Feasibility of using adaptive logic networks to predict compressor unit failure
Armstrong, W.W.; Chungying Chu; Thomas, M.M. [Dendronic Decisions Limited, Edmonton (Canada)] [and others
1995-12-31
In this feasibility study, an adaptive logic network (ALN) was trained to predict failures of turbine-driven compressor units using a large database of measurements. No expert knowledge about compressor systems was involved. The predictions used only the statistical properties of the measurements and the indications of failure types. A fuzzy set was used to model measurements typical of normal operation. It was constrained by a requirement imposed during ALN training, that it should have a shape similar to a Gaussian density, more precisely, that its logarithm should be convex-up. Initial results obtained using this approach to knowledge discovery in the database were encouraging.
Fuzzy-Logic Adaptive Queuing for a Heuristic TCP Performance in Mobile Wireless Networks
Ghaida A. AL-Suhail
2012-06-01
Full Text Available In this paper, we propose a new Fuzzy-Logic Adaptive Queuing controller (FLAQ based on a classical Random Early Detection (RED algorithm in wireless cellular network. The controller predicts dynamically the packet dropping rate and the corresponding average queue length. It relies on the average queue length at the base station router and the packet loss rate caused by the channel variations in mobile environment; assuming there is no buffer overflow due to the congestion. Using this model, a heuristic TCP performance can be estimated over a time-varying channel under different conditions of user’s mobility. The results show a significant improvement in TCP throughput performance when the user’s mobility is below 5 m/s; and becomes constant (i.e., close to i.i.d beyond this speed especially at 5% of predefined packet error rate.
Adaptive logic networks in rehabilitation of persons with incomplete spinal cord injury
Armstrong, W.W. [Univ. of Alberta, Edmonton (Canada)]|[Dendronic Decisions Limited, Edmonton (Canada)] [and others
1995-12-31
Persons with incomplete spinal cord injury are generally at least partially paralyzed and are often unable to walk. Manually-controlled electrical stimulation has been used to act upon nerves or muscles to cause leg movement so such persons can achieve functional walking. They use crutches or a mobile walker for support, and initiate each stimulus by pressing a button. Artificial intelligence and machine learning techniques are now making it possible to automate the process of stimulus-initiation. Supervised training of an automatic system can be based on samples of correct stimulation given by the patient or by a therapist, accompanied by data from sensors indicating the state of the person`s body and its relation to the ground during walking. A major issue is generalization, i.e. whether the result of training can be used for control at a later time or in somewhat different circumstances. As the possibilities grow for increasing the number and variety of sensors on a patient, and for easily implanting more numerous stimulation channels, the need is increasing for powerful learning systems which can automatically develop effective and safe control algorithms. This paper explains the foundations of adaptive logic networks, and illustrates how they have been used to develop an experimental walking prosthesis used in a laboratory setting. Successful generalization has been observed using parameters from training which took place minutes to days earlier.
Franceschet, Massimo
2010-01-01
Networks are pervasive in the real world. Nature, society, economy, and technology are supported by ostensibly different networks that in fact share an amazing number of interesting structural properties. Network thinking exploded in the last decade, boosted by the availability of large databases on the topology of various real networks, mainly the Web and biological networks, and converged to the new discipline of network analysis - the holistic analysis of complex systems through the study of the network that wires their components. Physicists mainly drove the investigation, studying the structure and function of networks using methods and tools of statistical mechanics. Here, we give an alternative perspective on network analysis, proposing a logic for specifying general properties of networks and a modular algorithm for checking these properties. The logic borrows from two intertwined computing fields: XML databases and model checking.
Bergenholtz, Carsten; Bjerregaard, Toke
The present study investigates how a high-tech-small-firm (HTSF) can carry out an inter-organizational search of actors located at universities. Responding to calls to study how firms navigate multiple institutional norms, this research examines the different strategies used by a HTSF to balance...... adopted academic norm-sets, commercial imperatives and formal regulations to support formation of networks and collaborations with universities. The findings show how the significance of weak and strong ties for the formation of collaborations and networks with universities is relative to the...... institutional embeddedness in different industries. The research thus illuminates how a HTSF effectively can combine different search and collaboration strategies depending on institutional contexts and logics governing R&D work. The concluding section outlines implications for future research and the...
Adaptation of the FPGA to Logic Failures
Tyurin S.F.; Grekov A.V.; Gromov O.A.
2013-01-01
The paper proposes the restoration of logic programmable logic integrated circuits such as FPGA (field-programmable gate array) for critical applications by adapting to failures of logic elements. The principle of adaptation FPGA is to switch to the remaining functionality of the LUT (Look Up Table), with the possibility of hardware and software they use in the event of hardware failure after massive failures. Asked to ensure the preservation of the basis in the sense of Post logic functions ...
Fuzzy logic and neural network technologies
Villarreal, James A.; Lea, Robert N.; Savely, Robert T.
1992-01-01
Applications of fuzzy logic technologies in NASA projects are reviewed to examine their advantages in the development of neural networks for aerospace and commercial expert systems and control. Examples of fuzzy-logic applications include a 6-DOF spacecraft controller, collision-avoidance systems, and reinforcement-learning techniques. The commercial applications examined include a fuzzy autofocusing system, an air conditioning system, and an automobile transmission application. The practical use of fuzzy logic is set in the theoretical context of artificial neural systems (ANSs) to give the background for an overview of ANS research programs at NASA. The research and application programs include the Network Execution and Training Simulator and faster training algorithms such as the Difference Optimized Training Scheme. The networks are well suited for pattern-recognition applications such as predicting sunspots, controlling posture maintenance, and conducting adaptive diagnoses.
Logical impossibilities in biological networks
Monendra Grover
2011-10-01
Full Text Available Biological networks are complex and involve several kinds of molecules. For proper biological function it is important for these biomolecules to act at an individual level and act at the level of interaction of these molecules. In this paper some of the logical impossibilities that may arise in the biological networks and their possible solutions are discussed. It may be important to understand these paradoxes and their possible solutions in order to develop a holistic view of biological function.
Logic reliability analysis of adaptive control strategies
An approach is developed for the evaluation of the reliability of logic of adaptive control strategies, taking into account logic structural complexity and potential failure of programming modules. Flaws in the control system algorithm may not be discovered during debugging or initial testing and may only affect the performance under abnormal situations although the system may appear reliable in normal operations. Considering an adaptive control system designed for use in control of equipment employed in nuclear power stations, logic reliability evaluation is demonstrated. The approach given is applicable to any other designs and may be used to compare different control system logic structures from the reliability viewpoint. Evaluation of the reliability of control systems is essential to automated operation of equipment used in nuclear power plants. (author)
Logical Modes of Attack in Argumentation Networks
Gabbay, Dov M.; Garcez, A. S. D. Avila
2009-01-01
This paper studies methodologically robust options for giving logical contents to nodes in abstract argumentation networks. It defines a variety of notions of attack in terms of the logical contents of the nodes in a network. General properties of logics are refined both in the object level and in the meta level to suit the needs of the application. The network-based system improves upon some of the attempts in the literature to define attacks in terms of defeasible proofs, the...
Neural logic networks a new class of neural networks
Heng, Teh Hoon
1995-01-01
This book is the first of a series of technical reports of a key research project of the Real-World Computing Program supported by the MITI of Japan.The main goal of the project is to model human intelligence by a special class of mathematical systems called neural logic networks.The book consists of three parts. Part 1 describes the general theory of neural logic networks and their potential applications. Part 2 discusses a new logic called Neural Logic which attempts to emulate more closely the logical thinking process of human. Part 3 studies the special features of neural logic networks wh
Reconfigurable logic in nanoelectronic switching networks
We demonstrate how to configure and reconfigure a nanoelectronic nonlinear network to a universal set of logic gates by applying sequences of voltage pulses to the edges of the network. The nanoelectronic device is designed to consist of a self-assembled network of nanoparticles connected by two-terminal linker elements with hysteretic behaviour, allowing voltage-controlled switching between a linear and nonlinear current-voltage characteristic (IVC), making reconfigurable logic possible
Anatomy Ontology Matching Using Markov Logic Networks
Li, Chunhua; Zhao, Pengpeng; Wu, Jian; Cui, Zhiming
2016-01-01
The anatomy of model species is described in ontologies, which are used to standardize the annotations of experimental data, such as gene expression patterns. To compare such data between species, we need to establish relationships between ontologies describing different species. Ontology matching is a kind of solutions to find semantic correspondences between entities of different ontologies. Markov logic networks which unify probabilistic graphical model and first-order logic provide an exc...
Fuzzy logic systems are equivalent to feedforward neural networks
无
2000-01-01
Fuzzy logic systems and feedforward neural networks are equivalent in essence. First, interpolation representations of fuzzy logic systems are introduced and several important conclusions are given. Then three important kinds of neural networks are defined, i.e. linear neural networks, rectangle wave neural networks and nonlinear neural networks. Then it is proved that nonlinear neural networks can be represented by rectangle wave neural networks. Based on the results mentioned above, the equivalence between fuzzy logic systems and feedforward neural networks is proved, which will be very useful for theoretical research or applications on fuzzy logic systems or neural networks by means of combining fuzzy logic systems with neural networks.
Logic Learning in Hopfield Networks
Sathasivam, Saratha
2008-01-01
Synaptic weights for neurons in logic programming can be calculated either by using Hebbian learning or by Wan Abdullah's method. In other words, Hebbian learning for governing events corresponding to some respective program clauses is equivalent with learning using Wan Abdullah's method for the same respective program clauses. In this paper we will evaluate experimentally the equivalence between these two types of learning through computer simulations.
Sorting Network for Reversible Logic Synthesis
Islam, Md Saiful; Mahmud, Abdullah Al; karim, Muhammad Rezaul
2010-01-01
In this paper, we have introduced an algorithm to implement a sorting network for reversible logic synthesis based on swapping bit strings. The algorithm first constructs a network in terms of n*n Toffoli gates read from left to right. The number of gates in the circuit produced by our algorithm is then reduced by template matching and removing useless gates from the network. We have also compared the efficiency of the proposed method with the existing ones.
Diffusion Adaptation over Networks
Sayed, Ali H
2012-01-01
Adaptive networks are well-suited to perform decentralized information processing and optimization tasks and to model various types of self organized and complex behavior encountered in nature. Adaptive networks consist of a collection of agents with processing and learning abilities. The agents are linked together through a connection topology, and they cooperate with each other through local interactions to solve distributed inference problems in real-time. The continuous diffusion of information across the network enables agents to adapt their performance in relation to changing data and network conditions; it also results in improved adaptation and learning performance relative to non-cooperative networks. This article provides an overview of diffusion strategies for adaptation and learning over networks. The article is divided into several sections: 1. Motivation; 2. Mean-Square-Error Estimation; 3. Distributed Optimization via Diffusion Strategies; 4. Adaptive Diffusion Strategies; 5. Performance of Ste...
Logic Tensor Networks: Deep Learning and Logical Reasoning from Data and Knowledge
Serafini, Luciano; Garcez, Artur d'Avila
2016-01-01
We propose Logic Tensor Networks: a uniform framework for integrating automatic learning and reasoning. A logic formalism called Real Logic is defined on a first-order language whereby formulas have truth-value in the interval [0,1] and semantics defined concretely on the domain of real numbers. Logical constants are interpreted as feature vectors of real numbers. Real Logic promotes a well-founded integration of deductive reasoning on a knowledge-base and efficient data-driven relational mac...
Fuzzy logic, neural networks, and soft computing
Zadeh, Lofti A.
1994-01-01
The past few years have witnessed a rapid growth of interest in a cluster of modes of modeling and computation which may be described collectively as soft computing. The distinguishing characteristic of soft computing is that its primary aims are to achieve tractability, robustness, low cost, and high MIQ (machine intelligence quotient) through an exploitation of the tolerance for imprecision and uncertainty. Thus, in soft computing what is usually sought is an approximate solution to a precisely formulated problem or, more typically, an approximate solution to an imprecisely formulated problem. A simple case in point is the problem of parking a car. Generally, humans can park a car rather easily because the final position of the car is not specified exactly. If it were specified to within, say, a few millimeters and a fraction of a degree, it would take hours or days of maneuvering and precise measurements of distance and angular position to solve the problem. What this simple example points to is the fact that, in general, high precision carries a high cost. The challenge, then, is to exploit the tolerance for imprecision by devising methods of computation which lead to an acceptable solution at low cost. By its nature, soft computing is much closer to human reasoning than the traditional modes of computation. At this juncture, the major components of soft computing are fuzzy logic (FL), neural network theory (NN), and probabilistic reasoning techniques (PR), including genetic algorithms, chaos theory, and part of learning theory. Increasingly, these techniques are used in combination to achieve significant improvement in performance and adaptability. Among the important application areas for soft computing are control systems, expert systems, data compression techniques, image processing, and decision support systems. It may be argued that it is soft computing, rather than the traditional hard computing, that should be viewed as the foundation for artificial
Fuzzy logic and neural networks basic concepts & application
Alavala, Chennakesava R
2008-01-01
About the Book: The primary purpose of this book is to provide the student with a comprehensive knowledge of basic concepts of fuzzy logic and neural networks. The hybridization of fuzzy logic and neural networks is also included. No previous knowledge of fuzzy logic and neural networks is required. Fuzzy logic and neural networks have been discussed in detail through illustrative examples, methods and generic applications. Extensive and carefully selected references is an invaluable resource for further study of fuzzy logic and neural networks. Each chapter is followed by a question bank
Quantum logic networks for probabilistic teleportation
刘金明; 张永生; 等
2003-01-01
By eans of the primitive operations consisting of single-qubit gates.two-qubit controlled-not gates,Von Neuman measurement and classically controlled operations.,we construct efficient quantum logic networks for implementing probabilistic teleportation of a single qubit,a two-particle entangled state,and an N-particle entanglement.Based on the quantum networks,we show that after the partially entangled states are concentrated into maximal entanglement,the above three kinds of probabilistic teleportation are the same as the standard teleportation using the corresponding maximally entangled states as the quantum channels.
Quantum logic networks for probabilistic teleportation
刘金明; 张永生; 郭光灿
2003-01-01
By means of the primitive operations consisting of single-qubit gates, two-qubit controlled-not gates, Von Neuman measurement and classically controlled operations, we construct efficient quantum logic networks for implementing probabilistic teleportation of a single qubit, atwo-particle entangled state, and an N-particle entanglement. Based on the quantum networks, we show that after the partially entangled states are concentrated into maximal entanglement,the above three kinds of probabilistic teleportation are the same as the standard teleportation using the corresponding maximally entangled states as the quantum channels.
Dynamic regimes of random fuzzy logic networks
Random multistate networks, generalizations of the Boolean Kauffman networks, are generic models for complex systems of interacting agents. Depending on their mean connectivity, these networks exhibit ordered as well as chaotic behavior with a critical boundary separating both regimes. Typically, the nodes of these networks are assigned single discrete states. Here, we describe nodes by fuzzy numbers, i.e. vectors of degree-of-membership (DOM) functions specifying the degree to which the nodes are in each of their discrete states. This allows our models to deal with imprecision and uncertainties. Compatible update rules are constructed by expressing the update rules of the multistate network in terms of Boolean operators and generalizing them to fuzzy logic (FL) operators. The standard choice for these generalizations is the Goedel FL, where AND and OR are replaced by the minimum and maximum of two DOMs, respectively. In mean-field approximations we are able to analytically describe the percolation and asymptotic distribution of DOMs in random Goedel FL networks. This allows us to characterize the different dynamic regimes of random multistate networks in terms of FL. In a low-dimensional example, we provide explicit computations and validate our mean-field results by showing that they agree well with network simulations.
Dynamic regimes of random fuzzy logic networks
Wittmann, Dominik M.; Theis, Fabian J.
2011-01-01
Random multistate networks, generalizations of the Boolean Kauffman networks, are generic models for complex systems of interacting agents. Depending on their mean connectivity, these networks exhibit ordered as well as chaotic behavior with a critical boundary separating both regimes. Typically, the nodes of these networks are assigned single discrete states. Here, we describe nodes by fuzzy numbers, i.e. vectors of degree-of-membership (DOM) functions specifying the degree to which the nodes are in each of their discrete states. This allows our models to deal with imprecision and uncertainties. Compatible update rules are constructed by expressing the update rules of the multistate network in terms of Boolean operators and generalizing them to fuzzy logic (FL) operators. The standard choice for these generalizations is the Gödel FL, where AND and OR are replaced by the minimum and maximum of two DOMs, respectively. In mean-field approximations we are able to analytically describe the percolation and asymptotic distribution of DOMs in random Gödel FL networks. This allows us to characterize the different dynamic regimes of random multistate networks in terms of FL. In a low-dimensional example, we provide explicit computations and validate our mean-field results by showing that they agree well with network simulations.
Chaudhuri, Arijit
2014-01-01
Combining the two statistical techniques of network sampling and adaptive sampling, this book illustrates the advantages of using them in tandem to effectively capture sparsely located elements in unknown pockets. It shows how network sampling is a reliable guide in capturing inaccessible entities through linked auxiliaries. The text also explores how adaptive sampling is strengthened in information content through subsidiary sampling with devices to mitigate unmanageable expanding sample sizes. Empirical data illustrates the applicability of both methods.
Adaptive network countermeasures.
McClelland-Bane, Randy; Van Randwyk, Jamie A.; Carathimas, Anthony G.; Thomas, Eric D.
2003-10-01
This report describes the results of a two-year LDRD funded by the Differentiating Technologies investment area. The project investigated the use of countermeasures in protecting computer networks as well as how current countermeasures could be changed in order to adapt with both evolving networks and evolving attackers. The work involved collaboration between Sandia employees and students in the Sandia - California Center for Cyber Defenders (CCD) program. We include an explanation of the need for adaptive countermeasures, a description of the architecture we designed to provide adaptive countermeasures, and evaluations of the system.
Compensatory fuzzy logic for intelligent social network analysis
Maikel Y. Leyva-Vázquez; Rafael Bello-Lara; Rafael Alejandro Espín-Andrade
2014-01-01
Fuzzy graph theory has gained in visibility for social network analysis. In this work fuzzy logic and their role in modeling social relational networks is discussed. We present a proposal for extending the fuzzy logic framework to intelligent social network analysis using the good properties of robustness and interpretability of compensatory fuzzy logic. We apply this approach to the concept path importance taking into account the length and strength of the connection. Results obtained with o...
NETWORK INTRUSION DETECTION SYSTEM USING FUZZY LOGIC
R. Shanmugavadivu
2011-02-01
Full Text Available IDS which are increasingly a key part of system defense are used to identify abnormal activities in a computer system. In general, the traditional intrusion detection relies on the extensive knowledge of security experts, in particular, on their familiarity with the computer system to be protected. To reduce this dependence, variousdata-mining and machine learning techniques have been used in the literature. In the proposed system, we have designed fuzzy logic-based system for effectively identifying the intrusion activities within a network. The proposed fuzzy logic-based system can be able to detect an intrusion behavior of the networks since the rule base contains a better set of rules. Here, we have used automated strategy for generation of fuzzy rules, which are obtained from the definite rules using frequent items. The experiments and evaluations of the proposed intrusion detection system are performed with the KDD Cup 99 intrusion detection dataset. The experimentalresults clearly show that the proposed system achieved higher precision in identifying whether the records are normal or attack one.
Probabilistic logic modeling of network reliability for hybrid network architectures
Wyss, G.D.; Schriner, H.K.; Gaylor, T.R.
1996-10-01
Sandia National Laboratories has found that the reliability and failure modes of current-generation network technologies can be effectively modeled using fault tree-based probabilistic logic modeling (PLM) techniques. We have developed fault tree models that include various hierarchical networking technologies and classes of components interconnected in a wide variety of typical and atypical configurations. In this paper we discuss the types of results that can be obtained from PLMs and why these results are of great practical value to network designers and analysts. After providing some mathematical background, we describe the `plug-and-play` fault tree analysis methodology that we have developed for modeling connectivity and the provision of network services in several current- generation network architectures. Finally, we demonstrate the flexibility of the method by modeling the reliability of a hybrid example network that contains several interconnected ethernet, FDDI, and token ring segments. 11 refs., 3 figs., 1 tab.
FUZZY LOGIC BASED ENERGY EFFICIENT PROTOCOL IN WIRELESS SENSOR NETWORKS
Zhan Wei Siew
2012-12-01
Full Text Available Wireless sensor networks (WSNs have been vastly developed due to the advances in microelectromechanical systems (MEMS using WSN to study and monitor the environments towards climates changes. In environmental monitoring, sensors are randomly deployed over the interest area to periodically sense the physical environments for a few months or even a year. Therefore, to prolong the network lifetime with limited battery capacity becomes a challenging issue. Low energy adaptive cluster hierarchical (LEACH is the common clustering protocol that aim to reduce the energy consumption by rotating the heavy workload cluster heads (CHs. The CHs election in LEACH is based on probability model which will lead to inefficient in energy consumption due to least desired CHs location in the network. In WSNs, the CHs location can directly influence the network energy consumption and further affect the network lifetime. In this paper, factors which will affect the network lifetime will be presented and the demonstration of fuzzy logic based CH selection conducted in base station (BS will also be carried out. To select suitable CHs that will prolong the network first node dies (FND round and consistent throughput to the BS, energy level and distance to the BS are selected as fuzzy inputs.
Compensatory fuzzy logic for intelligent social network analysis
Maikel Y. Leyva-Vázquez
2014-10-01
Full Text Available Fuzzy graph theory has gained in visibility for social network analysis. In this work fuzzy logic and their role in modeling social relational networks is discussed. We present a proposal for extending the fuzzy logic framework to intelligent social network analysis using the good properties of robustness and interpretability of compensatory fuzzy logic. We apply this approach to the concept path importance taking into account the length and strength of the connection. Results obtained with our model are more consistent with the way human make decisions. Additionally a case study to illustrate the applicability of the proposal on a coauthorship network is developed. Our main outcome is a new model for social network analysis based on compensatory fuzzy logic that gives more robust results and allows compensation. Moreover this approach makes emphasis in using language for social network analysis.
Communication in quantum networks of logical bus topology
Brougham, T; Jex, I
2009-01-01
Perfect state transfer (PST) is discussed in the context of passive quantum networks with logical bus topology, where many logical nodes communicate using the same shared media, without any external control. The conditions under which, a number of point-to-point PST links may serve as building blocks for the design of such multi-node networks are investigated. The implications of our results are discussed in the context of various Hamiltonians that act on the entire network, and are capable of providing PST between the logical nodes of a prescribed set in a deterministic manner.
Adaptive Dynamic Bayesian Networks
Ng, B M
2007-10-26
A discrete-time Markov process can be compactly modeled as a dynamic Bayesian network (DBN)--a graphical model with nodes representing random variables and directed edges indicating causality between variables. Each node has a probability distribution, conditional on the variables represented by the parent nodes. A DBN's graphical structure encodes fixed conditional dependencies between variables. But in real-world systems, conditional dependencies between variables may be unknown a priori or may vary over time. Model errors can result if the DBN fails to capture all possible interactions between variables. Thus, we explore the representational framework of adaptive DBNs, whose structure and parameters can change from one time step to the next: a distribution's parameters and its set of conditional variables are dynamic. This work builds on recent work in nonparametric Bayesian modeling, such as hierarchical Dirichlet processes, infinite-state hidden Markov networks and structured priors for Bayes net learning. In this paper, we will explain the motivation for our interest in adaptive DBNs, show how popular nonparametric methods are combined to formulate the foundations for adaptive DBNs, and present preliminary results.
Deep Space Network Antenna Logic Controller
Ahlstrom, Harlow; Morgan, Scott; Hames, Peter; Strain, Martha; Owen, Christopher; Shimizu, Kenneth; Wilson, Karen; Shaller, David; Doktomomtaz, Said; Leung, Patrick
2007-01-01
The Antenna Logic Controller (ALC) software controls and monitors the motion control equipment of the 4,000-metric-ton structure of the Deep Space Network 70-meter antenna. This program coordinates the control of 42 hydraulic pumps, while monitoring several interlocks for personnel and equipment safety. Remote operation of the ALC runs via the Antenna Monitor & Control (AMC) computer, which orchestrates the tracking functions of the entire antenna. This software provides a graphical user interface for local control, monitoring, and identification of faults as well as, at a high level, providing for the digital control of the axis brakes so that the servo of the AMC may control the motion of the antenna. Specific functions of the ALC also include routines for startup in cold weather, controlled shutdown for both normal and fault situations, and pump switching on failure. The increased monitoring, the ability to trend key performance characteristics, the improved fault detection and recovery, the centralization of all control at a single panel, and the simplification of the user interface have all reduced the required workforce to run 70-meter antennas. The ALC also increases the antenna availability by reducing the time required to start up the antenna, to diagnose faults, and by providing additional insight into the performance of key parameters that aid in preventive maintenance to avoid key element failure. The ALC User Display (AUD) is a graphical user interface with hierarchical display structure, which provides high-level status information to the operation of the ALC, as well as detailed information for virtually all aspects of the ALC via drill-down displays. The operational status of an item, be it a function or assembly, is shown in the higher-level display. By pressing the item on the display screen, a new screen opens to show more detail of the function/assembly. Navigation tools and the map button allow immediate access to all screens.
Adaptive process control using fuzzy logic and genetic algorithms
Karr, C. L.
1993-01-01
Researchers at the U.S. Bureau of Mines have developed adaptive process control systems in which genetic algorithms (GA's) are used to augment fuzzy logic controllers (FLC's). GA's are search algorithms that rapidly locate near-optimum solutions to a wide spectrum of problems by modeling the search procedures of natural genetics. FLC's are rule based systems that efficiently manipulate a problem environment by modeling the 'rule-of-thumb' strategy used in human decision making. Together, GA's and FLC's possess the capabilities necessary to produce powerful, efficient, and robust adaptive control systems. To perform efficiently, such control systems require a control element to manipulate the problem environment, and a learning element to adjust to the changes in the problem environment. Details of an overall adaptive control system are discussed. A specific laboratory acid-base pH system is used to demonstrate the ideas presented.
Adaptive Process Control with Fuzzy Logic and Genetic Algorithms
Karr, C. L.
1993-01-01
Researchers at the U.S. Bureau of Mines have developed adaptive process control systems in which genetic algorithms (GA's) are used to augment fuzzy logic controllers (FLC's). GA's are search algorithms that rapidly locate near-optimum solutions to a wide spectrum of problems by modeling the search procedures of natural genetics. FLC's are rule based systems that efficiently manipulate a problem environment by modeling the 'rule-of-thumb' strategy used in human decision-making. Together, GA's and FLC's possess the capabilities necessary to produce powerful, efficient, and robust adaptive control systems. To perform efficiently, such control systems require a control element to manipulate the problem environment, an analysis element to recognize changes in the problem environment, and a learning element to adjust to the changes in the problem environment. Details of an overall adaptive control system are discussed. A specific laboratory acid-base pH system is used to demonstrate the ideas presented.
Generalized Adaptive Artificial Neural Networks
Tawel, Raoul
1993-01-01
Mathematical model of supervised learning by artificial neural network provides for simultaneous adjustments of both temperatures of neurons and synaptic weights, and includes feedback as well as feedforward synaptic connections. Extension of mathematical model described in "Adaptive Neurons For Artificial Neural Networks" (NPO-17803). Dynamics of neural network represented in new model by less-restrictive continuous formalism.
Hardwired Logic and Multithread Design in Network Processors
李旭东; 徐扬; 刘斌; 王小军
2004-01-01
High-performance network processors are expected to play an important role in future high-speed routers. This paper focuses on two representative techniques needed for high-performance network processors: hardwired logic design and multithread design. Using hardwired logic, this paper compares a single-thread design with a multithread design, and proposes general models and principles to analyze the clock frequency and the resource cost for these environments. Then, two IP header processing schemes, one in single-thread mode and the other in double-thread mode, are developed using these principles and the implementation results verified the theoretical calculation.
Output-back fuzzy logic systems and equivalence with feedback neural networks
无
2000-01-01
A new idea, output-back fuzzy logic systems, is proposed. It is proved that output-back fuzzy logic systems must be equivalent to feedback neural networks. After the notion of generalized fuzzy logic systems is defined, which contains at least a typical fuzzy logic system and an output-back fuzzy logic system, one important conclusion is drawn that generalized fuzzy logic systems are almost equivalent to neural networks.
Stereo logical analysis of fracture networks
Fractures are always present in geological formations over a large range of scales and their characterization is drastically limited by the virtual impossibility of measuring them in-situ. Therefore, most analyses are based on 1 D and 2D measurements of fracture traces along boreholes or on exposed outcrops which necessitate extrapolation by stereo-logical techniques to 3D. Such extrapolations have already been made for specific fracture shapes by Warburton, Piggott, Berkowitz and Adler and Sisavath et al. The purpose of this communication is to survey our recent works in this field
Adaptive Background subtraction in Dynamic Environments Using Fuzzy Logic
Sivabalakrishnan.M
2010-03-01
Full Text Available Extracting a background from an image is the enabling step for many high-level vision processing tasks, such as object tracking andactivity analysis. Although there are a number of object extraction algorithms proposed in the literature, most approaches work efficiently only in constrained environments where the background isrelatively simple and static. We extracted features from image regions, accumulated the feature information over time, fused high-level knowledge with low-level features, and built a time-varyingbackground model. A problem with our system is that by adapting the background model, objects moved are difficult to handle. In order to reinsert them into the background, we run the risk of cutting off part of the object. In this paper, we develop a fuzzy logic inference system to detach the moving object from the background. Our experimental results demonstrate that the fuzzy inference system is very efficient and robust.
Povilaitytė, Živilė
2014-01-01
Given the novelty of political campaigning on social networking sites in Lithuania and the critique, it has received from social media experts, the object of the master thesis encompasses mediatization of Lithuanian politics on Facebook, when social media logic becomes adapted in political campaigning and integrated into the political agenda. Accordingly, the MA thesis developed its aim to define in what ways mediatization of Lithuanian politics manifests on Facebook and if public-relatio...
Inferring User Preferences by Probabilistic Logical Reasoning over Social Networks
Li, Jiwei; Ritter, Alan; Jurafsky, Dan
2014-01-01
We propose a framework for inferring the latent attitudes or preferences of users by performing probabilistic first-order logical reasoning over the social network graph. Our method answers questions about Twitter users like {\\em Does this user like sushi?} or {\\em Is this user a New York Knicks fan?} by building a probabilistic model that reasons over user attributes (the user's location or gender) and the social network (the user's friends and spouse), via inferences like homophily (I am mo...
A high-speed interconnect network using ternary logic
Madsen, Jens Kargaard; Long, S. I.
1995-01-01
This paper describes the design and implementation of a high-speed interconnect network (ICN) for a multiprocessor system using ternary logic. By using ternary logic and a fast point-to-point communication technique called STARI (Self-Timed At Receiver's Input), the communication between the...... of two LSI GaAs chips, Interface and Crossbar, which were implemented in a 0.8 μm MESFET process. In a 4×4 ICN, communication at 300 Mbit/s per wire was demonstrated, which is twice as fast as pure synchronous and four times faster than pure asynchronous communication in the specific test set-up...
Logical Graphics Design Technique for Drawing Distribution Networks
Al-A`Ali, Mansoor
Electricity distribution networks normally consist of tens of primary feeders, thousands of substations and switching stations spread over large geographical areas and thus require a complex system in order to manage them properly from within the distribution control centre. We show techniques for using Delphi Object Oriented components to automatically generate, display and manage graphically and logically the circuits of the network. The graphics components are dynamically interactive and thus the system allows switching operations as well as displays. The object oriented approach was developed to replace an older system, which used Microstation with MDL as the programming language and ORACLE as the DBMS. Before this, the circuits could only be displayed schematically, which has many inherent problems in speed and readability of large displays. Schematic graphics displays were cumbersome when adding or deleting stations; this problem is now resolved using our approach by logically generating the graphics from the database connectivity information. This paper demonstrates the method of designing these Object Oriented components and how they can be used in specially created algorithms to generate the necessary interactive graphics. Four different logical display algorithms were created and in this study we present samples of the four different outputs of these algorithms which prove that distribution engineers can work with logical display of the circuits which are aimed to speed up the switching operations and for better clarity of the display.
The Balanced Cross-Layer Design Routing Algorithm in Wireless Sensor Networks Using Fuzzy Logic.
Li, Ning; Martínez, José-Fernán; Hernández Díaz, Vicente
2015-01-01
Recently, the cross-layer design for the wireless sensor network communication protocol has become more and more important and popular. Considering the disadvantages of the traditional cross-layer routing algorithms, in this paper we propose a new fuzzy logic-based routing algorithm, named the Balanced Cross-layer Fuzzy Logic (BCFL) routing algorithm. In BCFL, we use the cross-layer parameters' dispersion as the fuzzy logic inference system inputs. Moreover, we give each cross-layer parameter a dynamic weight according the value of the dispersion. For getting a balanced solution, the parameter whose dispersion is large will have small weight, and vice versa. In order to compare it with the traditional cross-layer routing algorithms, BCFL is evaluated through extensive simulations. The simulation results show that the new routing algorithm can handle the multiple constraints without increasing the complexity of the algorithm and can achieve the most balanced performance on selecting the next hop relay node. Moreover, the Balanced Cross-layer Fuzzy Logic routing algorithm can adapt to the dynamic changing of the network conditions and topology effectively. PMID:26266412
Decoupled Adapt-then-Combine diffusion networks with adaptive combiners
Fernandez-Bes, Jesus; Arenas-García, Jerónimo; Silva, Magno T. M.; Azpicueta-Ruiz, Luis A.
2015-01-01
In this paper we analyze a novel diffusion strategy for adaptive networks called Decoupled Adapt-then-Combine, which keeps a fully local estimate of the solution for the adaptation step. Our strategy, which is specially convenient for heterogeneous networks, is compared with the standard Adapt-then-Combine scheme and theoretically analyzed using energy conservation arguments. Such comparison shows the need of implementing adaptive combiners for both schemes to obtain a good performance in cas...
Ramstroem, Erik [TPS Termiska Processer AB, Nykoeping (Sweden)
2002-04-01
Grate-control is a complex task in many ways. The relations between controlled variables and the values they depend on are mostly unknown. Research projects are going on to create grate models based on physical laws. Those models are too complex for control implementation. The evaluation time is to long for control use. Another fundamental difficulty is that the relationships are none linear. That is, for a specific change in control value, the change in controlled value depends on the original size of control value, process disturbances and controlled values. There are extensive theories for linear process control. Non-linear control theory is used in robotic applications, but not in process and combustion control. The aim of grate control is to use as much of the grate area as possible, without having unburned material in ash. The outlined strategy is: To keep the position of the final bum out zone constant and its extension controlled. The control variables should be primary airflow, distribution of primary air, and fuel flow. Disturbances that should be measured are the fuel moisture content, the temperature of primary air and the grate temperature under the fuel bed. Technologies used are, fuzzy-logic and neural networks. A combination of booth could be used as well as any of them separately. A Fuzzy-logic controller acts as a computerised operator. Rules are specified with 'if - then' thesis. An example of that is: - if temperature is low, then close the valve The boundaries between the rules are made fuzzy. That makes it possible for the temperature to be just a bit low, which makes the valve open a bit. A lot of rules are created so that the controller knows what to do in every situation. Neural networks are sort of multi dimensional curves, with arbitrary degrees of freedom. The nets are used to predict future process values from measured ones. The model is evaluated from collected data. Parameters are adjusted for best correspondence between
Development of standard logic network for PWR NSSS system design
The self-reliance of NSSS System Design is required not only the design capability to perform the system design but also the management capability to control the resource and time for the Project effectively. The purpose of this study is to develop the simplified standard Logic Network that is scheduled on the time and resource using the PERT/CPM method. That is mainly focused on Ulchin 3, 4 Project. We prepare the management tool of NSSS System Design project. And we can utilize it as a reference tool for the similar project which are complex and long term in a next project. (Author)
Fuzzy Logic Based Multi User Adaptive Test System
2014-01-01
The present proliferation of e-learning has been actively underway for the last 10 years. Current research in Adaptive Testing System focuses on the development of psychometric models with items selection strategies applicable to adaptive testing processes. The key aspect of proposed Adaptive Testing System is to develop an increasingly sophisticated latent trait model which can assist users in developing and enhancing their skills. Computerized Adaptive Test (CAT) System requires a lot of in...
Logical Sensor Network: An Abstraction of Sensor Data Processing over Multidomain Sensor Network
Naoya Namatame; Jin Nakazawa; Hideyuki Tokuda
2012-01-01
This paper focuses on a sensor network virtualization over multidomain sensor network and proposes an abstraction called “logical sensor network (LSN)” for sensor data processing. In the proposed abstraction, processing is a directed acyclic graph that consists of nodes and streams, which represents a small data processor and communication rules between them, respectively. We have added a notion of a trigger to this graph. A trigger represents a timing of the process execution. We have implem...
Uncovering transcriptional interactions via an adaptive fuzzy logic approach
Chen Chung-Ming
2009-12-01
Full Text Available Abstract Background To date, only a limited number of transcriptional regulatory interactions have been uncovered. In a pilot study integrating sequence data with microarray data, a position weight matrix (PWM performed poorly in inferring transcriptional interactions (TIs, which represent physical interactions between transcription factors (TF and upstream sequences of target genes. Inferring a TI means that the promoter sequence of a target is inferred to match the consensus sequence motifs of a potential TF, and their interaction type such as AT or RT is also predicted. Thus, a robust PWM (rPWM was developed to search for consensus sequence motifs. In addition to rPWM, one feature extracted from ChIP-chip data was incorporated to identify potential TIs under specific conditions. An interaction type classifier was assembled to predict activation/repression of potential TIs using microarray data. This approach, combining an adaptive (learning fuzzy inference system and an interaction type classifier to predict transcriptional regulatory networks, was named AdaFuzzy. Results AdaFuzzy was applied to predict TIs using real genomics data from Saccharomyces cerevisiae. Following one of the latest advances in predicting TIs, constrained probabilistic sparse matrix factorization (cPSMF, and using 19 transcription factors (TFs, we compared AdaFuzzy to four well-known approaches using over-representation analysis and gene set enrichment analysis. AdaFuzzy outperformed these four algorithms. Furthermore, AdaFuzzy was shown to perform comparably to 'ChIP-experimental method' in inferring TIs identified by two sets of large scale ChIP-chip data, respectively. AdaFuzzy was also able to classify all predicted TIs into one or more of the four promoter architectures. The results coincided with known promoter architectures in yeast and provided insights into transcriptional regulatory mechanisms. Conclusion AdaFuzzy successfully integrates multiple types of
Particle Swarm Optimization Based Adaptive Strategy for Tuning of Fuzzy Logic Controller
Sree Bash Chandra Debnath; Pintu Chandra Shill; Kazuyuki Murase
2013-01-01
This paper presents a new method for learning and tuning a fuzzy logic controller automatically by means of a particle swarm optimization (PSO). The proposed self-learning fuzzy logic control that uses the PSO with adaptive abilities can learn the fuzzy conclusion tables, their corresponding membership functions and fitness value where the optimization only considers certain points of the membership functions. To exhibit the effectiveness of proposed algorithm, it is used to optim...
An Adaptive Fuzzy-Logic Traffic Control System in Conditions of Saturated Transport Stream
Marakhimov, A. R.; Igamberdiev, H. Z.; Umarov, Sh. X.
2016-01-01
This paper considers the problem of building adaptive fuzzy-logic traffic control systems (AFLTCS) to deal with information fuzziness and uncertainty in case of heavy traffic streams. Methods of formal description of traffic control on the crossroads based on fuzzy sets and fuzzy logic are proposed. This paper also provides efficient algorithms for implementing AFLTCS and develops the appropriate simulation models to test the efficiency of suggested approach. PMID:27517081
Fluctuating epidemics on adaptive networks
Shaw, Leah B
2008-01-01
A model for epidemics on an adaptive network is considered. Nodes follow an SIRS (susceptible-infective-recovered-susceptible) pattern. Connections are rewired to break links from non-infected nodes to infected nodes and are reformed to connect to other non-infected nodes, as the nodes that are not infected try to avoid the infection. Monte Carlo simulation and numerical solution of a mean field model are employed. The introduction of rewiring affects both the network structure and the epidemic dynamics. Degree distributions are altered, and the average distance from a node to the nearest infective increases. The rewiring leads to regions of bistability where either an endemic or a disease-free steady state can exist. Fluctuations around the endemic state and the lifetime of the endemic state are considered. The fluctuations are found to exhibit power law behavior.
Sensor Network Self-Localization Using Fuzzy Logic
Arash Dana
2007-12-01
Full Text Available Location awareness is an important capability for a series of enhanced wireless businesses. sensor networks are dense wireless networks of small low cost sensors, which collect and disseminate environmental data, for monitoring, military application and so on. Localization is an unconstrained optimization problem. position estimation is based on various, distance / path measures, which include anchor and non-anchor nodes. Anchor positions, have been predetermined to help us localize other nodes. This study proposes using a combination of fuzzy techniques, and advanced APS method, to estimate unknown nodes. In a network with twenty hundred nodes of which twenty percent operates as anchors. These nodes localize the other one hundred and sixties. It is necessary to select the best four anchors for localizing. We suppose that the anchors neighbor to unknown nodes are the best. It is time-consuming to find the distance of unknown anchors in such a widespread network. Using the fuzzy logic, putting the limitation of distance, and selecting the nearest anchor to the unknown node, the nearest four anchoress can be selected. In this case the rate of localization error will be decreased due to selecting neighbor anchors. Therefore, we can localize nodes by using ad-hoc positioning system. Fuzzy rules help us to estimate position in less than 2.4 seconds with mean normal positioning deviation of z =0.4597.
An evidential path logic for multi-relational networks
Rodriguez, Marko A [Los Alamos National Laboratory; Geldart, Joe [UNIV OF DURHAM
2008-01-01
Multi-relational networks are used extensively to structure knowledge. Perhaps the most popular instance, due to the widespread adoption of the Semantic Web, is the Resource Description Framework (RDF). One of the primary purposes of a knowledge network is to reason; that is, to alter the topology of the network according to an algorithm that uses the existing topological structure as its input. There exist many such reasoning algorithms. With respect to the Semantic Web, the bivalent, axiomatic reasoners of the RDF Schema (RDFS) and the Web Ontology Language (OWL) are the most prevalent. However, nothing prevents other forms of reasoning from existing in the Semantic Web. This article presents a non-bivalent, non-axiomatic, evidential logic and reasoner that is an algebraic ring over a multi-relational network and two binary operations that can be composed to perform various forms of inference. Given its multi-relational grounding, it is possible to use the presented evidential framework as another method for structuring knowledge and reasoning in the Semantic Web. The benefits of this framework are that it works with arbitrary, partial, and contradictory knowledge while, at the same time, supporting a tractable approximate reasoning process.
Adaptive Dynamics of Regulatory Networks: Size Matters
Martinetz Thomas
2009-01-01
Full Text Available To accomplish adaptability, all living organisms are constructed of regulatory networks on different levels which are capable to differentially respond to a variety of environmental inputs. Structure of regulatory networks determines their phenotypical plasticity, that is, the degree of detail and appropriateness of regulatory replies to environmental or developmental challenges. This regulatory network structure is encoded within the genotype. Our conceptual simulation study investigates how network structure constrains the evolution of networks and their adaptive abilities. The focus is on the structural parameter network size. We show that small regulatory networks adapt fast, but not as good as larger networks in the longer perspective. Selection leads to an optimal network size dependent on heterogeneity of the environment and time pressure of adaptation. Optimal mutation rates are higher for smaller networks. We put special emphasis on discussing our simulation results on the background of functional observations from experimental and evolutionary biology.
Particle Swarm Optimization Based Adaptive Strategy for Tuning of Fuzzy Logic Controller
Sree Bash Chandra Debnath
2013-01-01
Full Text Available This paper presents a new method for learning and tuning a fuzzy logic controller automatically by means of a particle swarm optimization (PSO. The proposed self-learning fuzzy logic control that uses the PSO with adaptive abilities can learn the fuzzy conclusion tables, their corresponding membership functions and fitness value where the optimization only considers certain points of the membership functions. To exhibit the effectiveness of proposed algorithm, it is used to optimize the Gaussian membership functions of the fuzzy model of a nonlinear problem. Moreover, in order to design an effective adaptive fuzzy logic controller, an on line adaptive PSO based mechanism is presented to determine the parameters of the fuzzy mechanisms. Simulation results on two nonlinear problems are derived to demonstrate the powerful PSO learning algorithm and the proposed method is able to find good controllers better than neural controller and conventional controller for the target problem, cart pole type inverted pendulum system.
Evolution of a designless nanoparticle network into reconfigurable Boolean logic
Bose, S. K.; Lawrence, C. P.; Liu, Z.; Makarenko, K. S.; van Damme, R. M. J.; Broersma, H. J.; van der Wiel, W. G.
2015-12-01
Natural computers exploit the emergent properties and massive parallelism of interconnected networks of locally active components. Evolution has resulted in systems that compute quickly and that use energy efficiently, utilizing whatever physical properties are exploitable. Man-made computers, on the other hand, are based on circuits of functional units that follow given design rules. Hence, potentially exploitable physical processes, such as capacitive crosstalk, to solve a problem are left out. Until now, designless nanoscale networks of inanimate matter that exhibit robust computational functionality had not been realized. Here we artificially evolve the electrical properties of a disordered nanomaterials system (by optimizing the values of control voltages using a genetic algorithm) to perform computational tasks reconfigurably. We exploit the rich behaviour that emerges from interconnected metal nanoparticles, which act as strongly nonlinear single-electron transistors, and find that this nanoscale architecture can be configured in situ into any Boolean logic gate. This universal, reconfigurable gate would require about ten transistors in a conventional circuit. Our system meets the criteria for the physical realization of (cellular) neural networks: universality (arbitrary Boolean functions), compactness, robustness and evolvability, which implies scalability to perform more advanced tasks. Our evolutionary approach works around device-to-device variations and the accompanying uncertainties in performance. Moreover, it bears a great potential for more energy-efficient computation, and for solving problems that are very hard to tackle in conventional architectures.
Evolution of a designless nanoparticle network into reconfigurable Boolean logic.
Bose, S K; Lawrence, C P; Liu, Z; Makarenko, K S; van Damme, R M J; Broersma, H J; van der Wiel, W G
2015-12-01
Natural computers exploit the emergent properties and massive parallelism of interconnected networks of locally active components. Evolution has resulted in systems that compute quickly and that use energy efficiently, utilizing whatever physical properties are exploitable. Man-made computers, on the other hand, are based on circuits of functional units that follow given design rules. Hence, potentially exploitable physical processes, such as capacitive crosstalk, to solve a problem are left out. Until now, designless nanoscale networks of inanimate matter that exhibit robust computational functionality had not been realized. Here we artificially evolve the electrical properties of a disordered nanomaterials system (by optimizing the values of control voltages using a genetic algorithm) to perform computational tasks reconfigurably. We exploit the rich behaviour that emerges from interconnected metal nanoparticles, which act as strongly nonlinear single-electron transistors, and find that this nanoscale architecture can be configured in situ into any Boolean logic gate. This universal, reconfigurable gate would require about ten transistors in a conventional circuit. Our system meets the criteria for the physical realization of (cellular) neural networks: universality (arbitrary Boolean functions), compactness, robustness and evolvability, which implies scalability to perform more advanced tasks. Our evolutionary approach works around device-to-device variations and the accompanying uncertainties in performance. Moreover, it bears a great potential for more energy-efficient computation, and for solving problems that are very hard to tackle in conventional architectures. PMID:26389658
Fuzzy Logic Control Based QoS Management in Wireless Sensor/Actuator Networks
Xia, Feng; Sun, Youxian; Tian, Yu-Chu
2008-01-01
Wireless sensor/actuator networks (WSANs) are emerging rapidly as a new generation of sensor networks. Despite intensive research in wireless sensor networks (WSNs), limited work has been found in the open literature in the field of WSANs. In particular, quality-of-service (QoS) management in WSANs remains an important issue yet to be investigated. As an attempt in this direction, this paper develops a fuzzy logic control based QoS management (FLC-QM) scheme for WSANs with constrained resources and in dynamic and unpredictable environments. Taking advantage of the feedback control technology, this scheme deals with the impact of unpredictable changes in traffic load on the QoS of WSANs. It utilizes a fuzzy logic controller inside each source sensor node to adapt sampling period to the deadline miss ratio associated with data transmission from the sensor to the actuator. The deadline miss ratio is maintained at a pre-determined desired level so that the required QoS can be achieved. The FLC-QM has the advantag...
Fuzzy Optimized Metric for Adaptive Network Routing
Ahmad Khader Haboush
2012-04-01
Full Text Available Network routing algorithms used today calculate least cost (shortest paths between nodes. The cost of a path is the sum of the cost of all links on that path. The use of a single metric for adaptive routing is insufficient to reflect the actual state of the link. In general, there is a limitation on the accuracy of the link state information obtained by the routing protocol. Hence it becomes useful if two or more metrics can be associated to produce a single metric that can describe the state of the link more accurately. In this paper, a fuzzy inference rule base is implemented to generate the fuzzy cost of each candidate path to be used in routing the incoming calls. This fuzzy cost is based on the crisp values of the different metrics; a fuzzy membership function is defined. The parameters of these membership functions reflect dynamically the requirement of the incoming traffic service as well as the current state of the links in the path. And this paper investigates how three metrics, the mean link bandwidth, queue utilization and the mean link delay, can be related using a simple fuzzy logic algorithm to produce a optimized cost of the link for a certain interval that is more „precise‟ than either of the single metric, to solve routing problem .
The Adaptive Control of Nonlinear Systems Using the T-S-K Fuzzy Logic
Martin Kratmüller
2009-07-01
Full Text Available Fuzzy adaptive tracking controllers for a class of uncertain nonlinear dynamicalsystems are proposed and analyzed. The controller consists of adaptive and robustifyingcomponents whose role is to nullify the effect of uncertainties and achieve a desiredtracking performance. The interactions between the two components have beeninvestigated. We use the Takagi-Sugeno-Kang type of the fuzzy logic system to approximatethe controller. It is proved that the closed-loop system using this adaptive fuzzy controlleris globally stable in the sense that all signals involved are bounded. Finally, we apply themethod of direct adaptive fuzzy controllers to control an inverted pendulum and thesimulation results are included.
Learning ground CP-logic theories by leveraging Bayesian network learning techniques
Meert, Wannes; Struyf, Jan; Blockeel, Hendrik
2008-01-01
Causal relations are present in many application domains. Causal Probabilistic Logic (CP-logic) is a probabilistic modeling language that is especially designed to express such relations. This paper investigates the learning of CP-logic theories (CP-theories) from training data. Its ﬁrst contribution is SEM-CP-logic, an algorithm that learns CP-theories by leveraging Bayesian network (BN) learning techniques. SEM-CP-logic is based on a transformation between CP-theories and BNs. That is, the ...
Monitoring nuclear reactor systems using neural networks and fuzzy logic
A new approach is presented that demonstrates the potential of trained artificial neural networks (ANNs) as generators of membership functions for the purpose of monitoring nuclear reactor systems. ANN's provide a complex-to-simple mapping of reactor parameters in a process analogous to that of measurement. Through such virtual measurements the value of parameters with operational significance, e.g., control-valve-disk-position, valve-line-up-or performance can be determined. In the methodology presented the output of virtual measuring device is a set of membership functions which independently represent different states of the system. Utilizing a fuzzy logic representation offers the advantage of describing the state of the system in a condensed form, developed through linguistic descriptions and convenient for application in monitoring, diagnostics and generally control algorithms. The developed methodology is applied to the problem of measuring the disk position of the secondary flow control is clearly demonstrated as well as a method for selecting the actual output. The results suggest that it is possible to construct virtual measuring devices through artificial neural networks mapping dynamic time series to a set of membership functions and thus enhance the capability of monitoring systems
Monitoring nuclear reactor systems using neural networks and fuzzy logic
A new approach is presented that demonstrates the potential of trained artificial neural networks (ANNs) as generators of membership functions for the purpose of monitoring nuclear reactor systems. ANN's provide a complex-to-simple mapping of reactor parameters in a process analogous to that of measurement. Through such ''virtual measurements'' the value of parameters with operational significance, e.g., control-valve-disk-position, valve-line-up or performance can be determined. In the methodology presented the output of a virtual measuring device is a set of membership functions which independently represent different states of the system. Utilizing a fuzzy logic representation offers the advantage of describing the state of the system in a condensed form, developed through linguistic descriptions and convenient for application in monitoring, diagnostics and generally control algorithms. The developed methodology is applied to the problem of measuring the disk position of the secondary flow control valve of an experimental reactor using data obtained during a start-up. The enhanced noise tolerance of the methodology is clearly demonstrated as well as a method for selecting the actual output. The results suggest that it is possible to construct virtual measuring devices through artificial neural networks mapping dynamic time series to a set of membership functions and thus enhance the capability of monitoring systems. 8 refs., 11 figs., 1 tab
Fluid Intelligence and Psychosocial Outcome: From Logical Problem Solving to Social Adaptation
David Huepe; María Roca; Natalia Salas; Andrés Canales-Johnson; Álvaro A Rivera-Rei; Leandro Zamorano; Aimée Concepción; Facundo Manes; Agustín Ibañez
2011-01-01
BACKGROUND: While fluid intelligence has proved to be central to executive functioning, logical reasoning and other frontal functions, the role of this ability in psychosocial adaptation has not been well characterized. METHODOLOGY/PRINCIPAL FINDINGS: A random-probabilistic sample of 2370 secondary school students completed measures of fluid intelligence (Raven's Progressive Matrices, RPM) and several measures of psychological adaptation: bullying (Delaware Bullying Questionnaire), domestic a...
Computation emerges from adaptive synchronization of networking neurons.
Zanin, Massimiliano; Del Pozo, Francisco; Boccaletti, Stefano
2011-01-01
The activity of networking neurons is largely characterized by the alternation of synchronous and asynchronous spiking sequences. One of the most relevant challenges that scientists are facing today is, then, relating that evidence with the fundamental mechanisms through which the brain computes and processes information, as well as with the arousal (or progress) of a number of neurological illnesses. In other words, the problem is how to associate an organized dynamics of interacting neural assemblies to a computational task. Here we show that computation can be seen as a feature emerging from the collective dynamics of an ensemble of networking neurons, which interact by means of adaptive dynamical connections. Namely, by associating logical states to synchronous neuron's dynamics, we show how the usual Boolean logics can be fully recovered, and a universal Turing machine can be constructed. Furthermore, we show that, besides the static binary gates, a wider class of logical operations can be efficiently constructed as the fundamental computational elements interact within an adaptive network, each operation being represented by a specific motif. Our approach qualitatively differs from the past attempts to encode information and compute with complex systems, where computation was instead the consequence of the application of control loops enforcing a desired state into the specific system's dynamics. Being the result of an emergent process, the computation mechanism here described is not limited to a binary Boolean logic, but it can involve a much larger number of states. As such, our results can enlighten new concepts for the understanding of the real computing processes taking place in the brain. PMID:22073167
Computation emerges from adaptive synchronization of networking neurons.
Massimiliano Zanin
Full Text Available The activity of networking neurons is largely characterized by the alternation of synchronous and asynchronous spiking sequences. One of the most relevant challenges that scientists are facing today is, then, relating that evidence with the fundamental mechanisms through which the brain computes and processes information, as well as with the arousal (or progress of a number of neurological illnesses. In other words, the problem is how to associate an organized dynamics of interacting neural assemblies to a computational task. Here we show that computation can be seen as a feature emerging from the collective dynamics of an ensemble of networking neurons, which interact by means of adaptive dynamical connections. Namely, by associating logical states to synchronous neuron's dynamics, we show how the usual Boolean logics can be fully recovered, and a universal Turing machine can be constructed. Furthermore, we show that, besides the static binary gates, a wider class of logical operations can be efficiently constructed as the fundamental computational elements interact within an adaptive network, each operation being represented by a specific motif. Our approach qualitatively differs from the past attempts to encode information and compute with complex systems, where computation was instead the consequence of the application of control loops enforcing a desired state into the specific system's dynamics. Being the result of an emergent process, the computation mechanism here described is not limited to a binary Boolean logic, but it can involve a much larger number of states. As such, our results can enlighten new concepts for the understanding of the real computing processes taking place in the brain.
Holistic Evaluation of Novel Adaptation Logics for DASH and SVC
Sieber, Christian
2013-01-01
Streaming of videos has become the major traffic generator in today's Internet and the video traffic share is still increasing. According to Cisco's annual Visual Networking Index report, in 2012, 60% of the global Internet IP traffic was generated by video streaming services. Furthermore, the study predicts further increase to 73% by 2017. At the same time, advances in the fields of mobile communications and embedded devices lead to a widespread adoption of Internet video enabled mobile and ...
Adaptive Networks: the Governance for Sustainable Development
S.G. Nooteboom (Sibout)
2006-01-01
textabstractIn this book, I reconstruct how policy makers, working together in what I term adaptive networks, have enabled a breakthrough in thinking about sustainable mobility in certain policy circles. I define the conduct of leading actors in these adaptive networks as sustainable change manag
Quantum logic networks for cloning a quantum state near a given state
Two quantum logic networks are proposed to simulate a cloning machine that copies the states near a given one. Probabilistic cloning based on the first network is realized and the cloning probability of success based on the second network is 100%. Therefore, the second network is more motivative than the first one. (general)
Quantum logic networks for cloning a quantum state near a given state
Zhou Yan-Hui
2011-01-01
Two quantum logic networks are proposed to simulate a cloning machine that copies the states near a given one.Probabilistic cloning based on the first network is realized and the cloning probability of success based on the second network is 100%.Therefore,the second network is more motivative than the first one.
Dynamical Adaptation in Terrorist Cells/Networks
Hussain, Dil Muhammad Akbar; Ahmed, Zaki
2010-01-01
followers etc. In this research we analyze and predict the most likely role a particular node can adapt once a member of the network is either killed or caught. The adaptation is based on computing Bayes posteriori probability of each node and the level of the said node in the network structure.......Typical terrorist cells/networks have dynamical structure as they evolve or adapt to changes which may occur due to capturing or killing of a member of the cell/network. Analytical measures in graph theory like degree centrality, betweenness and closeness centralities are very common and have long...
Adaptive cluster synchronization in complex dynamical networks
Cluster synchronization is investigated in different complex dynamical networks. In this Letter, a novel adaptive strategy is proposed to make a complex dynamical network achieve cluster synchronization, where the adaptive strategy of one edge is adjusted only according to its local information. A sufficient condition about the global stability arbitrarily grouped of cluster synchronization is derived. Several numerical simulations show the effectiveness of the adaptive strategy.
Recruitment dynamics in adaptive social networks
Shkarayev, Maxim S.; Schwartz, Ira B.; Shaw, Leah B.
2011-01-01
We model recruitment in adaptive social networks in the presence of birth and death processes. Recruitment is characterized by nodes changing their status to that of the recruiting class as a result of contact with recruiting nodes. Only a susceptible subset of nodes can be recruited. The recruiting individuals may adapt their connections in order to improve recruitment capabilities, thus changing the network structure adaptively. We derive a mean field theory to predict the dependence of the...
Particle Swarm Optimization Based Adaptive Strategy for Tuning of Fuzzy Logic Controller
Sree Bash Chandra Debnath
2013-02-01
Full Text Available This paper presents a new method for learning and tuning a fuzzy logic controller automatically by meansof a particle swarm optimization (PSO. The proposed self-learning fuzzy logic control that uses the PSOwith adaptive abilities can learn the fuzzy conclusion tables, their corresponding membership functions andfitness value where the optimization only considers certain points of the membership functions. To exhibitthe effectiveness of proposed algorithm, it is used to optimize the Gaussian membership functions of thefuzzy model of a nonlinear problem. Moreover, in order to design an effective adaptive fuzzy logiccontroller, an on line adaptive PSO based mechanism is presented to determine the parameters of the fuzzymechanisms. Simulation results on two nonlinear problems are derived to demonstrate the powerful PSOlearning algorithm and the proposed method is able to find good controllers better than neural controllerand conventional controller for the target problem, cart pole type inverted pendulum system.
Zachariadis, S.; Mascolo, C.
2003-01-01
With the recent developments in wireless networks (Wavelan, Bluetooth) and the sales of mobile computers of any kind (such as laptopcomputers, Personal Digital Assistants (PDAs), mobile phones etc.) soaring, we are experiencing the availability of increasinglypowerful mobile computing environments that can roam across different types of networks. We have also recently witnessed theacceptance of Logical Mobility (LM) techniques, or the ability to ship part of an application or even a complete ...
The Balanced Cross-Layer Design Routing Algorithm in Wireless Sensor Networks Using Fuzzy Logic
Ning Li; José-Fernán Martínez; Vicente Hernández Díaz
2015-01-01
Recently, the cross-layer design for the wireless sensor network communication protocol has become more and more important and popular. Considering the disadvantages of the traditional cross-layer routing algorithms, in this paper we propose a new fuzzy logic-based routing algorithm, named the Balanced Cross-layer Fuzzy Logic (BCFL) routing algorithm. In BCFL, we use the cross-layer parameters’ dispersion as the fuzzy logic inference system inputs. Moreover, we give each cross-layer paramete...
Fuzzy Logic and Neural Networks - a Glimpse of the Future
Manley, Raymond
2015-01-01
In 1965 Lofti Zadeh published his paper on fuzzy set theory , putting it forward as a way of more closely realising the human thought process. Many systems developed to aid human activities have been based on definitive , yes/no, type decision making processes. An example is the way all computers are based on the binary logic system where only two possible and separate logic levels are allowed, a logic 1 or logic 0. However, we know from everyday experience that humans think in terms of vague...
Energy-efficient adaptive wireless network design
Havinga, Paul J. M.; Smit, Gerard J.M.; Bos, Martinus
2000-01-01
Energy efficiency is an important issue for mobile computers since they must rely on their batteries. We present an energy-efficient highly adaptive architecture of a network interface and novel data link layer protocol for wireless networks that provides quality of service (QoS) support for diverse traffic types. Due to the dynamic nature of wireless networks, adaptations are necessary to achieve energy efficiency and an acceptable quality of service. The paper provides a review of ideas and...
Adaptive Control Based On Neural Network
Wei, Sun; Lujin, Zhang; Jinhai, Zou; Siyi, Miao
2009-01-01
In this paper, the adaptive control based on neural network is studied. Firstly, a neural network based adaptive robust tracking control design is proposed for robotic systems under the existence of uncertainties. In this proposed control strategy, the NN is used to identify the modeling uncertainties, and then the disadvantageous effects caused by neural network approximating error and external disturbances in robotic system are counteracted by robust controller. Especially the proposed cont...
Radi Radi
2011-08-01
Full Text Available Constructive Back Propagation Neural Network (CBPNN is a kind of back propagation neural network trained with constructive algorithm. Training of CBPNN is mainly conducted by developing the network’s architecture which commonly done by adding a number of new neuron units on learning process. Training of the network usually implements fixed method to develop its structure gradually by adding new units constantly. Although this method is simple and able to create an adaptive network for data pattern complexity, but it is wasteful and inefficient for computing. New unit addition affects directly to the computational load of training, speed of convergence, and structure of the final neural network. While increases training load significantly, excessive addition of units also tends to generate a large size of final network. Moreover, addition pattern with small unit number tends to drop off the adaptability of the network and extends time of training. Therefore, there is important to design an adaptive structure development pattern for CBPNN in order to minimize computing load of training. This study proposes Fuzzy Logic (FL algorithm to manage and develop structure of CBPNN. FL method was implemented on two models of CBPNN, i.e. designed with one and two hidden layers, used to recognize aroma patterns on an electronic nose system. The results showed that this method is effective to be applied due to its capability to minimize time of training, to reduce load of computational learning, and generate small size of network.
Neural Network Adaptations to Hardware Implementations
Moerland, Perry,; Fiesler,Emile
1997-01-01
In order to take advantage of the massive parallelism offered by artificial neural networks, hardware implementations are essential.However, most standard neural network models are not very suitable for implementation in hardware and adaptations are needed. In this section an overview is given of the various issues that are encountered when mapping an ideal neural network model onto a compact and reliable neural network hardware implementation, like quantization, handling nonuniformities and ...
Neural Network Adaptations to Hardware Implementations
Moerland, Perry,; Fiesler,Emile; Beale, R
1997-01-01
In order to take advantage of the massive parallelism offered by artificial neural networks, hardware implementations are essential. However, most standard neural network models are not very suitable for implementation in hardware and adaptations are needed. In this section an overview is given of the various issues that are encountered when mapping an ideal neural network model onto a compact and reliable neural network hardware implementation, like quantization, handling nonuniformities and...
Quantum Logic Network for Cloning a State Near a Given One Based on Cavity QED
A quantum logic network is constructed to simulate a cloning machine which copies states near a given one. Meanwhile, a scheme for implementing this cloning network based on the technique of cavity quantum electrodynamics (QED) is presented. It is easy to implement this network of cloning machine in the framework of cavity QED and feasible in the experiment. (general)
Quantum Logic Network for Cloning a State Near a Given One Based on Cavity QED
ZHANG Da-Wei; SHAO Xiao-Qiang; ZHU Ai-Dong
2008-01-01
A quantum logic network is constructed to simulate a cloning machine which copies states near a given one. Meanwhile, a scheme for implementing this cloning network based on the technique of cavity quantum electrody-namics (QED) is presented. It is easy to implement this network of cloning machine in the framework of cavity QED and feasible in the experiment.
Computational intelligence synergies of fuzzy logic, neural networks and evolutionary computing
Siddique, Nazmul
2013-01-01
Computational Intelligence: Synergies of Fuzzy Logic, Neural Networks and Evolutionary Computing presents an introduction to some of the cutting edge technological paradigms under the umbrella of computational intelligence. Computational intelligence schemes are investigated with the development of a suitable framework for fuzzy logic, neural networks and evolutionary computing, neuro-fuzzy systems, evolutionary-fuzzy systems and evolutionary neural systems. Applications to linear and non-linear systems are discussed with examples. Key features: Covers all the aspect
Learning ground CP-logic theories by means of Bayesian network techniques
Meert, Wannes; Struyf, Jan; Blockeel, Hendrik
2007-01-01
Causal relationships are present in many application domains. CP-logic is a probabilistic modeling language that is especially designed to express such relationships. This paper investigates the learning of CP-theories from examples, and focusses on structure learning. The proposed approach is based on a transformation between CP-logic theories and Bayesian networks, that is, the method applies Bayesian network learning techniques to learn a CP-theory in the form of an equivalent Bayesian net...
Recruitment dynamics in adaptive social networks
We model recruitment in adaptive social networks in the presence of birth and death processes. Recruitment is characterized by nodes changing their status to that of the recruiting class as a result of contact with recruiting nodes. Only a susceptible subset of nodes can be recruited. The recruiting individuals may adapt their connections in order to improve recruitment capabilities, thus changing the network structure adaptively. We derive a mean-field theory to predict the dependence of the growth threshold of the recruiting class on the adaptation parameter. Furthermore, we investigate the effect of adaptation on the recruitment level, as well as on network topology. The theoretical predictions are compared with direct simulations of the full system. We identify two parameter regimes with qualitatively different bifurcation diagrams depending on whether nodes become susceptible frequently (multiple times in their lifetime) or rarely (much less than once per lifetime). (paper)
Recruitment dynamics in adaptive social networks.
Shkarayev, Maxim S; Schwartz, Ira B; Shaw, Leah B
2013-01-01
We model recruitment in adaptive social networks in the presence of birth and death processes. Recruitment is characterized by nodes changing their status to that of the recruiting class as a result of contact with recruiting nodes. Only a susceptible subset of nodes can be recruited. The recruiting individuals may adapt their connections in order to improve recruitment capabilities, thus changing the network structure adaptively. We derive a mean field theory to predict the dependence of the growth threshold of the recruiting class on the adaptation parameter. Furthermore, we investigate the effect of adaptation on the recruitment level, as well as on network topology. The theoretical predictions are compared with direct simulations of the full system. We identify two parameter regimes with qualitatively different bifurcation diagrams depending on whether nodes become susceptible frequently (multiple times in their lifetime) or rarely (much less than once per lifetime). PMID:25395989
Guarded Second-Order Logic, Spanning Trees, and Network Flows
Blumensath, Achim
2009-01-01
According to a theorem of Courcelle monadic second-order logic and guarded second-order logic (where one can also quantify over sets of edges) have the same expressive power over the class of all countable $k$-sparse hypergraphs. In the first part of the present paper we extend this result to hypergraphs of arbitrary cardinality. In the second part, we present a generalisation dealing with methods to encode sets of vertices by single vertices.
Obstacle avoidance for kinematically redundant robots using an adaptive fuzzy logic algorithm
In this paper the Adaptive Fuzzy Logic approach for solving the inverse kinematics of redundant robots in an environment with obstacles is presented. The obstacles are modeled as convex bodies. A fuzzy rule base that is updated via an adaptive law is used to solve the inverse kinematic problem. Additional rules have been introduced to take care of the obstacles avoidance problem. The proposed method has advantages such as high accuracy, simplicity of computations and generality for all redundant robots. Simulation results illustrate much better tracking performance than the dynamic base solution for a given trajectory in cartesian space, while guaranteeing a collision-free trajectory and observation of a mechanical joint limit
A computer program for the generation of logic networks from task chart data
Herbert, H. E.
1980-01-01
The Network Generation Program (NETGEN), which creates logic networks from task chart data is presented. NETGEN is written in CDC FORTRAN IV (Extended) and runs in a batch mode on the CDC 6000 and CYBER 170 series computers. Data is input via a two-card format and contains information regarding the specific tasks in a project. From this data, NETGEN constructs a logic network of related activities with each activity having unique predecessor and successor nodes, activity duration, descriptions, etc. NETGEN then prepares this data on two files that can be used in the Project Planning Analysis and Reporting System Batch Network Scheduling program and the EZPERT graphics program.
Network measures for characterising team adaptation processes
Barth, S.K.; Schraagen, J.M.C.; Schmettow, M.
2015-01-01
The aim of this study was to advance the conceptualisation of team adaptation by applying social network analysis (SNA) measures in a field study of a paediatric cardiac surgical team adapting to changes in task complexity and ongoing dynamic complexity. Forty surgical procedures were observed by tr
Neural network with dynamically adaptable neurons
Tawel, Raoul (Inventor)
1994-01-01
This invention is an adaptive neuron for use in neural network processors. The adaptive neuron participates in the supervised learning phase of operation on a co-equal basis with the synapse matrix elements by adaptively changing its gain in a similar manner to the change of weights in the synapse IO elements. In this manner, training time is decreased by as much as three orders of magnitude.
Computing single step operators of logic programming in radial basis function neural networks
Logic programming is the process that leads from an original formulation of a computing problem to executable programs. A normal logic program consists of a finite set of clauses. A valuation I of logic programming is a mapping from ground atoms to false or true. The single step operator of any logic programming is defined as a function (Tp:I→I). Logic programming is well-suited to building the artificial intelligence systems. In this study, we established a new technique to compute the single step operators of logic programming in the radial basis function neural networks. To do that, we proposed a new technique to generate the training data sets of single step operators. The training data sets are used to build the neural networks. We used the recurrent radial basis function neural networks to get to the steady state (the fixed point of the operators). To improve the performance of the neural networks, we used the particle swarm optimization algorithm to train the networks
Computing single step operators of logic programming in radial basis function neural networks
Hamadneh, Nawaf; Sathasivam, Saratha; Choon, Ong Hong [School of Mathematical Sciences, Universiti Sains Malaysia, 11800 USM, Penang (Malaysia)
2014-07-10
Logic programming is the process that leads from an original formulation of a computing problem to executable programs. A normal logic program consists of a finite set of clauses. A valuation I of logic programming is a mapping from ground atoms to false or true. The single step operator of any logic programming is defined as a function (T{sub p}:I→I). Logic programming is well-suited to building the artificial intelligence systems. In this study, we established a new technique to compute the single step operators of logic programming in the radial basis function neural networks. To do that, we proposed a new technique to generate the training data sets of single step operators. The training data sets are used to build the neural networks. We used the recurrent radial basis function neural networks to get to the steady state (the fixed point of the operators). To improve the performance of the neural networks, we used the particle swarm optimization algorithm to train the networks.
Adaptive Networks Theory, Models and Applications
Gross, Thilo
2009-01-01
With adaptive, complex networks, the evolution of the network topology and the dynamical processes on the network are equally important and often fundamentally entangled. Recent research has shown that such networks can exhibit a plethora of new phenomena which are ultimately required to describe many real-world networks. Some of those phenomena include robust self-organization towards dynamical criticality, formation of complex global topologies based on simple, local rules, and the spontaneous division of "labor" in which an initially homogenous population of network nodes self-organizes into functionally distinct classes. These are just a few. This book is a state-of-the-art survey of those unique networks. In it, leading researchers set out to define the future scope and direction of some of the most advanced developments in the vast field of complex network science and its applications.
联合模糊逻辑和神经网络的网络选择算法%Joint Fuzzy Logic and Neural Network for Network Selection
李航宇; 刘伟; 郭伟
2013-01-01
Propose a combination of fuzzy logic and neural network adaptive network selection algorithm for network selection in heterogeneous network environment. This method has the mechanism of reinforcement learning, therefore, the parameters of the membership function of fuzzy neural network can be dynamically adjusted online according to the output error, allowing the user to select the optimal network. Finally, a comparison wad made between the network selection algorithm of joint fuzzy logic and neural network and the network selection algorithm based on fuzzy logic. Simulation results show that this method can effectively guarantee that user comfort tends to the expectation of the ideal value to achieve the optimal network access options and to reduce the number of ping - pong effect, and have the higher user comfort compared with the non - adaptive fuzzy logic algorithm.%在网络优化选择问题的研究中,针对异构网络环境下的网络选择的问题,由于网络性能存在差异,提出一种联合模糊逻辑和神经网络的自适应网络选择算法.由于新方法具有学习训练的能力,所以能够根据输出误差对模糊神经网络的隶属度函数的参数进行动态的在线调整,从而使用户选择最优的网络.最后将联合模糊逻辑和神经网络的网络选择算法与基于模糊逻辑的网络选择算法进行了比较.仿真结果表明,改进方法能有效的保证用户舒适度比率趋于期望的理想值,实现了最优的网络接入选择,减少了乒乓效应发生的次数,并且相较于不自适应调整的模糊逻辑算法有更高的用户舒适度比率.
The Distributed Logical Reasoning Language D—Tuili and Its Implementation on Microcomputer Network
高全泉; 陆汝钤; 等
1992-01-01
D－Tuili,having been implemented on microcompute network,is a distributed logical reasoning programming language.D-Tuili supports parallel programming on the language level,and couples loosely with the distributed database management system,so data in distributed databases can be used in the distributed logic programs.In this paper,we mainly introduce the components of D-Tuili used to design distributed logic programs.Furthermore,the main principles to implement D-Tuili and the main technologies adopted in the implemented system of D-Tuili are described.
Performance of Networked DC Motor with Fuzzy Logic Controller
B. Sharmila; N. Devarajan
2010-01-01
In the recent years the usage of data networks has been increased due to its cost effective and flexible applications. A shared data network can effectively reduce complicated wiring connections, installation and maintenance for connecting a complex control system with various sensors, actuators, and controllers as a networked control system. For the time-sensitive application with networked control system the remote dc motor actuation control has been chosen. Due to time-varying network traf...
In-Network Adaptation of Video Streams Using Network Processors
Mohammad Shorfuzzaman
2009-01-01
problem can be addressed, near the network edge, by applying dynamic, in-network adaptation (e.g., transcoding of video streams to meet available connection bandwidth, machine characteristics, and client preferences. In this paper, we extrapolate from earlier work of Shorfuzzaman et al. 2006 in which we implemented and assessed an MPEG-1 transcoding system on the Intel IXP1200 network processor to consider the feasibility of in-network transcoding for other video formats and network processor architectures. The use of “on-the-fly” video adaptation near the edge of the network offers the promise of simpler support for a wide range of end devices with different display, and so forth, characteristics that can be used in different types of environments.
Flight test results of the fuzzy logic adaptive controller-helicopter (FLAC-H)
Wade, Robert L.; Walker, Gregory W.
1996-05-01
The fuzzy logic adaptive controller for helicopters (FLAC-H) demonstration is a cooperative effort between the US Army Simulation, Training, and Instrumentation Command (STRICOM), the US Army Aviation and Troop Command, and the US Army Missile Command to demonstrate a low-cost drone control system for both full-scale and sub-scale helicopters. FLAC-H was demonstrated on one of STRICOM's fleet of full-scale rotary-winged target drones. FLAC-H exploits fuzzy logic in its flight control system to provide a robust solution to the control of the helicopter's dynamic, nonlinear system. Straight forward, common sense fuzzy rules governing helicopter flight are processed instead of complex mathematical models. This has resulted in a simplified solution to the complexities of helicopter flight. Incorporation of fuzzy logic reduced the cost of development and should also reduce the cost of maintenance of the system. An adaptive algorithm allows the FLAC-H to 'learn' how to fly the helicopter, enabling the control system to adjust to varying helicopter configurations. The adaptive algorithm, based on genetic algorithms, alters the fuzzy rules and their related sets to improve the performance characteristics of the system. This learning allows FLAC-H to automatically be integrated into a new airframe, reducing the development costs associated with altering a control system for a new or heavily modified aircraft. Successful flight tests of the FLAC-H on a UH-1H target drone were completed in September 1994 at the White Sands Missile Range in New Mexico. This paper discuses the objective of the system, its design, and performance.
Genetic and logic networks with the signal-inhibitor-activator structure are dynamically robust
LI Fangting; TAN Ning
2006-01-01
The proteins, DNA and RNA interaction networks govern various biological functions in living cells, these networks should be dynamically robust in the intracellular and environmental fluctuations. Here, we use Boolean network to study the robust structure of both genetic and logic networks. First, SOS network in bacteria E. coli, which regulates cell survival and repair after DNA damage, is shown to be dynamically robust. Comparing with cell cycle network in budding yeast and flagella network in E. coli, we find the signal-inhibitor-activator (SIA) structure in transcription regulatory networks. Second, under the dynamical rule that inhibition is much stronger than activation, we have searched 3-node non-self-loop logical networks that are dynamically robust, and that if the attractive basin of a final attractor is as large as seven, and the final attractor has only one active node, then the active node acts as inhibitor, and the SIA and signal-inhibitor (SI) structures are fundamental architectures of robust networks. SIA and SI networks with dynamic robustness against environment uncertainties may be selected and maintained over the course of evolution, rather than blind trial-error testing and be ing an accidental consequence of particular evolutionary history. SIA network can perform a more complex process than SI network, andSIA might be used to design robust artificial genetic network. Our results provide dynamical support for why the inhibitors and SIA/SI structures are frequently employed in cellular regulatory networks.
Towards a logic-based method to infer provenance-aware molecular networks
Aslaoui-Errafi, Zahira; Cohen-Boulakia, Sarah; Froidevaux, Christine; Gloaguen, Pauline; Poupon, Anne; Rougny, Adrien; Yahiaoui, Meriem
2012-01-01
International audience Providing techniques to automatically infer molecular networks is particularly important to understand complex relationships between biological objects. We present a logic-based method to infer such networks and show how it allows inferring signalling networks from the design of a knowledge base. Provenance of inferred data has been carefully collected, allowing quality evaluation. More precisely, our method (i) takes into account various kinds of biological experime...
Traffic Engineering and Quality of Experience in MPLS Network by Fuzzy Logic characterization
Satya Prakash Rout; Palash Ghosal
2015-01-01
This paper proposes a load balancing algorithm using fuzzy logic so that maximum Quality of Experience can be achieved. Avoidance of congestion is one of the major performance objectives of traffic engineering in MPLS networks. Load balancing can prevent the congestion caused due to inefficient allocation of network resources. Another aspect of the network performance is Quality of Experience (QoE). QoE in telecommunications terminology, it is a measurement used to determine how w...
Secure Reprogramming of a Network Connected Device : Securing programmable logic controllers
Tesfaye, Mussie
2012-01-01
This is a master’s thesis project entitled “Secure reprogramming of network connected devices”. The thesis begins by providing some background information to enable the reader to understand the current vulnerabilities of network-connected devices, specifically with regard to cyber security and data integrity. Today supervisory control and data acquisition systems utilizing network connected programmable logic controllers are widely used in many industries and critical infrastructures. These n...
A FUZZY-LOGIC CONTROL ALGORITHM FOR ACTIVE QUEUE MANAGEMENT IN IP NETWORKS
无
2008-01-01
Active Queue Management (AQM) is an active research area in the Internet community. Random Early Detection (RED) is a typical AQM algorithm, but it is known that it is difficult to configure its parameters and its average queue length is closely related to the load level. This paper proposes an effective fuzzy congestion control algorithm based on fuzzy logic which uses the predominance of fuzzy logic to deal with uncertain events. The main advantage of this new congestion control algorithm is that it discards the packet dropping mechanism of RED, and calculates packet loss according to a preconfigured fuzzy logic by using the queue length and the buffer usage ratio. Theoretical analysis and Network Simulator (NS) simulation results show that the proposed algorithm achieves more throughput and more stable queue length than traditional schemes. It really improves a router's ability in network congestion control in IP network.
Fuzzy logic based Adaptive Modulation Using Non Data Aided SNR Estimation for OFDM system
K.SESHADRI SASTRY
2010-06-01
Full Text Available As demand for high quality transmission increases increase of spectrum efficiency and an improvement of error performance in wireless communication systems are important . One of the promising approaches to 4G is adaptive OFDM (AOFDM . Fixed modulation systems uses only one type of modulation scheme (or order, so that either performance or capacity should be compromised Adaptive modulated systems are superior to fixed modulated systems, since they change modulation order depending on present SNR. In an adaptive modulation system SNR estimation is important since performance of adaptive modulated system depends of estimated SNR. Non-data-Aided (NDA SNR estimation systems are gaining importance in recent days since they estimate SNR range and requires less data as input .In this paper we propose an adaptive modulated OFDM system which uses NDA(Non-data Aided SNR estimation using fuzzy logic interface.The proposed system is simulated in Matlab 7.4 and The results of computer simulation show the improvement in system capacity .
Fluid intelligence and psychosocial outcome: from logical problem solving to social adaptation.
David Huepe
Full Text Available BACKGROUND: While fluid intelligence has proved to be central to executive functioning, logical reasoning and other frontal functions, the role of this ability in psychosocial adaptation has not been well characterized. METHODOLOGY/PRINCIPAL FINDINGS: A random-probabilistic sample of 2370 secondary school students completed measures of fluid intelligence (Raven's Progressive Matrices, RPM and several measures of psychological adaptation: bullying (Delaware Bullying Questionnaire, domestic abuse of adolescents (Conflict Tactic Scale, drug intake (ONUDD, self-esteem (Rosenberg's Self Esteem Scale and the Perceived Mental Health Scale (Spanish adaptation. Lower fluid intelligence scores were associated with physical violence, both in the role of victim and victimizer. Drug intake, especially cannabis, cocaine and inhalants and lower self-esteem were also associated with lower fluid intelligence. Finally, scores on the perceived mental health assessment were better when fluid intelligence scores were higher. CONCLUSIONS/SIGNIFICANCE: Our results show evidence of a strong association between psychosocial adaptation and fluid intelligence, suggesting that the latter is not only central to executive functioning but also forms part of a more general capacity for adaptation to social contexts.
Fuzzy Logic Control of Adaptive ARQ for Video Distribution over a Bluetooth Wireless Link
R. Razavi
2007-01-01
Full Text Available Bluetooth's default automatic repeat request (ARQ scheme is not suited to video distribution resulting in missed display and decoded deadlines. Adaptive ARQ with active discard of expired packets from the send buffer is an alternative approach. However, even with the addition of cross-layer adaptation to picture-type packet importance, ARQ is not ideal in conditions of a deteriorating RF channel. The paper presents fuzzy logic control of ARQ, based on send buffer fullness and the head-of-line packet's deadline. The advantage of the fuzzy logic approach, which also scales its output according to picture type importance, is that the impact of delay can be directly introduced to the model, causing retransmissions to be reduced compared to all other schemes. The scheme considers both the delay constraints of the video stream and at the same time avoids send buffer overflow. Tests explore a variety of Bluetooth send buffer sizes and channel conditions. For adverse channel conditions and buffer size, the tests show an improvement of at least 4 dB in video quality compared to nonfuzzy schemes. The scheme can be applied to any codec with I-, P-, and (possibly B-slices by inspection of packet headers without the need for encoder intervention.
A hybrid neural networks-fuzzy logic-genetic algorithm for grade estimation
Tahmasebi, Pejman; Hezarkhani, Ardeshir
2012-05-01
The grade estimation is a quite important and money/time-consuming stage in a mine project, which is considered as a challenge for the geologists and mining engineers due to the structural complexities in mineral ore deposits. To overcome this problem, several artificial intelligence techniques such as Artificial Neural Networks (ANN) and Fuzzy Logic (FL) have recently been employed with various architectures and properties. However, due to the constraints of both methods, they yield the desired results only under the specific circumstances. As an example, one major problem in FL is the difficulty of constructing the membership functions (MFs).Other problems such as architecture and local minima could also be located in ANN designing. Therefore, a new methodology is presented in this paper for grade estimation. This method which is based on ANN and FL is called "Coactive Neuro-Fuzzy Inference System" (CANFIS) which combines two approaches, ANN and FL. The combination of these two artificial intelligence approaches is achieved via the verbal and numerical power of intelligent systems. To improve the performance of this system, a Genetic Algorithm (GA) - as a well-known technique to solve the complex optimization problems - is also employed to optimize the network parameters including learning rate, momentum of the network and the number of MFs for each input. A comparison of these techniques (ANN, Adaptive Neuro-Fuzzy Inference System or ANFIS) with this new method (CANFIS-GA) is also carried out through a case study in Sungun copper deposit, located in East-Azerbaijan, Iran. The results show that CANFIS-GA could be a faster and more accurate alternative to the existing time-consuming methodologies for ore grade estimation and that is, therefore, suggested to be applied for grade estimation in similar problems.
Logical Design and Control of Network in Local Mine Air-Reversing System
无
2001-01-01
This paper sets up a mathematical model of switching network and switching function by utilizing graphtheory to describe the logical function of different paths. The function varies with open and closed states of air doorsin a complex mine air sub-network, and the computer program for solving the switching function of complex net-works are offered. It gives the method for discriminating a reversible branch in a complex network by means of theswitching function, and the method of counter-inverted logical control of airflow inversion by means of open andshort circuit conversion of key branches. The research has solved the problem of the stablization of air flow for nor-mal ventination and reversing ventination in a diagonal network.
Design of intelligent systems based on fuzzy logic, neural networks and nature-inspired optimization
Castillo, Oscar; Kacprzyk, Janusz
2015-01-01
This book presents recent advances on the design of intelligent systems based on fuzzy logic, neural networks and nature-inspired optimization and their application in areas such as, intelligent control and robotics, pattern recognition, time series prediction and optimization of complex problems. The book is organized in eight main parts, which contain a group of papers around a similar subject. The first part consists of papers with the main theme of theoretical aspects of fuzzy logic, which basically consists of papers that propose new concepts and algorithms based on fuzzy systems. The second part contains papers with the main theme of neural networks theory, which are basically papers dealing with new concepts and algorithms in neural networks. The third part contains papers describing applications of neural networks in diverse areas, such as time series prediction and pattern recognition. The fourth part contains papers describing new nature-inspired optimization algorithms. The fifth part presents div...
Satisfiability of logic programming based on radial basis function neural networks
Hamadneh, Nawaf; Sathasivam, Saratha; Tilahun, Surafel Luleseged; Choon, Ong Hong [School of Mathematical Sciences, Universiti Sains Malaysia, 11800 USM, Penang (Malaysia)
2014-07-10
In this paper, we propose a new technique to test the Satisfiability of propositional logic programming and quantified Boolean formula problem in radial basis function neural networks. For this purpose, we built radial basis function neural networks to represent the proportional logic which has exactly three variables in each clause. We used the Prey-predator algorithm to calculate the output weights of the neural networks, while the K-means clustering algorithm is used to determine the hidden parameters (the centers and the widths). Mean of the sum squared error function is used to measure the activity of the two algorithms. We applied the developed technique with the recurrent radial basis function neural networks to represent the quantified Boolean formulas. The new technique can be applied to solve many applications such as electronic circuits and NP-complete problems.
Quantum Logic Networks for Probabilistic Teleportation of an Arbitrary Three-Particle State
QIAN Xue-Min; FANG Jian-Xing; ZHU Shi-Qun; XI Yong-Jun
2005-01-01
The scheme for probabilistic teleportation of an arbitrary three-particle state is proposed. By using single qubit gate and three two-qubit gates, efficient quantum logic networks for probabilistic teleportation of an arbitrary three-particle state are constructed.
Adaptation by Plasticity of Genetic Regulatory Networks
Brenner, Naama
2007-03-01
Genetic regulatory networks have an essential role in adaptation and evolution of cell populations. This role is strongly related to their dynamic properties over intermediate-to-long time scales. We have used the budding yeast as a model Eukaryote to study the long-term dynamics of the genetic regulatory system and its significance in evolution. A continuous cell growth technique (chemostat) allows us to monitor these systems over long times under controlled condition, enabling a quantitative characterization of dynamics: steady states and their stability, transients and relaxation. First, we have demonstrated adaptive dynamics in the GAL system, a classic model for a Eukaryotic genetic switch, induced and repressed by different carbon sources in the environment. We found that both induction and repression are only transient responses; over several generations, the system converges to a single robust steady state, independent of external conditions. Second, we explored the functional significance of such plasticity of the genetic regulatory network in evolution. We used genetic engineering to mimic the natural process of gene recruitment, placing the gene HIS3 under the regulation of the GAL system. Such genetic rewiring events are important in the evolution of gene regulation, but little is known about the physiological processes supporting them and the dynamics of their assimilation in a cell population. We have shown that cells carrying the rewired genome adapted to a demanding change of environment and stabilized a population, maintaining the adaptive state for hundreds of generations. Using genome-wide expression arrays we showed that underlying the observed adaptation is a global transcriptional programming that allowed tuning expression of the recruited gene to demands. Our results suggest that non-specific properties reflecting the natural plasticity of the regulatory network support adaptation of cells to novel challenges and enhance their evolvability.
Fuzzy-Logic Based Multi-Sensory Quality Evaluation via Communication Network
LI Li-xiong(李力雄); TAN Yue-mei(谭月梅); FEI Minrui(费敏锐); T.C.Yang
2004-01-01
In this paper, a multi-sensory quality evaluation using an array of instruments to measure different sensory qualities is established via communication network. The network is used to transmit quality data to evaluation computer. And the network-induced delays between instruments and computer may have negative influence on final evaluation results. The main goal of this paper is to analyze network delays' influence on evaluation results, and present a fuzzy-logic based solution to eliminate the impact and improve the precision of evaluation. And simulations are conducted to show the effectiveness of the proposed approach.
Benschop, Nico F
2009-01-01
""Associative Digital Network Theory"" is intended for researchers at industrial laboratories, teachers and students at technical universities, in electrical engineering, computer science and applied mathematics departments, interested in new developments of modeling and designing digital networks (DN: state machines, sequential and combinational logic) in general, as a combined math/engineering discipline. As background an undergraduate level of modern applied algebra (Birkhoff-Bartee: ""Modern Applied Algebra"" - 1970, and Hartmanis-Stearns: ""Algebraic Structure of Sequential Machines"" - 1
A survey of paraconsistent logics
Middelburg, C.A.
2011-01-01
A survey of paraconsistent logics that are prominent representatives of the different approaches that have been followed to develop paraconsistent logics is provided. The paraconsistent logics that will be discussed are an enrichment of Priest's logic LP, the logic RM3 from the school of relevance logic, da Costa's logics Cn, Jaskowski's logic D2, and Subrahmanian's logics Ptau. A deontic logic based on the first of these logics will be discussed as well. Moreover, some proposed adaptations o...
Prototype of an adaptive disruption predictor for JET based on fuzzy logic and regression trees
Disruptions remain one of the most hazardous events in the operation of a tokamak device, since they can cause damage to the vacuum vessel and surrounding structures. Their potential danger increases with the plasma volume and energy content and therefore they will constitute an even more serious issue for the next generation of machines. For these reasons, in the recent years a lot of attention has been devoted to devise predictors, capable of foreseeing the imminence of a disruption sufficiently in advance, to allow time for undertaking remedial actions. In this paper, the results of applying fuzzy logic and classification and regression trees (CART) to the problem of predicting disruptions at JET are reported. The conceptual tools of fuzzy logic, in addition to being well suited to accommodate the opinion of experts even if not formulated in mathematical but linguistic terms, are also fully transparent, since their governing rules are human defined. They can therefore help not only in forecasting disruptions but also in studying their behaviour. The analysis leading to the rules of the fuzzy predictor has been complemented with a systematic investigation of the correlation between the various experimental signals and the imminence of a disruption. This has been performed with an exhaustive, non-linear and unbiased method based on decision trees. This investigation has confirmed that the relative importance of various signals can change significantly depending on the plasma conditions. On the basis of the results provided by CART on the information content of the various quantities, the prototype of an adaptive fuzzy logic predictor was trained and tested on JET database. Its performance is significantly better than the previous static one, proving that more flexible prediction strategies, not uniform over the whole discharge but tuned to the operational region of the plasma at any given time, can be very competitive and should be investigated systematically
Logical Reduction of Biological Networks to Their Most Determinative Components.
Matache, Mihaela T; Matache, Valentin
2016-07-01
Boolean networks have been widely used as models for gene regulatory networks, signal transduction networks, or neural networks, among many others. One of the main difficulties in analyzing the dynamics of a Boolean network and its sensitivity to perturbations or mutations is the fact that it grows exponentially with the number of nodes. Therefore, various approaches for simplifying the computations and reducing the network to a subset of relevant nodes have been proposed in the past few years. We consider a recently introduced method for reducing a Boolean network to its most determinative nodes that yield the highest information gain. The determinative power of a node is obtained by a summation of all mutual information quantities over all nodes having the chosen node as a common input, thus representing a measure of information gain obtained by the knowledge of the node under consideration. The determinative power of nodes has been considered in the literature under the assumption that the inputs are independent in which case one can use the Bahadur orthonormal basis. In this article, we relax that assumption and use a standard orthonormal basis instead. We use techniques of Hilbert space operators and harmonic analysis to generate formulas for the sensitivity to perturbations of nodes, quantified by the notions of influence, average sensitivity, and strength. Since we work on finite-dimensional spaces, our formulas and estimates can be and are formulated in plain matrix algebra terminology. We analyze the determinative power of nodes for a Boolean model of a signal transduction network of a generic fibroblast cell. We also show the similarities and differences induced by the alternative complete orthonormal basis used. Among the similarities, we mention the fact that the knowledge of the states of the most determinative nodes reduces the entropy or uncertainty of the overall network significantly. In a special case, we obtain a stronger result than in previous
Fuzzy-Based Adaptive Hybrid Burst Assembly Technique for Optical Burst Switched Networks
Abubakar Muhammad Umaru; Muhammad Shafie Abd Latiff; Yahaya Coulibaly
2014-01-01
The optical burst switching (OBS) paradigm is perceived as an intermediate switching technology for future all-optical networks. Burst assembly that is the first process in OBS is the focus of this paper. In this paper, an intelligent hybrid burst assembly algorithm that is based on fuzzy logic is proposed. The new algorithm is evaluated against the traditional hybrid burst assembly algorithm and the fuzzy adaptive threshold (FAT) burst assembly algorithm via simulation. Simulation results sh...
Novel Spectrum Handoff in Cognitive Radio Networks Using Fuzzy Logic
Nisar A. Lala
2013-10-01
Full Text Available Cognitive radio is a technology initiated by many research organizations and academic institutions to raise the spectrum utilization of underutilized channels in order to alleviate spectrum scarcity problem to a larger extent. Spectrum handoff is initiated due to appearance of primary user (PU on the channels occupied by the secondary user (SU at that time and location or interference to the PU exceeds the certain threshold. In this paper, we propose a novel spectrum handoff algorithm using fuzzy logic based approach that does two important functions: 1 adjusts transmission power of SU intelligently in order to avoid handoff by reducing harmful interference to PUs and 2 takes handoff decisions intelligently in the light of new parameter such as expected holding time (HT of the channel as one of its antecedent. Simulated results show impact analysis of selection of the channel in the light of HT information and the comparison with random selection algorithm demonstrates that there is considerable reduction in handoff rate of SU.
Bayesian Network Models for Adaptive Testing
Plajner, Martin; Vomlel, Jiří
Achen: Sun SITE Central Europe, 2016 - (Agosta, J.; Carvalho, R.), s. 24-33. (CEUR Workshop Proceedings. Vol 1565). ISSN 1613-0073. [The Twelfth UAI Bayesian Modeling Applications Workshop (BMAW 2015). Amsterdam (NL), 16.07.2015] R&D Projects: GA ČR GA13-20012S Institutional support: RVO:67985556 Keywords : Bayesian networks * Computerized adaptive testing Subject RIV: JD - Computer Applications, Robotics http://library.utia.cas.cz/separaty/2016/MTR/plajner-0458062.pdf
Adaptive scheduling in cellular access, wireless mesh and IP networks
Nieminen, Johanna
2011-01-01
Networking scenarios in the future will be complex and will include fixed networks and hybrid Fourth Generation (4G) networks, consisting of both infrastructure-based and infrastructureless, wireless parts. In such scenarios, adaptive provisioning and management of network resources becomes of critical importance. Adaptive mechanisms are desirable since they enable a self-configurable network that is able to adjust itself to varying traffic and channel conditions. The operation of adaptive me...
A Neural Network for Generating Adaptive Lessons
Hassina Seridi-Bouchelaghem
2005-01-01
Full Text Available Traditional sequencing technology developed in the field of intelligent tutoring systems have not find an immediate place in large-scale Web-based education. This study investigates the use of computational intelligence for adaptive lesson generation in a distance learning environment over the Web. An approach for adaptive pedagogical hypermedia document generation is proposed and implemented in a prototype called KnowledgeClass. This approach is based on a specialized artificial neural network model. The system allows automatic generation of individualised courses according to the learners goal and previous knowledge and can dynamically adapt the course according to the learners success in acquiring knowledge. Several experiments showed the effectiveness of the proposed method.
The Fuzzy Logic of Network Connectivity in Mouse Visual Thalamus.
Morgan, Josh Lyskowski; Berger, Daniel Raimund; Wetzel, Arthur Willis; Lichtman, Jeff William
2016-03-24
In an attempt to chart parallel sensory streams passing through the visual thalamus, we acquired a 100-trillion-voxel electron microscopy (EM) dataset and identified cohorts of retinal ganglion cell axons (RGCs) that innervated each of a diverse group of postsynaptic thalamocortical neurons (TCs). Tracing branches of these axons revealed the set of TCs innervated by each RGC cohort. Instead of finding separate sensory pathways, we found a single large network that could not be easily subdivided because individual RGCs innervated different kinds of TCs and different kinds of RGCs co-innervated individual TCs. We did find conspicuous network subdivisions organized on the basis of dendritic rather than neuronal properties. This work argues that, in the thalamus, neural circuits are not based on a canonical set of connections between intrinsically different neuronal types but, rather, may arise by experience-based mixing of different kinds of inputs onto individual postsynaptic cells. PMID:27015312
Abdul Kareem
2012-07-01
Full Text Available This paper presents a novel fuzzy logic based Adaptive Super-twisting Sliding Mode Controller for the control of dynamic uncertain systems. The proposed controller combines the advantages of Second order Sliding Mode Control, Fuzzy Logic Control and Adaptive Control. The reaching conditions, stability and robustness of the system with the proposed controller are guaranteed. In addition, the proposed controller is well suited for simple design and implementation. The effectiveness of the proposed controller over the first order Sliding Mode Fuzzy Logic controller is illustrated by Matlab based simulations performed on a DC-DC Buck converter. Based on this comparison, the proposed controller is shown to obtain the desired transient response without causing chattering and error under steady-state conditions. The proposed controller is able to give robust performance in terms of rejection to input voltage variations and load variations.
Microsphere-based immunoassay integrated with a microfluidic network to perform logic operations
Lab on a chip (LOC) intelligent diagnostics can be described by molecular logic-based circuits. We report on the development of an LOC approach with logic capability for screening combinations of antigen and antibody in the same sample. A microsphere-based immunoassay was integrated with a microfluidic network device to perform the logic operations AND and INHIBIT. Using the clinically relevant biomarkers TNF-α cytokine and anti-TNF-α antibody, we obtained a fluorescent output in the presence of both inputs. This results in an AND operation, while the presence of only one specific input results in a different fluorescent signal, thereby indicating the INHIBIT operation. This approach demonstrates the effective use of molecular logic computation for developing portable, point-of-care technologies for diagnostic purposes due to fast detection times, minimal reagent consumption and low costs. This model system may be further expanded to screening of multiple disease markers, combinatorial logic applications, and developing “smart” sensors and therapeutic technologies. (author)
Fuzzy logic and neural networks in artificial intelligence and pattern recognition
Sanchez, Elie
1991-10-01
With the use of fuzzy logic techniques, neural computing can be integrated in symbolic reasoning to solve complex real world problems. In fact, artificial neural networks, expert systems, and fuzzy logic systems, in the context of approximate reasoning, share common features and techniques. A model of Fuzzy Connectionist Expert System is introduced, in which an artificial neural network is designed to construct the knowledge base of an expert system from, training examples (this model can also be used for specifications of rules in fuzzy logic control). Two types of weights are associated with the synaptic connections in an AND-OR structure: primary linguistic weights, interpreted as labels of fuzzy sets, and secondary numerical weights. Cell activation is computed through min-max fuzzy equations of the weights. Learning consists in finding the (numerical) weights and the network topology. This feedforward network is described and first illustrated in a biomedical application (medical diagnosis assistance from inflammatory-syndromes/proteins profiles). Then, it is shown how this methodology can be utilized for handwritten pattern recognition (characters play the role of diagnoses): in a fuzzy neuron describing a number for example, the linguistic weights represent fuzzy sets on cross-detecting lines and the numerical weights reflect the importance (or weakness) of connections between cross-detecting lines and characters.
On Capability-Related Adaptation in Networked Service Systems
Finn Arve Aagesen; Patcharee Thongtra
2012-01-01
Adaptability is a property related to engineering as well as to the execution of networked service systems. This publication considers issues of adaptability both within a general and a scoped view. The generalview considers issues of adaptation at two levels: 1) System of entities, functions and adaptability types, and 2) Architectures supporting adaptability. Adaptability types defined are capability-related, functionality-related and context-related adaptation. The scoped view of the publi...
GMAG Dissertation Award Talk: All Spin Logic -- Multimagnet Networks interacting via Spin currents
Srinivasan, Srikant
2012-02-01
Digital logic circuits have traditionally been based on storing information as charge on capacitors, and the stored information is transferred by controlling the flow of charge. However, electrons carry both charge and spin, the latter being responsible for magnetic phenomena. In the last few decades, there has been a significant improvement in our ability to control spins and their interaction with magnets. All Spin Logic (ASL) represents a new approach to information processing where spins and magnets now mirror the roles of charges and capacitors in conventional logic circuits. In this talk I first present a model [1] that couples non-collinear spin transport with magnet-dynamics to predict the switching behavior of the basic ASL device. This model is based on established physics and is benchmarked against available experimental data that demonstrate spin-torque switching in lateral structures. Next, the model is extended to simulate multi-magnet networks coupled with spin transport channels. The simulations suggest ASL devices have the essential characteristics for building logic circuits. In particular, (1) the example of an ASL ring oscillator [2, 3] is used to provide a clear signature of directed information transfer in cascaded ASL devices without the need for external control circuitry and (2) a simulated NAND [4] gate with fan-out of 2 suggests that ASL can implement universal logic and drive subsequent stages. Finally I will discuss how ASL based circuits could also have potential use in the design of neuromorphic circuits suitable for hybrid analog/digital information processing because of the natural mapping of ASL devices to neurons [4]. [4pt] [1] B. Behin-Aein, A. Sarkar, S. Srinivasan, and S. Datta, ``Switching Energy-Delay of All-Spin Logic devices,'' Appl. Phys. Lett., 98, 123510 (2011).[0pt] [2] S. Srinivasan, A. Sarkar, B. Behin-Aein, and S. Datta, ``All Spin Logic Device with Inbuilt Non-reciprocity,'' IEEE Trans. Magn., 47, 10 (2011).[0pt] [3
Hu, Junjie; Zecchino, Antonio; Marinelli, Mattia
2016-01-01
load and an electric vehicle, which has the vehicle-to-grid function. Three control logics of the OLTC transformer are described in the study. The three control logics are classified based on their control objectives and control inputs, which include network currents and voltages, and can be measured...
Traffic Engineering and Quality of Experience in MPLS Network by Fuzzy Logic characterization
Satya Prakash Rout,
2015-08-01
Full Text Available This paper proposes a load balancing algorithm using fuzzy logic so that maximum Quality of Experience can be achieved. Avoidance of congestion is one of the major performance objectives of traffic engineering in MPLS networks. Load balancing can prevent the congestion caused due to inefficient allocation of network resources. Another aspect of the network performance is Quality of Experience (QoE. QoE in telecommunications terminology, it is a measurement used to determine how well that network is satisfying the end user's requirements. The Mean Opinion Score (MOS is an important factor in determining the QoE. MOS is a measurement of the quality delivered by the network based on human perception at the destination end. Specifically we can tell mean opinion score (MOS provides a numerical indication of the perceived quality of received media after compression and/or transmission.
PID Neural Network Based Speed Control of Asynchronous Motor Using Programmable Logic Controller
MARABA, V. A.
2011-11-01
Full Text Available This paper deals with the structure and characteristics of PID Neural Network controller for single input and single output systems. PID Neural Network is a new kind of controller that includes the advantages of artificial neural networks and classic PID controller. Functioning of this controller is based on the update of controller parameters according to the value extracted from system output pursuant to the rules of back propagation algorithm used in artificial neural networks. Parameters obtained from the application of PID Neural Network training algorithm on the speed model of the asynchronous motor exhibiting second order linear behavior were used in the real time speed control of the motor. Programmable logic controller (PLC was used as real time controller. The real time control results show that reference speed successfully maintained under various load conditions.
Fuzzy Logic Based Anomaly Detection for Embedded Network Security Cyber Sensor
Ondrej Linda; Todd Vollmer; Jason Wright; Milos Manic
2011-04-01
Resiliency and security in critical infrastructure control systems in the modern world of cyber terrorism constitute a relevant concern. Developing a network security system specifically tailored to the requirements of such critical assets is of a primary importance. This paper proposes a novel learning algorithm for anomaly based network security cyber sensor together with its hardware implementation. The presented learning algorithm constructs a fuzzy logic rule based model of normal network behavior. Individual fuzzy rules are extracted directly from the stream of incoming packets using an online clustering algorithm. This learning algorithm was specifically developed to comply with the constrained computational requirements of low-cost embedded network security cyber sensors. The performance of the system was evaluated on a set of network data recorded from an experimental test-bed mimicking the environment of a critical infrastructure control system.
Class-Based Constraint-Based Routing with Implemented Fuzzy Logic in MPLS-TE Networks
Michal Pištek
2014-01-01
Full Text Available The paper deals with constraint-based routing (CBR in MPLS-TE networks and proposes a new CBR algorithm based on fuzzy logic called Fuzzy Class-Based Algorithm (FCBA. Multiprotocol label switching with traffic engineering (MPLS-TE networks represent a popular mechanism to effectively use resources of service providers’ core networks. The paths can be either built by administrators (explicit routing or built by using existing routing algorithms which mostly decide based on the shortest paths towards the destination which might not be sufficient in nowadays’ multimedia networks. To address this problem various CBR algorithms have emerged which take into consideration various aspects important to existing traffic like QoS parameters or administrative policies. FCBA makes routing decisions based on traffic classes and by using fuzzy logic we can assign normalized values to various constraints based on the traffic class’ preferences (e.g., low delay paths for voice traffic and network administrator’s preferences (e.g., avoiding congested links. The paper provides comparison of FCBA with existing CBR approaches based on their ability to provide QoS parameters loss. The simulations show that FCBA provides the best results for the highest priority traffic where it uses lower priority traffic to efficiently utilize the network.
Fuzzy-Logic Based Probabilistic Broadcasting Technique for Mobile Adhoc Networks
Tasneem Bano
2012-05-01
Full Text Available Broadcasting also called as flooding is one of the earliest research interests in mobile ad hoc networking. The main objective of broadcasting scheme is to avoid broadcast storm problem, provide good network performance and scalability. Existing probabilistic scheme consider only one parameter such as network density, node location or counter value in calculating probability value for broadcasting. These probability scheme cannot change their broadcasting approach even though the objectives of the system may change based on node characteristics or network conditions. There is no probabilistic broadcasting schemes which consider multiple parameters for determining the probability to broadcast. The parameters that greatly affect broadcasting is the network density, node location, etc. in this paper a new probabilistic broadcasting algorithm has been proposed that aims to utilize up to date local topological characteristics of intermediate nodes such as network density and node location. As we are using multiple values to calculate probability for broadcasting there is a need of optimization technique (such as Fuzzy Logic. With the use of fuzzy logic the proposed algorithm can keep good balance between rebroadcast efficiency, reachability and throughput.
Statistical, Logic-Based, and Neural Networks Based Methods for Mining Rules from Data
Holeňa, Martin
Dordrecht: Kluwer Academic Publishers, 2002 - (Hyder, A.; Shahbazian, E.; Waltz, E.), s. 511-532. (NATO Science Series). ISBN 1-4020-0722-1. [NATO Advanced study Institute on MSDF. Pitlochry (GB), 25.06.2000-07.07.2000] R&D Projects: GA AV ČR IAB2030007 Institutional research plan: AV0Z1030915 Keywords : data mining * integrative framework * observational logic * statistical hypotheses testing * rule extraction with artificial neural networks Subject RIV: BA - General Mathematics
Quantum Logic Networks for Probabilistic and Controlled Teleportation of Unknown Quantum States
GAO Ting
2004-01-01
We present simplification schemes for probabilistic and controlled teleportation of the unknown quantum states of both one particle and two particles and construct efficient quantum logic networks for implementing the new schemes by means of the primitive operations consisting of single-qubit gates, two-qubit controlled-not gates, Von Neumann measurement, and classically controlled operations. In these schemes the teleportation are not always successful but with certain probability.
Quantum logic networks for controlled teleportation of a single particle via W state
Yuan Hong-Chun; Qi Kai-Guo
2005-01-01
We discuss the scheme for probabilistic and controlled teleportation of an unknown state of one particle using the general three-particle W state as the quantum channel. The feature of this scheme is that teleportation between two sides depends on the agreement of the third side (Charlie), who may participate the process of quantum teleportation as a supervisor. In addition, we also construct efficient quantum logic networks for implementing the new scheme by means of the primitive operations.
Cooperative Media Streaming Using Adaptive Network Compression
Møller, Janus Heide; Sørensen, Jesper Hemming; Krigslund, Rasmus;
2008-01-01
an adaptive hybrid between LC and MDC. In order to facilitate the use of MDC-CC, a new overlay network approach is proposed, using tree of meshes. A control system for managing description distribution and compression in a small mesh is implemented in the discrete event simulator NS-2. The two...... media distribution using traditional approaches. In particular, the asymmetric relationship between the uplink and the downlink bandwidth makes the cooperative distribution difﬁcult. A promising concept, termed MDC with Conditional Compression (MDC-CC), has been proposed [11], which essentially acts as...
Adaptive-network models of swarm dynamics
Huepe, Cristian [614 N Paulina Street, Chicago, IL 60622-6062 (United States); Zschaler, Gerd; Do, Anne-Ly; Gross, Thilo, E-mail: cristian@northwestern.edu [Max-Planck-Institut fuer Physik komplexer Systeme, Noethnitzer Strasse 38, 01187 Dresden (Germany)
2011-07-15
We propose a simple adaptive-network model describing recent swarming experiments. Exploiting an analogy with human decision making, we capture the dynamics of the model using a low-dimensional system of equations permitting analytical investigation. We find that the model reproduces several characteristic features of swarms, including spontaneous symmetry breaking, noise- and density-driven order-disorder transitions that can be of first or second order, and intermittency. Reproducing these experimental observations using a non-spatial model suggests that spatial geometry may have less of an impact on collective motion than previously thought.
Adaptive-network models of swarm dynamics
We propose a simple adaptive-network model describing recent swarming experiments. Exploiting an analogy with human decision making, we capture the dynamics of the model using a low-dimensional system of equations permitting analytical investigation. We find that the model reproduces several characteristic features of swarms, including spontaneous symmetry breaking, noise- and density-driven order-disorder transitions that can be of first or second order, and intermittency. Reproducing these experimental observations using a non-spatial model suggests that spatial geometry may have less of an impact on collective motion than previously thought.
Quantitative Adaptive RED in Differentiated Service Networks
LONG KePing(隆克平); WANG Qian(王茜); CHENG ShiDuan(程时端); CHEN JunLiang(陈俊亮)
2003-01-01
This paper derives a quantitative model between RED (Random Early Detection)maxp and committed traffic rate for token-based marking schemes in DiffServ IP networks. Then,a DiffServ Quantitative RED (DQRED) is presented, which can adapt its dropping probabilityto marking probability of the edge router to reflect not only the sharing bandwidth but also therequirement of performance of these services. Hence, DQRED can cooperate with marking schemesto guarantee fairness between different DiffServ AF class services. A new marking probabilitymetering algorithm is also proposed to cooperate with DQRED. Simulation results verify thatDQRED mechanism can not only control congestion of DiffServ network very well, but also satisfydifferent quality requirements of AF class service. The performance of DQRED is better than thatof WRED.
Ondrej Linda; Todd Vollmer; Jim Alves-Foss; Milos Manic
2011-08-01
Resiliency and cyber security of modern critical infrastructures is becoming increasingly important with the growing number of threats in the cyber-environment. This paper proposes an extension to a previously developed fuzzy logic based anomaly detection network security cyber sensor via incorporating Type-2 Fuzzy Logic (T2 FL). In general, fuzzy logic provides a framework for system modeling in linguistic form capable of coping with imprecise and vague meanings of words. T2 FL is an extension of Type-1 FL which proved to be successful in modeling and minimizing the effects of various kinds of dynamic uncertainties. In this paper, T2 FL provides a basis for robust anomaly detection and cyber security state awareness. In addition, the proposed algorithm was specifically developed to comply with the constrained computational requirements of low-cost embedded network security cyber sensors. The performance of the system was evaluated on a set of network data recorded from an experimental cyber-security test-bed.
Adaptive bipartite consensus on coopetition networks
Hu, Jiangping; Zhu, Hong
2015-07-01
In this paper, a bipartite consensus tracking problem is considered for a group of autonomous agents on a coopetition network, on which the agents interact cooperatively and competitively simultaneously. The coopetition network involves positive and negative edges and is conveniently modeled by a signed graph. Additionally, the dynamics of all the agents are subjected to unknown disturbances, which are represented by linearly parameterized models. An adaptive estimation scheme is designed for each agent by virtue of the relative position measurements and the relative velocity measurements from its neighbors. Then a consensus tracking law is proposed for a new distributed system, which uses the relative measurements as the new state variables. The convergence of the consensus tracking error and the parameter estimation are analyzed even when the coopetition network is time-varying and no more global information about the bounds of the unknown disturbances is available to all the agents. Finally, some simulation results are provided to demonstrate the formation of the bipartite consensus on the coopetition network.
Probabilistic Adaptive Anonymous Authentication in Vehicular Networks
Yong Xi; Ke-Wei Sha; Wei-Song Shi; Loren Schwiebert; Tao Zhang
2008-01-01
Vehicular networks have attracted extensive attention in recent years for their promises in improving safety and enabling other value-added services. Most previous work focuses on designing the media access and physical layer protocols.Privacy issues in vehicular systems have not been well addressed. We argue that privacy is a user-specific concept, and a good privacy protection mechanism should allow users to select the levels of privacy they wish to have. To address this requirement, we propose an adaptive anonymous authentication mechanism that can trade off the anonymity level with computational and communication overheads (resource usage). This mechanism, to our knowledge, is the first effort on adaptive anonymous authentication. The resources used by our protocol are few. A high traffic volume of 2000 vehicles per hour consumes about 60kbps bandwidth, which is less than one percent of the bandwidth of DSRC (Dedicated Short Range Communications). By using adaptive anonymity, the protocol response time can further be improved 2～4 times with lessthan 20% bandwidth overheads.
Adaptive Fuzzy Logic Controllers for DC Drives: A Survey of the State of the art
E. E. El-kholy; A. M. Dabroom; Adel E. El-kholy
2006-01-01
Fuzzy Logic Control (FLC) has gained a great demand in process control applications. Fuzzy Logic (FL) technology enables the use of engineering experience and experimental results in designing an expert system capable of handling uncertain or fuzzy quantities. This paper presents a comprehensive review of FLC in the field of Direct Current (DC) motor drive systems. Firstly, the principles of fuzzy logic theory will be briefly presented. Secondly, the employment of the FL techniques in a contr...
Zhao, Yongli; Li, Shikun; Song, Yinan; Sun, Ji; Zhang, Jie
2015-12-01
Hierarchical control architecture is designed for software-defined multidomain optical networks (SD-MDONs), and a unified service logic processing model (USLPM) is first proposed for various applications. USLPM-based virtual optical network (VON) provisioning process is designed, and two VON mapping algorithms are proposed: random node selection and per controller computation (RNS&PCC) and balanced node selection and hierarchical controller computation (BNS&HCC). Then an SD-MDON testbed is built with OpenFlow extension in order to support optical transport equipment. Finally, VON provisioning service is experimentally demonstrated on the testbed along with performance verification.
Lu, Thomas; Pham, Timothy; Liao, Jason
2011-01-01
This paper presents the development of a fuzzy logic function trained by an artificial neural network to classify the system noise temperature (SNT) of antennas in the NASA Deep Space Network (DSN). The SNT data were classified into normal, marginal, and abnormal classes. The irregular SNT pattern was further correlated with link margin and weather data. A reasonably good correlation is detected among high SNT, low link margin and the effect of bad weather; however we also saw some unexpected non-correlations which merit further study in the future.
Mobile IPv6 (MIPv6) is one of the pioneer standards that support mobility in IPv6 environment. It has been designed to support different types of technologies for providing seamless communications in next generation network. However, MIPv6 and subsequent standards have some limitations due to its handoff latency. In this paper, a fuzzy logic based mechanism is proposed to reduce the handoff latency of MIPv6 for Layer 2 (L2) by scanning the Access Points (APs) while the Mobile Node (MN) is moving among different APs. Handoff latency occurs when the MN switches from one AP to another in L2. Heterogeneous network is considered in this research in order to reduce the delays in L2. Received Signal Strength Indicator (RSSI) and velocity of the MN are considered as the input of fuzzy logic technique. This technique helps the MN to measure optimum signal quality from APs for the speedy mobile node based on fuzzy logic input rules and makes a list of interfaces. A suitable interface from the list of available interfaces can be selected like WiFi, WiMAX or GSM. Simulation results show 55% handoff latency reduction and 50% packet loss improvement in L2 compared to standard to MIPv6
Designing Logical Topology for Wireless Sensor Networks: A Multi-Chain Oriented Approach
Quazi Mamun
2013-02-01
Full Text Available An optimal logical topology of a wireless sensor ne twork (WSN facilitates the deployed sensor nodes t o communicate with each other with little overheads, lowers energy consumption, lengthens lifetime of th e network, provides scalability, increases reliabilit y, and reduces latency. Designing an optimal logica l topology for a WSN thus needs to consider numerous factors. Chain oriented topologies have been found to offer a number of improvements in energy consump tions, lifetime, and load balancing than other topologies of WSNs. However, they usually suffer fr om latency, scalability, reliability and interferen ce problems. In this paper, we present a chain oriente d logical topology, which offers solutions to those problems. The proposed topology is designed such th at it retains the advantages of the chain oriented topologies, and at the same time, overcomes the pro blems of the chain oriented topology such as latenc y, scalability, and data reliability. The proposed top ology provides a communication abstraction, which c an be easily used to devise a range of application pro tocols. Moreover, the logical topology offers node management, resource management, and other services . The performance of the proposed topology is compared with other topologies in respect to total energy consumption and lifetime of the network.
Anwar, Farhat; Masud, Mosharrof H.; Latif, Suhaimi A.
2013-12-01
Mobile IPv6 (MIPv6) is one of the pioneer standards that support mobility in IPv6 environment. It has been designed to support different types of technologies for providing seamless communications in next generation network. However, MIPv6 and subsequent standards have some limitations due to its handoff latency. In this paper, a fuzzy logic based mechanism is proposed to reduce the handoff latency of MIPv6 for Layer 2 (L2) by scanning the Access Points (APs) while the Mobile Node (MN) is moving among different APs. Handoff latency occurs when the MN switches from one AP to another in L2. Heterogeneous network is considered in this research in order to reduce the delays in L2. Received Signal Strength Indicator (RSSI) and velocity of the MN are considered as the input of fuzzy logic technique. This technique helps the MN to measure optimum signal quality from APs for the speedy mobile node based on fuzzy logic input rules and makes a list of interfaces. A suitable interface from the list of available interfaces can be selected like WiFi, WiMAX or GSM. Simulation results show 55% handoff latency reduction and 50% packet loss improvement in L2 compared to standard to MIPv6.
Integrated Adaptive Analysis and Visualization of Satellite Network Data Project
National Aeronautics and Space Administration — We propose to develop a system that enables integrated and adaptive analysis and visualization of satellite network management data. Integrated analysis and...
Tuffy: Scaling up Statistical Inference in Markov Logic Networks using an RDBMS
Niu, Feng; Doan, AnHai; Shavlik, Jude
2011-01-01
Markov Logic Networks (MLNs) have emerged as a powerful framework that combines statistical and logical reasoning; they have been applied to many data intensive problems including information extraction, entity resolution, and text mining. Current implementations of MLNs do not scale to large real-world data sets, which is preventing their wide-spread adoption. We present Tuffy that achieves scalability via three novel contributions: (1) a bottom-up approach to grounding that allows us to leverage the full power of the relational optimizer, (2) a novel hybrid architecture that allows us to perform AI-style local search efficiently using an RDBMS, and (3) a theoretical insight that shows when one can (exponentially) improve the efficiency of stochastic local search. We leverage (3) to build novel partitioning, loading, and parallel algorithms. We show that our approach outperforms state-of-the-art implementations in both quality and speed on several publicly available datasets.
Opportunistic Adaptive Relaying in Cognitive Radio Networks
Jaafar, Wael; Haccoun, David
2012-01-01
Combining cognitive radio technology with user cooperation could be advantageous to both primary and secondary transmissions. In this paper, we propose a first relaying scheme for cognitive radio networks (called "Adaptive relaying scheme 1"), where one relay node can assist the primary or the secondary transmission with the objective of improving the outage probability of the secondary transmission with respect to a primary outage probability threshold. Upper bound expressions of the secondary outage probability using the proposed scheme are derived over Rayleigh fading channels. Numerical and simulation results show that the secondary outage probability using the proposed scheme is lower than that of other relaying schemes. Then, we extend the proposed scheme to the case where the relay node has the ability to decode both the primary and secondary signals and also can assist simultaneously both transmissions. Simulations show the performance improvement that can be obtained due to this extension in terms of...
Link-based formalism for time evolution of adaptive networks
Zhou, Jie; Chen, Guanrong
2013-01-01
Network topology and nodal dynamics are two fundamental stones of adaptive networks. Detailed and accurate knowledge of these two ingredients is crucial for understanding the evolution and mechanism of adaptive networks. In this paper, by adopting the framework of the adaptive SIS model proposed by Gross et al. [Phys. Rev. Lett. 96, 208701 (2006)] and carefully utilizing the information of degree correlation of the network, we propose a link-based formalism for describing the system dynamics with high accuracy and subtle details. Several specific degree correlation measures are introduced to reveal the coevolution of network topology and system dynamics.
Adaptive training of feedforward neural networks by Kalman filtering
Adaptive training of feedforward neural networks by Kalman filtering is described. Adaptive training is particularly important in estimation by neural network in real-time environmental where the trained network is used for system estimation while the network is further trained by means of the information provided by the experienced/exercised ongoing operation. As result of this, neural network adapts itself to a changing environment to perform its mission without recourse to re-training. The performance of the training method is demonstrated by means of actual process signals from a nuclear power plant. (orig.)
Song, Mingzhou (Joe) [New Mexico State University, Las Cruces; Lewis, Chris K. [New Mexico State University, Las Cruces; Lance, Eric [New Mexico State University, Las Cruces; Chesler, Elissa J [ORNL; Kirova, Roumyana [Bristol-Myers Squibb Pharmaceutical Research & Development, NJ; Langston, Michael A [University of Tennessee, Knoxville (UTK); Bergeson, Susan [Texas Tech University, Lubbock
2009-01-01
The problem of reconstructing generalized logical networks to account for temporal dependencies among genes and environmental stimuli from high-throughput transcriptomic data is addressed. A network reconstruction algorithm was developed that uses the statistical significance as a criterion for network selection to avoid false-positive interactions arising from pure chance. Using temporal gene expression data collected from the brains of alcohol-treated mice in an analysis of the molecular response to alcohol, this algorithm identified genes from a major neuronal pathway as putative components of the alcohol response mechanism. Three of these genes have known associations with alcohol in the literature. Several other potentially relevant genes, highlighted and agreeing with independent results from literature mining, may play a role in the response to alcohol. Additional, previously-unknown gene interactions were discovered that, subject to biological verification, may offer new clues in the search for the elusive molecular mechanisms of alcoholism.
Speed Adaptation in Urban Road Network Management
Raiyn Jamal
2016-06-01
Full Text Available Various forecasting schemes have been proposed to manage traffic data, which is collected by videos cameras, sensors, and mobile phone services. However, these are not sufficient for collecting data because of their limited coverage and high costs for installation and maintenance. To overcome the limitations of these tools, we introduce a hybrid scheme based on intelligent transportation system (ITS and global navigation satellite system (GNSS. Applying the GNSS to calculate travel time has proven efficient in terms of accuracy. In this case, GNSS data is managed to reduce traffic congestion and road accidents. This paper introduces a short-time forecasting model based on real-time travel time for urban heterogeneous road networks. Travel time forecasting has been achieved by predicting travel speeds using an optimized exponential moving Average (EMA model. Furthermore for speed adaptation in heterogeneous road networks, it is necessary to introduce asuitable control strategy for longitude, based on the GNSS. GNSS products provide worldwide and real-time services using precise timing information and, positioning technologies.
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.
Brain network adaptability across task states.
Elizabeth N Davison
2015-01-01
Full Text Available Activity in the human brain moves between diverse functional states to meet the demands of our dynamic environment, but fundamental principles guiding these transitions remain poorly understood. Here, we capitalize on recent advances in network science to analyze patterns of functional interactions between brain regions. We use dynamic network representations to probe the landscape of brain reconfigurations that accompany task performance both within and between four cognitive states: a task-free resting state, an attention-demanding state, and two memory-demanding states. Using the formalism of hypergraphs, we identify the presence of groups of functional interactions that fluctuate coherently in strength over time both within (task-specific and across (task-general brain states. In contrast to prior emphases on the complexity of many dyadic (region-to-region relationships, these results demonstrate that brain adaptability can be described by common processes that drive the dynamic integration of cognitive systems. Moreover, our results establish the hypergraph as an effective measure for understanding functional brain dynamics, which may also prove useful in examining cross-task, cross-age, and cross-cohort functional change.
Chang, H.-C.; Kopaska-Merkel, D. C.; Chen, H.-C.; Rocky, Durrans S.
2000-01-01
Lithofacies identification supplies qualitative information about rocks. Lithofacies represent rock textures and are important components of hydrocarbon reservoir description. Traditional techniques of lithofacies identification from core data are costly and different geologists may provide different interpretations. In this paper, we present a low-cost intelligent system consisting of three adaptive resonance theory neural networks and a rule-based expert system to consistently and objectively identify lithofacies from well-log data. The input data are altered into different forms representing different perspectives of observation of lithofacies. Each form of input is processed by a different adaptive resonance theory neural network. Among these three adaptive resonance theory neural networks, one neural network processes the raw continuous data, another processes categorial data, and the third processes fuzzy-set data. Outputs from these three networks are then combined by the expert system using fuzzy inference to determine to which facies the input data should be assigned. Rules are prioritized to emphasize the importance of firing order. This new approach combines the learning ability of neural networks, the adaptability of fuzzy logic, and the expertise of geologists to infer facies of the rocks. This approach is applied to the Appleton Field, an oil field located in Escambia County, Alabama. The hybrid intelligence system predicts lithofacies identity from log data with 87.6% accuracy. This prediction is more accurate than those of single adaptive resonance theory networks, 79.3%, 68.0% and 66.0%, using raw, fuzzy-set, and categorical data, respectively, and by an error-backpropagation neural network, 57.3%. (C) 2000 Published by Elsevier Science Ltd. All rights reserved.
A fuzzy logic based clustering strategy for improving vehicular ad-hoc network performance
Ali Çalhan
2015-04-01
This paper aims to improve the clustering of vehicles by using fuzzy logic in Vehicular Ad-Hoc Networks (VANETs) for making the network more robust and scalable. High mobility and scalability are two vital topics to be considered while providing efficient and reliable communication in VANETs. Clustering is of crucial significance in order to cope with the dynamic features of the VANET topologies. Plenty of parameters related to user preferences, network conditions and application requirements such as speed of mobile nodes, distance to cluster head, data rate and signal strength must be evaluated in the cluster head selection process together with the direction parameter for highly dynamic VANET structures. The prominent parameters speed, acceleration, distance and direction information are taken into account as inputs of the proposed cluster head selection algorithm. The simulation results show that developed fuzzy logic (FL) based cluster head selection algorithm (CHSA) has stable performance in various scenarios in VANETs. This study has also shown that the developed CHSAFL satisfies well the highly demanding requirements of both low speed and high speed vehicles on two-way multilane highway
Lam, H K; Nguyen, Hung T
2012-01-01
This book focuses on computational intelligence techniques and their applications - fast-growing and promising research topics that have drawn a great deal of attention from researchers over the years. It brings together many different aspects of the current research on intelligence technologies such as neural networks, support vector machines, fuzzy logic and evolutionary computation, and covers a wide range of applications from pattern recognition and system modeling, to intelligent control problems and biomedical applications.Fundamental concepts and essential analysis of various computatio
Fuzzy logic and artificial neural networks for nuclear power plant applications
This paper discusses the feasibility of applying fuzzy logic and neural networks to plant-wide monitoring, diagnostics, and control problems. Different data sets are gathered from several sources including two commercial Pressurized Water Reactors (PWR), the Experimental Breeder Reactor-II (EBR-II), and the conceptual design of Modular Liquid-Metal Reactor (PRISM). These data sets are used to illustrate applications to operating processes, and to PRISM design. The results show that the artificial intelligence approach to a number of operational tasks can considerably improve the safety and availability of nuclear power generation
Adaptive-impulsive synchronization of uncertain complex dynamical networks
This Letter studies adaptive-impulsive synchronization of uncertain complex dynamical networks. Based on the stability analysis of impulsive system, several network synchronization criteria for local and global adaptive-impulsive synchronization are established. Numerical example is also given to illustrate the results
A Survey of Paraconsistent Logics
Middelburg, C A
2011-01-01
A survey of paraconsistent logics that are prominent representatives of the different approaches that have been followed to develop paraconsistent logics is provided. The paraconsistent logics that will be discussed are an enrichment of Priest's logic LP, the logic RM3 from the school of relevance logic, da Costa's logics Cn, Jaskowski's logic D2, and Subrahmanian's logics Ptau. A deontic logic based on the first of these logics will be discussed as well. Moreover, some proposed adaptations of the AGM theory of belief revision to paraconsistent logics will be mentioned.
Lodowski Kerrie H
2009-01-01
Full Text Available Gene expression time course data can be used not only to detect differentially expressed genes but also to find temporal associations among genes. The problem of reconstructing generalized logical networks to account for temporal dependencies among genes and environmental stimuli from transcriptomic data is addressed. A network reconstruction algorithm was developed that uses statistical significance as a criterion for network selection to avoid false-positive interactions arising from pure chance. The multinomial hypothesis testing-based network reconstruction allows for explicit specification of the false-positive rate, unique from all extant network inference algorithms. The method is superior to dynamic Bayesian network modeling in a simulation study. Temporal gene expression data from the brains of alcohol-treated mice in an analysis of the molecular response to alcohol are used for modeling. Genes from major neuronal pathways are identified as putative components of the alcohol response mechanism. Nine of these genes have associations with alcohol reported in literature. Several other potentially relevant genes, compatible with independent results from literature mining, may play a role in the response to alcohol. Additional, previously unknown gene interactions were discovered that, subject to biological verification, may offer new clues in the search for the elusive molecular mechanisms of alcoholism.
Water level forecasting through fuzzy logic and artificial neural network approaches
S. Alvisi
2006-01-01
Full Text Available In this study three data-driven water level forecasting models are presented and discussed. One is based on the artificial neural networks approach, while the other two are based on the Mamdani and the Takagi-Sugeno fuzzy logic approaches, respectively. All of them are parameterised with reference to flood events alone, where water levels are higher than a selected threshold. The analysis of the three models is performed by using the same input and output variables. However, in order to evaluate their capability to deal with different levels of information, two different input sets are considered. The former is characterized by significant spatial and time aggregated rainfall information, while the latter considers rainfall information more distributed in space and time. The analysis is made with great attention to the reliability and accuracy of each model, with reference to the Reno river at Casalecchio di Reno (Bologna, Italy. It is shown that the two models based on the fuzzy logic approaches perform better when the physical phenomena considered are synthesised by both a limited number of variables and IF-THEN logic statements, while the ANN approach increases its performance when more detailed information is used. As regards the reliability aspect, it is shown that the models based on the fuzzy logic approaches may fail unexpectedly to forecast the water levels, in the sense that in the testing phase, some input combinations are not recognised by the rule system and thus no forecasting is performed. This problem does not occur in the ANN approach.
Synchronization of general complex networks via adaptive control schemes
Ping He; Chun-Guo Jing; Chang-Zhong Chen; Tao Fan; Hassan Saberi Nik
2014-03-01
In this paper, the synchronization problem of general complex networks is investigated by using adaptive control schemes. Time-delay coupling, derivative coupling, nonlinear coupling etc. exist universally in real-world complex networks. The adaptive synchronization scheme is designed for the complex network with multiple class of coupling terms. A criterion guaranteeing synchronization of such complex networks is established by employing the Lyapunov stability theorem and adaptive control schemes. Finally, an illustrative example with numerical simulation is given to show the feasibility and efficiency of theoretical results.
Traffic Signals Control with Adaptive Fuzzy Controller in Urban Road Network
LI Yan; FAN Xiao-ping
2008-01-01
An adaptive fuzzy logic controller (AFC) is presented for the signal control of the urban traffic network.The AFC is composed of the signal control system-oriented control level and the signal controller-oriented fuzzy rules regulation level.The control level decides the signal tunings in an intersection with a fuzzy logic controller.The regulation level optimizes the fuzzy rules by the Adaptive Rule Module in AFC according to both the system performance index in current control period and the traffic flows in the last one.Consequently the system performances are improved.A weight coefficient controller (WCC) is also developed to describe the interactions of traffic flow among the adjacent intersections.So the AFC combined with the WCC can be applied in a road network for signal timings.Simulations of the AFC on a real traffic scenario have been conducted.Simulation results indicate that the adaptive controller for traffic control shows better performance than the actuated one.
Adaptive Synchronization of Complex Dynamical Networks with State Predictor
Yuntao Shi; Bo Liu; Xiao Han
2013-01-01
This paper addresses the adaptive synchronization of complex dynamical networks with nonlinear dynamics. Based on the Lyapunov method, it is shown that the network can synchronize to the synchronous state by introducing local adaptive strategy to the coupling strengths. Moreover, it is also proved that the convergence speed of complex dynamical networks can be increased via designing a state predictor. Finally, some numerical simulations are worked out to illustrate the analytical results.
Electrooptical adaptive switching network for the hypercube computer
Chow, E.; Peterson, J.
1988-01-01
An all-optical network design for the hyperswitch network using regular free-space interconnects between electronic processor nodes is presented. The adaptive routing model used is described, and an adaptive routing control example is presented. The design demonstrates that existing electrooptical techniques are sufficient for implementing efficient parallel architectures without the need for more complex means of implementing arbitrary interconnection schemes. The electrooptical hyperswitch network significantly improves the communication performance of the hypercube computer.
Farnaz Pakdeland
2016-08-01
Full Text Available Wireless sensor network is comprised of several sensor nodes. The retaining factors influence the network operation. In the clustering structure the cluster head failure can cause loss of information.The aim of this paper is to increase tolerance error in the cluster head node. At first, paying attention to the producing balance in the density of the cluster cause to postpone the death time of the cluster head node and lessen the collision due to the lack of the energy balance in clusters. The innovation in this stage is formed by using two fuzzy logic systems. One in the phase of evaluation of the cluster head chance, and the other in the phase of producing balance and the nodes migration to the qualified clusters to increase balance, Then the focus is on recognizing and repairing the cluster head fault.
ARTIFICIAL NEURAL NETWORK AND FUZZY LOGIC CONTROLLER FOR GTAW MODELING AND CONTROL
无
2002-01-01
An artificial neural network(ANN) and a self-adjusting fuzzy logic controller(FLC) for modeling and control of gas tungsten arc welding(GTAW) process are presented. The discussion is mainly focused on the modeling and control of the weld pool depth with ANN and the intelligent control for weld seam tracking with FLC. The proposed neural network can produce highly complex nonlinear multi-variable model of the GTAW process that offers the accurate prediction of welding penetration depth. A self-adjusting fuzzy controller used for seam tracking adjusts the control parameters on-line automatically according to the tracking errors so that the torch position can be controlled accurately.
Intelligent control a hybrid approach based on fuzzy logic, neural networks and genetic algorithms
Siddique, Nazmul
2014-01-01
Intelligent Control considers non-traditional modelling and control approaches to nonlinear systems. Fuzzy logic, neural networks and evolutionary computing techniques are the main tools used. The book presents a modular switching fuzzy logic controller where a PD-type fuzzy controller is executed first followed by a PI-type fuzzy controller thus improving the performance of the controller compared with a PID-type fuzzy controller. The advantage of the switching-type fuzzy controller is that it uses one rule-base thus minimises the rule-base during execution. A single rule-base is developed by merging the membership functions for change of error of the PD-type controller and sum of error of the PI-type controller. Membership functions are then optimized using evolutionary algorithms. Since the two fuzzy controllers were executed in series, necessary further tuning of the differential and integral scaling factors of the controller is then performed. Neural-network-based tuning for the scaling parameters of t...
A hybrid neural network---fuzzy logic approach to nuclear power plant transient identification
A methodology is presented that couples pretrained artificial neural networks (ANNs) to rule-based fuzzy logic systems, for the purpose of distinguishing different transients in a Nuclear Power Plant (NPP). A model referenced approach is utilized in order to provide timely concise and task specific information about the status of the system under consideration. A rule based system integrated with a set of neural networks, that typify steady-state operation as well as different transients, diagnoses the state of the system and identifies the type of transient under development. ANNs produce their response in the form of membership functions which independently represent individual transients and the steady-state. Membership functions condense functionally relevant information in order for the overall system to successfully perform transient identification, in a time span faster or at least comparable to that of the transient development. To demonstrate the proposed methodology simulated accidents corresponding to a particular category of transients are used. The results obtained demonstrate the excellent noise tolerance of the ANNs and suggest a new approach for transient identification within the framework of fuzzy logic
Adaptive Mobile Positioning in WCDMA Networks
Dong B.
2005-01-01
Full Text Available We propose a new technique for mobile tracking in wideband code-division multiple-access (WCDMA systems employing multiple receive antennas. To achieve a high estimation accuracy, the algorithm utilizes the time difference of arrival (TDOA measurements in the forward link pilot channel, the angle of arrival (AOA measurements in the reverse-link pilot channel, as well as the received signal strength. The mobility dynamic is modelled by a first-order autoregressive (AR vector process with an additional discrete state variable as the motion offset, which evolves according to a discrete-time Markov chain. It is assumed that the parameters in this model are unknown and must be jointly estimated by the tracking algorithm. By viewing a nonlinear dynamic system such as a jump-Markov model, we develop an efficient auxiliary particle filtering algorithm to track both the discrete and continuous state variables of this system as well as the associated system parameters. Simulation results are provided to demonstrate the excellent performance of the proposed adaptive mobile positioning algorithm in WCDMA networks.
LTE Adaptation for Mobile Broadband Satellite Networks
Bastia Francesco
2009-01-01
Full Text Available One of the key factors for the successful deployment of mobile satellite systems in 4G networks is the maximization of the technology commonalities with the terrestrial systems. An effective way of achieving this objective consists in considering the terrestrial radio interface as the baseline for the satellite radio interface. Since the 3GPP Long Term Evolution (LTE standard will be one of the main players in the 4G scenario, along with other emerging technologies, such as mobile WiMAX; this paper analyzes the possible applicability of the 3GPP LTE interface to satellite transmission, presenting several enabling techniques for this adaptation. In particular, we propose the introduction of an inter-TTI interleaving technique that exploits the existing H-ARQ facilities provided by the LTE physical layer, the use of PAPR reduction techniques to increase the resilience of the OFDM waveform to non linear distortion, and the design of the sequences for Random Access, taking into account the requirements deriving from the large round trip times. The outcomes of this analysis show that, with the required proposed enablers, it is possible to reuse the existing terrestrial air interface to transmit over the satellite link.
Temporal percolation of a susceptible adaptive network
Valdez, L D; Braunstein, L A
2013-01-01
In the last decades, due to the appearance of many diseases such as SARS and the H1N1 flu strain, many authors studied the impact of the disease spreading in the evolution of the infected individuals using the susceptible-infected-recovered model. However, few authors focused on the temporal behavior of the susceptible individuals. Recently it was found that in an epidemic spreading, the dynamic of the size of the biggest susceptible cluster can be explained by a temporal node void percolation [Valdez et al PLoS ONE 7, e44188 (2012)]. It was shown that the size of the biggest susceptible cluster is the order parameter of this temporal percolation where the control parameter can be related to the number of links between susceptible individuals at a given time. As a consequence, there is a critical time at which the biggest susceptible cluster is destroyed. In this paper, we study the susceptible-infected-recovered model in an adaptive network where an intermittent social distancing strategy is applied. In this...
Geographic Routing Using Logical Levels in Wireless Sensor Networks for Sensor Mobility
Yassine SABRI; Najib EL KAMOUN
2015-01-01
In this paper we propose an improvement to the GRPW algorithm for wireless sensor networks called GRPW-M , which collects data in a wireless sensor network (WSN) using a mobile nodes. Performance of GRPW algorithm algorithm depends heavily on the immobile sensor nodes . This prediction can be hard to do. For that reason, we propose a modified algorithm that is able to adapt to the current situation in the network in which the sensor node considered mobile. The goal of the proposed algorithm i...
A fuzzy logic and neural network-based intelligent mine winder-motion control system. Part 1
Szklarski, L.; Fijalkowski, B. [Akademia Gorniczo-Hutnicza, Krakow (Poland)
1994-09-01
A fuzzy logic and neural network-based intelligent mine winder-motion control system would appear to be a useful contribution to mining automation where efforts are continually being made to reduce costs. This paper briefly describes macroelectronic and microelectronic mine winder propulsion and dispulsion spheres discussing their advantages and disadvantages. A fuzzy logic and neural network-based intelligent mine winder-motion control used in modern real-time expert hypersphere techniques is described. Amongst examples presented is an electromechanically-driven and fluidomechanically-braked intelligent mine winder. This incorporates a four-groove rope motorised pulley-fitted with an AC-AC, AC-DC-AC or DC-AC macrocommutator pulley-hub motor, and an artificial intelligence application-specific integrated circuit, which is fuzzy logic and neural network-based programmable, together with a learning PID microcontroller. 13 refs., 2 figs.
Dynamic multimedia stream adaptation and rate control for heterogeneous networks
SZWABE Andrzej; SCHORR Andreas; HAUCK Franz J.; KASSLER Andreas J.
2006-01-01
Dynamic adaptation of multimedia content is seen as an important feature of next generation networks and pervasive systems enabling terminals and applications to adapt to changes in e.g. context, access network, and available Quality-of-Service(QoS) due to mobility of users, devices or sessions. We present the architecture of a multimedia stream adaptation service which enables communication between terminals having heterogeneous hardware and software capabilities and served by heterogeneous networks. The service runs on special content adaptation nodes which can be placed at any location within the network. The flexible structure of our architecture allows using a variety of different adaptation engines. A generic transcoding engine is used to change the codec of streams. An MPEG-21 Digital Item Adaptation (DIA) based transformation engine allows adjusting the data rate of scalable media streams. An intelligent decision-taking engine implements adaptive flow control which takes into account current network QoS parameters and congestion information. Measurements demonstrate the quality gains achieved through adaptive congestion control mechanisms under conditions typical for a heterogeneous network.
Development of an Adaptive Routing Mechanism in Software-Deﬁned Networks
A. N. Noskov
2015-10-01
Full Text Available The purpose of this work is to develop a unitary mechanism of adaptive routing of diﬀerent kinds, basing on the current requirements on the quality of service. The software conﬁguration of a network is the technology of the future. The trend in communication systems constantly conﬁrms this fact. However, the application of this technology in its current form is justiﬁed only in large networks of technology giants and telecom operators. Today we have a large number of dynamic routing protocols to route big volume traﬃc in communication networks. Our task is to create the solution that can use the opportunities of each node to make a decision on the transmission of information by all possible means for each type of traﬃc. Achieving this goal is possible by solving the problem of the development of generalized metrics, which details the links between devices in the network, and the problem of establishing a framework of adaptive logical network topology (route management to ensure the quality of the whole network in order to meet the current requirements on the quality of a particular type service.
Gas Turbine Engine Control Design Using Fuzzy Logic and Neural Networks
M. Bazazzadeh
2011-01-01
Full Text Available This paper presents a successful approach in designing a Fuzzy Logic Controller (FLC for a specific Jet Engine. At first, a suitable mathematical model for the jet engine is presented by the aid of SIMULINK. Then by applying different reasonable fuel flow functions via the engine model, some important engine-transient operation parameters (such as thrust, compressor surge margin, turbine inlet temperature, etc. are obtained. These parameters provide a precious database, which train a neural network. At the second step, by designing and training a feedforward multilayer perceptron neural network according to this available database; a number of different reasonable fuel flow functions for various engine acceleration operations are determined. These functions are used to define the desired fuzzy fuel functions. Indeed, the neural networks are used as an effective method to define the optimum fuzzy fuel functions. At the next step, we propose a FLC by using the engine simulation model and the neural network results. The proposed control scheme is proved by computer simulation using the designed engine model. The simulation results of engine model with FLC illustrate that the proposed controller achieves the desired performance and stability.
Adapting Bayes Network Structures to Non-stationary Domains
Nielsen, Søren Holbech; Nielsen, Thomas Dyhre
2008-01-01
When an incremental structural learning method gradually modifies a Bayesian network (BN) structure to fit a sequential stream of observations, we call the process structural adaptation. Structural adaptation is useful when the learner is set to work in an unknown environment, where a BN is gradu......When an incremental structural learning method gradually modifies a Bayesian network (BN) structure to fit a sequential stream of observations, we call the process structural adaptation. Structural adaptation is useful when the learner is set to work in an unknown environment, where a BN...
Information Theoretic Adaptive Tracking of Epidemics in Complex Networks
Harrington, Patrick L
2013-01-01
Adaptively monitoring the states of nodes in a large complex network is of interest in domains such as national security, public health, and energy grid management. Here, we present an information theoretic adaptive tracking and sampling framework that recursively selects measurements using the feedback from performing inference on a dynamic Bayesian Network. We also present conditions for the existence of a network specific, observation dependent, phase transition in the updated posterior of hidden node states resulting from actively monitoring the network. Since traditional epidemic thresholds are derived using observation independent Markov chains, the threshold of the posterior should more accurately model the true phase transition of a network. The adaptive tracking framework and epidemic threshold should provide insight into modeling the dynamic response of the updated posterior to active intervention and control policies while monitoring modern complex networks.
Memristor-based programmable logic array (PLA) and analysis as Memristive networks.
Lee, Kwan-Hee; Lee, Sang-Jin; Kim, Seok-Man; Cho, Kyoungrok
2013-05-01
A Memristor theorized by Chua in 1971 has the potential to dramatically influence the way electronic circuits are designed. It is a two terminal device whose resistance state is based on the history of charge flow brought about as the result of the voltage being applied across its terminals and hence can be thought of as a special case of a reconfigurable resistor. Nanoscale devices using dense and regular fabrics such as Memristor cross-bar is promising new architecture for System-on-Chip (SoC) implementations in terms of not only the integration density that the technology can offer but also both improved performance and reduced power dissipation. Memristor has the capacity to switch between high and low resistance states in a cross-bar circuit configuration. The cross-bars are formed from an array of vertical conductive nano-wires cross a second array of horizontal conductive wires. Memristors are realized at the intersection of the two wires in the array through appropriate processing technology such that any particular wire in the vertical array can be connected to a wire in the horizontal array by switching the resistance of a particular intersection to a low state while other cross-points remain in a high resistance state. However the approach introduces a number of challenges. The lack of voltage gain prevents logic being cascaded and voltage level degradation affects robustness of the operation. Moreover the cross-bars introduce sneak current paths when two or more cross points are connected through the switched Memristor. In this paper, we propose Memristor-based programmable logic array (PLA) architecture and develop an analytical model to analyze the logic level on the memristive networks. The proposed PLA architecture has 12 inputs maximum and can be cascaded for more input variables with R(off)/R(on) ratio in the range from 55 to 160 of Memristors. PMID:23858841
Adaptive cluster synchronization of directed complex networks with time delays.
Heng Liu
Full Text Available This paper studied the cluster synchronization of directed complex networks with time delays. It is different from undirected networks, the coupling configuration matrix of directed networks cannot be assumed as symmetric or irreducible. In order to achieve cluster synchronization, this paper uses an adaptive controller on each node and an adaptive feedback strategy on the nodes which in-degree is zero. Numerical example is provided to show the effectiveness of main theory. This method is also effective when the number of clusters is unknown. Thus, it can be used in the community recognizing of directed complex networks.
Association Rule Mining Based Extraction of Semantic Relations Using Markov Logic Network
K.Karthikeyan
2014-10-01
Full Text Available Ontology may be a conceptualization of a website into a human understandable, however machine - readable format consisting of entities, attributes, relationships and axioms. Ontologies formalize the in tentional aspects of a site, whereas the denotative part is provided by a mental object that contains assertions about instances of concepts and relations. Semantic relation it might be potential to extract the whole family - tree of a outstanding personalit y employing a resource like Wikipedia. In a way, relations describe the linguistics relationships among the entities involve that is beneficial for a higher understanding of human language. The relation can be identified from the result of concept hierarch y extraction. The existing ontology learning process only produces the result of concept hierarchy extraction. It does not produce the semantic relation between the concepts. Here, we have to do the process of constructing the predicates and also first ord er logic formula. Here, also find the inference and learning weights using Markov Logic Network. To improve the relation of every input and also improve the relation between the contents we have to propose the concept of ARSRE. This method can find the fre quent items between concepts and converting the extensibility of existing lightweight ontologies to formal one. The experimental results can produce the good extraction of semantic relations compared to state - of - art method
Robust adaptive neural network control with supervisory controller
张天平; 梅建东
2004-01-01
The problem of direct adaptive neural network control for a class of uncertain nonlinear systems with unknown constant control gain is studied in this paper. Based on the supervisory control strategy and the approximation capability of multilayer neural networks (MNNs), a novel design scheme of direct adaptive neural network controller is proposed.The adaptive law of the adjustable parameter vector and the matrix of weights in the neural networks and the gain of sliding mode control term to adaptively compensate for the residual and the approximation error of MNNs is determined by using a Lyapunov method. The approach does not require the optimal approximation error to be square-integrable or the supremum of the optimal approximation error to be known. By theoretical analysis, the closed-loop control system is proven to be globally stable in the sense that all signals involved are bounded, with tracking error converging to zero.Simulation results demonstrate the effectiveness of the approach.
Hybrid Method for the Navigation of Mobile Robot Using Fuzzy Logic and Spiking Neural Networks
Zineb LAOUICI
2014-11-01
Full Text Available the aim of this paper is to present a strategy describing a hybrid approach for the navigation of a mobile robot in a partially known environment. The main idea is to combine between fuzzy logic approach suitable for the navigation in an unknown environment and spiking neural networks approach for solving the problem of navigation in a known environment. In the literature, many approaches exist for the navigation purpose, for solving separately the problem in both situations. Our idea is based on the fact that we consider a mixed environment, and try to exploit the known environment parts for improving the path and time of navigation between the starting point and the target. The Simulation results, which are shown on two simulated scenarios, indicate that the hybridization improves the performance of robot navigation with regard to path length and the time of navigation.
A review on application of neural networks and fuzzy logic to solve hydrothermal scheduling problem
Electrical power system is highly complicated having hydro and thermal mix with large number of machines. To reduce power production cost, hydro and thermal resources are mixed. Hydrothermal scheduling is the optimal coordination of hydro and thermal plants to meet the system load demand at minimum possible operational cost while satisfying the system constraints. Hydrothermal scheduling is dynamic, large scale, non-linear and non-convex optimization problem. The classical techniques have failed in solving such problem. Artificial Intelligence Tools based techniques are used now a day to solve this complex optimization problem because of their no requirements on the nature of the problem. The aim of this research paper is to provide a comprehensive survey of literature related to both Artificial Neural Network (ANN) and Fuzzy Logic (FL) as effective optimization algorithms for the hydrothermal scheduling problem. The outcomes along with the merits and demerits of individual techniques are also discussed. (author)
On the Performance of Adaptive Modulation in Cognitive Radio Networks
Foukalas, F.; Karetsos, G. T.
2013-01-01
We study the performance of cognitive radio networks (CRNs) when incorporating adaptive modulation at the physical layer. Three types of CRNs are considered, namely opportunistic spectrum access (OSA), spectrum sharing (SS) and sensing-based SS. We obtain closed-form expressions for the average spectral efficiency achieved at the secondary network and the optimal power allocation for both continuous and discrete rate types of adaptive modulation assuming perfect channel state information. The...
K. Nattar Kannan
2014-11-01
Full Text Available Wireless Sensor Networks (WSNs is a emerging technology of real time embedded systems for a variety of applications. In general, WSNs has great challenges in the factor of limited computation, energy and memory resources. Clustering techniques play a vital role in WSNs to increase the network lifetime and also made energy efficiency. Existing clustering approaches like LEACH uses neighboring information of the nodes for selecting cluster heads and other nodes spent more energy for transmitting data to cluster head. It was not considered the expected residual energy for selecting a cluster head. In this study, Genetic Algorithm (GA is used to form optimal clusters based on fitness parameters including Cluster Distance (CD, Direct Distance to Base Station (DDBS and Energy of nodes. Also, fuzzy logic approach is applied to select optimal cluster head by using expected residual energy that increases the network lifetime. The aim of the study is providing a solution for unbalanced energy consumption problem in a WSN. The simulation results show that the proposed protocol performs well than other protocols like LEACH and LEACH_ERE.
黄刚; 杨华中; 罗嵘; 汪蕙
2002-01-01
In deep submicron (DSM) integrated circuits (IC), coupling capacitors between inter-connects become dominant over grounded capacitors. As a result, the dynamic power dissipationof one node is no longer only in relation to the signal on that node, and it also depends on signalson its neighbor nodes through coupling capacitors. Thus, for their limitation in dealing with ca-pacitively coupled nets, past jobs on power estimation are facing rigorous challenges and need tobe ameliorated. This paper proposes and proves a simple and fast approach to predicting dynamicpower dissipation of coupled interconnect networks: a coupling capacitor in dynamic CMOS logiccircuits is decoupled and mapped into an equivalent cell containing an XOR gate and a groundedcapacitor, and the whole circuit after mapping, consuming the same power as the original one,could be easily managed by generally-used gate-level power estimation tools. This paper also in-vestigates the correlation coefficient method (CCM). Given the signal probabilities and the correla-tion coefficients between signals, the dynamic power of interconnect networks can be calculatedby using CCM. It can be proved that the decoupling method and CCM draw identical results, that isto say, the decoupling method implicitly preserves correlation properties between signals and thereis no accuracy loss in the decoupling process. Moreover, it is addressed that the coupling capaci-tors in static CMOS circuits could be decoupled and mapped into an equivalent cell containing amore complicated logic block, and the power can be obtained by the probability method for dy-namic CMOS logic circuits.
Adaptive nonlinear control using input normalized neural networks
An adaptive feedback linearization technique combined with the neural network is addressed to control uncertain nonlinear systems. The neural network-based adaptive control theory has been widely studied. However, the stability analysis of the closed-loop system with the neural network is rather complicated and difficult to understand, and sometimes unnecessary assumptions are involved. As a result, unnecessary assumptions for stability analysis are avoided by using the neural network with input normalization technique. The ultimate boundedness of the tracking error is simply proved by the Lyapunov stability theory. A new simple update law as an adaptive nonlinear control is derived by the simplification of the input normalized neural network assuming the variation of the uncertain term is sufficiently small
Traffic flow on realistic road networks with adaptive traffic lights
de Gier, Jan; Rojas, Omar
2010-01-01
We present a model of traffic flow on generic urban road networks based on cellular automata. We apply this model to an existing road network in the Australian city of Melbourne, using empirical data as input. For comparison, we also apply this model to a square-grid network using hypothetical input data. On both networks we compare the effects of non-adative vs adaptive traffic lights, in which instantaneous traffic state information feeds back into the traffic signal schedule. We observe that not only do adaptive traffic lights result in better averages of network observables, they also lead to significantly smaller fluctuations in these observables. We furthermore compare two different systems of adaptive traffic signals, one which is informed by the traffic state on both upstream and downstream links, and one which is informed by upstream links only. We find that, in general, the total travel time is smallest when using the joint upstream-downstream control strategy.
Stochastic analysis of epidemics on adaptive time varying networks
Kotnis, Bhushan; Kuri, Joy
2013-06-01
Many studies investigating the effect of human social connectivity structures (networks) and human behavioral adaptations on the spread of infectious diseases have assumed either a static connectivity structure or a network which adapts itself in response to the epidemic (adaptive networks). However, human social connections are inherently dynamic or time varying. Furthermore, the spread of many infectious diseases occur on a time scale comparable to the time scale of the evolving network structure. Here we aim to quantify the effect of human behavioral adaptations on the spread of asymptomatic infectious diseases on time varying networks. We perform a full stochastic analysis using a continuous time Markov chain approach for calculating the outbreak probability, mean epidemic duration, epidemic reemergence probability, etc. Additionally, we use mean-field theory for calculating epidemic thresholds. Theoretical predictions are verified using extensive simulations. Our studies have uncovered the existence of an “adaptive threshold,” i.e., when the ratio of susceptibility (or infectivity) rate to recovery rate is below the threshold value, adaptive behavior can prevent the epidemic. However, if it is above the threshold, no amount of behavioral adaptations can prevent the epidemic. Our analyses suggest that the interaction patterns of the infected population play a major role in sustaining the epidemic. Our results have implications on epidemic containment policies, as awareness campaigns and human behavioral responses can be effective only if the interaction levels of the infected populace are kept in check.
Linking Individual and Collective Behavior in Adaptive Social Networks
Pinheiro, Flávio L.; Santos, Francisco C.; Pacheco, Jorge M.
2016-03-01
Adaptive social structures are known to promote the evolution of cooperation. However, up to now the characterization of the collective, population-wide dynamics resulting from the self-organization of individual strategies on a coevolving, adaptive network has remained unfeasible. Here we establish a (reversible) link between individual (micro)behavior and collective (macro)behavior for coevolutionary processes. We demonstrate that an adaptive network transforms a two-person social dilemma locally faced by individuals into a collective dynamics that resembles that associated with an N -person coordination game, whose characterization depends sensitively on the relative time scales between the entangled behavioral and network evolutions. In particular, we show that the faster the relative rate of adaptation of the network, the smaller the critical fraction of cooperators required for cooperation to prevail, thus establishing a direct link between network adaptation and the evolution of cooperation. The framework developed here is general and may be readily applied to other dynamical processes occurring on adaptive networks, notably, the spreading of contagious diseases or the diffusion of innovations.
Implementation of an Adaptive Learning System Using a Bayesian Network
Yasuda, Keiji; Kawashima, Hiroyuki; Hata, Yoko; Kimura, Hiroaki
2015-01-01
An adaptive learning system is proposed that incorporates a Bayesian network to efficiently gauge learners' understanding at the course-unit level. Also, learners receive content that is adapted to their measured level of understanding. The system works on an iPad via the Edmodo platform. A field experiment using the system in an elementary school…
Adaptation to synchronization in phase-oscillator networks
Arizmendi, Fernando; Zanette, Damian H.
2008-01-01
We introduce an adaptation algorithm by which an ensemble of coupled oscillators with attractive and repulsive interactions is induced to adopt a prescribed synchronized state. While the performance of adaptation is controlled by measuring a macroscopic quantity, which characterizes the achieved degree of synchronization, adaptive changes are introduced at the microscopic level of the interaction network, by modifying the configuration of repulsive interactions. This scheme emulates the disti...
Sunal, Cynthia Szymanski; Karr, Charles L.; Sunal, Dennis W.
2003-01-01
Students' conceptions of three major artificial intelligence concepts used in the modeling of systems in science, fuzzy logic, neural networks, and genetic algorithms were investigated before and after a higher education science course. Students initially explored their prior ideas related to the three concepts through active tasks. Then,…
Collaborative Trust Networks in Engineering Design Adaptation
Atkinson, Simon Reay; Maier, Anja; Caldwell, Nicholas;
2011-01-01
); applying the Change Prediction Method (CPM) tool. It posits the idea of the ‘Networks-in-Being’ with varying individual and collective characteristics. [Social] networks are considered to facilitate information exchange between actors. At the same time, networks failing to provide trusted-information can...... hinder effective communication and collaboration. Different combinations of trust may therefore improve or impair the likelihood of information flow, transfer and subsequent action (cause and effect). This paper investigates how analysing different types of network-structures-in-being can support......Within organisations, decision makers have to rely on collaboration with other actors from different disciplines working within highly dynamic and distributed associated networks of varying size and scales. This paper develops control and influence networks within Design Structure Matrices (DSM...
Adaptive optimization and control using neural networks
Mead, W.C.; Brown, S.K.; Jones, R.D.; Bowling, P.S.; Barnes, C.W.
1993-10-22
Recent work has demonstrated the ability of neural-network-based controllers to optimize and control machines with complex, non-linear, relatively unknown control spaces. We present a brief overview of neural networks via a taxonomy illustrating some capabilities of different kinds of neural networks. We present some successful control examples, particularly the optimization and control of a small-angle negative ion source.
Meng, Fanyu; Zhu, Aidong
2008-10-01
A quantum logic network to implement quantum telecloning is presented in this paper. The network includes two parts: the first part is used to create the telecloning channel and the second part to teleport the state. It can be used not only to implement universal telecloning for a bipartite entangled state which is completely unknown, but also to implement the phase-covariant telecloning for one that is partially known. Furthermore, the network can also be used to construct a tele-triplicator. It can easily be implemented in experiment because only single- and two-qubit operations are used in the network.
Meng Fanyu; Zhu Aidong [Department of Physics, College of Science, Yanbian University, Yanji, Jilin 133002 (China)], E-mail: adzhu@ybu.edu.cn
2008-10-28
A quantum logic network to implement quantum telecloning is presented in this paper. The network includes two parts: the first part is used to create the telecloning channel and the second part to teleport the state. It can be used not only to implement universal telecloning for a bipartite entangled state which is completely unknown, but also to implement the phase-covariant telecloning for one that is partially known. Furthermore, the network can also be used to construct a tele-triplicator. It can easily be implemented in experiment because only single- and two-qubit operations are used in the network.
This paper presents a new robust adaptive synchronization method for a class of uncertain dynamical complex networks with network failures and coupling time-varying delays. Adaptive schemes are proposed to adjust controller parameters for the faulty network compensations, as well as to estimate the upper and lower bounds of delayed state errors and perturbations to compensate the effects of delay and perturbation on-line without assuming symmetry or irreducibility of networks. It is shown that, through Lyapunov stability theory, distributed adaptive controllers constructed by the adaptive schemes are successful in ensuring the achievement of asymptotic synchronization of networks in the present of faulty and delayed networks, and perturbation inputs. A Chua's circuit network example is finally given to show the effectiveness of the proposed synchronization criteria. (general)
Adaptive swarm-based routing in communication networks
吕勇; 赵光宙; 苏凡军; 历小润
2004-01-01
Swarm intelligence inspired by the social behavior of ants boasts a number of attractive features, including adaptation, robustness and distributed, decentralized nature, which are well suited for routing in modern communication networks. This paper describes an adaptive swarm-based routing algorithm that increases convergence speed, reduces routing instabilities and oscillations by using a novel variation of reinforcement learning and a technique called momentum.Experiment on the dynamic network showed that adaptive swarm-based routing learns the optimum routing in terms of convergence speed and average packet latency.
Adaptive swarm-based routing in communication networks
吕勇; 赵光宙; 苏凡军; 历小润
2004-01-01
Swarm intelligence inspired by the social behavior of ants boasts a number of attractive features,including adaptation,robustness and distributed,decentralized nature,which are well suited for routing in modern communication networks.This paper describes an adaptive swarm-based routing algorithm that increases convergence speed,reduces routing instabilities and oscillations by using a novel variation of reinforcement learning and a technique called momentum.Experiment on the dynamic network showed that adaptive swarm-based routing learns the optimum routing in terms of convergence speed and average packet latency.
Model for cascading failures with adaptive defense in complex networks
This paper investigates cascading failures in networks by considering interplay between the flow dynamic and the network topology, where the fluxes exchanged between a pair of nodes can be adaptively adjusted depending on the changes of the shortest path lengths between them. The simulations on both an artificially created scale-free network and the real network structure of the power grid reveal that the adaptive adjustment of the fluxes can drastically enhance the robustness of complex networks against cascading failures. Particularly, there exists an optimal region where the propagation of the cascade is significantly suppressed and the fluxes supported by the network are maximal. With this understanding, a costless strategy of defense for preventing cascade breakdown is proposed. It is shown to be more effective for suppressing the propagation of the cascade than the recent proposed strategy of defense based on the intentional removal of nodes. (general)
Adaptive Neurons For Artificial Neural Networks
Tawel, Raoul
1990-01-01
Training time decreases dramatically. In improved mathematical model of neural-network processor, temperature of neurons (in addition to connection strengths, also called weights, of synapses) varied during supervised-learning phase of operation according to mathematical formalism and not heuristic rule. Evidence that biological neural networks also process information at neuronal level.
Network-topology-adaptive quantum conference protocols
Zhang Sheng; Wang Jian; Tang Chao-Jing; Zhang Quan
2011-01-01
As an important application of the quantum network communication,quantum multiparty conference has made multiparty secret communication possible.Previous quantum multiparty conference schemes based on quantum data encryption are insensitive to network topology.However,the topology of the quantum network significantly affects the communication efficiency,e.g.,parallel transmission in a channel with limited bandwidth.We have proposed two distinctive protocols,which work in two basic network topologies with efficiency higher than the existing ones.We first present a protocol which works in the reticulate network using Greeberger-Horne-Zeilinger states and entanglement swapping.Another protocol,based on quantum multicasting with quantum data compression,which can improve the efficiency of the network,works in the star-like network.The security of our protocols is guaranteed by quantum key distribution and one-time-pad encryption.In general,the two protocols can be applied to any quantum network where the topology can be equivalently transformed to one of the two structures we propose in our protocols.
A Neural Network for Generating Adaptive Lessons
Hassina Seridi-Bouchelaghem; Toufik Sari; Mokhtar Sellami
2005-01-01
Traditional sequencing technology developed in the field of intelligent tutoring systems have not find an immediate place in large-scale Web-based education. This study investigates the use of computational intelligence for adaptive lesson generation in a distance learning environment over the Web. An approach for adaptive pedagogical hypermedia document generation is proposed and implemented in a prototype called KnowledgeClass. This approach is based on a specialized art...
TCP-Adaptive in High Speed Long Distance Networks
Quan Liu
2014-02-01
Full Text Available With the development of high performance computing and increasing of network bandwidth, more and more applications require fast data transfer over high-speed long-distance networks. Research shows that the standard TCP Reno cannot fulfill the requirement of fast transfer of massive data due to its conservative congestion control mechanism. Some works have been proposed to improve the TCP throughput performance using more aggressive window increasing tactics and obtain substantial achievements. However, they cannot be strictly proved to be comprehensively suitable for high-speed complex network environments. In this paper, we propose TCP-Adaptive, an adaptive congestion control algorithm adjusting the increasing congestion window dynamically. The algorithm improves logarithmic detection procedure for available bandwidth in the flow path by distinguishing the first detection in congestion avoidance and retransmission timeout. On the other hand, an adaptive control algorithm is proposed to achieve better performance in high-speed long-distance networks. The algorithm uses round trip time (RTT variations to predict the congestion trends to update the increments of congestion window. Simulations verify the property of TCP-Adaptive and show satisfying performance in throughput, RTT fairness aspects over high-speed long-distance networks. Especially in sporadic loss environment, TCP-Adaptive shows a significant adaptability with the variations of link quality
Temporal and structural heterogeneities emerging in adaptive temporal networks
Aoki, Takaaki; Gross, Thilo
2015-01-01
We introduce a model of adaptive temporal networks whose evolution is regulated by an interplay between node activity and dynamic exchange of information through links. We study the model by using a master equation approach. Starting from a homogeneous initial configuration, we show that temporal and structural heterogeneities, characteristic of real-world networks, spontaneously emerge. This theoretically tractable model thus contributes to the understanding of the dynamics of human activity and interaction networks.
Temporal and structural heterogeneities emerging in adaptive temporal networks
Aoki, Takaaki; Rocha, Luis E. C.; Gross, Thilo
2016-04-01
We introduce a model of adaptive temporal networks whose evolution is regulated by an interplay between node activity and dynamic exchange of information through links. We study the model by using a master equation approach. Starting from a homogeneous initial configuration, we show that temporal and structural heterogeneities, characteristic of real-world networks, spontaneously emerge. This theoretically tractable model thus contributes to the understanding of the dynamics of human activity and interaction networks.
Adaptive Energy-Aware Gathering Strategy for Wireless Sensor Networks
E M Saad; Awadalla, M. H.; R. R. Darwish
2009-01-01
Energy hole problem is considered one of the most severe threats in wireless sensor networks. In this paper the idea of exploiting sink mobility for the purpose of culling the energy hole problem in hierarchical large-scale wireless sensor networks based on bees algorithm is presented. In the proposed scheme, a mobile sink equipped with a powerful transceiver and battery, traverses the entire field, and periodically gathers data from network cluster heads. The mobile sink follows an adaptive ...
An Adaptive Handover Prediction Scheme for Seamless Mobility Based Wireless Networks
Ali Safa Sadiq
2014-01-01
Full Text Available We propose an adaptive handover prediction (AHP scheme for seamless mobility based wireless networks. That is, the AHP scheme incorporates fuzzy logic with AP prediction process in order to lend cognitive capability to handover decision making. Selection metrics, including received signal strength, mobile node relative direction towards the access points in the vicinity, and access point load, are collected and considered inputs of the fuzzy decision making system in order to select the best preferable AP around WLANs. The obtained handover decision which is based on the calculated quality cost using fuzzy inference system is also based on adaptable coefficients instead of fixed coefficients. In other words, the mean and the standard deviation of the normalized network prediction metrics of fuzzy inference system, which are collected from available WLANs are obtained adaptively. Accordingly, they are applied as statistical information to adjust or adapt the coefficients of membership functions. In addition, we propose an adjustable weight vector concept for input metrics in order to cope with the continuous, unpredictable variation in their membership degrees. Furthermore, handover decisions are performed in each MN independently after knowing RSS, direction toward APs, and AP load. Finally, performance evaluation of the proposed scheme shows its superiority compared with representatives of the prediction approaches.
Time scales in evolutionary game on adaptive networks
Most previous studies concerning spatial games have assumed strategy updating occurs with a fixed ratio relative to interactions. We here set up a coevolutionary model to investigate how different ratio affects the evolution of cooperation on adaptive networks. Simulation results demonstrate that cooperation can be significantly enhanced under our rewiring mechanism, especially with slower natural selection. Meanwhile, slower selection induces larger network heterogeneity. Strong selection contracts the parameter area where cooperation thrives. Therefore, cooperation prevails whenever individuals are offered enough chances to adapt to the environment. Robustness of the results has been checked under rewiring cost or varied networks.
Time scales in evolutionary game on adaptive networks
Cong, Rui, E-mail: congrui0000@126.com [School of Mechano-Electronic Engineering, Xidian University, Xi' an (China); Wu, Te; Qiu, Yuan-Ying [School of Mechano-Electronic Engineering, Xidian University, Xi' an (China); Wang, Long [School of Mechano-Electronic Engineering, Xidian University, Xi' an (China); Center for Systems and Control, State Key Laboratory for Turbulence and Complex Systems, College of Engineering, Peking University, Beijing (China)
2014-02-01
Most previous studies concerning spatial games have assumed strategy updating occurs with a fixed ratio relative to interactions. We here set up a coevolutionary model to investigate how different ratio affects the evolution of cooperation on adaptive networks. Simulation results demonstrate that cooperation can be significantly enhanced under our rewiring mechanism, especially with slower natural selection. Meanwhile, slower selection induces larger network heterogeneity. Strong selection contracts the parameter area where cooperation thrives. Therefore, cooperation prevails whenever individuals are offered enough chances to adapt to the environment. Robustness of the results has been checked under rewiring cost or varied networks.
Adaptive Network Dynamics and Evolution of Leadership in Collective Migration
Pais, Darren
2013-01-01
The evolution of leadership in migratory populations depends not only on costs and benefits of leadership investments but also on the opportunities for individuals to rely on cues from others through social interactions. We derive an analytically tractable adaptive dynamic network model of collective migration with fast timescale migration dynamics and slow timescale adaptive dynamics of individual leadership investment and social interaction. For large populations, our analysis of bifurcations with respect to investment cost explains the observed hysteretic effect associated with recovery of migration in fragmented environments. Further, we show a minimum connectivity threshold above which there is evolutionary branching into leader and follower populations. For small populations, we show how the topology of the underlying social interaction network influences the emergence and location of leaders in the adaptive system. Our model and analysis can describe other adaptive network dynamics involving collective...
Time-adaptive and history-adaptive multicriterion routing in stochastic, time-dependent networks
Pretolani, Daniele; Nielsen, Lars Relund; Andersen, Kim Allan;
2009-01-01
We compare two different models for multicriterion routing in stochastic time-dependent networks: the classic "time-adaptive'' model and the more flexible "history-adaptive'' one. We point out several properties of the sets of efficient solutions found under the two models. We also devise a method...
Adaptive intelligent power systems: Active distribution networks
Electricity networks are extensive and well established. They form a key part of the infrastructure that supports industrialised society. These networks are moving from a period of stability to a time of potentially major transition, driven by a need for old equipment to be replaced, by government policy commitments to cleaner and renewable sources of electricity generation, and by change in the power industry. This paper looks at moves towards active distribution networks. The novel transmission and distribution systems of the future will challenge today's system designs. They will cope with variable voltages and frequencies, and will offer more flexible, sustainable options. Intelligent power networks will need innovation in several key areas of information technology. Active control of flexible, large-scale electrical power systems is required. Protection and control systems will have to react to faults and unusual transient behaviour and ensure recovery after such events. Real-time network simulation and performance analysis will be needed to provide decision support for system operators, and the inputs to energy and distribution management systems. Advanced sensors and measurement will be used to achieve higher degrees of network automation and better system control, while pervasive communications will allow networks to be reconfigured by intelligent systems
Network-based business process management: embedding business logic in communications networks
Pau, Louis-François; Vervest, Peter
2003-01-01
textabstractAdvanced Business Process Management (BPM) tools enable the decomposition of previously integrated and often ill-defined processes into re-usable process modules. These process modules can subsequently be distributed on the Internet over a variety of many different actors, each with their own specialization and economies-of-scale. The economic benefits of process specialization can be huge. However, how should such actors in a business network find, select, and control, the best p...
Concurrent enhancement of percolation and synchronization in adaptive networks
Eom, Young-Ho; Boccaletti, Stefano; Caldarelli, Guido
2016-06-01
Co-evolutionary adaptive mechanisms are not only ubiquitous in nature, but also beneficial for the functioning of a variety of systems. We here consider an adaptive network of oscillators with a stochastic, fitness-based, rule of connectivity, and show that it self-organizes from fragmented and incoherent states to connected and synchronized ones. The synchronization and percolation are associated to abrupt transitions, and they are concurrently (and significantly) enhanced as compared to the non-adaptive case. Finally we provide evidence that only partial adaptation is sufficient to determine these enhancements. Our study, therefore, indicates that inclusion of simple adaptive mechanisms can efficiently describe some emergent features of networked systems’ collective behaviors, and suggests also self-organized ways to control synchronization and percolation in natural and social systems.
Controling contagious processes on temporal networks via adaptive rewiring
Belik, Vitaly; Hövel, Philipp
2015-01-01
We consider recurrent contagious processes on a time-varying network. As a control procedure to mitigate the epidemic, we propose an adaptive rewiring mechanism for temporary isolation of infected nodes upon their detection. As a case study, we investigate the network of pig trade in Germany. Based on extensive numerical simulations for a wide range of parameters, we demonstrate that the adaptation mechanism leads to a significant extension of the parameter range, for which most of the index nodes (origins of the epidemic) lead to vanishing epidemics. We find that diseases with detection times around a week and infectious periods up to 3 months can be effectively controlled. Furthermore the performance of adaptation is very heterogeneous with respect to the index node. We identify index nodes that are most responsive to the adaptation strategy and quantify the success of the proposed adaptation scheme in dependence on the infectious period and detection times.
Jafri, Madiha J.; Ely, Jay J.; Vahala, Linda L.
2007-01-01
In this paper, neural network (NN) modeling is combined with fuzzy logic to estimate Interference Path Loss measurements on Airbus 319 and 320 airplanes. Interference patterns inside the aircraft are classified and predicted based on the locations of the doors, windows, aircraft structures and the communication/navigation system-of-concern. Modeled results are compared with measured data. Combining fuzzy logic and NN modeling is shown to improve estimates of measured data over estimates obtained with NN alone. A plan is proposed to enhance the modeling for better prediction of electromagnetic coupling problems inside aircraft.
Adaptive computational resource allocation for sensor networks
WANG Dian-hong; FEI E; YAN Yu-jie
2008-01-01
To efficiently utilize the limited computational resource in real-time sensor networks, this paper focu-ses on the challenge of computational resource allocation in sensor networks and provides a solution with the method of economies. It designs a mieroeconomic system in which the applications distribute their computational resource consumption across sensor networks by virtue of mobile agent. Further, it proposes the market-based computational resource allocation policy named MCRA which satisfies the uniform consumption of computational energy in network and the optimal division of the single computational capacity for multiple tasks. The simula-tion in the scenario of target tracing demonstrates that MCRA realizes an efficient allocation of computational re-sources according to the priority of tasks, achieves the superior allocation performance and equilibrium perform-ance compared to traditional allocation policies, and ultimately prolongs the system lifetime.
Adaptive Capacity Management in Bluetooth Networks
Son, L.T.
With the Internet and mobile wireless development, accelerated by high-speed and low cost VLSI device evolution, short range wireless communications have become more and more popular, especially Bluetooth. Bluetooth is a new short range radio technology that promises to be very convenient, low...... power, and low cost mobile ad hoc solution for the global interconnection of all mobile devices. To implement Bluetooth network as a true mobile ad hoc wireless network operating in short radio range, highly dynamic network environment, low power, and scarce resources, many new research challenges occur......, such as limited wireless bandwidth operation, routing, scheduling, network control, etc. Currently Bluetooth specification particularly does not describe in details about how to implement Quality of Service and Resource Management in Bluetooth protocol stacks. These issues become significant, when the number...
Improved Adaptive Routing for Multihop IEEE 802.15.6 Wireless Body Area Networks
Shariar Imtiaz
2013-11-01
Full Text Available Wireless Body Area Network has the ability to collect and send data on body measurement to the server through PDA or other device. Nodes (sensors collect vital signs from the body or environmental factor and check them. In IEEE 802.15.6 routing is discussed as a part of the link layer where multihop is not fully considered. Improving network performance, reducing energy consumption, thus extending the network lifetime is the main challenge in BANs. Several studies mention that multihop for BANs helps for achieving network performance, reducing energy consumption and extending network lifetime. One work presents the Adaptive multihop tree-based Routing (AMR protocol that is extensively evaluated in a real testbed deployment. They use fuzzy logic to combine all metrics they use. Another limitation is that they have used Prim's algorithm which is not a realistic approach. So in this work we have improved their multihop tree-based Routing (AMR protocol using Kruskal's algorithm instead of Prim's algorithm. The time complexity of Kruskal's algorithm is way less than prims's algorithm. We have used network simulator 3 (NS3 to simulate and found that our algorithm is better than AMR if many of nodes.
Yuanjiang Huang
2014-01-01
Full Text Available The sensor nodes in the Wireless Sensor Networks (WSNs are prone to failures due to many reasons, for example, running out of battery or harsh environment deployment; therefore, the WSNs are expected to be able to maintain network connectivity and tolerate certain amount of node failures. By applying fuzzy-logic approach to control the network topology, this paper aims at improving the network connectivity and fault-tolerant capability in response to node failures, while taking into account that the control approach has to be localized and energy efficient. Two fuzzy controllers are proposed in this paper: one is Learning-based Fuzzy-logic Topology Control (LFTC, of which the fuzzy controller is learnt from a training data set; another one is Rules-based Fuzzy-logic Topology Control (RFTC, of which the fuzzy controller is obtained through designing if-then rules and membership functions. Both LFTC and RFTC do not rely on location information, and they are localized. Comparing them with other three representative algorithms (LTRT, List-based, and NONE through extensive simulations, our two proposed fuzzy controllers have been proved to be very energy efficient to achieve desired node degree and improve the network connectivity when sensor nodes run out of battery or are subject to random attacks.
Geographic Routing Using Logical Levels in Wireless Sensor Networks for Sensor Mobility
Yassine SABRI
2015-07-01
Full Text Available In this paper we propose an improvement to the GRPW algorithm for wireless sensor networks called GRPW-M , which collects data in a wireless sensor network (WSN using a mobile nodes. Performance of GRPW algorithm algorithm depends heavily on the immobile sensor nodes . This prediction can be hard to do. For that reason, we propose a modified algorithm that is able to adapt to the current situation in the network in which the sensor node considered mobile. The goal of the proposed algorithm is to decrease the reconstruction cost and increase the data delivery ratio. In comparing the GRPW-M protocol with GRPW protocol in simulation, this paper demonstrates that adjustment process executed by GRPW-M does in fact decrease the reconstruction cost and increase the data delivery ratio . Simulations were performed on GRPW as well as on the proposed Routing algorithm. The efficiency factors that were evaluated was total number of transmissions in the network and total delivery rate. And in general the proposed Routing algorithm may perform reasonable well for a large number network setups.
Bifurcation Analysis of Equilibria in Competitive Logistic Networks with Adaptation
Raimondi, A.; Tebaldi, C.
2008-04-01
A general n-node network is considered for which, in absence of interactions, each node is governed by a logistic equation. Interactions among the nodes take place in the form of competition, which also includes adaptive abilities through a (short term) memory effect. As a consequence the dynamics of the network is governed by a system of n2 nonlinear ordinary differential equations. As a first step, equilibria and their stability are investigated analytically for the general network in dependence of the relevant parameters, namely the strength of competition, the adaptation rate and the network size. The existence of classes of invariant subspaces, related to symmetries, allows the introduction of a reduced model, four dimensional, where n appears as a parameter, which give full account of existence and stability for the equilibria in the network.
Discrete Logic Modelling Optimization to Contextualize Prior Knowledge Networks Using PRUNET.
Rodriguez, Ana; Crespo, Isaac; Androsova, Ganna; del Sol, Antonio
2015-01-01
High-throughput technologies have led to the generation of an increasing amount of data in different areas of biology. Datasets capturing the cell's response to its intra- and extra-cellular microenvironment allows such data to be incorporated as signed and directed graphs or influence networks. These prior knowledge networks (PKNs) represent our current knowledge of the causality of cellular signal transduction. New signalling data is often examined and interpreted in conjunction with PKNs. However, different biological contexts, such as cell type or disease states, may have distinct variants of signalling pathways, resulting in the misinterpretation of new data. The identification of inconsistencies between measured data and signalling topologies, as well as the training of PKNs using context specific datasets (PKN contextualization), are necessary conditions to construct reliable, predictive models, which are current challenges in the systems biology of cell signalling. Here we present PRUNET, a user-friendly software tool designed to address the contextualization of a PKNs to specific experimental conditions. As the input, the algorithm takes a PKN and the expression profile of two given stable steady states or cellular phenotypes. The PKN is iteratively pruned using an evolutionary algorithm to perform an optimization process. This optimization rests in a match between predicted attractors in a discrete logic model (Boolean) and a Booleanized representation of the phenotypes, within a population of alternative subnetworks that evolves iteratively. We validated the algorithm applying PRUNET to four biological examples and using the resulting contextualized networks to predict missing expression values and to simulate well-characterized perturbations. PRUNET constitutes a tool for the automatic curation of a PKN to make it suitable for describing biological processes under particular experimental conditions. The general applicability of the implemented algorithm
Discrete Logic Modelling Optimization to Contextualize Prior Knowledge Networks Using PRUNET.
Ana Rodriguez
Full Text Available High-throughput technologies have led to the generation of an increasing amount of data in different areas of biology. Datasets capturing the cell's response to its intra- and extra-cellular microenvironment allows such data to be incorporated as signed and directed graphs or influence networks. These prior knowledge networks (PKNs represent our current knowledge of the causality of cellular signal transduction. New signalling data is often examined and interpreted in conjunction with PKNs. However, different biological contexts, such as cell type or disease states, may have distinct variants of signalling pathways, resulting in the misinterpretation of new data. The identification of inconsistencies between measured data and signalling topologies, as well as the training of PKNs using context specific datasets (PKN contextualization, are necessary conditions to construct reliable, predictive models, which are current challenges in the systems biology of cell signalling. Here we present PRUNET, a user-friendly software tool designed to address the contextualization of a PKNs to specific experimental conditions. As the input, the algorithm takes a PKN and the expression profile of two given stable steady states or cellular phenotypes. The PKN is iteratively pruned using an evolutionary algorithm to perform an optimization process. This optimization rests in a match between predicted attractors in a discrete logic model (Boolean and a Booleanized representation of the phenotypes, within a population of alternative subnetworks that evolves iteratively. We validated the algorithm applying PRUNET to four biological examples and using the resulting contextualized networks to predict missing expression values and to simulate well-characterized perturbations. PRUNET constitutes a tool for the automatic curation of a PKN to make it suitable for describing biological processes under particular experimental conditions. The general applicability of the
Tri Kusnandi Fazarudin
2015-12-01
Full Text Available Multi-Depot Vehicle Routing Problem with Time Window (MDVRPTW is a problem of finding an optimal route for a supplier. The supplier needs to deliver goods to a number of customers using the vehicles located in a number of depots. Each delivery must be done within the service time specified by each customer The vehicles used have a maximum limit on the amount of goods that can be loaded and the maximum time the vehicle may be used. MDVRPTW is one of the variations of Vehicle Routing Problem (VRP. There are various algorithms that have been used to solve VRP problems. Some of them are Genetic Algorithm (GA, Tabu Search, and Adaptive GA with Artificial Bee Colony. GA can solve the problem within a shorter time, but it is vulnerable to get trapped in a local optimum. A strategy to reduce the probability of it is to make the GA adaptive. In this research, MDVRPTW is solved with GA. To reduce the probability of getting trapped in a local optimum, the GA parameters are made adaptive using Fuzzy Logic Controller (FLC. Based on the results of this research, using FLC on GA causes the average of the solution to be better than the solution produced using GA without FLC.
Scalable Lunar Surface Networks and Adaptive Orbit Access
Wang, Xudong
2015-01-01
Teranovi Technologies, Inc., has developed innovative network architecture, protocols, and algorithms for both lunar surface and orbit access networks. A key component of the overall architecture is a medium access control (MAC) protocol that includes a novel mechanism of overlaying time division multiple access (TDMA) and carrier sense multiple access with collision avoidance (CSMA/CA), ensuring scalable throughput and quality of service. The new MAC protocol is compatible with legacy Institute of Electrical and Electronics Engineers (IEEE) 802.11 networks. Advanced features include efficiency power management, adaptive channel width adjustment, and error control capability. A hybrid routing protocol combines the advantages of ad hoc on-demand distance vector (AODV) routing and disruption/delay-tolerant network (DTN) routing. Performance is significantly better than AODV or DTN and will be particularly effective for wireless networks with intermittent links, such as lunar and planetary surface networks and orbit access networks.
Explosive Synchronization and Emergence of Assortativity on Adaptive Networks
JIANG Hui-Jun; WU Hao; HOU Zhong-Huai
2011-01-01
@@ We report an explosive transition from incoherence to synchronization of coupled phase oscillators on adaptive networks,following an Achlioptas process based on dynamic clustering information.During each adaptive step of the network topology,a portion of the links is randomly removed and the same amount of new links is generated following the so-called product rules(PRs) applied to the dynamic clusters.Particularly,two types of PRs are considered,namely,the min-PR and max-PR.We demonstrate that the synchronization transition becomes explosive in both cases.Interestingly,we find that the min-PR rule can lead to disassortativity of the network topology,while the max-PR rule leads to assortativity.%We report an explosive transition from incoherence to synchronization of coupled phase oscillators on adaptive networks, following an Achlioptas process based on dynamic clustering information. During each adaptive step of the network topology, a portion of the links is randomly removed and the same amount of new links is generated following the so-called product rules (PRs) applied to the dynamic clusters. Particularly, two types of PRs are considered, namely, the min-PR and max-PR. We demonstrate that the synchronization transition becomes explosive in both cases. Interestingly, we find that the min-PR rule can lead to disassortativity of the network topology, while the max-PR rule leads to assortativity.
Global network reorganization during dynamic adaptations of Bacillus subtilis metabolism
Buescher, Joerg Martin; Liebermeister, Wolfram; Jules, Matthieu;
2012-01-01
known transcription regulation network. Interactions across multiple levels of regulation were involved in adaptive changes that could also be achieved by controlling single genes. Our analysis suggests that global trade-offs and evolutionary constraints provide incentives to favor complex control......Adaptation of cells to environmental changes requires dynamic interactions between metabolic and regulatory networks, but studies typically address only one or a few layers of regulation. For nutritional shifts between two preferred carbon sources of Bacillus subtilis, we combined statistical and...... model-based data analyses of dynamic transcript, protein, and metabolite abundances and promoter activities. Adaptation to malate was rapid and primarily controlled posttranscriptionally compared with the slow, mainly transcriptionally controlled adaptation to glucose that entailed nearly half of the...
Analysis of adaptive algorithms for an integrated communication network
Reed, Daniel A.; Barr, Matthew; Chong-Kwon, Kim
1985-01-01
Techniques were examined that trade communication bandwidth for decreased transmission delays. When the network is lightly used, these schemes attempt to use additional network resources to decrease communication delays. As the network utilization rises, the schemes degrade gracefully, still providing service but with minimal use of the network. Because the schemes use a combination of circuit and packet switching, they should respond to variations in the types and amounts of network traffic. Also, a combination of circuit and packet switching to support the widely varying traffic demands imposed on an integrated network was investigated. The packet switched component is best suited to bursty traffic where some delays in delivery are acceptable. The circuit switched component is reserved for traffic that must meet real time constraints. Selected packet routing algorithms that might be used in an integrated network were simulated. An integrated traffic places widely varying workload demands on a network. Adaptive algorithms were identified, ones that respond to both the transient and evolutionary changes that arise in integrated networks. A new algorithm was developed, hybrid weighted routing, that adapts to workload changes.
A candidate multimodal functional genetic network for thermal adaptation
Katharina C. Wollenberg Valero
2014-09-01
Full Text Available Vertebrate ectotherms such as reptiles provide ideal organisms for the study of adaptation to environmental thermal change. Comparative genomic and exomic studies can recover markers that diverge between warm and cold adapted lineages, but the genes that are functionally related to thermal adaptation may be difficult to identify. We here used a bioinformatics genome-mining approach to predict and identify functions for suitable candidate markers for thermal adaptation in the chicken. We first established a framework of candidate functions for such markers, and then compiled the literature on genes known to adapt to the thermal environment in different lineages of vertebrates. We then identified them in the genomes of human, chicken, and the lizard Anolis carolinensis, and established a functional genetic interaction network in the chicken. Surprisingly, markers initially identified from diverse lineages of vertebrates such as human and fish were all in close functional relationship with each other and more associated than expected by chance. This indicates that the general genetic functional network for thermoregulation and/or thermal adaptation to the environment might be regulated via similar evolutionarily conserved pathways in different vertebrate lineages. We were able to identify seven functions that were statistically overrepresented in this network, corresponding to four of our originally predicted functions plus three unpredicted functions. We describe this network as multimodal: central regulator genes with the function of relaying thermal signal (1, affect genes with different cellular functions, namely (2 lipoprotein metabolism, (3 membrane channels, (4 stress response, (5 response to oxidative stress, (6 muscle contraction and relaxation, and (7 vasodilation, vasoconstriction and regulation of blood pressure. This network constitutes a novel resource for the study of thermal adaptation in the closely related nonavian reptiles and
Adaptive Media Access Control for Energy Harvesting - Wireless Sensor Networks
Fafoutis, Xenofon; Dragoni, Nicola
2012-01-01
ODMAC (On-Demand Media Access Control) is a recently proposed MAC protocol designed to support individual duty cycles for Energy Harvesting — Wireless Sensor Networks (EH-WSNs). Individual duty cycles are vital for EH-WSNs, because they allow nodes to adapt their energy consumption to the ever...... three key properties of EH-WSNs: adaptability of energy consumption, distributed energy-aware load balancing and support for different application-specific requirements....
Scalable Harmonization of Complex Networks With Local Adaptive Controllers
Kárný, Miroslav; Herzallah, R.
-, - (2016). ISSN 2168-2216 R&D Projects: GA ČR GA13-13502S Institutional support: RVO:67985556 Keywords : Adaptive control * Adaptive estimation * Bayes methods * Complex networks * Decentralized control * Feedback * Feedforward systems * Recursive estimation Subject RIV: BB - Applied Statistics, Operational Research Impact factor: 1.699, year: 2014 http://library.utia.cas.cz/separaty/2016/AS/karny-0457337.pdf
Adaptive Network Dynamics and Evolution of Leadership in Collective Migration
Pais, Darren; Leonard, Naomi Ehrich
2013-01-01
The evolution of leadership in migratory populations depends not only on costs and benefits of leadership investments but also on the opportunities for individuals to rely on cues from others through social interactions. We derive an analytically tractable adaptive dynamic network model of collective migration with fast timescale migration dynamics and slow timescale adaptive dynamics of individual leadership investment and social interaction. For large populations, our analysis of bifurcatio...