obtained through direct visualization. The slip length was matched to scaling laws with good agreement. Roughness at high air fractions led to a reduced pressure drop and higher velocities, demonstrating the effectiveness of the considered surface in terms of reduced resistance to flow. We conclude that the observed air fraction under flow conditions is the primary factor determining the response in fluid flow. Such behavior correlated well with the hydrophobic or superhydrophobic response, indicating significant potential for practical use in enhancing fluid flow efficiency.
Dynamically unpolarized single-photon source in diamond with intrinsic randomness
Abe, Naofumi; Mitsumori, Yasuyoshi; Sadgrove, Mark; Edamatsu, Keiichi
2017-01-01
Polarization is one of the fundamental properties of light, providing numerous applications in science and technology. While ?dynamically unpolarized? single-photon sources are demanded for various quantum applications, such sources have never been explored. Here we demonstrate dynamically unpolarized single-photon emission from a single [111]-oriented nitrogen- vacancy centre in diamond, in which the single-photon stream is unpolarized, exhibiting intrinsic randomness with vanishing polariza...
On fuzzy almost continuous convergence in fuzzy function spaces
Directory of Open Access Journals (Sweden)
A.I. Aggour
2013-10-01
Full Text Available In this paper, we study the fuzzy almost continuous convergence of fuzzy nets on the set FAC(X, Y of all fuzzy almost continuous functions of a fuzzy topological space X into another Y. Also, we introduce the notions of fuzzy splitting and fuzzy jointly continuous topologies on the set FAC(X, Y and study some of its basic properties.
A NOTE ON FUZZY CLOSURE OF A FUZZY SET
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Bhimraj Basumatary
2015-10-01
Full Text Available In this article fuzzy closure has been discussed with the help of extended definition of fuzzy set on the assumption that the union of a fuzzy set and its complement is universal set and intersection of a fuzzy set and its complement is empty set. Also we have discussed some proposition of fuzzy closure with the help of numerical example on the basis of extended definition of fuzzy set.
Multiformity of inherent randomicity and visitation density in n symbolic dynamics
International Nuclear Information System (INIS)
Zhang Yagang; Wang Changjiang
2007-01-01
The multiformity of inherent randomicity and visitation density in n symbolic dynamics will be clarified in this paper. These stochastic symbolic sequences bear three features. The distribution of frequency, inter-occurrence times and the alignment of two random sequences are amplified in detail. The features of visitation density in surjective maps presents catholicity and the catholicity in n letters randomicity has the same measure foundation. We hope to offer a symbolic platform that satisfies these stochastic properties and to attempt to study certain properties of DNA base sequences, 20 amino acids symbolic sequences of proteid structure, and the time series that can be symbolic in finance market et al
Fuzzy soft connected sets in fuzzy soft topological spaces II
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A. Kandil
2017-04-01
Full Text Available In this paper, we introduce some different types of fuzzy soft connected components related to the different types of fuzzy soft connectedness and based on an equivalence relation defined on the set of fuzzy soft points of X. We have investigated some very interesting properties for fuzzy soft connected components. We show that the fuzzy soft C5-connected component may be not exists and if it exists, it may not be fuzzy soft closed set. Also, we introduced some very interesting properties for fuzzy soft connected components in discrete fuzzy soft topological spaces which is a departure from the general topology.
Probabilistic fuzzy clustering algorithm for fuzzy rules decomposition
Salgado, Paulo; Igrejas, Getúlio
2007-01-01
The Fuzzy C-Means (FCM) clustering algorithm is the best known and the most used method for fuzzy clustering and is generally applied to well defined sets of data. In this work a generalized Probabilistic Fuzzy C-Means (PFCM) algorithm is proposed and applied to fuzzy sets clustering. The methodology presented leads to a fuzzy partition of the fuzzy rules, one for each cluster, which corresponds to a new set of fuzzy sub-systems. When applied to the clustering of a flat fuzzy system the resul...
Robust fuzzy logic stabilization with disturbance elimination.
Danapalasingam, Kumeresan A
2014-01-01
A robust fuzzy logic controller is proposed for stabilization and disturbance rejection in nonlinear control systems of a particular type. The dynamic feedback controller is designed as a combination of a control law that compensates for nonlinear terms in a control system and a dynamic fuzzy logic controller that addresses unknown model uncertainties and an unmeasured disturbance. Since it is challenging to derive a highly accurate mathematical model, the proposed controller requires only nominal functions of a control system. In this paper, a mathematical derivation is carried out to prove that the controller is able to achieve asymptotic stability by processing state measurements. Robustness here refers to the ability of the controller to asymptotically steer the state vector towards the origin in the presence of model uncertainties and a disturbance input. Simulation results of the robust fuzzy logic controller application in a magnetic levitation system demonstrate the feasibility of the control design.
A dynamic random effects multinomial logit model of household car ownership
DEFF Research Database (Denmark)
Bue Bjørner, Thomas; Leth-Petersen, Søren
2007-01-01
Using a large household panel we estimate demand for car ownership by means of a dynamic multinomial model with correlated random effects. Results suggest that the persistence in car ownership observed in the data should be attributed to both true state dependence and to unobserved heterogeneity ...
Cosmic and terrestrial single-event radiation effects in dynamic random access memories
International Nuclear Information System (INIS)
Massengill, L.W.
1996-01-01
A review of the literature on single-event radiation effects (SEE) on MOS integrated-circuit dynamic random access memories (DRAM's) is presented. The sources of single-event (SE) radiation particles, causes of circuit information loss, experimental observations of SE information upset, technological developments for error mitigation, and relationships of developmental trends to SE vulnerability are discussed
Decomposition of fuzzy continuity and fuzzy ideal continuity via fuzzy idealization
International Nuclear Information System (INIS)
Zahran, A.M.; Abbas, S.E.; Abd El-baki, S.A.; Saber, Y.M.
2009-01-01
Recently, El-Naschie has shown that the notion of fuzzy topology may be relevant to quantum paretical physics in connection with string theory and E-infinity space time theory. In this paper, we study the concepts of r-fuzzy semi-I-open, r-fuzzy pre-I-open, r-fuzzy α-I-open and r-fuzzy β-I-open sets, which is properly placed between r-fuzzy openness and r-fuzzy α-I-openness (r-fuzzy pre-I-openness) sets regardless the fuzzy ideal topological space in Sostak sense. Moreover, we give a decomposition of fuzzy continuity, fuzzy ideal continuity and fuzzy ideal α-continuity, and obtain several characterization and some properties of these functions. Also, we investigate their relationship with other types of function.
FUZZY RINGS AND ITS PROPERTIES
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Karyati Karyati
2017-01-01
One of algebraic structure that involves a binary operation is a group that is defined an un empty set (classical with an associative binary operation, it has identity elements and each element has an inverse. In the structure of the group known as the term subgroup, normal subgroup, subgroup and factor group homomorphism and its properties. Classical algebraic structure is developed to algebraic structure fuzzy by the researchers as an example semi group fuzzy and fuzzy group after fuzzy sets is introduced by L. A. Zadeh at 1965. It is inspired of writing about semi group fuzzy and group of fuzzy, a research on the algebraic structure of the ring is held with reviewing ring fuzzy, ideal ring fuzzy, homomorphism ring fuzzy and quotient ring fuzzy with its properties. The results of this study are obtained fuzzy properties of the ring, ring ideal properties fuzzy, properties of fuzzy ring homomorphism and properties of fuzzy quotient ring by utilizing a subset of a subset level and strong level as well as image and pre-image homomorphism fuzzy ring. Keywords: fuzzy ring, subset level, homomorphism fuzzy ring, fuzzy quotient ring
ON SOME DECOMPOSITIONS OF FUZZY SOFT CONTINUITY
Gain, Pradip Kumar; Mukherjee, Prakash; Chakraborty, Ramkrishna Prasad
2015-01-01
– In this article, some open-like fuzzy soft sets such as fuzzy soft semi-open set, fuzzy soft preopen set, fuzzy soft α-open set and corresponding variants of fuzzy soft continuous functions are introduced and discussed. Some other variants of fuzzy soft sets such as fuzzy soft semi-preclosed set, fuzzy soft t-set, fuzzy soft α*-set, fuzzy soft regular open set, fuzzy soft B-set, fuzzy soft C-set and fuzzy soft D(α)-set are defined and some properties of these sets are studied and investigat...
DEFF Research Database (Denmark)
Jantzen, Jan
The objective of this textbook is to acquire an understanding of the behaviour of fuzzy logic controllers. Under certain conditions a fuzzy controller is equivalent to a proportional-integral-derivative (PID) controller. Using that equivalence as a link, the book applies analysis methods from...... linear and nonlinear control theory. In the linear domain, PID tuning methods and stability analyses are transferred to linear fuzzy controllers. The Nyquist plot shows the robustness of different settings of the fuzzy gain parameters. As a result, a fuzzy controller is guaranteed to perform as well...... as any PID controller. In the nonlinear domain, the stability of four standard control surfaces is analysed by means of describing functions and Nyquist plots. The self-organizing controller (SOC) is shown to be a model reference adaptive controller. There is a possibility that a nonlinear fuzzy PID...
Lei, Qian
2017-01-01
This book offers a comprehensive and systematic review of the latest research findings in the area of intuitionistic fuzzy calculus. After introducing the intuitionistic fuzzy numbers’ operational laws and their geometrical and algebraic properties, the book defines the concept of intuitionistic fuzzy functions and presents the research on the derivative, differential, indefinite integral and definite integral of intuitionistic fuzzy functions. It also discusses some of the methods that have been successfully used to deal with continuous intuitionistic fuzzy information or data, which are different from the previous aggregation operators focusing on discrete information or data. Mainly intended for engineers and researchers in the fields of fuzzy mathematics, operations research, information science and management science, this book is also a valuable textbook for postgraduate and advanced undergraduate students alike.
Statistical Stability Analysis for Particle Swarm Optimization Dynamics with Random Coefficients
Koguma, Yuji; Aiyhosi, Eitaro
Particle Swarm Optimization (PSO), a meta-heuristic global optimization method, has attracted special interest for its simple algorithm and high searching ability. The updating formula of PSO involves coefficients with random numbers as parameters to enhance diversification ability in searching for the global optimum. However, the randomness makes stability of the searching points difficult to be analyzed mathematically, and the users need to adjust the parameter values by trial and error. In this paper, stability of the stochastic dynamics of PSO is analyzed mathematically and exact stability condition taking the randomness into consideration is presented with an index “statistical eigenvalue”, which is a new concept to evaluate the degree of the stability of PSO dynamics. Accuracy and effectiveness of the proposed stability discrimination using the presented index are certified in numerical simulation for simple examples.
3D Multisource Full‐Waveform Inversion using Dynamic Random Phase Encoding
Boonyasiriwat, Chaiwoot
2010-10-17
We have developed a multisource full‐waveform inversion algorithm using a dynamic phase encoding strategy with dual‐randomization—both the position and polarity of simultaneous sources are randomized and changed every iteration. The dynamic dual‐randomization is used to promote the destructive interference of crosstalk noise resulting from blending a large number of common shot gathers into a supergather. We compare our multisource algorithm with various algorithms in a numerical experiment using the 3D SEG/EAGE overthrust model and show that our algorithm provides a higher‐quality velocity tomogram than the other methods that use only monorandomization. This suggests that increasing the degree of randomness in phase encoding should improve the quality of the inversion result.
Disjointness of Fuzzy Coalitions
Czech Academy of Sciences Publication Activity Database
Mareš, Milan; Vlach, M.
2008-01-01
Roč. 44, č. 3 (2008), s. 416-429 ISSN 0023-5954 R&D Projects: GA AV ČR IAA1075301; GA ČR GA402/04/1026; GA MŠk 1M0572 Institutional research plan: CEZ:AV0Z10750506 Keywords : Fuzzy coalitionla game * Disjointness of fuzzy sets * Fuzzy coalition Subject RIV: BD - Theory of Information Impact factor: 0.281, year: 2008
Metamathematics of fuzzy logic
Hájek, Petr
1998-01-01
This book presents a systematic treatment of deductive aspects and structures of fuzzy logic understood as many valued logic sui generis. Some important systems of real-valued propositional and predicate calculus are defined and investigated. The aim is to show that fuzzy logic as a logic of imprecise (vague) propositions does have well-developed formal foundations and that most things usually named `fuzzy inference' can be naturally understood as logical deduction.
DEFF Research Database (Denmark)
Dotoli, M.; Jantzen, Jan
1999-01-01
The tutorial concerns automatic control of an inverted pendulum, especially rule based control by means of fuzzy logic. A ball balancer, implemented in a software simulator in Matlab, is used as a practical case study. The objectives of the tutorial are to teach the basics of fuzzy control......, and to show how to apply fuzzy logic in automatic control. The tutorial is distance learning, where students interact one-to-one with the teacher using e-mail....
Wang, Xin-Fan; Wang, Jian-Qiang; Deng, Sheng-Yue
2013-01-01
We investigate the dynamic stochastic multicriteria decision making (SMCDM) problems, in which the criterion values take the form of log-normally distributed random variables, and the argument information is collected from different periods. We propose two new geometric aggregation operators, such as the log-normal distribution weighted geometric (LNDWG) operator and the dynamic log-normal distribution weighted geometric (DLNDWG) operator, and develop a method for dynamic SMCDM with log-normally distributed random variables. This method uses the DLNDWG operator and the LNDWG operator to aggregate the log-normally distributed criterion values, utilizes the entropy model of Shannon to generate the time weight vector, and utilizes the expectation values and variances of log-normal distributions to rank the alternatives and select the best one. Finally, an example is given to illustrate the feasibility and effectiveness of this developed method.
Fuzzy control and identification
Lilly, John H
2010-01-01
This book gives an introduction to basic fuzzy logic and Mamdani and Takagi-Sugeno fuzzy systems. The text shows how these can be used to control complex nonlinear engineering systems, while also also suggesting several approaches to modeling of complex engineering systems with unknown models. Finally, fuzzy modeling and control methods are combined in the book, to create adaptive fuzzy controllers, ending with an example of an obstacle-avoidance controller for an autonomous vehicle using modus ponendo tollens logic.
T Atanassov, Krassimir
2017-01-01
The book offers a comprehensive survey of intuitionistic fuzzy logics. By reporting on both the author’s research and others’ findings, it provides readers with a complete overview of the field and highlights key issues and open problems, thus suggesting new research directions. Starting with an introduction to the basic elements of intuitionistic fuzzy propositional calculus, it then provides a guide to the use of intuitionistic fuzzy operators and quantifiers, and lastly presents state-of-the-art applications of intuitionistic fuzzy sets. The book is a valuable reference resource for graduate students and researchers alike.
Advances in fuzzy implication functions
Beliakov, Gleb; Sola, Humberto; Pradera, Ana
2013-01-01
Fuzzy implication functions are one of the main operations in fuzzy logic. They generalize the classical implication, which takes values in the set {0,1}, to fuzzy logic, where the truth values belong to the unit interval [0,1]. These functions are not only fundamental for fuzzy logic systems, fuzzy control, approximate reasoning and expert systems, but they also play a significant role in mathematical fuzzy logic, in fuzzy mathematical morphology and image processing, in defining fuzzy subsethood measures and in solving fuzzy relational equations. This volume collects 8 research papers on fuzzy implication functions. Three articles focus on the construction methods, on different ways of generating new classes and on the common properties of implications and their dependencies. Two articles discuss implications defined on lattices, in particular implication functions in interval-valued fuzzy set theories. One paper summarizes the sufficient and necessary conditions of solutions for one distributivity equation...
2010-07-28
... INTERNATIONAL TRADE COMMISSION [Investigation No. 337-TA-707] In the Matter of Certain Dynamic Random Access Memory Semiconductors and Products Containing Same, Including Memory Modules; Notice of a... importation of certain dynamic random access memory semiconductors and products containing same, including...
Souza Soler, de L.; Kok, K.; Câmara, G.; Veldkamp, A.
2011-01-01
In this study we developed a methodology to identify and quantify the relationships among determinants of land cover change using a regional case study in the Brazilian Amazon. The method is based on the application of fuzzy cognitive maps (FCMs), a semi-quantitative tool that provides a structured
Fuzzy logic based robotic controller
Attia, F.; Upadhyaya, M.
1994-01-01
Existing Proportional-Integral-Derivative (PID) robotic controllers rely on an inverse kinematic model to convert user-specified cartesian trajectory coordinates to joint variables. These joints experience friction, stiction, and gear backlash effects. Due to lack of proper linearization of these effects, modern control theory based on state space methods cannot provide adequate control for robotic systems. In the presence of loads, the dynamic behavior of robotic systems is complex and nonlinear, especially where mathematical modeling is evaluated for real-time operators. Fuzzy Logic Control is a fast emerging alternative to conventional control systems in situations where it may not be feasible to formulate an analytical model of the complex system. Fuzzy logic techniques track a user-defined trajectory without having the host computer to explicitly solve the nonlinear inverse kinematic equations. The goal is to provide a rule-based approach, which is closer to human reasoning. The approach used expresses end-point error, location of manipulator joints, and proximity to obstacles as fuzzy variables. The resulting decisions are based upon linguistic and non-numerical information. This paper presents a solution to the conventional robot controller which is independent of computationally intensive kinematic equations. Computer simulation results of this approach as obtained from software implementation are also discussed.
Some Properties of Fuzzy Soft Proximity Spaces
Demir, İzzettin; Özbakır, Oya Bedre
2015-01-01
We study the fuzzy soft proximity spaces in Katsaras's sense. First, we show how a fuzzy soft topology is derived from a fuzzy soft proximity. Also, we define the notion of fuzzy soft δ-neighborhood in the fuzzy soft proximity space which offers an alternative approach to the study of fuzzy soft proximity spaces. Later, we obtain the initial fuzzy soft proximity determined by a family of fuzzy soft proximities. Finally, we investigate relationship between fuzzy soft proximities and proximities. PMID:25793224
On Intuitionistic Fuzzy Filters of Intuitionistic Fuzzy Coframes
Directory of Open Access Journals (Sweden)
Rajesh K. Thumbakara
2013-01-01
Full Text Available Frame theory is the study of topology based on its open set lattice, and it was studied extensively by various authors. In this paper, we study quotients of intuitionistic fuzzy filters of an intuitionistic fuzzy coframe. The quotients of intuitionistic fuzzy filters are shown to be filters of the given intuitionistic fuzzy coframe. It is shown that the collection of all intuitionistic fuzzy filters of a coframe and the collection of all intutionistic fuzzy quotient filters of an intuitionistic fuzzy filter are coframes.
Directory of Open Access Journals (Sweden)
Lei Wang
2014-01-01
Full Text Available A dynamic model of gear transmission system of wind turbine is built with consideration of randomness of loads and parameters. The dynamic response of the system is obtained using the theory of random sampling and the Runge-Kutta method. According to rain flow counting principle, the dynamic meshing forces are converted into a series of luffing fatigue load spectra. The amplitude and frequency of the equivalent stress are obtained using equivalent method of Geber quadratic curve. Moreover, the dynamic reliability model of components and system is built according to the theory of probability of cumulative fatigue damage. The system reliability with the random variation of parameters is calculated and the influence of random parameters on dynamic reliability of components is analyzed. In the end, the results of the proposed method are compared with that of Monte Carlo method. This paper can be instrumental in the design of wind turbine gear transmission system with more advantageous dynamic reliability.
Activated aging dynamics and effective trap model description in the random energy model
Baity-Jesi, M.; Biroli, G.; Cammarota, C.
2018-01-01
We study the out-of-equilibrium aging dynamics of the random energy model (REM) ruled by a single spin-flip Metropolis dynamics. We focus on the dynamical evolution taking place on time-scales diverging with the system size. Our aim is to show to what extent the activated dynamics displayed by the REM can be described in terms of an effective trap model. We identify two time regimes: the first one corresponds to the process of escaping from a basin in the energy landscape and to the subsequent exploration of high energy configurations, whereas the second one corresponds to the evolution from a deep basin to the other. By combining numerical simulations with analytical arguments we show why the trap model description does not hold in the former but becomes exact in the second.
Type-2 Fuzzy Logic in Intelligent Control Applications
Castillo, Oscar
2012-01-01
We describe in this book, hybrid intelligent systems based mainly on type-2 fuzzy logic for intelligent control. Hybrid intelligent systems combine several intelligent computing paradigms, including fuzzy logic, and bio-inspired optimization algorithms, which can be used to produce powerful automatic control systems. The book is organized in three main parts, which contain a group of chapters 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 can be the basis for achieving intelligent control with interval type-2 fuzzy logic. The second part of the book is comprised of chapters with the main theme of evolutionary optimization of type-2 fuzzy systems in intelligent control with the aim of designing optimal type-2 fuzzy controllers for complex control problems in diverse areas of application, including mobile robotics, aircraft dynamics systems and hardware implementations. Th...
Fuzzy model predictive control algorithm applied in nuclear power plant
International Nuclear Information System (INIS)
Zuheir, Ahmad
2006-01-01
The aim of this paper is to design a predictive controller based on a fuzzy model. The Takagi-Sugeno fuzzy model with an Adaptive B-splines neuro-fuzzy implementation is used and incorporated as a predictor in a predictive controller. An optimization approach with a simplified gradient technique is used to calculate predictions of the future control actions. In this approach, adaptation of the fuzzy model using dynamic process information is carried out to build the predictive controller. The easy description of the fuzzy model and the easy computation of the gradient sector during the optimization procedure are the main advantages of the computation algorithm. The algorithm is applied to the control of a U-tube steam generation unit (UTSG) used for electricity generation. (author)
Maximum dynamic responses using matched filter theory and random process theory
Pototzky, Anthony S.; Zeiler, Thomas A.; Perry, Boyd, III
1988-01-01
This paper describes and illustrates two ways of performing time-correlated gust-load calculations. The first is based on Matched Filter Theory; the second on Random Process Theory. The two yield theoretically identical results and both employ novel applications of the theories and unconventional interpretations of the intermediate and final results. Both approaches are computationally fast and are general enough to be applied to dynamic-response problems other than gust loads. A brief mathematical development and example calculations using both Matched Filter Theory and Random Process Theory are presented.
Properties of Bipolar Fuzzy Hypergraphs
Akram, M.; Dudek, W. A.; Sarwar, S.
2013-01-01
In this article, we apply the concept of bipolar fuzzy sets to hypergraphs and investigate some properties of bipolar fuzzy hypergraphs. We introduce the notion of $A-$ tempered bipolar fuzzy hypergraphs and present some of their properties. We also present application examples of bipolar fuzzy hypergraphs.
Directory of Open Access Journals (Sweden)
Radko Mesiar
2017-11-01
Full Text Available A revised definition for fuzzy bags is reviewed, developing the concept of bags given by Delgado et al. 2009 from which each bag has two parts, function and summary information. Then, the definitions of fuzzy bag expected value, bag entropy and bag similarity are introduced. By some examples, the new concepts are illustrated.
Extended Fuzzy Clustering Algorithms
U. Kaymak (Uzay); M. Setnes
2000-01-01
textabstractFuzzy clustering is a widely applied method for obtaining fuzzy models from data. It has been applied successfully in various fields including finance and marketing. Despite the successful applications, there are a number of issues that must be dealt with in practical applications of
Clustering by fuzzy neural gas and evaluation of fuzzy clusters.
Geweniger, Tina; Fischer, Lydia; Kaden, Marika; Lange, Mandy; Villmann, Thomas
2013-01-01
We consider some modifications of the neural gas algorithm. First, fuzzy assignments as known from fuzzy c-means and neighborhood cooperativeness as known from self-organizing maps and neural gas are combined to obtain a basic Fuzzy Neural Gas. Further, a kernel variant and a simulated annealing approach are derived. Finally, we introduce a fuzzy extension of the ConnIndex to obtain an evaluation measure for clusterings based on fuzzy vector quantization.
Clustering by Fuzzy Neural Gas and Evaluation of Fuzzy Clusters
Geweniger, Tina; Fischer, Lydia; Kaden, Marika; Lange, Mandy; Villmann, Thomas
2013-01-01
We consider some modifications of the neural gas algorithm. First, fuzzy assignments as known from fuzzy c-means and neighborhood cooperativeness as known from self-organizing maps and neural gas are combined to obtain a basic Fuzzy Neural Gas. Further, a kernel variant and a simulated annealing approach are derived. Finally, we introduce a fuzzy extension of the ConnIndex to obtain an evaluation measure for clusterings based on fuzzy vector quantization.
Effects of random tooth profile errors on the dynamic behaviors of planetary gears
Xun, Chao; Long, Xinhua; Hua, Hongxing
2018-02-01
In this paper, a nonlinear random model is built to describe the dynamics of planetary gear trains (PGTs), in which the time-varying mesh stiffness, tooth profile modification (TPM), tooth contact loss, and random tooth profile error are considered. A stochastic method based on the method of multiple scales (MMS) is extended to analyze the statistical property of the dynamic performance of PGTs. By the proposed multiple-scales based stochastic method, the distributions of the dynamic transmission errors (DTEs) are investigated, and the lower and upper bounds are determined based on the 3σ principle. Monte Carlo method is employed to verify the proposed method. Results indicate that the proposed method can be used to determine the distribution of the DTE of PGTs high efficiently and allow a link between the manufacturing precision and the dynamical response. In addition, the effects of tooth profile modification on the distributions of vibration amplitudes and the probability of tooth contact loss with different manufacturing tooth profile errors are studied. The results show that the manufacturing precision affects the distribution of dynamic transmission errors dramatically and appropriate TPMs are helpful to decrease the nominal value and the deviation of the vibration amplitudes.
Design of optimal Mamdani-type fuzzy controller for nonholonomic wheeled mobile robots
Directory of Open Access Journals (Sweden)
Davood Nazari Maryam Abadi
2015-01-01
Full Text Available In this paper, in order to cope with both parametric and nonparametric uncertainties in the robot model, an optimal Mamdani-type fuzzy logic controller is introduced for trajectory tracking of wheeled mobile robots (WMRs. The dynamic model of a nonholonomic mobile robot was implemented in the Matlab/Simulink environment. The parameters of input and output membership functions, and PID controller coefficients are optimized simultaneously by random inertia weight Particle Swarm Optimization (RNW-PSO. Simulation results show the system performance is desirable.
Statistical Methods for Fuzzy Data
Viertl, Reinhard
2011-01-01
Statistical data are not always precise numbers, or vectors, or categories. Real data are frequently what is called fuzzy. Examples where this fuzziness is obvious are quality of life data, environmental, biological, medical, sociological and economics data. Also the results of measurements can be best described by using fuzzy numbers and fuzzy vectors respectively. Statistical analysis methods have to be adapted for the analysis of fuzzy data. In this book, the foundations of the description of fuzzy data are explained, including methods on how to obtain the characterizing function of fuzzy m
Directory of Open Access Journals (Sweden)
Zoltan Erdei
2011-12-01
Full Text Available In this paper the authors present the usefulness of fuzzy logic in controlling engineering processes or applications. Although fuzzy logic does not represent a novelty for the scientific and engineering field, it enjoys a great appreciation from those involved in the two domains. The fact that fuzzy logic uses sentences kindred with the natural language make it easier to comprehend that a complex mathematical model required by the classic control theory. In MatLab software there are dedicated toolboxes to this subject that make the design of a fuzzy controller a facile one. In the paper design methods of a fuzzy controller are being presented both in Simulink and MatLab.
DEFF Research Database (Denmark)
Jantzen, Jan
1998-01-01
Design of a fuzzy controller requires more design decisions than usual, for example regarding rule base, inference engine, defuzzification, and data pre- and post processing. This tutorial paper identifies and describes the design choices related to single-loop fuzzy control, based on an internat...... on an international standard which is underway. The paper contains also a design approach, which uses a PID controller as a starting point. A design engineer can view the paper as an introduction to fuzzy controller design.......Design of a fuzzy controller requires more design decisions than usual, for example regarding rule base, inference engine, defuzzification, and data pre- and post processing. This tutorial paper identifies and describes the design choices related to single-loop fuzzy control, based...
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.
Directory of Open Access Journals (Sweden)
Chao Luo
Full Text Available A novel algebraic approach is proposed to study dynamics of asynchronous random Boolean networks where a random number of nodes can be updated at each time step (ARBNs. In this article, the logical equations of ARBNs are converted into the discrete-time linear representation and dynamical behaviors of systems are investigated. We provide a general formula of network transition matrices of ARBNs as well as a necessary and sufficient algebraic criterion to determine whether a group of given states compose an attractor of length[Formula: see text] in ARBNs. Consequently, algorithms are achieved to find all of the attractors and basins in ARBNs. Examples are showed to demonstrate the feasibility of the proposed scheme.
All-time dynamics of continuous-time random walks on complex networks.
Teimouri, Hamid; Kolomeisky, Anatoly B
2013-02-28
The concept of continuous-time random walks (CTRW) is a generalization of ordinary random walk models, and it is a powerful tool for investigating a broad spectrum of phenomena in natural, engineering, social, and economic sciences. Recently, several theoretical approaches have been developed that allowed to analyze explicitly dynamics of CTRW at all times, which is critically important for understanding mechanisms of underlying phenomena. However, theoretical analysis has been done mostly for systems with a simple geometry. Here we extend the original method based on generalized master equations to analyze all-time dynamics of CTRW models on complex networks. Specific calculations are performed for models on lattices with branches and for models on coupled parallel-chain lattices. Exact expressions for velocities and dispersions are obtained. Generalized fluctuations theorems for CTRW models on complex networks are discussed.
Czech Academy of Sciences Publication Activity Database
Maslowski, Bohdan; Schmalfuss, B.
2004-01-01
Roč. 22, č. 6 (2004), s. 1577-1607 ISSN 0736-2994 R&D Projects: GA ČR GA201/01/1197 Institutional research plan: CEZ:AV0Z1019905 Keywords : fractional Brownian motion * random dynamical systems * stationary solutions Subject RIV: BA - General Mathematics Impact factor: 0.290, year: 2004 http://www.tandfonline.com/doi/full/10.1081/ SAP -200029498
Hesitant intuitionistic fuzzy soft sets
Nazra, Admi; Syafruddin; Lestari, Riri; Catur Wicaksono, Gandung
2017-09-01
This paper aims to extend the hesitant fuzzy soft sets to hesitant intuitionistic fuzzy soft sets by merging the concept of hesitant intuitionistic fuzzy sets and soft sets. The authors define some operations on hesitant intuitionistic fuzzy sets, such as complement, union and intersection, and obtain related properties. The similar operations are defined on hesitant intuitionistic fuzzy soft sets, and also some properties such as assosiative and De Morgan’s laws are obtained.
Heyl, Markus; Vojta, Matthias
2015-09-01
In this work we formulate the nonequilibrium dynamical renormalization group (ndRG). The ndRG represents a general renormalization-group scheme for the analytical description of the real-time dynamics of complex quantum many-body systems. In particular, the ndRG incorporates time as an additional scale which turns out to be important for the description of the long-time dynamics. It can be applied to both translational-invariant and disordered systems. As a concrete application, we study the real-time dynamics after a quench between two quantum critical points of different universality classes. We achieve this by switching on weak disorder in a one-dimensional transverse-field Ising model initially prepared at its clean quantum critical point. By comparing to numerically exact simulations for large systems, we show that the ndRG is capable of analytically capturing the full crossover from weak to infinite randomness. We analytically study signatures of localization in both real space and Fock space.
Fuzzy methods and design; Fuzzy shuho to sekkei
Energy Technology Data Exchange (ETDEWEB)
Furuta, H. [Kwansei Gakuin Univ., Hyogo (Japan)
1996-03-05
This paper explains the application of the fuzzy theory to a design. A rational decision in design with only an objective logic requires conditions such that a set of selectable alternative plans and the results of executing them are known, and that a rule or a sequential relation exists to decide the order of preference of the alternative plans. In a case where the optimum anti-earthquake design was applied, for example, the seismic motion, subsoil and properties of materials or the like used to be treated stochastically and statistically as being of random nature. However, elements of uncertainty are actually involved other than the randomness, in consideration of cost effectiveness, safety and such. In the problems of anti-earthquake design by the fuzzy theory, the restrictive conditions are stipulated with a membership function respectively, such that the design earthquake motion is in a range larger than the maximum motion, and that the stress or displacement is each in the range smaller than the allowable stress or displacement of members; in addition, the weight is expressed to be the minimum as the objective function. 9 refs., 1 fig.
Interval-Valued Model Level Fuzzy Aggregation-Based Background Subtraction.
Chiranjeevi, Pojala; Sengupta, Somnath
2017-09-01
In a recent work, the effectiveness of neighborhood supported model level fuzzy aggregation was shown under dynamic background conditions. The multi-feature fuzzy aggregation used in that approach uses real fuzzy similarity values, and is robust for low and medium-scale dynamic background conditions such as swaying vegetation, sprinkling water, etc. The technique, however, exhibited some limitations under heavily dynamic background conditions, as features have high uncertainty under such noisy conditions and these uncertainties were not captured by real fuzzy similarity values. Our proposed algorithm is particularly focused toward improving the detection under heavy dynamic background conditions by modeling uncertainties in the data by interval-valued fuzzy set. In this paper, real-valued fuzzy aggregation has been extended to interval-valued fuzzy aggregation by considering uncertainties over real similarity values. We build up a procedure to calculate the uncertainty that varies for each feature, at each pixel, and at each time instant. We adaptively determine membership values at each pixel by the Gaussian of uncertainty value instead of fixed membership values used in recent fuzzy approaches, thereby, giving importance to a feature based on its uncertainty. Interval-valued Choquet integral is evaluated using interval similarity values and the membership values in order to calculate interval-valued fuzzy similarity between model and current. Adequate qualitative and quantitative studies are carried out to illustrate the effectiveness of the proposed method in mitigating heavily dynamic background situations as compared to state-of-the-art.
Musharbash, Eleonora; Nobile, Fabio
2018-02-01
In this paper we propose a method for the strong imposition of random Dirichlet boundary conditions in the Dynamical Low Rank (DLR) approximation of parabolic PDEs and, in particular, incompressible Navier Stokes equations. We show that the DLR variational principle can be set in the constrained manifold of all S rank random fields with a prescribed value on the boundary, expressed in low rank format, with rank smaller then S. We characterize the tangent space to the constrained manifold by means of a Dual Dynamically Orthogonal (Dual DO) formulation, in which the stochastic modes are kept orthonormal and the deterministic modes satisfy suitable boundary conditions, consistent with the original problem. The Dual DO formulation is also convenient to include the incompressibility constraint, when dealing with incompressible Navier Stokes equations. We show the performance of the proposed Dual DO approximation on two numerical test cases: the classical benchmark of a laminar flow around a cylinder with random inflow velocity, and a biomedical application for simulating blood flow in realistic carotid artery reconstructed from MRI data with random inflow conditions coming from Doppler measurements.
Local random configuration-tree theory for string repetition and facilitated dynamics of glass
Lam, Chi-Hang
2018-02-01
We derive a microscopic theory of glassy dynamics based on the transport of voids by micro-string motions, each of which involves particles arranged in a line hopping simultaneously displacing one another. Disorder is modeled by a random energy landscape quenched in the configuration space of distinguishable particles, but transient in the physical space as expected for glassy fluids. We study the evolution of local regions with m coupled voids. At a low temperature, energetically accessible local particle configurations can be organized into a random tree with nodes and edges denoting configurations and micro-string propagations respectively. Such trees defined in the configuration space naturally describe systems defined in two- or three-dimensional physical space. A micro-string propagation initiated by a void can facilitate similar motions by other voids via perturbing the random energy landscape, realizing path interactions between voids or equivalently string interactions. We obtain explicit expressions of the particle diffusion coefficient and a particle return probability. Under our approximation, as temperature decreases, random trees of energetically accessible configurations exhibit a sequence of percolation transitions in the configuration space, with local regions containing fewer coupled voids entering the non-percolating immobile phase first. Dynamics is dominated by coupled voids of an optimal group size, which increases as temperature decreases. Comparison with a distinguishable-particle lattice model (DPLM) of glass shows very good quantitative agreements using only two adjustable parameters related to typical energy fluctuations and the interaction range of the micro-strings.
Construction of fuzzy automata by fuzzy experiments
International Nuclear Information System (INIS)
Mironov, A.
1994-01-01
The solving the problem of canonical realization of partial reaction morphisms (PRM) for automata in toposes and fuzzy automata is addressed. This problem extends the optimal construction problem for finite deterministic automata by experiments. In the present paper the conception of canonical realization of PRM for automata in toposes is introduced and the sufficient conditions for the existence of canonical realizations for PRM in toposes are presented. As a consequence of this result the existence of canonical realizations for PRM in the category of fuzzy sets over arbitrary complete chain is proven
Random Vibration and Dynamic Analysis of a Planetary Gear Train in a Wind Turbine
Directory of Open Access Journals (Sweden)
Jianming Yang
2016-01-01
Full Text Available Premature failure of gearboxes is a big challenge facing the wind power industry. It highly depends on fully understanding the embedded dynamics to solve this problem. To this end, this paper investigates the random vibration and dynamics of planetary gear trains (PGTs in wind turbines under the excitation of wind turbulence. The turbulence is represented by the Von Karmon spectrum and implemented by passing white noise through a 2nd-order shaping filter. Then, extra equations are formed and added to the original governing equations of motion. With this augmented equation set, a recursive numerical algorithm based on stochastic Newmark scheme is applied to solve for the statistics of the responses starting from initial conditions. After simulation, the variances of the vibration responses and the dynamic meshing forces at gear meshes are obtained.
A Markov model for the temporal dynamics of balanced random networks of finite size
Lagzi, Fereshteh; Rotter, Stefan
2014-01-01
The balanced state of recurrent networks of excitatory and inhibitory spiking neurons is characterized by fluctuations of population activity about an attractive fixed point. Numerical simulations show that these dynamics are essentially nonlinear, and the intrinsic noise (self-generated fluctuations) in networks of finite size is state-dependent. Therefore, stochastic differential equations with additive noise of fixed amplitude cannot provide an adequate description of the stochastic dynamics. The noise model should, rather, result from a self-consistent description of the network dynamics. Here, we consider a two-state Markovian neuron model, where spikes correspond to transitions from the active state to the refractory state. Excitatory and inhibitory input to this neuron affects the transition rates between the two states. The corresponding nonlinear dependencies can be identified directly from numerical simulations of networks of leaky integrate-and-fire neurons, discretized at a time resolution in the sub-millisecond range. Deterministic mean-field equations, and a noise component that depends on the dynamic state of the network, are obtained from this model. The resulting stochastic model reflects the behavior observed in numerical simulations quite well, irrespective of the size of the network. In particular, a strong temporal correlation between the two populations, a hallmark of the balanced state in random recurrent networks, are well represented by our model. Numerical simulations of such networks show that a log-normal distribution of short-term spike counts is a property of balanced random networks with fixed in-degree that has not been considered before, and our model shares this statistical property. Furthermore, the reconstruction of the flow from simulated time series suggests that the mean-field dynamics of finite-size networks are essentially of Wilson-Cowan type. We expect that this novel nonlinear stochastic model of the interaction between
Neuro-Fuzzy Sensor Fault Diagnosis of an Induction Motor
Directory of Open Access Journals (Sweden)
M. L. Benloucif
2011-06-01
Full Text Available In this paper, a neuro-fuzzy fault diagnosis scheme is presented and its ability to detect and isolate sensor faults in an induction motor is assessed. This fault detection and isolation (FDI approach relies on a combination of neural modelling and fuzzy logic techniques which can deal effectively with nonlinear dynamics and uncertainties. It is based on a two step neural network procedure: a first neural network is used for residual generation and a second fuzzy neural network performs residual evaluation. Simulation results are given to demonstrate the efficiency of this FDI approach.
Approximations of Fuzzy Systems
Directory of Open Access Journals (Sweden)
Vinai K. Singh
2013-03-01
Full Text Available A fuzzy system can uniformly approximate any real continuous function on a compact domain to any degree of accuracy. Such results can be viewed as an existence of optimal fuzzy systems. Li-Xin Wang discussed a similar problem using Gaussian membership function and Stone-Weierstrass Theorem. He established that fuzzy systems, with product inference, centroid defuzzification and Gaussian functions are capable of approximating any real continuous function on a compact set to arbitrary accuracy. In this paper we study a similar approximation problem by using exponential membership functions
Reichhardt, C.; Olson Reichhardt, C. J.
2017-02-01
We review the depinning and nonequilibrium phases of collectively interacting particle systems driven over random or periodic substrates. This type of system is relevant to vortices in type-II superconductors, sliding charge density waves, electron crystals, colloids, stripe and pattern forming systems, and skyrmions, and could also have connections to jamming, glassy behaviors, and active matter. These systems are also ideal for exploring the broader issues of characterizing transient and steady state nonequilibrium flow phases as well as nonequilibrium phase transitions between distinct dynamical phases, analogous to phase transitions between different equilibrium states. We discuss the differences between elastic and plastic depinning on random substrates and the different types of nonequilibrium phases which are associated with specific features in the velocity-force curves, fluctuation spectra, scaling relations, and local or global particle ordering. We describe how these quantities can change depending on the dimension, anisotropy, disorder strength, and the presence of hysteresis. Within the moving phase we discuss how there can be a transition from a liquid-like state to dynamically ordered moving crystal, smectic, or nematic states. Systems with periodic or quasiperiodic substrates can have multiple nonequilibrium second or first order transitions in the moving state between chaotic and coherent phases, and can exhibit hysteresis. We also discuss systems with competing repulsive and attractive interactions, which undergo dynamical transitions into stripes and other complex morphologies when driven over random substrates. Throughout this work we highlight open issues and future directions such as absorbing phase transitions, nonequilibrium work relations, inertia, the role of non-dissipative dynamics such as Magnus effects, and how these results could be extended to the broader issues of plasticity in crystals, amorphous solids, and jamming phenomena.
Implementation of random set-up errors in Monte Carlo calculated dynamic IMRT treatment plans
International Nuclear Information System (INIS)
Stapleton, S; Zavgorodni, S; Popescu, I A; Beckham, W A
2005-01-01
The fluence-convolution method for incorporating random set-up errors (RSE) into the Monte Carlo treatment planning dose calculations was previously proposed by Beckham et al, and it was validated for open field radiotherapy treatments. This study confirms the applicability of the fluence-convolution method for dynamic intensity modulated radiotherapy (IMRT) dose calculations and evaluates the impact of set-up uncertainties on a clinical IMRT dose distribution. BEAMnrc and DOSXYZnrc codes were used for Monte Carlo calculations. A sliding window IMRT delivery was simulated using a dynamic multi-leaf collimator (DMLC) transport model developed by Keall et al. The dose distributions were benchmarked for dynamic IMRT fields using extended dose range (EDR) film, accumulating the dose from 16 subsequent fractions shifted randomly. Agreement of calculated and measured relative dose values was well within statistical uncertainty. A clinical seven field sliding window IMRT head and neck treatment was then simulated and the effects of random set-up errors (standard deviation of 2 mm) were evaluated. The dose-volume histograms calculated in the PTV with and without corrections for RSE showed only small differences indicating a reduction of the volume of high dose region due to set-up errors. As well, it showed that adequate coverage of the PTV was maintained when RSE was incorporated. Slice-by-slice comparison of the dose distributions revealed differences of up to 5.6%. The incorporation of set-up errors altered the position of the hot spot in the plan. This work demonstrated validity of implementation of the fluence-convolution method to dynamic IMRT Monte Carlo dose calculations. It also showed that accounting for the set-up errors could be essential for correct identification of the value and position of the hot spot
Implementation of random set-up errors in Monte Carlo calculated dynamic IMRT treatment plans
Stapleton, S.; Zavgorodni, S.; Popescu, I. A.; Beckham, W. A.
2005-02-01
The fluence-convolution method for incorporating random set-up errors (RSE) into the Monte Carlo treatment planning dose calculations was previously proposed by Beckham et al, and it was validated for open field radiotherapy treatments. This study confirms the applicability of the fluence-convolution method for dynamic intensity modulated radiotherapy (IMRT) dose calculations and evaluates the impact of set-up uncertainties on a clinical IMRT dose distribution. BEAMnrc and DOSXYZnrc codes were used for Monte Carlo calculations. A sliding window IMRT delivery was simulated using a dynamic multi-leaf collimator (DMLC) transport model developed by Keall et al. The dose distributions were benchmarked for dynamic IMRT fields using extended dose range (EDR) film, accumulating the dose from 16 subsequent fractions shifted randomly. Agreement of calculated and measured relative dose values was well within statistical uncertainty. A clinical seven field sliding window IMRT head and neck treatment was then simulated and the effects of random set-up errors (standard deviation of 2 mm) were evaluated. The dose-volume histograms calculated in the PTV with and without corrections for RSE showed only small differences indicating a reduction of the volume of high dose region due to set-up errors. As well, it showed that adequate coverage of the PTV was maintained when RSE was incorporated. Slice-by-slice comparison of the dose distributions revealed differences of up to 5.6%. The incorporation of set-up errors altered the position of the hot spot in the plan. This work demonstrated validity of implementation of the fluence-convolution method to dynamic IMRT Monte Carlo dose calculations. It also showed that accounting for the set-up errors could be essential for correct identification of the value and position of the hot spot.
Sokouti, Babak; Rezvan, Farshad; Dastmalchi, Siavoush
2015-08-01
G protein-coupled receptors (GPCRs) constitute the largest superfamily of integral membrane proteins (IMPs) and they tremendously contribute in the flow of information into cells. In this study, the random forest (RF) and the subtractive fuzzy c-means clustering (SBC) methods have been used to determine the importance of input variables and discriminate GPCRs from non-GPCRs using twenty amino acid and fifty pseudo amino acid compositions derived from GPCR sequences. The studied dataset was retrieved from the UniProt/SWISSPROT database and consists of 1000 GPCR and 1000 non-GPCR reviewed sequences. The top ranked RF-SBC-based model discriminates GPCRs and non-GPCRs successfully with the accuracy, sensitivity, specificity and Matthew's coefficient correlation (MCC) rates of 99.15%, 99.60%, 98.70% and 0.983%, respectively. These rates were obtained from averaged values of 5-fold cross validation using only twenty four out of fifty pseudo amino acid composition features. The results show that the proposed RF-SBC-based model outperforms other existing algorithms in terms of the evaluated performance criteria. The webserver for the proposed algorithm is available at http://brcinfo.shinyapps.io/GPCRIden.
Some Results on Fuzzy Soft Topological Spaces
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Cigdem Gunduz (Aras
2013-01-01
Full Text Available We introduce some important properties of fuzzy soft topological spaces. Furthermore, fuzzy soft continuous mapping, fuzzy soft open and fuzzy soft closed mappings, and fuzzy soft homeomorphism for fuzzy soft topological spaces are given and structural characteristics are discussed and studied.
NeCamp, Timothy; Kilbourne, Amy; Almirall, Daniel
2017-08-01
Cluster-level dynamic treatment regimens can be used to guide sequential treatment decision-making at the cluster level in order to improve outcomes at the individual or patient-level. In a cluster-level dynamic treatment regimen, the treatment is potentially adapted and re-adapted over time based on changes in the cluster that could be impacted by prior intervention, including aggregate measures of the individuals or patients that compose it. Cluster-randomized sequential multiple assignment randomized trials can be used to answer multiple open questions preventing scientists from developing high-quality cluster-level dynamic treatment regimens. In a cluster-randomized sequential multiple assignment randomized trial, sequential randomizations occur at the cluster level and outcomes are observed at the individual level. This manuscript makes two contributions to the design and analysis of cluster-randomized sequential multiple assignment randomized trials. First, a weighted least squares regression approach is proposed for comparing the mean of a patient-level outcome between the cluster-level dynamic treatment regimens embedded in a sequential multiple assignment randomized trial. The regression approach facilitates the use of baseline covariates which is often critical in the analysis of cluster-level trials. Second, sample size calculators are derived for two common cluster-randomized sequential multiple assignment randomized trial designs for use when the primary aim is a between-dynamic treatment regimen comparison of the mean of a continuous patient-level outcome. The methods are motivated by the Adaptive Implementation of Effective Programs Trial which is, to our knowledge, the first-ever cluster-randomized sequential multiple assignment randomized trial in psychiatry.
Synchronization of fractional fuzzy cellular neural networks with interactions
Ma, Weiyuan; Li, Changpin; Wu, Yujiang; Wu, Yongqing
2017-10-01
In this paper, we introduce fuzzy theory into the fractional cellular neural networks to dynamically enhance the coupling strength and propose a fractional fuzzy neural network model with interactions. Using the Lyapunov principle of fractional differential equations, we design the adaptive control schemes to realize the synchronization and obtain the synchronization criteria. Finally, we provide some numerical examples to show the effectiveness of our obtained results.
Extinction transition in stochastic population dynamics in a random, convective environment
International Nuclear Information System (INIS)
Juhász, Róbert
2013-01-01
Motivated by modeling the dynamics of a population living in a flowing medium where the environmental factors are random in space, we have studied an asymmetric variant of the one-dimensional contact process, where the quenched random reproduction rates are systematically greater in one direction than in the opposite one. The spatial disorder turns out to be a relevant perturbation but, according to results of Monte Carlo simulations, the behavior of the model at the extinction transition is different from the (infinite-randomness) critical behavior of the disordered symmetric contact process. Depending on the strength a of the asymmetry, the critical population drifts either with a finite velocity or with an asymptotically vanishing velocity as x(t) ∼ t μ(a) , where μ(a) < 1. Dynamical quantities are non-self-averaging at the extinction transition; the survival probability, for instance, shows multiscaling, i.e. it is characterized by a broad spectrum of effective exponents. For a sufficiently weak asymmetry, a Griffiths phase appears below the extinction transition, where the survival probability decays as a non-universal power of the time while, above the transition, another extended phase emerges, where the front of the population advances anomalously with a diffusion exponent continuously varying with the control parameter. (paper)
Bandemer, Hans
1992-01-01
Fuzzy data such as marks, scores, verbal evaluations, imprecise observations, experts' opinions and grey tone pictures, are quite common. In Fuzzy Data Analysis the authors collect their recent results providing the reader with ideas, approaches and methods for processing such data when looking for sub-structures in knowledge bases for an evaluation of functional relationship, e.g. in order to specify diagnostic or control systems. The modelling presented uses ideas from fuzzy set theory and the suggested methods solve problems usually tackled by data analysis if the data are real numbers. Fuzzy Data Analysis is self-contained and is addressed to mathematicians oriented towards applications and to practitioners in any field of application who have some background in mathematics and statistics.
Malhas, Othman Qasim
1993-10-01
The concept of “abacus logic” has recently been developed by the author (Malhas, n.d.). In this paper the relation of abacus logic to the concept of fuzziness is explored. It is shown that if a certain “regularity” condition is met, concepts from fuzzy set theory arise naturally within abacus logics. In particular it is shown that every abacus logic then has a “pre-Zadeh orthocomplementation”. It is also shown that it is then possible to associate a fuzzy set with every proposition of abacus logic and that the collection of all such sets satisfies natural conditions expected in systems of fuzzy logic. Finally, the relevance to quantum mechanics is discussed.
Proceedings of the Second Joint Technology Workshop on Neural Networks and Fuzzy Logic, volume 1
Lea, Robert N. (Editor); Villarreal, James (Editor)
1991-01-01
Documented here are papers presented at the Neural Networks and Fuzzy Logic Workshop sponsored by NASA and the University of Houston, Clear Lake. The workshop was held April 11 to 13 at the Johnson Space Flight Center. Technical topics addressed included adaptive systems, learning algorithms, network architectures, vision, robotics, neurobiological connections, speech recognition and synthesis, fuzzy set theory and application, control and dynamics processing, space applications, fuzzy logic and neural network computers, approximate reasoning, and multiobject decision making.
A neuro-fuzzy monitoring system. Application to flexible production systems.
Palluat, Nicolas; Racoceanu, Daniel; Zerhouni, Noureddine
2006-01-01
The multiple reconfiguration and the complexity of the modern production system lead to design intelligent monitoring aid systems. Accordingly, the use of neuro-fuzzy technics seems very promising. In this paper, we propose a new monitoring aid system composed by a dynamic neural network detection tool and a neuro-fuzzy diagnosis tool. Learning capabilities due to the neural structure permit us to update the monitoring aid system. The neuro-fuzzy network provides and abductive diagnosis. More...
Zhijia Chen; Yuanchang Zhu; Yanqiang Di; Shaochong Feng
2015-01-01
In IaaS (infrastructure as a service) cloud environment, users are provisioned with virtual machines (VMs). To allocate resources for users dynamically and effectively, accurate resource demands predicting is essential. For this purpose, this paper proposes a self-adaptive prediction method using ensemble model and subtractive-fuzzy clustering based fuzzy neural network (ESFCFNN). We analyze the characters of user preferences and demands. Then the architecture of the prediction model is const...
Random and systematic beam modulator errors in dynamic intensity modulated radiotherapy
International Nuclear Information System (INIS)
Parsai, Homayon; Cho, Paul S; Phillips, Mark H; Giansiracusa, Robert S; Axen, David
2003-01-01
This paper reports on the dosimetric effects of random and systematic modulator errors in delivery of dynamic intensity modulated beams. A sliding-widow type delivery that utilizes a combination of multileaf collimators (MLCs) and backup diaphragms was examined. Gaussian functions with standard deviations ranging from 0.5 to 1.5 mm were used to simulate random positioning errors. A clinical example involving a clival meningioma was chosen with optic chiasm and brain stem as limiting critical structures in the vicinity of the tumour. Dose calculations for different modulator fluctuations were performed, and a quantitative analysis was carried out based on cumulative and differential dose volume histograms for the gross target volume and surrounding critical structures. The study indicated that random modulator errors have a strong tendency to reduce minimum target dose and homogeneity. Furthermore, it was shown that random perturbation of both MLCs and backup diaphragms in the order of σ = 1 mm can lead to 5% errors in prescribed dose. In comparison, when MLCs or backup diaphragms alone was perturbed, the system was more robust and modulator errors of at least σ = 1.5 mm were required to cause dose discrepancies greater than 5%. For systematic perturbation, even errors in the order of ±0.5 mm were shown to result in significant dosimetric deviations
Random and systematic beam modulator errors in dynamic intensity modulated radiotherapy
Energy Technology Data Exchange (ETDEWEB)
Parsai, Homayon [Department of Radiation Oncology, University of Washington, Box 356043, Seattle, WA 98195 (United States); Cho, Paul S [Department of Radiation Oncology, University of Washington, Box 356043, Seattle, WA 98195 (United States); Phillips, Mark H [Department of Radiation Oncology, University of Washington, Box 356043, Seattle, WA 98195 (United States); Giansiracusa, Robert S [Department of Radiation Oncology, University of Washington, Box 356043, Seattle, WA 98195 (United States); Axen, David [Department of Physics and Astronomy, University of British Columbia, Vancouver, BC, V6T 1Z1 (Canada)
2003-05-07
This paper reports on the dosimetric effects of random and systematic modulator errors in delivery of dynamic intensity modulated beams. A sliding-widow type delivery that utilizes a combination of multileaf collimators (MLCs) and backup diaphragms was examined. Gaussian functions with standard deviations ranging from 0.5 to 1.5 mm were used to simulate random positioning errors. A clinical example involving a clival meningioma was chosen with optic chiasm and brain stem as limiting critical structures in the vicinity of the tumour. Dose calculations for different modulator fluctuations were performed, and a quantitative analysis was carried out based on cumulative and differential dose volume histograms for the gross target volume and surrounding critical structures. The study indicated that random modulator errors have a strong tendency to reduce minimum target dose and homogeneity. Furthermore, it was shown that random perturbation of both MLCs and backup diaphragms in the order of {sigma} = 1 mm can lead to 5% errors in prescribed dose. In comparison, when MLCs or backup diaphragms alone was perturbed, the system was more robust and modulator errors of at least {sigma} = 1.5 mm were required to cause dose discrepancies greater than 5%. For systematic perturbation, even errors in the order of {+-}0.5 mm were shown to result in significant dosimetric deviations.
Realistic Many-Body Quantum Systems vs. Full Random Matrices: Static and Dynamical Properties
Directory of Open Access Journals (Sweden)
Eduardo Jonathan Torres-Herrera
2016-10-01
Full Text Available We study the static and dynamical properties of isolated many-body quantum systems and compare them with the results for full random matrices. In doing so, we link concepts from quantum information theory with those from quantum chaos. In particular, we relate the von Neumann entanglement entropy with the Shannon information entropy and discuss their relevance for the analysis of the degree of complexity of the eigenstates, the behavior of the system at different time scales and the conditions for thermalization. A main advantage of full random matrices is that they enable the derivation of analytical expressions that agree extremely well with the numerics and provide bounds for realistic many-body quantum systems.
A new logistic dynamic particle swarm optimization algorithm based on random topology.
Ni, Qingjian; Deng, Jianming
2013-01-01
Population topology of particle swarm optimization (PSO) will directly affect the dissemination of optimal information during the evolutionary process and will have a significant impact on the performance of PSO. Classic static population topologies are usually used in PSO, such as fully connected topology, ring topology, star topology, and square topology. In this paper, the performance of PSO with the proposed random topologies is analyzed, and the relationship between population topology and the performance of PSO is also explored from the perspective of graph theory characteristics in population topologies. Further, in a relatively new PSO variant which named logistic dynamic particle optimization, an extensive simulation study is presented to discuss the effectiveness of the random topology and the design strategies of population topology. Finally, the experimental data are analyzed and discussed. And about the design and use of population topology on PSO, some useful conclusions are proposed which can provide a basis for further discussion and research.
A New Logistic Dynamic Particle Swarm Optimization Algorithm Based on Random Topology
Directory of Open Access Journals (Sweden)
Qingjian Ni
2013-01-01
Full Text Available Population topology of particle swarm optimization (PSO will directly affect the dissemination of optimal information during the evolutionary process and will have a significant impact on the performance of PSO. Classic static population topologies are usually used in PSO, such as fully connected topology, ring topology, star topology, and square topology. In this paper, the performance of PSO with the proposed random topologies is analyzed, and the relationship between population topology and the performance of PSO is also explored from the perspective of graph theory characteristics in population topologies. Further, in a relatively new PSO variant which named logistic dynamic particle optimization, an extensive simulation study is presented to discuss the effectiveness of the random topology and the design strategies of population topology. Finally, the experimental data are analyzed and discussed. And about the design and use of population topology on PSO, some useful conclusions are proposed which can provide a basis for further discussion and research.
Directory of Open Access Journals (Sweden)
Wen-Min Zhou
2013-01-01
Full Text Available This paper is concerned with the consensus problem of general linear discrete-time multiagent systems (MASs with random packet dropout that happens during information exchange between agents. The packet dropout phenomenon is characterized as being a Bernoulli random process. A distributed consensus protocol with weighted graph is proposed to address the packet dropout phenomenon. Through introducing a new disagreement vector, a new framework is established to solve the consensus problem. Based on the control theory, the perturbation argument, and the matrix theory, the necessary and sufficient condition for MASs to reach mean-square consensus is derived in terms of stability of an array of low-dimensional matrices. Moreover, mean-square consensusable conditions with regard to network topology and agent dynamic structure are also provided. Finally, the effectiveness of the theoretical results is demonstrated through an illustrative example.
Erhard, D; Hollander, den, WTF Frank; Maillard, G Gregory
2014-01-01
In this paper we study the parabolic Anderson equation $\\partial u(x,t)/\\partial t=\\kappa\\varDelta u(x,t)+\\xi(x,t)u(x,t)$, $x\\in\\mathbb{Z}^{d}$, $t\\geq0$, where the $u$-field and the $\\xi$-field are $\\mathbb{R}$-valued, $\\kappa\\in[0,\\infty)$ is the diffusion constant, and $\\varDelta $ is the discrete Laplacian. The $\\xi$-field plays the role of a dynamic random environment that drives the equation. The initial condition $u(x,0)=u_{0}(x)$, $x\\in\\mathbb{Z}^{d}$, is taken to be non-negative and ...
Random Linear Network Coding is Key to Data Survival in Highly Dynamic Distributed Storage
DEFF Research Database (Denmark)
Sipos, Marton A.; Fitzek, Frank; Roetter, Daniel Enrique Lucani
2015-01-01
as the number of available nodes varies greatly over time and keeping track of the system's state becomes unfeasible. As a consequence, conventional erasure correction approaches are ill-suited for maintaining data integrity. In this highly dynamic context, random linear network coding (RLNC) provides...... an interesting solution. Our goal is to characterize RLNC's guaranteed data integrity region in terms of the total number of storage devices that need to be available and stored data per device. We compare our fully distributed RLNC approach to centralized (genie aided) and fully decentralized replication...
Fuzzy Logic and Neuro-fuzzy Systems: A Systematic Introduction
Yue Wu; Biaobiao Zhang; Jiabin Lu; K. -L. Du
2011-01-01
Fuzzy logic is a rigorous mathematical field, and it provides an effective vehicle for modeling the uncertainty in human reasoning. In fuzzy logic, the knowledge of experts is modeled by linguistic rules represented in the form of IF-THEN logic. Like neural network models such as the multilayer perceptron (MLP) and the radial basis function network (RBFN), some fuzzy inference systems (FISs) have the capability of universal approximation. Fuzzy logic can be used in most areas where neural net...
Fuzzy variable linear programming with fuzzy technical coefficients
Directory of Open Access Journals (Sweden)
Sanwar Uddin Ahmad
2012-11-01
Full Text Available Normal 0 false false false EN-US X-NONE X-NONE Fuzzy linear programming is an application of fuzzy set theory in linear decision making problems and most of these problems are related to linear programming with fuzzy variables. In this paper an approximate but convenient method for solving these problems with fuzzy non-negative technical coefficient and without using the ranking functions, is proposed. With the help of numerical examples, the method is illustrated.
A random walk description of the heterogeneous glassy dynamics of attracting colloids
Energy Technology Data Exchange (ETDEWEB)
Chaudhuri, Pinaki; Berthier, Ludovic; Kob, Walter [Laboratoire des Colloides, Verres et Nanomateriaux, UMR 5587, Universite Montpellier II and CNRS, 34095 Montpellier (France); Gao Yongxiang; Kilfoil, Maria [Department of Physics, McGill University, Montreal, H3A 2T8 (Canada)], E-mail: berthier@lcvn.univ-montp2.fr
2008-06-18
We study the heterogeneous dynamics of attractive colloidal particles close to the gel transition using confocal microscopy experiments combined with a theoretical statistical analysis. We focus on single particle dynamics and show that the self-part of the van Hove distribution function is not the Gaussian expected for a Fickian process, but that it reflects instead the existence, at any given time, of colloids with widely different mobilities. Our confocal microscopy measurements can be described well by a simple analytical model based on a conventional continuous time random walk picture, as already found for several other glassy materials. In particular, the theory successfully accounts for the presence of broad tails in the van Hove distributions that exhibit exponential, rather than Gaussian, decay at large distance.
A New Class of Particle Filters for Random Dynamic Systems with Unknown Statistics
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Joaquín Míguez
2004-11-01
Full Text Available In recent years, particle filtering has become a powerful tool for tracking signals and time-varying parameters of random dynamic systems. These methods require a mathematical representation of the dynamics of the system evolution, together with assumptions of probabilistic models. In this paper, we present a new class of particle filtering methods that do not assume explicit mathematical forms of the probability distributions of the noise in the system. As a consequence, the proposed techniques are simpler, more robust, and more flexible than standard particle filters. Apart from the theoretical development of specific methods in the new class, we provide computer simulation results that demonstrate the performance of the algorithms in the problem of autonomous positioning of a vehicle in a 2-dimensional space.
Magnetic field line random walk in two-dimensional dynamical turbulence
Wang, J. F.; Qin, G.; Ma, Q. M.; Song, T.; Yuan, S. B.
2017-08-01
The field line random walk (FLRW) of magnetic turbulence is one of the important topics in plasma physics and astrophysics. In this article, by using the field line tracing method, the mean square displacement (MSD) of FLRW is calculated on all possible length scales for pure two-dimensional turbulence with the damping dynamical model. We demonstrate that in order to describe FLRW with the damping dynamical model, a new dimensionless quantity R is needed to be introduced. On different length scales, dimensionless MSD shows different relationships with the dimensionless quantity R. Although the temporal effect affects the MSD of FLRW and even changes regimes of FLRW, it does not affect the relationship between the dimensionless MSD and dimensionless quantity R on all possible length scales.
International Nuclear Information System (INIS)
Duan Ya-Fan; Xu Zhen; Qian Jun; Sun Jian-Fang; Jiang Bo-Nan; Hong Tao
2011-01-01
We numerically analyze the dynamic behavior of Bose—Einstein condensate (BEC) in a one-dimensional disordered potential before it completely loses spatial quantum coherence. We find that both the disorder statistics and the atom interactions produce remarkable effects on localization. We also find that the single phase of the initial condensate is broken into many small pieces while the system approaches localization, showing a counter-intuitive step-wise phase but not a thoroughly randomized phase. Although the condensates as a whole show less flow and expansion, the currents between adjacent phase steps retain strong time dependence. Thus we show explicitly that the localization of a finite size Bose—Einstein condensate is a dynamic equilibrium state. (general)
Ha, Seung-Yeal; Xiao, Qinghua; Zhang, Xiongtao
2018-04-01
We study the dynamics of infinitely many Cucker-Smale (C-S) flocking particles under the interplay of random communication and incompressible fluids. For the dynamics of an ensemble of flocking particles, we use the kinetic Cucker-Smale-Fokker-Planck (CS-FP) equation with a degenerate diffusion, whereas for the fluid component, we use the incompressible Navier-Stokes (N-S) equations. These two subsystems are coupled via the drag force. For this coupled model, we present the global existence of weak and strong solutions in Rd (d = 2 , 3). Under the extra regularity assumptions of the initial data, the unique solvability of strong solutions is also established in R2. In a large coupling regime and periodic spatial domain T2 : =R2 /Z2, we show that the velocities of C-S particles and fluids are asymptotically aligned to two constant velocities which may be different.
Applications of fuzzy theories to multi-objective system optimization
Rao, S. S.; Dhingra, A. K.
1991-01-01
Most of the computer aided design techniques developed so far deal with the optimization of a single objective function over the feasible design space. However, there often exist several engineering design problems which require a simultaneous consideration of several objective functions. This work presents several techniques of multiobjective optimization. In addition, a new formulation, based on fuzzy theories, is also introduced for the solution of multiobjective system optimization problems. The fuzzy formulation is useful in dealing with systems which are described imprecisely using fuzzy terms such as, 'sufficiently large', 'very strong', or 'satisfactory'. The proposed theory translates the imprecise linguistic statements and multiple objectives into equivalent crisp mathematical statements using fuzzy logic. The effectiveness of all the methodologies and theories presented is illustrated by formulating and solving two different engineering design problems. The first one involves the flight trajectory optimization and the main rotor design of helicopters. The second one is concerned with the integrated kinematic-dynamic synthesis of planar mechanisms. The use and effectiveness of nonlinear membership functions in fuzzy formulation is also demonstrated. The numerical results indicate that the fuzzy formulation could yield results which are qualitatively different from those provided by the crisp formulation. It is felt that the fuzzy formulation will handle real life design problems on a more rational basis.
Life and Death of Stationary Linear Response in Anomalous Continuous Time Random Walk Dynamics
Igor, Goychuk
2014-10-01
Linear theory of stationary response in systems at thermal equilibrium requires to find equilibrium correlation function of unperturbed responding system. Studies of the response of the systems exhibiting anomalously slow dynamics are often based on the continuous time random walk description (CTRW) with divergent mean waiting times. The bulk of the literature on anomalous response contains linear response functions like one by Cole-Cole calculated from such a CTRW theory and applied to systems at thermal equilibrium. Here we show within a fairly simple and general model that for the systems with divergent mean waiting times the stationary response at thermal equilibrium is absent, in accordance with some recent studies. The absence of such stationary response (or dying to zero non-stationary response in aging experiments) would confirm CTRW with divergent mean waiting times as underlying physical relaxation mechanism, but reject it otherwise. We show that the absence of stationary response is closely related to the breaking of ergodicity of the corresponding dynamical variable. As an important new result, we derive a generalized Cole-Cole response within ergodic CTRW dynamics with finite waiting time. Moreover, we provide a physically reasonable explanation of the origin and wide presence of 1/f noise in condensed matter for ergodic dynamics close to normal, rather than strongly deviating.
Directory of Open Access Journals (Sweden)
Dhvani N Shah
2014-10-01
Full Text Available Background: Balance is a key component of normal daily activities. Therefore, it is necessary to find various programs to improve balance. The core functions to maintain postural alignment and balance during functional activities. The purpose was to study the effects of the core stability training on dynamic balance in healthy, young adults. Methods: It was an interventional study, in which 60 healthy young adults were selected. They were randomly divided into two groups of 30 each, one being experimental group and other control group. Measurement of their height, weight, BMI and leg length was taken. Subjects in both the groups were assessed for core stability with pressure biofeedback unit (PBU and dynamic balance using Star Excursion Balance Test (SEBT pre and post intervention. Subjects in the experimental group underwent progressive core stability training program for six weeks (3days/week and control group was refrained from any type of structured training program. Results: There was statistically significant improvement in core stability and dynamic balance of the experimental group after six weeks of intervention. Conclusion: It is concluded that core stability training of six weeks duration is effective in improving dynamic balance in healthy, young adults.
From Fuzzy Logic to Fuzzy Mathematics: A Methodological Manifesto
Czech Academy of Sciences Publication Activity Database
Běhounek, Libor; Cintula, Petr
2006-01-01
Roč. 157, č. 5 (2006), s. 642-646 ISSN 0165-0114 R&D Projects: GA AV ČR KJB100300502 Institutional research plan: CEZ:AV0Z10300504 Keywords : non-classical logics * formal fuzzy logic * formal fuzzy mathematics * high-order fuzzy logic Subject RIV: BA - General Mathematics Impact factor: 1.181, year: 2006
Fuzzy logic an introductory course for engineering students
Trillas, Enric
2015-01-01
This book introduces readers to fundamental concepts in fuzzy logic. It describes the necessary theoretical background and a number of basic mathematical models. Moreover, it makes them familiar with fuzzy control, an important topic in the engineering field. The book offers an unconventional introductory textbook on fuzzy logic, presenting theory together with examples and not always following the typical mathematical style of theorem-corollaries. Primarily intended to support engineers during their university studies, and to spark their curiosity about fuzzy logic and its applications, the book is also suitable for self-study, providing a valuable resource for engineers and professionals who deal with imprecision and non-random uncertainty in real-world applications.
Prediction of conductivity by adaptive neuro-fuzzy model.
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S Akbarzadeh
Full Text Available Electrochemical impedance spectroscopy (EIS is a key method for the characterizing the ionic and electronic conductivity of materials. One of the requirements of this technique is a model to forecast conductivity in preliminary experiments. The aim of this paper is to examine the prediction of conductivity by neuro-fuzzy inference with basic experimental factors such as temperature, frequency, thickness of the film and weight percentage of salt. In order to provide the optimal sets of fuzzy logic rule bases, the grid partition fuzzy inference method was applied. The validation of the model was tested by four random data sets. To evaluate the validity of the model, eleven statistical features were examined. Statistical analysis of the results clearly shows that modeling with an adaptive neuro-fuzzy is powerful enough for the prediction of conductivity.
On fuzzy multiset regular languages
Directory of Open Access Journals (Sweden)
B. K. Sharma
2017-03-01
Full Text Available The purpose of present work is to study some algebraic aspect of fuzzy multiset regular languages. In between, we show the equivalence of multiset regular language and fuzzy multiset regular language. Finally, we introduce the concept of pumping lemma for fuzzy multiset regular languages, which we use to establish a necessary and sufficient condition for a fuzzy multiset language to be non-constant.
Shapley's value for fuzzy games
Directory of Open Access Journals (Sweden)
Raúl Alvarado Sibaja
2009-02-01
Full Text Available This is the continuation of a previous article titled "Fuzzy Games", where I defined a new type of games based on the Multilinear extensions f, of characteristic functions and most of standard theorems for cooperative games also hold for this new type of games: The fuzzy games. Now we give some other properties and the extension of the definition of Shapley¨s Value for Fuzzy Games Keywords: game theory, fuzzy sets, multiattribute decisions.
Fuzzy HRRN CPU Scheduling Algorithm
Bashir Alam; R. Biswas; M. Alam
2011-01-01
There are several scheduling algorithms like FCFS, SRTN, RR, priority etc. Scheduling decisions of these algorithms are based on parameters which are assumed to be crisp. However, in many circumstances these parameters are vague. The vagueness of these parameters suggests that scheduler should use fuzzy technique in scheduling the jobs. In this paper we have proposed a novel CPU scheduling algorithm Fuzzy HRRN that incorporates fuzziness in basic HRRN using fuzzy Technique FIS.
Deng, Zhaohong; Choi, Kup-Sze; Cao, Longbing; Wang, Shitong
2014-04-01
A challenge in modeling type-2 fuzzy logic systems is the development of efficient learning algorithms to cope with the ever increasing size of real-world data sets. In this paper, the extreme learning strategy is introduced to develop a fast training algorithm for interval type-2 Takagi-Sugeno-Kang fuzzy logic systems. The proposed algorithm, called type-2 fuzzy extreme learning algorithm (T2FELA), has two distinctive characteristics. First, the parameters of the antecedents are randomly generated and parameters of the consequents are obtained by a fast learning method according to the extreme learning mechanism. In addition, because the obtained parameters are optimal in the sense of minimizing the norm, the resulting fuzzy systems exhibit better generalization performance. The experimental results clearly demonstrate that the training speed of the proposed T2FELA algorithm is superior to that of the existing state-of-the-art algorithms. The proposed algorithm also shows competitive performance in generalization abilities.
T-->0 mean-field population dynamics approach for the random 3-satisfiability problem.
Zhou, Haijun
2008-06-01
During the past decade, phase-transition phenomena in the random 3-satisfiability ( 3 -SAT) problem has been intensively studied by statistical physics methods. In this work, we study the random 3 -SAT problem by the mean-field first-step replica-symmetry-broken cavity theory at the limit of temperature T-->0 . The reweighting parameter y of the cavity theory is allowed to approach infinity together with the inverse temperature beta with fixed ratio r=ybeta . Focusing on the system's space of satisfiable configurations, we carry out extensive population dynamics simulations using the technique of importance sampling, and we obtain the entropy density s(r) and complexity Sigma(r) of zero-energy clusters at different r values. We demonstrate that the population dynamics may reach different fixed points with different types of initial conditions. By knowing the trends of s(r) and Sigma(r) with r , we can judge whether a certain type of initial condition is appropriate at a given r value. This work complements and confirms the results of several other very recent theoretical studies.
Effective speed and agility conditioning methodology for random intermittent dynamic type sports.
Bloomfield, Jonathan; Polman, Remco; O'Donoghue, Peter; McNaughton, Lars
2007-11-01
Different coaching methods are often used to improve performance. This study compared the effectiveness of 2 methodologies for speed and agility conditioning for random, intermittent, and dynamic activity sports (e.g., soccer, tennis, hockey, basketball, rugby, and netball) and the necessity for specialized coaching equipment. Two groups were delivered either a programmed method (PC) or a random method (RC) of conditioning with a third group receiving no conditioning (NC). PC participants used the speed, agility, quickness (SAQ) conditioning method, and RC participants played supervised small-sided soccer games. PC was also subdivided into 2 groups where participants either used specialized SAQ equipment or no equipment. A total of 46 (25 males and 21 females) untrained participants received (mean +/- SD) 12.2 +/- 2.1 hours of physical conditioning over 6 weeks between a battery of speed and agility parameter field tests. Two-way analysis of variance results indicated that both conditioning groups showed a significant decrease in body mass and body mass index, although PC achieved significantly greater improvements on acceleration, deceleration, leg power, dynamic balance, and the overall summation of % increases when compared to RC and NC (p speed and agility parameters; however, this study found that specialized SAQ equipment was not a requirement to observe significant improvements. Further research is required to establish whether these benefits transfer to sport-specific tasks as well as to the underlying mechanisms resulting in improved performance.
Compactness in fuzzy function spaces
African Journals Online (AJOL)
In [3] we defined a notion of compactness in FCS, the category of fuzzy convergence spaces as defined by Lowen/Lowen/Wuyts [8]. In their paper the latter also introduced a fuzzy convergence structure c-lim for fuzzy function spaces thus proving that FCS is a topological quasitopos. In this paper we start the investigation of ...
Clustering algorithms for fuzzy rules decomposition
Salgado, Paulo; Igrejas, Getúlio
2007-01-01
This paper presents the development, testing and evaluation of generalized Possibilistic fuzzy c-means (FCM) algorithms applied to fuzzy sets. Clustering is formulated as a constrained minimization problem, whose solution depends on the constraints imposed on the membership function of the cluster and on the relevance measure of the fuzzy rules. This fuzzy clustering of fuzzy rules leads to a fuzzy partition of the fuzzy rules, one for each cluster, which corresp...
Adaptive neuro-fuzzy controller of switched reluctance motor
Directory of Open Access Journals (Sweden)
Tahour Ahmed
2007-01-01
Full Text Available This paper presents an application of adaptive neuro-fuzzy (ANFIS control for switched reluctance motor (SRM speed. The ANFIS has the advantages of expert knowledge of the fuzzy inference system and the learning capability of neural networks. An adaptive neuro-fuzzy controller of the motor speed is then designed and simulated. Digital simulation results show that the designed ANFIS speed controller realizes a good dynamic behaviour of the motor, a perfect speed tracking with no overshoot and a good rejection of impact loads disturbance. The results of applying the adaptive neuro-fuzzy controller to a SRM give better performance and high robustness than those obtained by the application of a conventional controller (PI.
Adaptive fuzzy control for a simulation of hydraulic analogy of a nuclear reactor
International Nuclear Information System (INIS)
Ruan, D.; Li, X.; Eynde, G. van den
2000-01-01
In the framework of the on-going R and D project on fuzzy control applications to the Belgian Reactor 1 (BR1) at the Belgian Nuclear Research Centre (SCK-CEN), we have constructed a real fuzzy-logic-control demo model. The demo model is suitable for us to test and compare some new algorithms of fuzzy control and intelligent systems, which is advantageous because it is always difficult and time consuming, due to safety aspects, to do all experiments in a real nuclear environment. In this chapter, we first report briefly on the construction of the demo model, and then introduce the results of a fuzzy control, a proportional-integral-derivative (PID) control and an advanced fuzzy control, in which the advanced fuzzy control is a fuzzy control with an adaptive function that can self-regulate the fuzzy control rules. Afterwards, we present a comparative study of those three methods. The results have shown that fuzzy control has more advantages in terms of flexibility, robustness, and easily updated facilities with respect to the PID control of the demo model, but that PID control has much higher regulation resolution due to its integration terms. The adaptive fuzzy control can dynamically adjust the rule base, therefore it is more robust and suitable to those very uncertain occasions. (orig.)
International Nuclear Information System (INIS)
Cheng, J-C; Rahmim, Arman; Blinder, Stephan; Camborde, Marie-Laure; Raywood, Kelvin; Sossi, Vesna
2007-01-01
We describe an ordinary Poisson list-mode expectation maximization (OP-LMEM) algorithm with a sinogram-based scatter correction method based on the single scatter simulation (SSS) technique and a random correction method based on the variance-reduced delayed-coincidence technique. We also describe a practical approximate scatter and random-estimation approach for dynamic PET studies based on a time-averaged scatter and random estimate followed by scaling according to the global numbers of true coincidences and randoms for each temporal frame. The quantitative accuracy achieved using OP-LMEM was compared to that obtained using the histogram-mode 3D ordinary Poisson ordered subset expectation maximization (3D-OP) algorithm with similar scatter and random correction methods, and they showed excellent agreement. The accuracy of the approximated scatter and random estimates was tested by comparing time activity curves (TACs) as well as the spatial scatter distribution from dynamic non-human primate studies obtained from the conventional (frame-based) approach and those obtained from the approximate approach. An excellent agreement was found, and the time required for the calculation of scatter and random estimates in the dynamic studies became much less dependent on the number of frames (we achieved a nearly four times faster performance on the scatter and random estimates by applying the proposed method). The precision of the scatter fraction was also demonstrated for the conventional and the approximate approach using phantom studies
A Spectral Approach for Quenched Limit Theorems for Random Expanding Dynamical Systems
Dragičević, D.; Froyland, G.; González-Tokman, C.; Vaienti, S.
2018-01-01
We prove quenched versions of (i) a large deviations principle (LDP), (ii) a central limit theorem (CLT), and (iii) a local central limit theorem for non-autonomous dynamical systems. A key advance is the extension of the spectral method, commonly used in limit laws for deterministic maps, to the general random setting. We achieve this via multiplicative ergodic theory and the development of a general framework to control the regularity of Lyapunov exponents of twisted transfer operator cocycles with respect to a twist parameter. While some versions of the LDP and CLT have previously been proved with other techniques, the local central limit theorem is, to our knowledge, a completely new result, and one that demonstrates the strength of our method. Applications include non-autonomous (piecewise) expanding maps, defined by random compositions of the form {T_{σ^{n-1} ω} circ\\cdotscirc T_{σω}circ T_ω} . An important aspect of our results is that we only assume ergodicity and invertibility of the random driving {σ:Ω\\toΩ} ; in particular no expansivity or mixing properties are required.
Modeling Invasion Dynamics with Spatial Random-Fitness Due to Micro-Environment.
Manem, V S K; Kaveh, K; Kohandel, M; Sivaloganathan, S
2015-01-01
Numerous experimental studies have demonstrated that the microenvironment is a key regulator influencing the proliferative and migrative potentials of species. Spatial and temporal disturbances lead to adverse and hazardous microenvironments for cellular systems that is reflected in the phenotypic heterogeneity within the system. In this paper, we study the effect of microenvironment on the invasive capability of species, or mutants, on structured grids (in particular, square lattices) under the influence of site-dependent random proliferation in addition to a migration potential. We discuss both continuous and discrete fitness distributions. Our results suggest that the invasion probability is negatively correlated with the variance of fitness distribution of mutants (for both advantageous and neutral mutants) in the absence of migration of both types of cells. A similar behaviour is observed even in the presence of a random fitness distribution of host cells in the system with neutral fitness rate. In the case of a bimodal distribution, we observe zero invasion probability until the system reaches a (specific) proportion of advantageous phenotypes. Also, we find that the migrative potential amplifies the invasion probability as the variance of fitness of mutants increases in the system, which is the exact opposite in the absence of migration. Our computational framework captures the harsh microenvironmental conditions through quenched random fitness distributions and migration of cells, and our analysis shows that they play an important role in the invasion dynamics of several biological systems such as bacterial micro-habitats, epithelial dysplasia, and metastasis. We believe that our results may lead to more experimental studies, which can in turn provide further insights into the role and impact of heterogeneous environments on invasion dynamics.
Directory of Open Access Journals (Sweden)
Amiya Kumar Dash
2013-01-01
Full Text Available This paper discusses the multicrack detection of structure using fuzzy Gaussian technique. The vibration parameters derived from the numerical methods of the cracked cantilever beam are used to set several fuzzy rules for designing the fuzzy controller used to predict the crack location and depth. Relative crack locations and relative crack depths are the output parameters from the fuzzy inference system. The method proposed in the current analysis is used to evaluate the dynamic response of cracked cantilever beam. The results of the proposed method are in good agreement with the results obtained from the developed experimental setup.
DEFF Research Database (Denmark)
Li, Yan-Fu; Ding, Yi; Zio, Enrico
2014-01-01
. In this work, we extend the traditional universal generating function (UGF) approach for multi-state system (MSS) availability and reliability assessment to account for both aleatory and epistemic uncertainties. First, a theoretical extension, named hybrid UGF (HUGF), is made to introduce the use of random...... allow quantifying different levels of imprecision in system availability and reliability estimation. The HUGF approach is demonstrated with a numerical example, and applied to study a distributed generation system, with a comparison to the widely used Monte Carlo simulation method....
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...
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 of the ru...
An adaptive fuzzy neural network for MIMO system model approximation in high-dimensional spaces.
Chak, C K; Feng, G; Ma, J
1998-01-01
An adaptive fuzzy system implemented within the framework of neural network is proposed. The integration of the fuzzy system into a neural network enables the new fuzzy system to have learning and adaptive capabilities. The proposed fuzzy neural network can locate its rules and optimize its membership functions by competitive learning, Kalman filter algorithm and extended Kalman filter algorithms. A key feature of the new architecture is that a high dimensional fuzzy system can be implemented with fewer number of rules than the Takagi-Sugeno fuzzy systems. A number of simulations are presented to demonstrate the performance of the proposed system including modeling nonlinear function, operator's control of chemical plant, stock prices and bioreactor (multioutput dynamical system).
Chaotic System Identification Based on a Fuzzy Wiener Model with Particle Swarm Optimization
International Nuclear Information System (INIS)
Yong, Li; Ying-Gan, Tang
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
A neural fuzzy controller learning by fuzzy error propagation
Nauck, Detlef; Kruse, Rudolf
1992-01-01
In this paper, we describe a procedure to integrate techniques for the adaptation of membership functions in a linguistic variable based fuzzy control environment by using neural network learning principles. This is an extension to our work. We solve this problem by defining a fuzzy error that is propagated back through the architecture of our fuzzy controller. According to this fuzzy error and the strength of its antecedent each fuzzy rule determines its amount of error. Depending on the current state of the controlled system and the control action derived from the conclusion, each rule tunes the membership functions of its antecedent and its conclusion. By this we get an unsupervised learning technique that enables a fuzzy controller to adapt to a control task by knowing just about the global state and the fuzzy error.
International Nuclear Information System (INIS)
Da-Zhong, Ma; Hua-Guang, Zhang; Zhan-Shan, Wang; Jian, Feng
2010-01-01
In this paper the fault tolerant synchronization of two chaotic systems based on fuzzy model and sample data is investigated. The problem of fault tolerant synchronization is formulated to study the global asymptotical stability of the error system with the fuzzy sampled-data controller which contains a state feedback controller and a fault compensator. The synchronization can be achieved no matter whether the fault occurs or not. To investigate the stability of the error system and facilitate the design of the fuzzy sampled-data controller, a Takagi–Sugeno (T–S) fuzzy model is employed to represent the chaotic system dynamics. To acquire good performance and produce a less conservative analysis result, a new parameter-dependent Lyapunov–Krasovksii functional and a relaxed stabilization technique are considered. The stability conditions based on linear matrix inequality are obtained to achieve the fault tolerant synchronization of the chaotic systems. Finally, a numerical simulation is shown to verify the results. (general)
T-S Fuzzy Modelling and H∞ Attitude Control for Hypersonic Gliding Vehicles
Directory of Open Access Journals (Sweden)
Weidong Zhang
2017-01-01
Full Text Available This paper addresses the T-S fuzzy modelling and H∞ attitude control in three channels for hypersonic gliding vehicles (HGVs. First, the control-oriented affine nonlinear model has been established which is transformed from the reentry dynamics. Then, based on Taylor’s expansion approach and the fuzzy linearization approach, the homogeneous T-S local modelling technique for HGVs is proposed. Given the approximation accuracy and controller design complexity, appropriate fuzzy premise variables and operating points of interest are selected to construct the T-S homogeneous submodels. With so-called fuzzy blending, the original plant is transformed into the overall T-S fuzzy model with disturbance. By utilizing Lyapunov functional approach, a state feedback fuzzy controller has been designed based on relaxed linear matrix inequality (LMI conditions to stable the original plants with a prescribed H∞ performance of disturbance. Finally, numerical simulations are performed to demonstrate the effectiveness of the proposed H∞ T-S fuzzy controller for the original attitude dynamics; the superiority of the designed T-S fuzzy controller compared with other local controllers based on the constructed fuzzy model is shown as well.
Activity patterns on random scale-free networks: global dynamics arising from local majority rules
Zhou, Haijun; Lipowsky, Reinhard
2007-01-01
Activity or spin patterns on a random scale-free network are studied using mean field analysis and computer simulations. These activity patterns evolve in time according to local majority rule dynamics which is implemented using (i) parallel or synchronous updating and (ii) random sequential or asynchronous updating. Our mean field calculations predict that the relaxation processes of disordered activity patterns become much more efficient as the scaling exponent γ of the scale-free degree distribution changes from γ > 5/2 to γ 5/2, the corresponding decay times increase as ln(N) with increasing network size N whereas they are independent of N for γ networks: (A) multi-networks as generated by the configuration method, which typically leads to many self-connections and multiple edges, and (B) simple networks without self-connections and multiple edges. We find that the mean field predictions are confirmed (i) for random sequential updating of multi-networks and (ii) for both parallel and random sequential updating of simple networks with γ = 2.25 and 2.6. For γ = 2.4, the data for the simple networks seem to be consistent with mean field theory as well, whereas we cannot draw a definite conclusion from the simulation data for the multi-networks. The latter difficulty can be understood in terms of an effective scaling exponent γeff = γeff(γ, N) for multi-networks. This effective exponent is determined by removing all self-connections and multiple edges; it satisfies γeff >= γ and decreases towards γ with increasing network size N. For γ = 2.4, we find γeff gtrsim 5/2 up to N = 217.
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.
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.
The foundations of fuzzy control
Lewis, Harold W
1997-01-01
Harold Lewis applied a cross-disciplinary approach in his highly accessible discussion of fuzzy control concepts. With the aid of fifty-seven illustrations, he thoroughly presents a unique mathematical formalism to explain the workings of the fuzzy inference engine and a novel test plant used in the research. Additionally, the text posits a new viewpoint on why fuzzy control is more popular in some countries than in others. A direct and original view of Japanese thinking on fuzzy control methods, based on the author's personal knowledge of - and association with - Japanese fuzzy research, is also included.
Udupa, Jayaram K.; Odhner, Dewey; Falcao, Alexandre X.; Ciesielski, Krzysztof C.; Miranda, Paulo A. V.; Vaideeswaran, Pavithra; Mishra, Shipra; Grevera, George J.; Saboury, Babak; Torigian, Drew A.
2011-03-01
To make Quantitative Radiology (QR) a reality in routine clinical practice, computerized automatic anatomy recognition (AAR) becomes essential. As part of this larger goal, we present in this paper a novel fuzzy strategy for building bodywide group-wise anatomic models. They have the potential to handle uncertainties and variability in anatomy naturally and to be integrated with the fuzzy connectedness framework for image segmentation. Our approach is to build a family of models, called the Virtual Quantitative Human, representing normal adult subjects at a chosen resolution of the population variables (gender, age). Models are represented hierarchically, the descendents representing organs contained in parent organs. Based on an index of fuzziness of the models, 32 thorax data sets, and 10 organs defined in them, we found that the hierarchical approach to modeling can effectively handle the non-linear relationships in position, scale, and orientation that exist among organs in different patients.
A transductive neuro-fuzzy controller: application to a drilling process.
Gajate, Agustín; Haber, Rodolfo E; Vega, Pastora I; Alique, José R
2010-07-01
Recently, new neuro-fuzzy inference algorithms have been developed to deal with the time-varying behavior and uncertainty of many complex systems. This paper presents the design and application of a novel transductive neuro-fuzzy inference method to control force in a high-performance drilling process. The main goal is to study, analyze, and verify the behavior of a transductive neuro-fuzzy inference system for controlling this complex process, specifically addressing the dynamic modeling, computational efficiency, and viability of the real-time application of this algorithm as well as assessing the topology of the neuro-fuzzy system (e.g., number of clusters, number of rules). A transductive reasoning method is used to create local neuro-fuzzy models for each input/output data set in a case study. The direct and inverse dynamics of a complex process are modeled using this strategy. The synergies among fuzzy, neural, and transductive strategies are then exploited to deal with process complexity and uncertainty through the application of the neuro-fuzzy models within an internal model control (IMC) scheme. A comparative study is made of the adaptive neuro-fuzzy inference system (ANFIS) and the suggested method inspired in a transductive neuro-fuzzy inference strategy. The two neuro-fuzzy strategies are evaluated in a real drilling force control problem. The experimental results demonstrated that the transductive neuro-fuzzy control system provides a good transient response (without overshoot) and better error-based performance indices than the ANFIS-based control system. In particular, the IMC system based on a transductive neuro-fuzzy inference approach reduces the influence of the increase in cutting force that occurs as the drill depth increases, reducing the risk of rapid tool wear and catastrophic tool breakage.
Randomized dynamical decoupling strategies and improved one-way key rates for quantum cryptography
International Nuclear Information System (INIS)
Kern, Oliver
2009-01-01
The present thesis deals with various methods of quantum error correction. It is divided into two parts. In the first part, dynamical decoupling methods are considered which have the task of suppressing the influence of residual imperfections in a quantum memory. Such imperfections might be given by couplings between the finite dimensional quantum systems (qudits) constituting the quantum memory, for instance. The suppression is achieved by altering the dynamics of an imperfect quantum memory with the help of a sequence of local unitary operations applied to the qudits. Whereas up to now the operations of such decoupling sequences have been constructed in a deterministic fashion, strategies are developed in this thesis which construct the operations by random selection from a suitable set. Formulas are derived which estimate the average performance of such strategies. As it turns out, randomized decoupling strategies offer advantages and disadvantages over deterministic ones. It is possible to benefit from the advantages of both kind of strategies by designing combined strategies. Furthermore, it is investigated if and how the discussed decoupling strategies can be employed to protect a quantum computation running on the quantum memory. It is shown that a purely randomized decoupling strategy may be used by applying the decoupling operations and adjusted gates of the quantum algorithm in an alternating fashion. Again this method can be enhanced by the means of deterministic methods in order to obtain a combined decoupling method for quantum computations analogously to the combining strategies for quantum memories. The second part of the thesis deals with quantum error-correcting codes and protocols for quantum key distribution. The focus is on the BB84 and the 6-state protocol making use of only one-way communication during the error correction and privacy amplification steps. It is shown that by adding additional errors to the preliminary key (a process called
Randomized dynamical decoupling strategies and improved one-way key rates for quantum cryptography
Energy Technology Data Exchange (ETDEWEB)
Kern, Oliver
2009-05-25
The present thesis deals with various methods of quantum error correction. It is divided into two parts. In the first part, dynamical decoupling methods are considered which have the task of suppressing the influence of residual imperfections in a quantum memory. Such imperfections might be given by couplings between the finite dimensional quantum systems (qudits) constituting the quantum memory, for instance. The suppression is achieved by altering the dynamics of an imperfect quantum memory with the help of a sequence of local unitary operations applied to the qudits. Whereas up to now the operations of such decoupling sequences have been constructed in a deterministic fashion, strategies are developed in this thesis which construct the operations by random selection from a suitable set. Formulas are derived which estimate the average performance of such strategies. As it turns out, randomized decoupling strategies offer advantages and disadvantages over deterministic ones. It is possible to benefit from the advantages of both kind of strategies by designing combined strategies. Furthermore, it is investigated if and how the discussed decoupling strategies can be employed to protect a quantum computation running on the quantum memory. It is shown that a purely randomized decoupling strategy may be used by applying the decoupling operations and adjusted gates of the quantum algorithm in an alternating fashion. Again this method can be enhanced by the means of deterministic methods in order to obtain a combined decoupling method for quantum computations analogously to the combining strategies for quantum memories. The second part of the thesis deals with quantum error-correcting codes and protocols for quantum key distribution. The focus is on the BB84 and the 6-state protocol making use of only one-way communication during the error correction and privacy amplification steps. It is shown that by adding additional errors to the preliminary key (a process called
Dynamical properties of the S =1/2 random Heisenberg chain
Shu, Yu-Rong; Dupont, Maxime; Yao, Dao-Xin; Capponi, Sylvain; Sandvik, Anders W.
2018-03-01
We study dynamical properties at finite temperature (T ) of Heisenberg spin chains with random antiferromagnetic exchange couplings, which realize the random singlet phase in the low-energy limit, using three complementary numerical methods: exact diagonalization, matrix-product-state algorithms, and stochastic analytic continuation of quantum Monte Carlo results in imaginary time. Specifically, we investigate the dynamic spin structure factor S (q ,ω ) and its ω →0 limit, which are closely related to inelastic neutron scattering and nuclear magnetic resonance (NMR) experiments (through the spin-lattice relaxation rate 1 /T1 ). Our study reveals a continuous narrow band of low-energy excitations in S (q ,ω ) , extending throughout the q space, instead of being restricted to q ≈0 and q ≈π as found in the uniform system. Close to q =π , the scaling properties of these excitations are well captured by the random-singlet theory, but disagreements also exist with some aspects of the predicted q dependence further away from q =π . Furthermore we also find spin diffusion effects close to q =0 that are not contained within the random-singlet theory but give non-negligible contributions to the mean 1 /T1 . To compare with NMR experiments, we consider the distribution of the local relaxation rates 1 /T1 . We show that the local 1 /T1 values are broadly distributed, approximately according to a stretched exponential. The mean 1 /T1 first decreases with T , but below a crossover temperature it starts to increase and likely diverges in the limit of a small nuclear resonance frequency ω0. Although a similar divergent behavior has been predicted and experimentally observed for the static uniform susceptibility, this divergent behavior of the mean 1 /T1 has never been experimentally observed. Indeed, we show that the divergence of the mean 1 /T1 is due to rare events in the disordered chains and is concealed in experiments, where the typical 1 /T1 value is accessed.
The first order fuzzy predicate logic (I)
International Nuclear Information System (INIS)
Sheng, Y.M.
1986-01-01
Some analysis tools of fuzzy measures, Sugeno's integrals, etc. are introduced into the semantic of the first order predicate logic to explain the concept of fuzzy quantifiers. The truth value of a fuzzy quantification proposition is represented by Sugeno's integral. With this framework, several important notions of formation rules, fuzzy valutions and fuzzy validity are discussed
Fuzzy linear programming approach for solving transportation ...
Indian Academy of Sciences (India)
ALI EBRAHIMNEJAD
fuzzy modified distribution method to obtain the optimal solution in terms of fuzzy numbers. Pandian & Natarajan. [13] introduced a new algorithm namely, fuzzy zero point method for finding fuzzy optimal solution for such FTP in which the transportation cost, supply and demand are represented by trapezoidal fuzzy numbers.
Directory of Open Access Journals (Sweden)
Fu-Gui Shi
2010-01-01
Full Text Available The notion of (L,M-fuzzy σ-algebras is introduced in the lattice value fuzzy set theory. It is a generalization of Klement's fuzzy σ-algebras. In our definition of (L,M-fuzzy σ-algebras, each L-fuzzy subset can be regarded as an L-measurable set to some degree.
Directory of Open Access Journals (Sweden)
Shawkat Alkhazaleh
2012-01-01
Full Text Available In 1999 Molodtsov introduced the concept of soft set theory as a general mathematical tool for dealing with uncertainty. Alkhazaleh et al. in 2011 introduced the definition of a soft multiset as a generalization of Molodtsov's soft set. In this paper we give the definition of fuzzy soft multiset as a combination of soft multiset and fuzzy set and study its properties and operations. We give examples for these concepts. Basic properties of the operations are also given. An application of this theory in decision-making problems is shown.
Fuzzy efficiency without convexity
DEFF Research Database (Denmark)
Hougaard, Jens Leth; Balezentis, Tomas
2014-01-01
In this paper we develop a fuzzy version of the crisp Free Disposal Hull (FDH) method for measuring technical efficiency for samples of similar production units. The FDH-method is basically Data Envelopment Analysis (DEA) without the underlying assumption of convexity of the technology set. Our...... approach builds directly upon the definition of Farrell's indexes of technical efficiency used in crisp FDH. Therefore we do not require the use of fuzzy programming techniques but only utilize ranking probabilities of intervals as well as a related definition of dominance between pairs of intervals. We...
Nonstationary dynamics of encounters: Mean valuable territory covered by a random searcher
Campos, Daniel; Bartumeus, Frederic; Méndez, Vicenç
2017-09-01
Inspired by recent experiments on the organism Caenorhabditis elegans we present a stochastic problem to capture the adaptive dynamics of search in living beings, which involves the exploration-exploitation dilemma between remaining in a previously preferred area and relocating to new places. We assess the question of search efficiency by introducing a new magnitude, the mean valuable territory covered by a Browinan searcher, for the case where each site in the domain becomes valuable only after a random time controlled by a nonhomogeneous rate which expands from the origin outwards. We explore analytically this magnitude for domains of dimensions 1, 2, and 3 and discuss the theoretical and applied (biological) interest of our approach. As the main results here, we (i) report the existence of some universal scaling properties for the mean valuable territory covered as a function of time and (ii) reveal the emergence of an optimal diffusivity which appears only for domains in two and higher dimensions.
Glassy dynamics of 2D colloid crystals in a random pinning potential
Kim, Sungcheol; Pertsinidis, Alexandros; Ling, Xinsheng
2011-03-01
Recently, we have demonstrated that a monolayer charged colloidal crystal confined to a rough charged surface provides a realization of the Larkin-Ovchinnikov random-pinning model in two dimensions. The statics of the system is found to agree with Larkin's prediction of balkanization into small ordered domains. However, the dynamics are in disagreement with the collective creep model. Detailed analysis of the particle trajectories suggest that collective creep is preempted by channel flow. We also find that the velocity response to a step-like driving force shows a stretched exponential behavior similar to that found in structural glasses. Here, we provide a detailed analysis of this process. This research was supported by the NFS-DMR.
Directory of Open Access Journals (Sweden)
Jadwiga Zaród
2011-01-01
Full Text Available The farms of Western Pomerania province possess a large surplus of manpower. The dynamic optimization models with random constraints served the investigation of the possibilities of implementation of the unused man-hours. Those models regarded four successive years 2003-2006. The solution proceeded in two steps. The first step let us construct the assumption of the surplus or the deficiency of production factors. In the next step additional variables regarding the lease of arable grounds were introduced while the unused man-hours were implemented with various probability. The optimal solutions indicated the area of particular crops, the quantity of livestock and the farm income dependent on the use of the existing employment. This study aims at the presentation of the possibility of implementation of unused man-hours in farms dealing solely with the crop production and also the production of crop and livestock.
Dynamic connectivity algorithms for Monte Carlo simulations of the random-cluster model
Metin Elçi, Eren; Weigel, Martin
2014-05-01
We review Sweeny's algorithm for Monte Carlo simulations of the random cluster model. Straightforward implementations suffer from the problem of computational critical slowing down, where the computational effort per edge operation scales with a power of the system size. By using a tailored dynamic connectivity algorithm we are able to perform all operations with a poly-logarithmic computational effort. This approach is shown to be efficient in keeping online connectivity information and is of use for a number of applications also beyond cluster-update simulations, for instance in monitoring droplet shape transitions. As the handling of the relevant data structures is non-trivial, we provide a Python module with a full implementation for future reference.
Dynamic connectivity algorithms for Monte Carlo simulations of the random-cluster model
International Nuclear Information System (INIS)
Elçi, Eren Metin; Weigel, Martin
2014-01-01
We review Sweeny's algorithm for Monte Carlo simulations of the random cluster model. Straightforward implementations suffer from the problem of computational critical slowing down, where the computational effort per edge operation scales with a power of the system size. By using a tailored dynamic connectivity algorithm we are able to perform all operations with a poly-logarithmic computational effort. This approach is shown to be efficient in keeping online connectivity information and is of use for a number of applications also beyond cluster-update simulations, for instance in monitoring droplet shape transitions. As the handling of the relevant data structures is non-trivial, we provide a Python module with a full implementation for future reference.
Directory of Open Access Journals (Sweden)
Guitao Zhang
2014-01-01
Full Text Available The advertisement can increase the consumers demand; therefore it is one of the most important marketing strategies in the operations management of enterprises. This paper aims to analyze the impact of advertising investment on a discrete dynamic supply chain network which consists of suppliers, manufactures, retailers, and demand markets associated at different tiers under random demand. The impact of advertising investment will last several planning periods besides the current period due to delay effect. Based on noncooperative game theory, variational inequality, and Lagrange dual theory, the optimal economic behaviors of the suppliers, the manufactures, the retailers, and the consumers in the demand markets are modeled. In turn, the supply chain network equilibrium model is proposed and computed by modified project contraction algorithm with fixed step. The effectiveness of the model is illustrated by numerical examples, and managerial insights are obtained through the analysis of advertising investment in multiple periods and advertising delay effect among different periods.
Static and dynamic properties of frictional phenomena in a one-dimensional system with randomness
International Nuclear Information System (INIS)
Kawaguchi, T.; Matsukawa, H.
1997-01-01
Static and dynamic frictional phenomena at the interface with random impurities are investigated in a two-chain model with incommensurate structure. Static frictional force is caused by the impurity pinning and/or by the pinning due to the regular potential, which is responsible for the breaking of analyticity transition for impurity-free cases. It is confirmed that the static frictional force is always finite in the presence of impurities, in contrast to the impurity-free system. The nature of impurity pinning is discussed in connection with that in density waves. The kinetic frictional force of a steady sliding state is also investigated numerically. The relationship between the sliding velocity dependence of the kinetic frictional force and the strength of impurity potential is discussed. copyright 1997 The American Physical Society
Fuzzy Control of Flexible-Link Manipulators: A Review
Akbarzadeh-T, M.-R.; Quintana, S.; Jamshidi, M.
1998-01-01
Several recent research efforts are reviewed here which have applied fuzzy logic in control of flexible-link manipulators. A flexible robot is a distributed parameter system represented by complex nonlinear dynamics, its actuator and the control parameters are non-colocated, and lastly, unstructured/unknown parameters play a significant role in model dynamics of a flexible robot operating in the real world. As a result, control of flexible robots is considered a promising area for application of intelligent control methodologies such as fuzzy logic, genetic algorithms, and neural networks.
Directory of Open Access Journals (Sweden)
Peng Wang
2017-01-01
Full Text Available Maritime piracy is posing a genuine threat to maritime transport. The main purpose of simulation is to predict the behaviors of many actual systems, and it has been successfully applied in many fields. But the application of simulation in the maritime domain is still scarce. The rapid development of network and measurement technologies brings about higher accuracy and better availability of online measurements. This makes the simulation paradigm named as dynamic data driven simulation increasingly popular. It can assimilate the online measurements into the running simulation models and ensure much more accurate prediction of the complex systems under study. In this paper, we study how to utilize the online measurements in the agent based simulation of the maritime pirate activity. A new random finite set based data assimilation algorithm is proposed to overcome the limitations of the conventional vectors based data assimilation algorithms. The random finite set based general data model, measurement model, and simulation model are introduced to support the proposed algorithm. The details of the proposed algorithm are presented in the context of agent based simulation of maritime pirate activity. Two groups of experiments are used to practically prove the effectiveness and superiority of the proposed algorithm.
Directory of Open Access Journals (Sweden)
Behrang Mohajer
2013-01-01
Full Text Available A new algorithm named random particle optimization algorithm (RPOA for local path planning problem of mobile robots in dynamic and unknown environments is proposed. The new algorithm inspired from bacterial foraging technique is based on particles which are randomly distributed around a robot. These particles search the optimal path toward the target position while avoiding the moving obstacles by getting help from the robot’s sensors. The criterion of optimal path selection relies on the particles distance to target and Gaussian cost function assign to detected obstacles. Then, a high level decision making strategy will decide to select best mobile robot path among the proceeded particles, and finally a low level decision control provides a control signal for control of considered holonomic mobile robot. This process is implemented without requirement to tuning algorithm or complex calculation, and furthermore, it is independent from gradient base methods such as heuristic (artificial potential field methods. Therefore, in this paper, the problem of local mobile path planning is free from getting stuck in local minima and is easy computed. To evaluate the proposed algorithm, some simulations in three various scenarios are performed and results are compared by the artificial potential field.
MODELING URBAN DYNAMICS USING RANDOM FOREST: IMPLEMENTING ROC AND TOC FOR MODEL EVALUATION
Directory of Open Access Journals (Sweden)
M. Ahmadlou
2016-06-01
Full Text Available The importance of spatial accuracy of land use/cover change maps necessitates the use of high performance models. To reach this goal, calibrating machine learning (ML approaches to model land use/cover conversions have received increasing interest among the scholars. This originates from the strength of these techniques as they powerfully account for the complex relationships underlying urban dynamics. Compared to other ML techniques, random forest has rarely been used for modeling urban growth. This paper, drawing on information from the multi-temporal Landsat satellite images of 1985, 2000 and 2015, calibrates a random forest regression (RFR model to quantify the variable importance and simulation of urban change spatial patterns. The results and performance of RFR model were evaluated using two complementary tools, relative operating characteristics (ROC and total operating characteristics (TOC, by overlaying the map of observed change and the modeled suitability map for land use change (error map. The suitability map produced by RFR model showed 82.48% area under curve for the ROC model which indicates a very good performance and highlights its appropriateness for simulating urban growth.
Gan, Chun-Biao; Ding, Chang-Tao; Yang, Shi-Xi
2014-12-01
During bipedal walking, it is critical to detect and adjust the robot postures by feedback control to maintain its normal state amidst multi-source random disturbances arising from some unavoidable uncertain factors. The radical basis function (RBF) neural network model of a five-link biped robot is established, and two certain disturbances and a randomly uncertain disturbance are then mixed with the optimal torques in the network model to study the performance of the biped robot by several evaluation indices and a specific Poincaré map. In contrast with the simulations, the response varies as desired under optimal inputting while the output is fluctuating in the situation of disturbance driving. Simulation results from noise inputting also show that the dynamics of the robot is less sensitive to the disturbance of knee joint input of the swing leg than those of the other three joints, the response errors of the biped will be increasing with higher disturbance levels, and especially there are larger output fluctuations in the knee and hip joints of the swing leg.
Dynamic approach to space and habitat use based on biased random bridges.
Benhamou, Simon
2011-01-26
Although habitat use reflects a dynamic process, most studies assess habitat use statically as if an animal's successively recorded locations reflected a point rather than a movement process. By relying on the activity time between successive locations instead of the local density of individual locations, movement-based methods can substantially improve the biological relevance of utilization distribution (UD) estimates (i.e. the relative frequencies with which an animal uses the various areas of its home range, HR). One such method rests on Brownian bridges (BBs). Its theoretical foundation (purely and constantly diffusive movements) is paradoxically inconsistent with both HR settlement and habitat selection. An alternative involves movement-based kernel density estimation (MKDE) through location interpolation, which may be applied to various movement behaviours but lacks a sound theoretical basis. I introduce the concept of a biased random (advective-diffusive) bridge (BRB) and show that the MKDE method is a practical means to estimate UDs based on simplified (isotropically diffusive) BRBs. The equation governing BRBs is constrained by the maximum delay between successive relocations warranting constant within-bridge advection (allowed to vary between bridges) but remains otherwise similar to the BB equation. Despite its theoretical inconsistencies, the BB method can therefore be applied to animals that regularly reorientate within their HRs and adapt their movements to the habitats crossed, provided that they were relocated with a high enough frequency. Biased random walks can approximate various movement types at short times from a given relocation. Their simplified form constitutes an effective trade-off between too simple, unrealistic movement models, such as Brownian motion, and more sophisticated and realistic ones, such as biased correlated random walks (BCRWs), which are too complex to yield functional bridges. Relying on simplified BRBs proves to be the
Spinodals with Disorder: From Avalanches in Random Magnets to Glassy Dynamics
Nandi, Saroj Kumar; Biroli, Giulio; Tarjus, Gilles
2016-04-01
We revisit the phenomenon of spinodals in the presence of quenched disorder and develop a complete theory for it. We focus on the spinodal of an Ising model in a quenched random field (RFIM), which has applications in many areas from materials to social science. By working at zero temperature in the quasistatically driven RFIM, thermal fluctuations are eliminated and one can give a rigorous content to the notion of spinodal. We show that the latter is due to the depinning and the subsequent expansion of rare droplets. We work out the associated critical behavior, which, in any finite dimension, is very different from the mean-field one: the characteristic length diverges exponentially and the thermodynamic quantities display very mild nonanalyticities much like in a Griffith phenomenon. From the recently established connection between the spinodal of the RFIM and glassy dynamics, our results also allow us to conclusively assess the physical content and the status of the dynamical transition predicted by the mean-field theory of glass-forming liquids.
Limits and Dynamics of Stochastic Neuronal Networks with Random Heterogeneous Delays
Touboul, Jonathan
2012-11-01
Realistic networks display heterogeneous transmission delays. We analyze here the limits of large stochastic multi-populations networks with stochastic coupling and random interconnection delays. We show that depending on the nature of the delays distributions, a quenched or averaged propagation of chaos takes place in these networks, and that the network equations converge towards a delayed McKean-Vlasov equation with distributed delays. Our approach is mostly fitted to neuroscience applications. We instantiate in particular a classical neuronal model, the Wilson and Cowan system, and show that the obtained limit equations have Gaussian solutions whose mean and standard deviation satisfy a closed set of coupled delay differential equations in which the distribution of delays and the noise levels appear as parameters. This allows to uncover precisely the effects of noise, delays and coupling on the dynamics of such heterogeneous networks, in particular their role in the emergence of synchronized oscillations. We show in several examples that not only the averaged delay, but also the dispersion, govern the dynamics of such networks.
Language Recognition Using Latent Dynamic Conditional Random Field Model with Phonological Features
Directory of Open Access Journals (Sweden)
Sirinoot Boonsuk
2014-01-01
Full Text Available Spoken language recognition (SLR has been of increasing interest in multilingual speech recognition for identifying the languages of speech utterances. Most existing SLR approaches apply statistical modeling techniques with acoustic and phonotactic features. Among the popular approaches, the acoustic approach has become of greater interest than others because it does not require any prior language-specific knowledge. Previous research on the acoustic approach has shown less interest in applying linguistic knowledge; it was only used as supplementary features, while the current state-of-the-art system assumes independency among features. This paper proposes an SLR system based on the latent-dynamic conditional random field (LDCRF model using phonological features (PFs. We use PFs to represent acoustic characteristics and linguistic knowledge. The LDCRF model was employed to capture the dynamics of the PFs sequences for language classification. Baseline systems were conducted to evaluate the features and methods including Gaussian mixture model (GMM based systems using PFs, GMM using cepstral features, and the CRF model using PFs. Evaluated on the NIST LRE 2007 corpus, the proposed method showed an improvement over the baseline systems. Additionally, it showed comparable result with the acoustic system based on i-vector. This research demonstrates that utilizing PFs can enhance the performance.
Emergent dynamics of Cucker-Smale flocking particles in a random environment
Ha, Seung-Yeal; Jeong, Jiin; Noh, Se Eun; Xiao, Qinghua; Zhang, Xiongtao
2017-02-01
We present a new kinetic Cucker-Smale-Fokker-Planck (CS-FP) type equation with a degenerate diffusion, which describes the dynamics for an ensemble of infinitely many Cucker-Smale particles in a random environment. The asymptotic dynamics of the CS-FP equation exhibits a threshold-like phenomenon depending on the relative strength between the coupling strength and the noise strength. In the small coupling regime, the noise effect becomes dominant, which induces the velocity variance to increase to infinity exponentially fast. In contrast, the velocity alignment effect is strong in the large coupling regime, and the velocity variance tends to zero exponentially fast. We present the global existence of classical solutions to the CS-FP equation for a sufficiently smooth initial datum without smallness in its size. For the kinetic CS-FP equation with a metric dependent communication weight, we provide a uniform-in-time mean-field limit from the stochastic CS-model to the kinetic CS-FP equation without convergence rate.
Fuzzy multivariable control of domestic heat pumps
International Nuclear Information System (INIS)
Underwood, C.P.
2015-01-01
Poor control has been identified as one of the reasons why recent field trials of domestic heat pumps in the UK have produced disappointing results. Most of the technology in use today uses a thermostatically-controlled fixed speed compressor with a mechanical expansion device. This article investigates improved control of these heat pumps through the design and evaluation of a new multivariable fuzzy logic control system utilising a variable speed compressor drive with capacity control linked through to evaporator superheat control. A new dynamic thermal model of a domestic heat pump validated using experimental data forms the basis of the work. The proposed control system is evaluated using median and extreme daily heating demand profiles for a typical UK house compared with a basic thermostatically-controlled alternative. Results show good tracking of the heating temperature and superheat control variables, reduced cycling and an improvement in performance averaging 20%. - Highlights: • A new dynamic model of a domestic heat pump is developed and validated. • A new multivariable fuzzy logic heat pump control system is developed/reported. • The fuzzy controller regulates both plant capacity and evaporator superheat degree. • Thermal buffer storage is also considered as well as compressor cycling. • The new controller shows good variable tracking and a reduction in energy of 20%.
DEFF Research Database (Denmark)
Franco de los Rios, Camilo Andres; Hougaard, Jens Leth; Nielsen, Kurt
for decision support and multidimensional interval analysis. First, the original approach is extended using fuzzy set theory which makes it possible to handle both non-interval and interval data. Second, we re-examine the ranking procedure based on semi-equivalence classes and suggest a new complementary...
Fuzzy knowledge management for the semantic web
Ma, Zongmin; Yan, Li; Cheng, Jingwei
2014-01-01
This book goes to great depth concerning the fast growing topic of technologies and approaches of fuzzy logic in the Semantic Web. The topics of this book include fuzzy description logics and fuzzy ontologies, queries of fuzzy description logics and fuzzy ontology knowledge bases, extraction of fuzzy description logics and ontologies from fuzzy data models, storage of fuzzy ontology knowledge bases in fuzzy databases, fuzzy Semantic Web ontology mapping, and fuzzy rules and their interchange in the Semantic Web. The book aims to provide a single record of current research in the fuzzy knowledge representation and reasoning for the Semantic Web. The objective of the book is to provide the state of the art information to researchers, practitioners and graduate students of the Web intelligence and at the same time serve the knowledge and data engineering professional faced with non-traditional applications that make the application of conventional approaches difficult or impossible.
Dynamic fair node spectrum allocation for ad hoc networks using random matrices
Rahmes, Mark; Lemieux, George; Chester, Dave; Sonnenberg, Jerry
2015-05-01
Dynamic Spectrum Access (DSA) is widely seen as a solution to the problem of limited spectrum, because of its ability to adapt the operating frequency of a radio. Mobile Ad Hoc Networks (MANETs) can extend high-capacity mobile communications over large areas where fixed and tethered-mobile systems are not available. In one use case with high potential impact, cognitive radio employs spectrum sensing to facilitate the identification of allocated frequencies not currently accessed by their primary users. Primary users own the rights to radiate at a specific frequency and geographic location, while secondary users opportunistically attempt to radiate at a specific frequency when the primary user is not using it. We populate a spatial radio environment map (REM) database with known information that can be leveraged in an ad hoc network to facilitate fair path use of the DSA-discovered links. Utilization of high-resolution geospatial data layers in RF propagation analysis is directly applicable. Random matrix theory (RMT) is useful in simulating network layer usage in nodes by a Wishart adjacency matrix. We use the Dijkstra algorithm for discovering ad hoc network node connection patterns. We present a method for analysts to dynamically allocate node-node path and link resources using fair division. User allocation of limited resources as a function of time must be dynamic and based on system fairness policies. The context of fair means that first available request for an asset is not envied as long as it is not yet allocated or tasked in order to prevent cycling of the system. This solution may also save money by offering a Pareto efficient repeatable process. We use a water fill queue algorithm to include Shapley value marginal contributions for allocation.
Bhadauria, Esha A.; Gurudut, Peeyoosha
2017-01-01
The aim of the present study was to compare three different forms of exercises namely lumbar stabilization, dynamic strengthening, and Pilates on chronic low back pain (LBP) in terms of pain, range of motion, core strength and function. In this study, 44 subjects suffering from non-specific LBP for more than 3 months were randomly allocated into the lumbar stabilization group, the dynamic strengthening group, and the Pilates group. Ten sessions of exercises for 3 weeks were prescribed along w...
Hierarchical type-2 fuzzy aggregation of fuzzy controllers
Cervantes, Leticia
2016-01-01
This book focuses on the fields of fuzzy logic, granular computing and also considering the control area. These areas can work together to solve various control problems, the idea is that this combination of areas would enable even more complex problem solving and better results. In this book we test the proposed method using two benchmark problems: the total flight control and the problem of water level control for a 3 tank system. When fuzzy logic is used it make it easy to performed the simulations, these fuzzy systems help to model the behavior of a real systems, using the fuzzy systems fuzzy rules are generated and with this can generate the behavior of any variable depending on the inputs and linguistic value. For this reason this work considers the proposed architecture using fuzzy systems and with this improve the behavior of the complex control problems.
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.
A New Fuzzy-Evidential Controller for Stabilization of the Planar Inverted Pendulum System
Tang, Yongchuan; Zhou, Deyun
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. PMID:27482707
Word Similarity from Dictionaries: Inferring Fuzzy Measures from Fuzzy Graphs
Directory of Open Access Journals (Sweden)
Vicenc Torra
2008-01-01
Full Text Available WORD SIMILARITY FROM DICTIONARIES: INFERRING FUZZY MEASURES FROM FUZZY GRAPHS The computation of similarities between words is a basic element of information retrieval systems, when retrieval is not solely based on word matching. In this work we consider a measure between words based on dictionaries. This is achieved assuming that a dictionary is formalized as a fuzzy graph. We show that the approach permits to compute measures not only for pairs of words but for sets of them.
Directory of Open Access Journals (Sweden)
Yachun Pang
2012-01-01
Full Text Available This paper presents a novel two-step approach that incorporates fuzzy c-means (FCMs clustering and gradient vector flow (GVF snake algorithm for lesions contour segmentation on breast magnetic resonance imaging (BMRI. Manual delineation of the lesions by expert MR radiologists was taken as a reference standard in evaluating the computerized segmentation approach. The proposed algorithm was also compared with the FCMs clustering based method. With a database of 60 mass-like lesions (22 benign and 38 malignant cases, the proposed method demonstrated sufficiently good segmentation performance. The morphological and texture features were extracted and used to classify the benign and malignant lesions based on the proposed computerized segmentation contour and radiologists’ delineation, respectively. Features extracted by the computerized characterization method were employed to differentiate the lesions with an area under the receiver-operating characteristic curve (AUC of 0.968, in comparison with an AUC of 0.914 based on the features extracted from radiologists’ delineation. The proposed method in current study can assist radiologists to delineate and characterize BMRI lesion, such as quantifying morphological and texture features and improving the objectivity and efficiency of BMRI interpretation with a certain clinical value.
COMPACTNESS IN INTUITIONISTIC FUZZY MULTISET TOPOLOGY
Kunnambath, Shinoj Thekke; John, Sunil Jacob
2017-01-01
– In this paper, we discussVarious properties of Compact and Homeomorphic Intuitionistic Fuzzy Multiset Topological spacesarious properties of Compact and Homeomorphic Intuitionistic Fuzzy Multiset Topological spaces
Probabilistic clustering algorithms for fuzzy rules decomposition
Salgado, Paulo; Igrejas, Getúlio
2007-01-01
The fuzzy c-means (FCM) clustering algorithm is the best known and used method in fuzzy clustering and is generally applied to well defined set of data. In this paper a generalized Probabilistic fuzzy c-means (FCM) algorithm is proposed and applied to clustering fuzzy sets. This technique leads to a fuzzy partition of the fuzzy rules, one for each cluster, which corresponds to a new set of fuzzy sub-systems. When applied to the clustering of a flat fuzzy system results a set of...
A NEURO FUZZY MODEL FOR THE INVESTIGATION OF ...
African Journals Online (AJOL)
Several factors may contribute directly or indirectly to the structural failure of metallic pipes. The most important of which is corrosion. Corrosivity of pipes is not a directly measurable parameter as pipe corrosion is a very random phenomenon. The main aim of the present study is to develop a neuro-fuzzy model capable of ...
Fuzzy logic particle tracking velocimetry
Wernet, Mark P.
1993-01-01
Fuzzy logic has proven to be a simple and robust method for process control. Instead of requiring a complex model of the system, a user defined rule base is used to control the process. In this paper the principles of fuzzy logic control are applied to Particle Tracking Velocimetry (PTV). Two frames of digitally recorded, single exposure particle imagery are used as input. The fuzzy processor uses the local particle displacement information to determine the correct particle tracks. Fuzzy PTV is an improvement over traditional PTV techniques which typically require a sequence (greater than 2) of image frames for accurately tracking particles. The fuzzy processor executes in software on a PC without the use of specialized array or fuzzy logic processors. A pair of sample input images with roughly 300 particle images each, results in more than 200 velocity vectors in under 8 seconds of processing time.
A Comparison of Neural, Fuzzy, Evolutionary, and Adaptive Approaches for Carrier Landing
National Research Council Canada - National Science Library
Steinberg, Marc
2001-01-01
.... The control law approaches examined are: fuzzy logic, two neural network approaches, indirect adaptive and non-adaptive versions of dynamic inversion, and a hybrid approach that combines direct and indirect adaptive elements...
Inner Product over Fuzzy Matrices
Directory of Open Access Journals (Sweden)
A. Nagoor Gani
2016-01-01
Full Text Available The purpose of this study was to introduce the inner product over fuzzy matrices. By virtue of this definition, α-norm is defined and the parallelogram law is proved. Again the relative fuzzy norm with respect to the inner product over fuzzy matrices is defined. Moreover Cauchy Schwarz inequality, Pythagoras, and Fundamental Minimum Principle are established. Some equivalent conditions are also proved.
Intuitionistic fuzzy aggregation and clustering
Xu, Zeshui
2012-01-01
This book offers a systematic introduction to the clustering algorithms for intuitionistic fuzzy values, the latest research results in intuitionistic fuzzy aggregation techniques, the extended results in interval-valued intuitionistic fuzzy environments, and their applications in multi-attribute decision making, such as supply chain management, military system performance evaluation, project management, venture capital, information system selection, building materials classification, and operational plan assessment, etc.
2010-04-20
... DEPARTMENT OF COMMERCE International Trade Administration [C-580-851] Dynamic Random Access Memory Semiconductors from the Republic of Korea: Extension of Time Limit for Preliminary Results of Countervailing Duty... access memory semiconductors from the Republic of Korea, covering the period January 1, 2008 through...
Czech Academy of Sciences Publication Activity Database
Klvana, M.; Pavlová, M.; Koudeláková, T.; Chaloupková, R.; Dvořák, P.; Prokop, Z.; Stsiapanava, A.; Kutý, Michal; Kutá-Smatanová, Ivana; Dohnálek, Jan; Kulhánek, P.; Damborský, J.
2009-01-01
Roč. 392, č. 5 (2009), s. 1339-1356 ISSN 0022-2836 R&D Projects: GA MŠk(CZ) LC06010 Institutional research plan: CEZ:AV0Z40500505; CEZ:AV0Z60870520 Keywords : haloalkane dehalogenase * product release * random acceleration molecular dynamics Subject RIV: CD - Macromolecular Chemistry Impact factor: 3.871, year: 2009
Palasantzas, G.
2007-01-01
The authors investigate the simultaneous influence of thermomechanical and momentum exchange noise on the linear dynamic range DR of nanoresonators with random rough surfaces. The latter are characterized by the roughness amplitude w, the lateral correlation length xi, and the roughness exponent 0
DEFF Research Database (Denmark)
Fosgerau, Mogens
2010-01-01
This paper investigates the distribution of delays during a repeatedly occurring demand peak in a congested facility with random capacity and demand, such as an airport or an urban road. Congestion is described in the form of a dynamic queue using the Vickrey bottleneck model and assuming Nash...... phenomenon that has now been observed a number of times. Empirical illustration is provided....
Directory of Open Access Journals (Sweden)
A.A. Fahmy
2013-12-01
Full Text Available This paper presents a new neuro-fuzzy controller for robot manipulators. First, an inductive learning technique is applied to generate the required inverse modeling rules from input/output data recorded in the off-line structure learning phase. Second, a fully differentiable fuzzy neural network is developed to construct the inverse dynamics part of the controller for the online parameter learning phase. Finally, a fuzzy-PID-like incremental controller was employed as Feedback servo controller. The proposed control system was tested using dynamic model of a six-axis industrial robot. The control system showed good results compared to the conventional PID individual joint controller.
Fuzzy Objects and Noncommutative Solitons
Kobayashi, Shinpei; Asakawa, Tsuguhiko
2015-01-01
The fuzzy disc is a disc-shaped region in a noncommutative plane, and is a fuzzy approximation of a commutative disc. We showed that one can introduce a concept of angles to the fuzzy disc, by using the phase operator and phase states known in quantum optics. We also constructed fan-shaped soliton solutions, which would be identified with D-branes, of a scalar field theory on the fuzzy disc and applied this concept to a theory of noncommutative gravity. This proceeding is based on our previous work.
Fuzzy algebraic hyperstructures an introduction
Davvaz, Bijan
2015-01-01
This book is intended as an introduction to fuzzy algebraic hyperstructures. As the first in its genre, it includes a number of topics, most of which reflect the authors’ past research and thus provides a starting point for future research directions. The book is organized in five chapters. The first chapter introduces readers to the basic notions of algebraic structures and hyperstructures. The second covers fuzzy sets, fuzzy groups and fuzzy polygroups. The following two chapters are concerned with the theory of fuzzy Hv-structures: while the third chapter presents the concept of fuzzy Hv-subgroup of Hv-groups, the fourth covers the theory of fuzzy Hv-ideals of Hv-rings. The final chapter discusses several connections between hypergroups and fuzzy sets, and includes a study on the association between hypergroupoids and fuzzy sets endowed with two membership functions. In addition to providing a reference guide to researchers, the book is also intended as textbook for undergraduate and graduate students.
Directory of Open Access Journals (Sweden)
Wang Yajun
2008-12-01
Full Text Available In order to address the complex uncertainties caused by interfacing between the fuzziness and randomness of the safety problem for embankment engineering projects, and to evaluate the safety of embankment engineering projects more scientifically and reasonably, this study presents the fuzzy logic modeling of the stochastic finite element method (SFEM based on the harmonious finite element (HFE technique using a first-order approximation theorem. Fuzzy mathematical models of safety repertories were introduced into the SFEM to analyze the stability of embankments and foundations in order to describe the fuzzy failure procedure for the random safety performance function. The fuzzy models were developed with membership functions with half depressed gamma distribution, half depressed normal distribution, and half depressed echelon distribution. The fuzzy stochastic mathematical algorithm was used to comprehensively study the local failure mechanism of the main embankment section near Jingnan in the Yangtze River in terms of numerical analysis for the probability integration of reliability on the random field affected by three fuzzy factors. The result shows that the middle region of the embankment is the principal zone of concentrated failure due to local fractures. There is also some local shear failure on the embankment crust. This study provides a referential method for solving complex multi-uncertainty problems in engineering safety analysis.
Chance-constrained programming models for capital budgeting with NPV as fuzzy parameters
Huang, Xiaoxia
2007-01-01
In an uncertain economic environment, experts' knowledge about outlays and cash inflows of available projects consists of much vagueness instead of randomness. Investment outlays and annual net cash flows of a project are usually predicted by using experts' knowledge. Fuzzy variables can overcome the difficulties in predicting these parameters. In this paper, capital budgeting problem with fuzzy investment outlays and fuzzy annual net cash flows is studied based on credibility measure. Net present value (NPV) method is employed, and two fuzzy chance-constrained programming models for capital budgeting problem are provided. A fuzzy simulation-based genetic algorithm is provided for solving the proposed model problems. Two numerical examples are also presented to illustrate the modelling idea and the effectiveness of the proposed algorithm.
International Nuclear Information System (INIS)
Baron, Jorge H.; Rivera, S.S.
2000-01-01
The so-called vulnerability matrix is used in the evaluation part of the probabilistic safety assessment for a nuclear power plant, during the containment event trees calculations. This matrix is established from what is knows as Numerical Categories for Engineering Judgement. This matrix is usually established with numerical values obtained with traditional arithmetic using the set theory. The representation of this matrix with fuzzy numbers is much more adequate, due to the fact that the Numerical Categories for Engineering Judgement are better represented with linguistic variables, such as 'highly probable', 'probable', 'impossible', etc. In the present paper a methodology to obtain a Fuzzy Vulnerability Matrix is presented, starting from the recommendations on the Numerical Categories for Engineering Judgement. (author)
Fuzzy Perfect Mappings and Q-Compactness in Smooth Fuzzy Topological Spaces
Directory of Open Access Journals (Sweden)
C. Kalaivani
2014-03-01
Full Text Available We point out that the product of two fuzzy closed sets of smooth fuzzy topological spaces need not be fuzzy closed with respect to the the existing notion of product smooth fuzzy topology. To get this property, we introduce a new suitable product smooth fuzzy topology. We investigate whether F1×F2 and (F,H are weakly smooth fuzzy continuity whenever F1, F2, F and H are weakly smooth fuzzy continuous. Using this new product smooth fuzzy topology, we define smooth fuzzy perfect mapping and prove that composition of two smooth fuzzy perfect mappings is smooth fuzzy perfect under some additional conditions. We also introduce two new notions of compactness called Q-compactness and Q-α-compactness; and discuss the compactness of the image of a Q-compact set (Q-α-compact set under a weakly smooth fuzzy continuous function ((α,β-weakly smooth fuzzy continuous function.
Dynamical effects and the critical behavior of random-field systems (invited)
International Nuclear Information System (INIS)
Shapir, Y.
1985-01-01
A variety of phenomena is observed experimentally in random-field (RF) systems realized by the application of an external field to diluted antiferromagnets. At low temperatures, infinitely long hysteretic effects are manifested by the history dependence of the final states: long-range order is observed if the field is applied after cooling, while domain states are reached when field cooled. While no indications for critical fluctuations are detected in 2-D systems, scaling behavior, for both the correlation length and the specific heat, is observed in 3-D systems over an intermediate range of temperatures. The related critical properties seem to be well described by the corresponding ones in the 2-D pure Ising model. The renormalization-group approach, which yields for the equilibrium critical exponents their values of the pure model in d-2 dimensions, is reviewed. A generalization of the dimensional-reduction approach, which accounts self-consistently for renormalized responses of the RF system, is presented. The dynamical effects are implicitly incorporated through the variation in the critical response between the local and the global regimes, associated with short- and long-time scales, respectively. In both regimes the lower critical dimension is found to be d = 2 in accordance with stability arguments. The short-time critical behavior indicates a dimensional reduction by one for the 3-D thermal exponents, in agreement with the experimental results
Dynamical effects and the critical behavior of random-field systems
International Nuclear Information System (INIS)
Shapir, Y.
1985-01-01
A variety of phenomena is observed experimentally in random-field (RF) systems realized by the application of an external field to diluted antiferromagnets. At low temperatures, infinitely long hysteretic effects are manifested by the history dependence of the final states: long-range order is observed if the field is applied after cooling, while domain states are reached when field cooled. While no indications for critical fluctuations are detected in 2-D systems, scaling behavior, for both the correlation length and the specific heat, is observed in 3-D systems over an intermediate range of temperatures. The related critical properties seem to be well described by the corresponding ones in the 2-D pure Ising model. The renormalization-group approach, which yields for the equilibrium critical exponents their values of the pure model in d-2 dimensions, is reviewed. A generalization of the dimensional-reduction approach, which accounts self-consistently for renormalized responses of the RF system, is presented. The dynamical effects are implicitly incorporated through the variation in the critical response between the local and the global regimes, associated with short- and long-time scales, respectively. In both regimes the lower critical dimension is found to be d = 2 in accordance with stability arguments. The short-time critical behavior indicates a dimensional reduction by one for the 3-D thermal exponents, in agreement with the experimental results. 37 references
Fuzzy Logic Unmanned Air Vehicle Motion Planning
Directory of Open Access Journals (Sweden)
Chelsea Sabo
2012-01-01
Full Text Available There are a variety of scenarios in which the mission objectives rely on an unmanned aerial vehicle (UAV being capable of maneuvering in an environment containing obstacles in which there is little prior knowledge of the surroundings. With an appropriate dynamic motion planning algorithm, UAVs would be able to maneuver in any unknown environment towards a target in real time. This paper presents a methodology for two-dimensional motion planning of a UAV using fuzzy logic. The fuzzy inference system takes information in real time about obstacles (if within the agent's sensing range and target location and outputs a change in heading angle and speed. The FL controller was validated, and Monte Carlo testing was completed to evaluate the performance. Not only was the path traversed by the UAV often the exact path computed using an optimal method, the low failure rate makes the fuzzy logic controller (FLC feasible for exploration. The FLC showed only a total of 3% failure rate, whereas an artificial potential field (APF solution, a commonly used intelligent control method, had an average of 18% failure rate. These results highlighted one of the advantages of the FLC method: its adaptability to complex scenarios while maintaining low control effort.
Czech Academy of Sciences Publication Activity Database
Coufal, David
2017-01-01
Roč. 319, 15 July (2017), s. 1-27 ISSN 0165-0114 R&D Projects: GA MŠk(CZ) LD13002 Institutional support: RVO:67985807 Keywords : fuzzy systems * radial functions * coherence Subject RIV: BA - General Mathematics OBOR OECD: Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8) Impact factor: 2.718, year: 2016
Heinrich, Berthold
Neben der klassischen Regelungstechnik gewinnt heute eine andere Art der Herangehensweise an Regelungsaufgaben Bedeutung, die mit vermeintlich unscharfen (engl.: fuzzy) Begriffen wie ‚Temperatur ist viel zu hoch`, ‚Laufkatze ist weit weg`, ‚Ventil wird weit geöffnet` arbeitet. Zufällig oder unscharf ist diese Art der Regelung nicht, sondern sie führt über ein präzises Regelwerk zu genau determinierten Ergebnissen.
Multiple Instance Fuzzy Inference
2015-12-02
and learn the fuzzy inference system’s parameters [24, 25]. In this later technique, supervised and unsupervised learning algorithms are devised to...algorithm ( unsupervised learning ) can be used to identify local contexts of the input space, and a linear classifier (supervised learning ) can be used...instance level (patch-level) labels and would require the image to be correctly segmented and labeled prior to learning . Figure 1.1: Example of an image
A fuzzy logic based clustering strategy for improving vehicular ad ...
Indian Academy of Sciences (India)
ITS proposes to manage vehicle traffic, support drivers with safety .... the same time. The vehicle that sends firstly a message for inviting the vehicles to join and has more cluster members will be elected as a cluster head. There are ... In this study, an alternative approach using fuzzy logic under dynamic network conditions.
Performance comparison of fuzzy and non-fuzzy classification methods
Directory of Open Access Journals (Sweden)
B. Simhachalam
2016-07-01
Full Text Available In data clustering, partition based clustering algorithms are widely used clustering algorithms. Among various partition algorithms, fuzzy algorithms, Fuzzy c-Means (FCM, Gustafson–Kessel (GK and non-fuzzy algorithm, k-means (KM are most popular methods. k-means and Fuzzy c-Means use standard Euclidian distance measure and Gustafson–Kessel uses fuzzy covariance matrix in their distance metrics. In this work, a comparative study of these algorithms with different famous real world data sets, liver disorder and wine from the UCI repository is presented. The performance of the three algorithms is analyzed based on the clustering output criteria. The results were compared with the results obtained from the repository. The results showed that Gustafson–Kessel produces close results to Fuzzy c-Means. Further, the experimental results demonstrate that k-means outperforms the Fuzzy c-Means and Gustafson–Kessel algorithms. Thus the efficiency of k-means is better than that of Fuzzy c-Means and Gustafson–Kessel algorithms.
Feasibility analysis of fuzzy logic control for ITER Poloidal field (PF) AC/DC converter system
Energy Technology Data Exchange (ETDEWEB)
Hassan, Mahmood Ul; Fu, Peng [Institute of Plasma Physics, Chinese Academy of Sciences, Hefei 230031 (China); University of Science and Technology of China (China); Song, Zhiquan, E-mail: zhquansong@ipp.ac.cn [Institute of Plasma Physics, Chinese Academy of Sciences, Hefei 230031 (China); Chen, Xiaojiao [Institute of Plasma Physics, Chinese Academy of Sciences, Hefei 230031 (China); University of Science and Technology of China (China); Zhang, Xiuqing [Institute of Plasma Physics, Chinese Academy of Sciences, Hefei 230031 (China); Humayun, Muhammad [Shanghai Jiaotong University (China)
2017-05-15
Highlights: • The implementation of the Fuzzy controller for the ITER PF converter system is presented. • The comparison of the FLC and PI simulation are investigated. • The FLC single and parallel bridge operation are presented. • Fuzzification and Defuzzification algorithms are presented using FLC controller. - Abstract: This paper describes the feasibility analysis of the fuzzy logic control to increase the performance of the ITER poloidal field (PF) converter systems. A fuzzy-logic-based controller is designed for ITER PF converter system, using the traditional PI controller and Fuzzy controller (FC), the dynamic behavior and transient response of the PF converter system are compared under normal operation by analysis and simulation. The analysis results show that the fuzzy logic control can achieve better operation performance than PI control.
Adaptive fuzzy sliding-mode control for multi-input multi-output chaotic systems
International Nuclear Information System (INIS)
Poursamad, Amir; Markazi, Amir H.D.
2009-01-01
This paper describes an adaptive fuzzy sliding-mode control algorithm for controlling unknown or uncertain, multi-input multi-output (MIMO), possibly chaotic, dynamical systems. The control approach encompasses a fuzzy system and a robust controller. The fuzzy system is designed to mimic an ideal sliding-mode controller, and the robust controller compensates the difference between the fuzzy controller and the ideal one. The parameters of the fuzzy system, as well as the uncertainty bound of the robust controller, are tuned adaptively. The adaptive laws are derived in the Lyapunov sense to guarantee the asymptotic stability and tracking of the controlled system. The effectiveness of the proposed method is shown by applying it to some well-known chaotic systems.