Optimal Channel Width Adaptation, Logical Topology Design, and Routing in Wireless Mesh Networks
Directory of Open Access Journals (Sweden)
Li Li
2009-01-01
Full Text Available Radio frequency spectrum is a finite and scarce resource. How to efficiently use the spectrum resource is one of the fundamental issues for multi-radio multi-channel wireless mesh networks. However, past research efforts that attempt to exploit multiple channels always assume channels of fixed predetermined width, which prohibits the further effective use of the spectrum resource. In this paper, we address how to optimally adapt channel width to more efficiently utilize the spectrum in IEEE802.11-based multi-radio multi-channel mesh networks. We mathematically formulate the channel width adaptation, logical topology design, and routing as a joint mixed 0-1 integer linear optimization problem, and we also propose our heuristic assignment algorithm. Simulation results show that our method can significantly improve spectrum use efficiency and network performance.
Feasibility of using adaptive logic networks to predict compressor unit failure
Energy Technology Data Exchange (ETDEWEB)
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
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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
Energy Technology Data Exchange (ETDEWEB)
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.
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
Directory of Open Access Journals (Sweden)
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.
A Logic for Diffusion in Social Networks
Christoff, Z.; Hansen, J.U.
2015-01-01
This paper introduces a general logical framework for reasoning about diffusion processes within social networks. The new "Logic for Diffusion in Social Networks" is a dynamic extension of standard hybrid logic, allowing to model complex phenomena involving several properties of agents. We provide a
Limitations of Passively Mapping Logical Network Topologies
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Ayodeji J. Akande
2017-02-01
Full Text Available Understanding logical network connectivity is essential in network topology mapping especially in a fast growing network where knowing what is happening on the network is critical for security purposes and where knowing how network resources are being used is highly important. Mapping logical communication topology is important for network auditing, network maintenance and governance, network optimization, and network security. However, the process of capturing network traffic to generate the logical network topology may have a great influence on the operation of the network. In hierarchically structured networks such as control systems, typical active network mapping techniques are not employable as they can affect time-sensitive cyber-physical processes, hence, passive network mapping is required. Though passive network mapping does not modify or disrupt existing traffic, current passive mapping techniques ignore many practical issues when used to generate logical communication topologies. In this paper, we present a methodology which compares topologies from an idealized mapping process with what is actually achievable using passive network mapping and identify some of the factors that can cause inaccuracies in logical maps derived from passively monitored network traffic. We illustrate these factors using a case study involving a hierarchical control network.
Parallel optical logic operations on reversible networks
Shamir, Joseph
2013-03-01
A generic optical network architecture is proposed for the implementation of programmable logic operations. Based on reversible optical gate elements the processor is highly energy efficient and intrinsically fast. In this architecture the whole logic operation is executed by light propagating through the system with no energy dissipation. Energy must be spent only at the input interface and at discrete locations where the logic operation results are to be detected. As a consequence, the theoretical lower limit for energy dissipation in logic operations must be reconsidered. The strength of this approach is demonstrated by examples showing the implementation of various lossless logic operations, including Half Adder and Full Adder.
Boolean logic functions of a synthetic peptide network.
Ashkenasy, Gonen; Ghadiri, M Reza
2004-09-15
Living cells can process rapidly and simultaneously multiple extracellular input signals through the complex networks of evolutionary selected biomolecular interactions and chemical transformations. Recent approaches to molecular computation have increasingly sought to mimic or exploit various aspects of biology. A number of studies have adapted nucleic acids and proteins to the design of molecular logic gates and computational systems, while other works have affected computation in living cells via biochemical pathway engineering. Here we report that de novo designed synthetic peptide networks can also mimic some of the basic logic functions of the more complex biological networks. We show that segments of a small network whose graph structure is composed of five nodes and 15 directed edges can express OR, NOR, and NOTIF logic.
Optimal Power Flow Using Adaptive Fuzzy Logic Controllers
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Abdullah M. Abusorrah
2013-01-01
Full Text Available This paper presents an approach for optimum reactive power dispatch through the power network with flexible AC transmission systems (FACTSs devices, using adaptive fuzzy logic controller (AFLC driven by adaptive fuzzy sets (AFSs. The membership functions of AFLC are optimized based on 2nd-order fuzzy set specifications. The operation of FACTS devices (particularly, static VAR compensator (SVC and the setting of their control parameters (QSVC are optimized dynamically based on the proposed AFLC to enhance the power system stability in addition to their main function of power flow control. The proposed AFLC is compared with a static fuzzy logic controller (SFLC, driven by a fixed fuzzy set (FFS. Simulation studies were carried out and validated on the standard IEEE 30-bus test system.
Fuzzy logic systems are equivalent to feedforward neural networks
Institute of Scientific and Technical Information of China (English)
李洪兴
2000-01-01
Fuzzy logic systems and feedforward neural networks are equivalent in essence. First, interpolation representations of fuzzy logic systems are introduced and several important conclusions are given. Then three important kinds of neural networks are defined, i.e. linear neural networks, rectangle wave neural networks and nonlinear neural networks. Then it is proved that nonlinear neural networks can be represented by rectangle wave neural networks. Based on the results mentioned above, the equivalence between fuzzy logic systems and feedforward neural networks is proved, which will be very useful for theoretical research or applications on fuzzy logic systems or neural networks by means of combining fuzzy logic systems with neural networks.
Automatic self-configuration of the logical network using distributed software agents
Marzo i Lázaro, Josep Lluís; Vilà Talleda, Pere; Bueno Delgado, Antonio; Fàbrega i Soler, Lluís; Calle Ortega, Eusebi
2004-01-01
We present a system for dynamic network resource configuration in environments with bandwidth reservation. The proposed system is completely distributed and automates the mechanisms for adapting the logical network to the offered load. The system is able to manage dynamically a logical network such as a virtual path network in ATM or a label switched path network in MPLS or GMPLS. The system design and implementation is based on a multi-agent system (MAS) which make the decisions of when and ...
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.
Modelling defeasible reasoning by means of adaptive logic games
P. Verdée
2011-01-01
In this article, I present a dynamic logic game for defeasible reasoning. I argue that, as far as defeasible reasoning is concerned, one should distinguish between practical and ideal rationality. Starting from the adaptive logic framework, I formalize both rationality notions by means of logic game
Logic Mining Using Neural Networks
Sathasivam, Saratha
2008-01-01
Knowledge could be gained from experts, specialists in the area of interest, or it can be gained by induction from sets of data. Automatic induction of knowledge from data sets, usually stored in large databases, is called data mining. Data mining methods are important in the management of complex systems. There are many technologies available to data mining practitioners, including Artificial Neural Networks, Regression, and Decision Trees. Neural networks have been successfully applied in wide range of supervised and unsupervised learning applications. Neural network methods are not commonly used for data mining tasks, because they often produce incomprehensible models, and require long training times. One way in which the collective properties of a neural network may be used to implement a computational task is by way of the concept of energy minimization. The Hopfield network is well-known example of such an approach. The Hopfield network is useful as content addressable memory or an analog computer for s...
Cascaded logic gates in nanophotonic plasmon networks.
Wei, Hong; Wang, Zhuoxian; Tian, Xiaorui; Käll, Mikael; Xu, Hongxing
2011-07-12
Optical computing has been pursued for decades as a potential strategy for advancing beyond the fundamental performance limitations of semiconductor-based electronic devices, but feasible on-chip integrated logic units and cascade devices have not been reported. Here we demonstrate that a plasmonic binary NOR gate, a 'universal logic gate', can be realized through cascaded OR and NOT gates in four-terminal plasmonic nanowire networks. This finding provides a path for the development of novel nanophotonic on-chip processor architectures for future optical computing technologies.
Logic integer programming models for signaling networks.
Haus, Utz-Uwe; Niermann, Kathrin; Truemper, Klaus; Weismantel, Robert
2009-05-01
We propose a static and a dynamic approach to model biological signaling networks, and show how each can be used to answer relevant biological questions. For this, we use the two different mathematical tools of Propositional Logic and Integer Programming. The power of discrete mathematics for handling qualitative as well as quantitative data has so far not been exploited in molecular biology, which is mostly driven by experimental research, relying on first-order or statistical models. The arising logic statements and integer programs are analyzed and can be solved with standard software. For a restricted class of problems the logic models reduce to a polynomial-time solvable satisfiability algorithm. Additionally, a more dynamic model enables enumeration of possible time resolutions in poly-logarithmic time. Computational experiments are included.
Anatomy Ontology Matching Using Markov Logic Networks
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Chunhua Li
2016-01-01
Full Text Available 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 excellent framework for ontology matching. We combine several different matching strategies through first-order logic formulas according to the structure of anatomy ontologies. Experiments on the adult mouse anatomy and the human anatomy have demonstrated the effectiveness of proposed approach in terms of the quality of result alignment.
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, 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
Networking development by Boolean logic
Tu, Shikui; Pederson, Thoru; Weng, Zhiping
2013-01-01
Eric Davidson at Caltech has spent several decades investigating the molecular basis of animal development using the sea urchin embryo as an experimental system1,2 although his scholarship extends to all of embryology as embodied in several editions of his landmark book.3 In recent years his laboratory has become a leading force in constructing gene regulatory networks (GRNs) operating in sea urchin development.4 This axis of his work has its roots in this laboratory’s cDNA cloning of an actin mRNA from the sea urchin embryo (for the timeline, see ref. 1)—one of the first eukaryotic mRNAs to be cloned as it turned out. From that point of departure, the Davidson lab has drilled down into other genes and gene families and the factors that regulate their coordinated regulation, leading them into the GRN era (a field they helped to define) and the development of the computational tools needed to consolidate and advance the GRN field. PMID:23412653
Quantum logic networks for probabilistic teleportation
Institute of Scientific and Technical Information of China (English)
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.
Quantum logic networks for probabilistic teleportation
Institute of Scientific and Technical Information of China (English)
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.
Probabilistic logic networks a comprehensive framework for uncertain inference
Goertzel, Ben; Goertzel, Izabela Freire; Heljakka, Ari
2008-01-01
This comprehensive book describes Probabilistic Logic Networks (PLN), a novel conceptual, mathematical and computational approach to uncertain inference. A broad scope of reasoning types are considered.
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.
Energy Technology Data Exchange (ETDEWEB)
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.
Reconfiguring the Logical Topology With Performance Guarantees in WDM Networks
Institute of Scientific and Technical Information of China (English)
无
2003-01-01
To improve the network performance after traffic demand changes, reconfiguring the logical topology is necessary. We present an ILP algorithm to find out the least lightpath changes needed with guaranteed network performance.
FUZZY LOGIC BASED ENERGY EFFICIENT PROTOCOL IN WIRELESS SENSOR NETWORKS
Directory of Open Access Journals (Sweden)
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
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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.
Implementation of Adaptive Digital Controllers on Programmable Logic Devices
Gwaltney, David A.; King, Kenneth D.; Smith, Keary J.; Monenegro, Justino (Technical Monitor)
2002-01-01
Much has been made of the capabilities of FPGA's (Field Programmable Gate Arrays) in the hardware implementation of fast digital signal processing. Such capability also makes an FPGA a suitable platform for the digital implementation of closed loop controllers. Other researchers have implemented a variety of closed-loop digital controllers on FPGA's. Some of these controllers include the widely used proportional-integral-derivative (PID) controller, state space controllers, neural network and fuzzy logic based controllers. There are myriad advantages to utilizing an FPGA for discrete-time control functions which include the capability for reconfiguration when SRAM-based FPGA's are employed, fast parallel implementation of multiple control loops and implementations that can meet space level radiation tolerance requirements in a compact form-factor. Generally, a software implementation on a DSP (Digital Signal Processor) or microcontroller is used to implement digital controllers. At Marshall Space Flight Center, the Control Electronics Group has been studying adaptive discrete-time control of motor driven actuator systems using digital signal processor (DSP) devices. While small form factor, commercial DSP devices are now available with event capture, data conversion, pulse width modulated (PWM) outputs and communication peripherals, these devices are not currently available in designs and packages which meet space level radiation requirements. In general, very few DSP devices are produced that are designed to meet any level of radiation tolerance or hardness. The goal of this effort is to create a fully digital, flight ready controller design that utilizes an FPGA for implementation of signal conditioning for control feedback signals, generation of commands to the controlled system, and hardware insertion of adaptive control algorithm approaches. An alternative is required for compact implementation of such functionality to withstand the harsh environment
FPGA-oriented synthesis of multivalued logical networks
Deniziak, S.; Wiśniewski, M.; Kurczyna, K.
2016-12-01
Multivalued logical network consists of modules connected by multivalued signals. During synthesis each module is decomposed into smaller ones using the symbolic decomposition. Since the efficiency of the decomposition strongly depends on encoding of multivalued signals, the result of synthesis depends on the order, in which the consecutive modules are implemented. This paper presents the method of FPGA-oriented synthesis of multivalued logical networks. Experimental results showed that our approach significantly reduces the cost of implementation.
Logical Modeling and Dynamical Analysis of Cellular Networks.
Abou-Jaoudé, Wassim; Traynard, Pauline; Monteiro, Pedro T; Saez-Rodriguez, Julio; Helikar, Tomáš; Thieffry, Denis; Chaouiya, Claudine
2016-01-01
The logical (or logic) formalism is increasingly used to model regulatory and signaling networks. Complementing these applications, several groups contributed various methods and tools to support the definition and analysis of logical models. After an introduction to the logical modeling framework and to several of its variants, we review here a number of recent methodological advances to ease the analysis of large and intricate networks. In particular, we survey approaches to determine model attractors and their reachability properties, to assess the dynamical impact of variations of external signals, and to consistently reduce large models. To illustrate these developments, we further consider several published logical models for two important biological processes, namely the differentiation of T helper cells and the control of mammalian cell cycle.
Fusion Control of Flexible Logic Control and Neural Network
Directory of Open Access Journals (Sweden)
Lihua Fu
2014-01-01
Full Text Available Based on the basic physical meaning of error E and error variety EC, this paper analyzes the logical relationship between them and uses Universal Combinatorial Operation Model in Universal Logic to describe it. Accordingly, a flexible logic control method is put forward to realize effective control on multivariable nonlinear system. In order to implement fusion control with artificial neural network, this paper proposes a new neuron model of Zero-level Universal Combinatorial Operation in Universal Logic. And the artificial neural network of flexible logic control model is implemented based on the proposed neuron model. Finally, stability control, anti-interference control of double inverted-pendulum system, and free walking of cart pendulum system on a level track are realized, showing experimentally the feasibility and validity of this method.
Identifying network public opinion leaders based on Markov Logic Networks.
Zhang, Weizhe; Li, Xiaoqiang; He, Hui; Wang, Xing
2014-01-01
Public opinion emergencies have important effect on social activities. Recognition of special communities like opinion leaders can contribute to a comprehensive understanding of the development trend of public opinion. In this paper, a network opinion leader recognition method based on relational data was put forward, and an opinion leader recognition system integrating public opinion data acquisition module, data characteristic selection, and fusion module as well as opinion leader discovery module based on Markov Logic Networks was designed. The designed opinion leader recognition system not only can overcome the incomplete data acquisition and isolated task of traditional methods, but also can recognize opinion leaders comprehensively with considerations to multiple problems by using the relational model. Experimental results demonstrated that, compared with the traditional methods, the proposed method can provide a more accurate opinion leader recognition and has good noise immunity.
Information diffusion on adaptive network
Institute of Scientific and Technical Information of China (English)
Hu Ke; Tang Yi
2008-01-01
Based on the adaptive network,the feedback mechanism and interplay between the network topology and the diffusive process of information are studied.The results reveal that the adaptation of network topology can drive systems into the scale-free one with the assortative or disassortative degree correlations,and the hierarchical clustering.Meanwhile,the processes of the information diffusion are extremely speeded up by the adaptive changes of network topology.
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.
Neural networks and logical reasoning systems: a translation table.
Martins, J; Mendes, R V
2001-04-01
A correspondence is established between the basic elements of logic reasoning systems (knowledge bases, rules, inference and queries) and the structure and dynamical evolution laws of neural networks. The correspondence is pictured as a translation dictionary which might allow to go back and forth between symbolic and network formulations, a desirable step in learning-oriented systems and multicomputer networks. In the framework of Horn clause logics, it is found that atomic propositions with n arguments correspond to nodes with nth order synapses, rules to synaptic intensity constraints, forward chaining to synaptic dynamics and queries either to simple node activation or to a query tensor dynamics.
Using fuzzy logic to integrate neural networks and knowledge-based systems
Yen, John
1991-01-01
Outlined here is a novel hybrid architecture that uses fuzzy logic to integrate neural networks and knowledge-based systems. The author's approach offers important synergistic benefits to neural nets, approximate reasoning, and symbolic processing. Fuzzy inference rules extend symbolic systems with approximate reasoning capabilities, which are used for integrating and interpreting the outputs of neural networks. The symbolic system captures meta-level information about neural networks and defines its interaction with neural networks through a set of control tasks. Fuzzy action rules provide a robust mechanism for recognizing the situations in which neural networks require certain control actions. The neural nets, on the other hand, offer flexible classification and adaptive learning capabilities, which are crucial for dynamic and noisy environments. By combining neural nets and symbolic systems at their system levels through the use of fuzzy logic, the author's approach alleviates current difficulties in reconciling differences between low-level data processing mechanisms of neural nets and artificial intelligence systems.
Selection Shapes Transcriptional Logic and Regulatory Specialization in Genetic Networks.
Directory of Open Access Journals (Sweden)
Karl Fogelmark
Full Text Available Living organisms need to regulate their gene expression in response to environmental signals and internal cues. This is a computational task where genes act as logic gates that connect to form transcriptional networks, which are shaped at all scales by evolution. Large-scale mutations such as gene duplications and deletions add and remove network components, whereas smaller mutations alter the connections between them. Selection determines what mutations are accepted, but its importance for shaping the resulting networks has been debated.To investigate the effects of selection in the shaping of transcriptional networks, we derive transcriptional logic from a combinatorially powerful yet tractable model of the binding between DNA and transcription factors. By evolving the resulting networks based on their ability to function as either a simple decision system or a circadian clock, we obtain information on the regulation and logic rules encoded in functional transcriptional networks. Comparisons are made between networks evolved for different functions, as well as with structurally equivalent but non-functional (neutrally evolved networks, and predictions are validated against the transcriptional network of E. coli.We find that the logic rules governing gene expression depend on the function performed by the network. Unlike the decision systems, the circadian clocks show strong cooperative binding and negative regulation, which achieves tight temporal control of gene expression. Furthermore, we find that transcription factors act preferentially as either activators or repressors, both when binding multiple sites for a single target gene and globally in the transcriptional networks. This separation into positive and negative regulators requires gene duplications, which highlights the interplay between mutation and selection in shaping the transcriptional networks.
Hardwired Logic and Multithread Design in Network Processors
Institute of Scientific and Technical Information of China (English)
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
Institute of Scientific and Technical Information of China (English)
无
2000-01-01
A new idea, output-back fuzzy logic systems, is proposed. It is proved that output-back fuzzy logic systems must be equivalent to feedback neural networks. After the notion of generalized fuzzy logic systems is defined, which contains at least a typical fuzzy logic system and an output-back fuzzy logic system, one important conclusion is drawn that generalized fuzzy logic systems are almost equivalent to neural networks.
BSSSN: Bit String Swapping Sorting Network for Reversible Logic Synthesis
Islam, Md Saiful
2010-01-01
In this paper, we have introduced the notion of UselessGate and ReverseOperation. We have also given an algorithm to implement a sorting network for reversible logic synthesis based on swapping bit strings. The network is constructed in terms of n*n Toffoli Gates read from left to right and it has shown that there will be no more gates than the number of swappings the algorithm requires. The gate complexity of the network is O(n2). The number of gates in the network can be further reduced by template reduction technique and removing UselessGate from the network.
On Memory Capacity of the Probabilistic Logic Neuron Network
Institute of Scientific and Technical Information of China (English)
无
1993-01-01
In this paper,the memory capacity of Probabilistic Logic Neuron(PLN) network is discussed.We obtain two main results:(1)the method for constructing a PLN network with a given memory capacity;(2)the relationship between the memory capacity and the size of a PLN network.We show that the memory capacity of a PLN network depends on not only the number of input ports of its element but also the number of elements themselves.The results provide a new method for designing a PLN network.
Mobile Robot Navigation using Fuzzy Logic and Wavelet Network
Directory of Open Access Journals (Sweden)
Mustafa I. Hamzah
2014-05-01
Full Text Available This paper presents the proposed autonomous mobile robot navigation scheme. The navigation of mobile robot in unknown environment with obstacle avoidance is based on using fuzzy logic and wavelet network. Several cases are designed and modeled in Simulink and MATLAB. Simulation results show good performance for the proposed scheme.
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...
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.
Performance of Networked DC Motor with Fuzzy Logic Controller
Directory of Open Access Journals (Sweden)
B. Sharmila
2010-07-01
Full Text Available 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 traffic demands and disturbances, the guarantee of transmitting signals without any delays or data losses plays a vital role for the performances in using networked control systems. This paper proposes Fuzzy Logic Controller methodology in the networked dc motor control and the results are compared with the performance of the system with Ziegler-Nichols Tuned Proportional-Integral-Derivative Controller and Fuzzy Modulated Proportional-Integral-Derivative Controller. Simulations results are presented to demonstrate the proposed schemes in a closed loop control. The effective results show that the performance of networked control dc motor is improved by using Fuzzy Logic Controller than the other controllers.
Access Network Selection Based on Fuzzy Logic and Genetic Algorithms
Directory of Open Access Journals (Sweden)
Mohammed Alkhawlani
2008-01-01
Full Text Available In the next generation of heterogeneous wireless networks (HWNs, a large number of different radio access technologies (RATs will be integrated into a common network. In this type of networks, selecting the most optimal and promising access network (AN is an important consideration for overall networks stability, resource utilization, user satisfaction, and quality of service (QoS provisioning. This paper proposes a general scheme to solve the access network selection (ANS problem in the HWN. The proposed scheme has been used to present and design a general multicriteria software assistant (SA that can consider the user, operator, and/or the QoS view points. Combined fuzzy logic (FL and genetic algorithms (GAs have been used to give the proposed scheme the required scalability, flexibility, and simplicity. The simulation results show that the proposed scheme and SA have better and more robust performance over the random-based selection.
Phylogenetically informed logic relationships improve detection of biological network organization
2011-01-01
Background A "phylogenetic profile" refers to the presence or absence of a gene across a set of organisms, and it has been proven valuable for understanding gene functional relationships and network organization. Despite this success, few studies have attempted to search beyond just pairwise relationships among genes. Here we search for logic relationships involving three genes, and explore its potential application in gene network analyses. Results Taking advantage of a phylogenetic matrix constructed from the large orthologs database Roundup, we invented a method to create balanced profiles for individual triplets of genes that guarantee equal weight on the different phylogenetic scenarios of coevolution between genes. When we applied this idea to LAPP, the method to search for logic triplets of genes, the balanced profiles resulted in significant performance improvement and the discovery of hundreds of thousands more putative triplets than unadjusted profiles. We found that logic triplets detected biological network organization and identified key proteins and their functions, ranging from neighbouring proteins in local pathways, to well separated proteins in the whole pathway, and to the interactions among different pathways at the system level. Finally, our case study suggested that the directionality in a logic relationship and the profile of a triplet could disclose the connectivity between the triplet and surrounding networks. Conclusion Balanced profiles are superior to the raw profiles employed by traditional methods of phylogenetic profiling in searching for high order gene sets. Gene triplets can provide valuable information in detection of biological network organization and identification of key genes at different levels of cellular interaction. PMID:22172058
A high-speed interconnect network using ternary logic
DEFF Research Database (Denmark)
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 proc......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 processors is free of clock skew and insensitive to any delay differences in buffers and wires. In addition, the number of signal wires and pins are reduced by 50 percent in comparison with a similar binary implementation. The ICN architecture is based on a crossbar topology and the high-speed part consists...... 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...
Sentiment classification technology based on Markov logic networks
He, Hui; Li, Zhigang; Yao, Chongchong; Zhang, Weizhe
2016-07-01
With diverse online media emerging, there is a growing concern of sentiment classification problem. At present, text sentiment classification mainly utilizes supervised machine learning methods, which feature certain domain dependency. On the basis of Markov logic networks (MLNs), this study proposed a cross-domain multi-task text sentiment classification method rooted in transfer learning. Through many-to-one knowledge transfer, labeled text sentiment classification, knowledge was successfully transferred into other domains, and the precision of the sentiment classification analysis in the text tendency domain was improved. The experimental results revealed the following: (1) the model based on a MLN demonstrated higher precision than the single individual learning plan model. (2) Multi-task transfer learning based on Markov logical networks could acquire more knowledge than self-domain learning. The cross-domain text sentiment classification model could significantly improve the precision and efficiency of text sentiment classification.
Innovation barriers originating from the differing logics of network actors:
DEFF Research Database (Denmark)
Aarikka-Stenroos, Leena; Alaranta, Mar
2016-01-01
. The heterogeneity can originate also from the differences in the priorities, interests, and interactional goals of companies (and other organizations) that are labelled as “logics” of innovating firms. Since the different organizations' logics set the structural conditions for innovation and cause both positive...... and negative consequences, such as innovation barriers, it is important to capture how logics are interconnected and how certain organizations influence the conditions of others. we lack understanding of how the diversity challenges innovating in innovation networks and systems by setting innovation barriers....... Hence, we will examine innovation barriers originating from the diversity and heterogeneity in an innovation network/system. We draw on a single and extensive multi-actor case study on the national nanoceramics innovation system in Argentina. We will identify innovation barriers at intrafirm/organization...
Fuzzy Logic-Based Guaranteed Lifetime Protocol for Real-Time Wireless Sensor Networks.
Shah, Babar; Iqbal, Farkhund; Abbas, Ali; Kim, Ki-Il
2015-08-18
Few techniques for guaranteeing a network lifetime have been proposed despite its great impact on network management. Moreover, since the existing schemes are mostly dependent on the combination of disparate parameters, they do not provide additional services, such as real-time communications and balanced energy consumption among sensor nodes; thus, the adaptability problems remain unresolved among nodes in wireless sensor networks (WSNs). To solve these problems, we propose a novel fuzzy logic model to provide real-time communication in a guaranteed WSN lifetime. The proposed fuzzy logic controller accepts the input descriptors energy, time and velocity to determine each node's role for the next duration and the next hop relay node for real-time packets. Through the simulation results, we verified that both the guaranteed network's lifetime and real-time delivery are efficiently ensured by the new fuzzy logic model. In more detail, the above-mentioned two performance metrics are improved up to 8%, as compared to our previous work, and 14% compared to existing schemes, respectively.
The Balanced Cross-Layer Design Routing Algorithm in Wireless Sensor Networks Using Fuzzy Logic
Li, Ning; Martínez, José-Fernán; Díaz, Vicente Hernández
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
The Balanced Cross-Layer Design Routing Algorithm in Wireless Sensor Networks Using Fuzzy Logic.
Li, Ning; Martínez, José-Fernán; Hernández Díaz, Vicente
2015-08-10
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.
Fuzzy Logic-Based Guaranteed Lifetime Protocol for Real-Time Wireless Sensor Networks
Directory of Open Access Journals (Sweden)
Babar Shah
2015-08-01
Full Text Available Few techniques for guaranteeing a network lifetime have been proposed despite its great impact on network management. Moreover, since the existing schemes are mostly dependent on the combination of disparate parameters, they do not provide additional services, such as real-time communications and balanced energy consumption among sensor nodes; thus, the adaptability problems remain unresolved among nodes in wireless sensor networks (WSNs. To solve these problems, we propose a novel fuzzy logic model to provide real-time communication in a guaranteed WSN lifetime. The proposed fuzzy logic controller accepts the input descriptors energy, time and velocity to determine each node’s role for the next duration and the next hop relay node for real-time packets. Through the simulation results, we verified that both the guaranteed network’s lifetime and real-time delivery are efficiently ensured by the new fuzzy logic model. In more detail, the above-mentioned two performance metrics are improved up to 8%, as compared to our previous work, and 14% compared to existing schemes, respectively.
Parallel logic gates in synthetic gene networks induced by non-Gaussian noise.
Xu, Yong; Jin, Xiaoqin; Zhang, Huiqing
2013-11-01
The recent idea of logical stochastic resonance is verified in synthetic gene networks induced by non-Gaussian noise. We realize the switching between two kinds of logic gates under optimal moderate noise intensity by varying two different tunable parameters in a single gene network. Furthermore, in order to obtain more logic operations, thus providing additional information processing capacity, we obtain in a two-dimensional toggle switch model two complementary logic gates and realize the transformation between two logic gates via the methods of changing different parameters. These simulated results contribute to improve the computational power and functionality of the networks.
Energy Technology Data Exchange (ETDEWEB)
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
Abdul Kareem; Mohammad Fazle Azeem
2012-01-01
This paper presents a novel fuzzy logic based Adaptive Super-twisting Sliding Mode Controller for the control of dynamic uncertain systems. The proposed controller combines the advantages of Second order Sliding Mode Control, Fuzzy Logic Control and Adaptive Control. The reaching conditions, stability and robustness of the system with the proposed controller are guaranteed. In addition, the proposed controller is well suited for simple design and implementation. The effectiveness ...
Genetic optimization of neural network and fuzzy logic for oil bubble point pressure modeling
Energy Technology Data Exchange (ETDEWEB)
Afshar, Mohammad [Islamic Azad University, Kharg (Iran, Islamic Republic of); Gholami, Amin [Petroleum University of Technology, Abadan (Iran, Islamic Republic of); Asoodeh, Mojtaba [Islamic Azad University, Birjand (Iran, Islamic Republic of)
2014-03-15
Bubble point pressure is a critical pressure-volume-temperature (PVT) property of reservoir fluid, which plays an important role in almost all tasks involved in reservoir and production engineering. We developed two sophisticated models to estimate bubble point pressure from gas specific gravity, oil gravity, solution gas oil ratio, and reservoir temperature. Neural network and adaptive neuro-fuzzy inference system are powerful tools for extracting the underlying dependency of a set of input/output data. However, the mentioned tools are in danger of sticking in local minima. The present study went further by optimizing fuzzy logic and neural network models using the genetic algorithm in charge of eliminating the risk of being exposed to local minima. This strategy is capable of significantly improving the accuracy of both neural network and fuzzy logic models. The proposed methodology was successfully applied to a dataset of 153 PVT data points. Results showed that the genetic algorithm can serve the neural network and neuro-fuzzy models from local minima trapping, which might occur through back-propagation algorithm.
An evidential path logic for multi-relational networks
Energy Technology Data Exchange (ETDEWEB)
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.
An Evidential Path Logic for Multi-Relational Networks
Rodriguez, Marko A
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, monotonic 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-monotonic, evidential logic and reasoner that is an algebraic ring over a multi-relational network equipped with two binary operations that can be composed to execute various forms of inference. Given its multi-relational grounding, it is possible to use the presented evidential framework as another...
Uncovering transcriptional interactions via an adaptive fuzzy logic approach
Directory of Open Access Journals (Sweden)
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
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
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.
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...
A Qualitative Comparison of Different Logical Topologies for Wireless Sensor Networks
Directory of Open Access Journals (Sweden)
Quazi Mamun
2012-11-01
Full Text Available Wireless Sensor Networks (WSNs are formed by a large collection of power-conscious wireless-capable sensors without the support of pre-existing infrastructure, possibly by unplanned deployment. With a sheer number of sensor nodes, their unattended deployment and hostile environment very often preclude reliance on physical configuration or physical topology. It is, therefore, often necessary to depend on the logical topology. Logical topologies govern how a sensor node communicates with other nodes in the network. In this way, logical topologies play a vital role for resource-constraint sensor networks. It is thus more intuitive to approach the constraint minimizing problems from (logical topological point of view. Hence, this paper aims to study the logical topologies of WSNs. In doing so, a set of performance metrics is identified first. We identify various logical topologies from different application protocols of WSNs, and then compare the topologies using the set of performance metrics.
A qualitative comparison of different logical topologies for Wireless Sensor Networks.
Mamun, Quazi
2012-11-05
Wireless Sensor Networks (WSNs) are formed by a large collection of power-conscious wireless-capable sensors without the support of pre-existing infrastructure, possibly by unplanned deployment. With a sheer number of sensor nodes, their unattended deployment and hostile environment very often preclude reliance on physical configuration or physical topology. It is, therefore, often necessary to depend on the logical topology. Logical topologies govern how a sensor node communicates with other nodes in the network. In this way, logical topologies play a vital role for resource-constraint sensor networks. It is thus more intuitive to approach the constraint minimizing problems from (logical) topological point of view. Hence, this paper aims to study the logical topologies of WSNs. In doing so, a set of performance metrics is identified first. We identify various logical topologies from different application protocols of WSNs, and then compare the topologies using the set of performance metrics.
Noise-aided Logic in an Electronic Analog of Synthetic Genetic Networks
Hellen, Edward H; Kurths, Jurgen; Sinha, Sudeshna
2012-01-01
We report the experimental verification of noise-enhanced logic behaviour in an electronic analog of a synthetic genetic network, composed of two repressors and two constructive promoters. We observe good agreement between circuit measurements and numerical prediction, with the circuit allowing for robust logic operations in an optimal window of noise. Namely, the input-output characteristics of a logic gate is reproduced faithfully under moderate noise, which is a manifestation of the phenomenon known as Logical Stochastic Resonance. Interestingly, the two dynamical variables in the system yield complementary logic behaviour simultaneously, indicating strong potential for parallel processing.
Network Threat Ratings in Conventional DREAD Model Using Fuzzy Logic
Directory of Open Access Journals (Sweden)
Ak.Ashakumar Singh
2012-01-01
Full Text Available One of the most popular techniques to deal with ever growing risks associated with security threats is DREAD model. It is used for rating risk of network threats identified in the abuser stories. In this model network threats needs to be defined by sharp cutoffs. However, such precise distribution is not suitable for risk categorization as risks are vague in nature and deals with high level of uncertainty. In view of these risk factors, the paper proposes a novel fuzzy approach using DREAD model for computing risk level that ensures better evaluation of imprecise concepts. Thus, it provides the capacity to include subjectivity and uncertainty during risk ranking. These threat parameters need to be frequently updated based on feedback from implementation of previous parameters. These feedback are always stated in the form of ordinal ratings, e.g. "high speed", "average performance", "good condition". Different people can describe different values to these ordinal ratings without a clear-cut reason or scientific basis. There is need for a way or means to transform vague ordinal ratings to more appreciable and precise numerical estimates. The paper transforms the ordinal performance ratings of some system performance parameters to numerical ratings using Fuzzy Logic.
VLSI Circuit Configuration Using Satisfiability Logic in Hopfield Network
Directory of Open Access Journals (Sweden)
Mohd Asyraf Mansor
2016-09-01
Full Text Available Very large scale integration (VLSI circuit comprises of integrated circuit (IC with transistors in a single chip, widely used in many sophisticated electronic devices. In our paper, we proposed VLSI circuit design by implementing satisfiability problem in Hopfield neural network as circuit verification technique. We restrict our logic construction to 2-Satisfiability (2-SAT and 3- Satisfiability (3-SAT clauses in order to suit with the transistor configuration in VLSI circuit. In addition, we developed VLSI circuit based on Hopfield neural network in order to detect any possible error earlier than the manual circuit design. Microsoft Visual C++ 2013 is used as a platform for training, testing and validating of our proposed design. Hence, the performance of our proposed technique evaluated based on global VLSI configuration, circuit accuracy and the runtime. It has been observed that the VLSI circuits (HNN-2SAT and HNN-3SAT circuit developed by proposed design are better than the conventional circuit due to the early error detection in our circuit.
Adaptive Dynamics of Regulatory Networks: Size Matters
Directory of Open Access Journals (Sweden)
2009-03-01
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.
Adaptive Dynamics of Regulatory Networks: Size Matters
Directory of Open Access Journals (Sweden)
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.
The programmable (logic) controller: Adapting in an environment of change
Energy Technology Data Exchange (ETDEWEB)
Levine, P.S. [ed.
1995-03-01
Reports of the imminent death of the PLC (programmable logic controller) were greatly exaggerated, to paraphrase Mark Twain. In fact, the PLC is not only alive and working worldwide in thousands of applications, but it is also integrating well with related technologies. Long-term survival is a larger question - probably unanswerable given the pace of technological change. However, a few questions arise about the PLC today and in the immediate future: (1) What`s happening with programming languages? (2) Will there continue to be a {open_quotes}blurring of the lines{close_quotes} between the PLC and other technologies, and what role will software play in this integration? (3) How will the PLC`s cost and size affect the market?
Data Dissemination Based on Fuzzy Logic and Network Coding in Vehicular Networks
Directory of Open Access Journals (Sweden)
Xiaolan Tang
2017-01-01
Full Text Available Vehicular networks, as a significant technology in intelligent transportation systems, improve the convenience, efficiency, and safety of driving in smart cities. However, because of the high velocity, the frequent topology change, and the limited bandwidth, it is difficult to efficiently propagate data in vehicular networks. This paper proposes a data dissemination scheme based on fuzzy logic and network coding for vehicular networks, named SFN. It uses fuzzy logic to compute a transmission ability for each vehicle by comprehensively considering the effects of three factors: the velocity change rate, the velocity optimization degree, and the channel quality. Then, two nodes with high abilities are selected as primary backbone and slave backbone in every road segment, which propagate data to other vehicles in this segment and forward them to the backbones in the next segment. The backbone network helps to increase the delivery ratio and avoid invalid transmissions. Additionally, network coding is utilized to reduce transmission overhead and accelerate data retransmission in interbackbone forwarding and intrasegment broadcasting. Experiments show that, compared with existing schemes, SFN has a high delivery ratio and a short dissemination delay, while the backbone network keeps high reliability.
Quantum logic networks for cloning a quantum state near a given state
Institute of Scientific and Technical Information of China (English)
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.
Mailloux, Shay; Halámek, Jan; Katz, Evgeny
2014-03-07
A new Sense-and-Act system was realized by the integration of a biocomputing system, performing analytical processes, with a signal-responsive electrode. A drug-mimicking release process was triggered by biomolecular signals processed by different logic networks, including three concatenated AND logic gates or a 3-input OR logic gate. Biocatalytically produced NADH, controlled by various combinations of input signals, was used to activate the electrochemical system. A biocatalytic electrode associated with signal-processing "biocomputing" systems was electrically connected to another electrode coated with a polymer film, which was dissolved upon the formation of negative potential releasing entrapped drug-mimicking species, an enzyme-antibody conjugate, operating as a model for targeted immune-delivery and consequent "prodrug" activation. The system offers great versatility for future applications in controlled drug release and personalized medicine.
Fuzzy knowledge base construction through belief networks based on Lukasiewicz logic
Lara-Rosano, Felipe
1992-01-01
In this paper, a procedure is proposed to build a fuzzy knowledge base founded on fuzzy belief networks and Lukasiewicz logic. Fuzzy procedures are developed to do the following: to assess the belief values of a consequent, in terms of the belief values of its logical antecedents and the belief value of the corresponding logical function; and to update belief values when new evidence is available.
Adaptive disturbance attenuation via logic-based switching
Battistelli, Giorgio; Mari, Daniele; Selvi, Daniela; Tesi, Alberto; Tesi, Pietro
2014-01-01
The problem of attenuating unknown and possibly time-varying disturbances acting on a linear time-invariant dynamical system is addressed by means of an adaptive switching control approach. Given a family of pre-designed stabilizing controllers, a supervisory unit infers in real-time the potential b
An adaptive fuzzy logic controller for robot-manipulator
Directory of Open Access Journals (Sweden)
Tran Thu Ha
2008-11-01
Full Text Available In this paper, an adaptive fuzzy controller is designed for the robot-manipulator. The synthesized controller ensures that 1 the close-loop system is globally stable and 2 the tracking error converges to zero asymptotically and a cost function is minimized. The fuzzy controller is synthesized from a collection of IF-THEN rules. The parameters of the membership functions characterizing the linguistic terms change according to some adaptive law for the purpose of controlling a plant to track a reference trajectory. The proposed control scheme is demonstrated in a typical nonlinear plant two link manipulator. The computer simulation of control is done by the language MATLAB. The results of simulation show that the adaptipresented results are analyzed.
A fuzzy logic based network intrusion detection system for predicting the TCP SYN flooding attack
CSIR Research Space (South Africa)
Mkuzangwe, Nenekazi NP
2017-04-01
Full Text Available presents a fuzzy logic based network intrusion detection system to predict neptune which is a type of a Transmission Control Protocol Synchronized (TCP SYN) flooding attack. The performance of the proposed fuzzy logic based system is compared to that of a...
Learning a Markov Logic network for supervised gene regulatory network inference.
Brouard, Céline; Vrain, Christel; Dubois, Julie; Castel, David; Debily, Marie-Anne; d'Alché-Buc, Florence
2013-09-12
Gene regulatory network inference remains a challenging problem in systems biology despite the numerous approaches that have been proposed. When substantial knowledge on a gene regulatory network is already available, supervised network inference is appropriate. Such a method builds a binary classifier able to assign a class (Regulation/No regulation) to an ordered pair of genes. Once learnt, the pairwise classifier can be used to predict new regulations. In this work, we explore the framework of Markov Logic Networks (MLN) that combine features of probabilistic graphical models with the expressivity of first-order logic rules. We propose to learn a Markov Logic network, e.g. a set of weighted rules that conclude on the predicate "regulates", starting from a known gene regulatory network involved in the switch proliferation/differentiation of keratinocyte cells, a set of experimental transcriptomic data and various descriptions of genes all encoded into first-order logic. As training data are unbalanced, we use asymmetric bagging to learn a set of MLNs. The prediction of a new regulation can then be obtained by averaging predictions of individual MLNs. As a side contribution, we propose three in silico tests to assess the performance of any pairwise classifier in various network inference tasks on real datasets. A first test consists of measuring the average performance on balanced edge prediction problem; a second one deals with the ability of the classifier, once enhanced by asymmetric bagging, to update a given network. Finally our main result concerns a third test that measures the ability of the method to predict regulations with a new set of genes. As expected, MLN, when provided with only numerical discretized gene expression data, does not perform as well as a pairwise SVM in terms of AUPR. However, when a more complete description of gene properties is provided by heterogeneous sources, MLN achieves the same performance as a black-box model such as a
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
Quantum Logic Network for Cloning a State Near a Given One Based on Cavity QED
Institute of Scientific and Technical Information of China (English)
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.
Adaptively managing wildlife for climate change: a fuzzy logic approach.
Prato, Tony
2011-07-01
Wildlife managers have little or no control over climate change. However, they may be able to alleviate potential adverse impacts of future climate change by adaptively managing wildlife for climate change. In particular, wildlife managers can evaluate the efficacy of compensatory management actions (CMAs) in alleviating potential adverse impacts of future climate change on wildlife species using probability-based or fuzzy decision rules. Application of probability-based decision rules requires managers to specify certain probabilities, which is not possible when they are uncertain about the relationships between observed and true ecological conditions for a species. Under such uncertainty, the efficacy of CMAs can be evaluated and the best CMA selected using fuzzy decision rules. The latter are described and demonstrated using three constructed cases that assume: (1) a single ecological indicator (e.g., population size for a species) in a single time period; (2) multiple ecological indicators for a species in a single time period; and (3) multiple ecological conditions for a species in multiple time periods.
Design of adaptive fuzzy logic controller based on linguistic-hedge concepts and genetic algorithms.
Liu, B D; Chen, C Y; Tsao, J Y
2001-01-01
In this paper, we propose a novel fuzzy logic controller, called linguistic hedge fuzzy logic controller, to simplify the membership function constructions and the rule developments. The design methodology of linguistic hedge fuzzy logic controller is a hybrid model based on the concepts of the linguistic hedges and the genetic algorithms. The linguistic hedge operators are used to adjust the shape of the system membership functions dynamically, and ran speed up the control result to fit the system demand. The genetic algorithms are adopted to search the optimal linguistic hedge combination in the linguistic hedge module, According to the proposed methodology, the linguistic hedge fuzzy logic controller has the following advantages: 1) it needs only the simple-shape membership functions rather than the carefully designed ones for characterizing the related variables; 2) it is sufficient to adopt a fewer number of rules for inference; 3) the rules are developed intuitionally without heavily depending on the endeavor of experts; 4) the linguistic hedge module associated with the genetic algorithm enables it to be adaptive; 5) it performs better than the conventional fuzzy logic controllers do; and 6) it can be realized with low design complexity and small hardware overhead. Furthermore, the proposed approach has been applied to design three well-known nonlinear systems. The simulation and experimental results demonstrate the effectiveness of this design.
Computing single step operators of logic programming in radial basis function neural networks
Energy Technology Data Exchange (ETDEWEB)
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.
Computing single step operators of logic programming in radial basis function neural networks
Hamadneh, Nawaf; Sathasivam, Saratha; Choon, Ong Hong
2014-07-01
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.
Benefits and challenges of controlling a LED AFS (Adaptive Front-lighting System) using fuzzy logic
2011-01-01
Texto completo: acesso restrito. p.579−588 The vehicular illumination system has undergone considerable technological advances in recent decades such as the use of a Light Emitting Diode (LED) Adaptive Front-lighting System (AFS), which represents an industry breakthrough in lighting technology and is rapidly becoming one of the most important innovative technologies around the world in the lighting community. This paper presents AFS control alternatives using fuzzy logic (types 1...
Systems chemistry: logic gates, arithmetic units, and network motifs in small networks.
Wagner, Nathaniel; Ashkenasy, Gonen
2009-01-01
A mixture of molecules can be regarded as a network if all the molecular components participate in some kind of interaction with other molecules--either physical or functional interactions. Template-assisted ligation reactions that direct replication processes can serve as the functional elements that connect two members of a chemical network. In such a process, the template does not necessarily catalyze its own formation, but rather the formation of another molecule, which in turn can operate as a template for reactions within the network medium. It was postulated that even networks made up of small numbers of molecules possess a wealth of molecular information sufficient to perform rather complex behavior. To probe this assumption, we have constructed virtual arrays consisting of three replicating molecules, in which dimer templates are capable of catalyzing reactants to form additional templates. By using realistic parameters from peptides or DNA replication experiments, we simulate the construction of various functional motifs within the networks. Specifically, we have designed and implemented each of the three-element Boolean logic gates, and show how these networks are assembled from four basic "building blocks". We also show how the catalytic pathways can be wired together to perform more complex arithmetic units and network motifs, such as the half adder and half subtractor computational modules, and the coherent feed-forward loop network motifs under different sets of parameters. As in previous studies of chemical networks, some of the systems described display behavior that would be difficult to predict without the numerical simulations. Furthermore, the simulations reveal trends and characteristics that should be useful as "recipes" for future design of experimental functional motifs and for potential integration into modular circuits and molecular computation devices.
Construction of cell type-specific logic models of signaling networks using CellNOpt.
Morris, Melody K; Melas, Ioannis; Saez-Rodriguez, Julio
2013-01-01
Mathematical models are useful tools for understanding protein signaling networks because they provide an integrated view of pharmacological and toxicological processes at the molecular level. Here we describe an approach previously introduced based on logic modeling to generate cell-specific, mechanistic and predictive models of signal transduction. Models are derived from a network encoding prior knowledge that is trained to signaling data, and can be either binary (based on Boolean logic) or quantitative (using a recently developed formalism, constrained fuzzy logic). The approach is implemented in the freely available tool CellNetOptimizer (CellNOpt). We explain the process CellNOpt uses to train a prior knowledge network to data and illustrate its application with a toy example as well as a realistic case describing signaling networks in the HepG2 liver cancer cell line.
Dynamic logics of networks: Information flow and the spread of opinion
Christoff, Z.L.
2016-01-01
This thesis uses logical tools to investigate a number of basic features of social networks and their evolution over time, including flow of information and spread of opinions. Part I contains the preliminaries, including an introduction to the basic phenomena in social networks that call for a logi
The Distributed Logical Reasoning Language D—Tuili and Its Implementation on Microcomputer Network
Institute of Scientific and Technical Information of China (English)
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.
Abdul Kareem; Mohammad Fazle Azeem
2012-01-01
This paper presents a novel fuzzy logic based Adaptive Super-twisting Sliding Mode Controller for the control of dynamic uncertain systems. The proposed controller combines the advantages of Second order Sliding Mode Control, Fuzzy Logic Control and Adaptive Control. The reaching conditions, stability and robustness of the system with the proposed controller are guaranteed. In addition, the proposed controller is well suited for simple design and implementation. The effectiveness ...
Genetic and logic networks with the signal-inhibitor-activator structure are dynamically robust
Institute of Scientific and Technical Information of China (English)
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.
Traffic-Based Reconfiguration for Logical Topologies in Large-Scale WDM Optical Networks
Zhang, Yongbing; Murata, Masaki; Takagi, Hideaki; Ji, Yusheng
2005-10-01
Wavelength-division multiplexing (WDM) technology has emerged as a promising technology for backbone networks. The optical layer based on WDM technology provides optical routing services to the upper layers such as the packet-switching layer and the time-division multiplexing (TDM) layer over the generalized multiprotocol label-switching (GMPLS) paradigm. The set of all-optical communication channels (lightpaths) in the optical layer defines the logical topology for the upper layer applications. Since the traffic demand of upper layer applications fluctuates from time to time, it is required to reconfigure the underlying logical topology in the optical layer accordingly. However, the reconfiguration for the logical topology is reluctantly disruptive to the network since some lightpaths should be torn down and some traffic has to be buffered or rerouted during the reconfiguration process. Therefore, it needs to have an efficient transition method to shift the current logical topology to the new one so as to minimize the effect of the reconfiguration on the upper layer traffic. This paper proposes several heuristic algorithms that move the current logical topology efficiently to the given target logical topology in large-scale wavelength-routed optical networks. In the proposed algorithms, the performance improvement/degradation of data transmission [transmission delay or distance between a source-destination (s-d) pair] caused by a new lightpath is considered as benefit for establishing the new lightpath. The proposed algorithms construct the new logical topology starting from a lightpath with the largest benefit to the user traffic. Simulation experiments have been performed to evaluate the proposed algorithms in comparison with existing algorithms in a National Science Foundation Network (NSFNET)-like network model with 16 nodes and 25 links. The results show that the proposed algorithms yield much better performance (shorter average packet hot distance) than
Adaptation Methods in Mobile Communication Networks
Vladimir Wieser
2003-01-01
Adaptation methods are the main tool for transmission rate maximization through the mobile channel and today the great attention is directed to them not only in theoretical domain but in standardization process, too. The review of adaptation methods for system and technical parameters of mobile cellular networks (2.5G and 3G) is carricd out.
Train velocity estimation method based on an adaptive filter with fuzzy logic
Pichlík, Petr; Zděnek, Jiří
2017-03-01
The train velocity is difficult to determine when the velocity is measured only on the driven or braked locomotive wheelsets. In this case, the calculated train velocity is different from the actual train velocity due to slip velocity or skid velocity respectively. The train velocity is needed for a locomotive controller proper work. For this purpose, an adaptive filter that is tuned by a fuzzy logic is designed and described in the paper. The filter calculates the train longitudinal velocity based on locomotive wheelset velocity. The fuzzy logic is used for the tuning of the filter according to actual wheelset acceleration and wheelset jerk. The simulation results are based on real measured data on a freight train. The results show that the calculated velocity corresponds to the actual train velocity.
A FUZZY-LOGIC CONTROL ALGORITHM FOR ACTIVE QUEUE MANAGEMENT IN IP NETWORKS
Institute of Scientific and Technical Information of China (English)
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.
Dynamical Adaptation in Terrorist Cells/Networks
DEFF Research Database (Denmark)
Hussain, Dil Muhammad Akbar; Ahmed, Zaki
2010-01-01
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...
Energy-efficient adaptive wireless network design
Havinga, Paul J.M.; Smit, Gerardus Johannes Maria; Bos, M.
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
QoS Adaptive Topology Configuration in Synchronous Wireless Sensor Networks
Institute of Scientific and Technical Information of China (English)
杨挺; 武娇雯; 李昂; 张志东
2010-01-01
By using hyper-graph theory,this paper proposes a QoS adaptive topology configuration(QATC) algorithm to effectively control large-scale topology and achieve robust data transmitting in synchronous wireless sensor networks.Firstly,a concise hyper-graph model is abstracted to analyze the large-scale and high-connectivity network.Secondly,based on the control theory of biologic "Cell Mergence",a novel self-adaptive topology configuration algorithm is used to build homologous perceptive data logic sub-network ...
Synchronization in complex networks with adaptive coupling
Zhang, Rong; Hu, Manfeng; Xu, Zhenyuan
2007-08-01
Generally it is very difficult to realized synchronization for some complex networks. In order to synchronize, the coupling coefficient of networks has to be very large, especially when the number of coupled nodes is larger. In this Letter, we consider the problem of synchronization in complex networks with adaptive coupling. A new concept about asymptotic stability is presented, then we proved by using the well-known LaSalle invariance principle, that the state of such a complex network can synchronize an arbitrary assigned state of an isolated node of the network as long as the feedback gain is positive. Unified system is simulated as the nodes of adaptive coupling complex networks with different topologies.
Synchronization in complex networks with adaptive coupling
Energy Technology Data Exchange (ETDEWEB)
Zhang Rong [School of Science, Southern Yangtze University, Wuxi 214122 (China); School of Information Engineering, Southern Yangtze University, Wuxi 214122 (China)], E-mail: ronia62@yahoo.com; Hu Manfeng [School of Science, Southern Yangtze University, Wuxi 214122 (China); School of Information Engineering, Southern Yangtze University, Wuxi 214122 (China); Xu Zhenyuan [School of Science, Southern Yangtze University, Wuxi 214122 (China)
2007-08-20
Generally it is very difficult to realized synchronization for some complex networks. In order to synchronize, the coupling coefficient of networks has to be very large, especially when the number of coupled nodes is larger. In this Letter, we consider the problem of synchronization in complex networks with adaptive coupling. A new concept about asymptotic stability is presented, then we proved by using the well-known LaSalle invariance principle, that the state of such a complex network can synchronize an arbitrary assigned state of an isolated node of the network as long as the feedback gain is positive. Unified system is simulated as the nodes of adaptive coupling complex networks with different topologies.
Ostrowski, M; Paulevé, L; Schaub, T; Siegel, A; Guziolowski, C
2016-11-01
Boolean networks (and more general logic models) are useful frameworks to study signal transduction across multiple pathways. Logic models can be learned from a prior knowledge network structure and multiplex phosphoproteomics data. However, most efficient and scalable training methods focus on the comparison of two time-points and assume that the system has reached an early steady state. In this paper, we generalize such a learning procedure to take into account the time series traces of phosphoproteomics data in order to discriminate Boolean networks according to their transient dynamics. To that end, we identify a necessary condition that must be satisfied by the dynamics of a Boolean network to be consistent with a discretized time series trace. Based on this condition, we use Answer Set Programming to compute an over-approximation of the set of Boolean networks which fit best with experimental data and provide the corresponding encodings. Combined with model-checking approaches, we end up with a global learning algorithm. Our approach is able to learn logic models with a true positive rate higher than 78% in two case studies of mammalian signaling networks; for a larger case study, our method provides optimal answers after 7min of computation. We quantified the gain in our method predictions precision compared to learning approaches based on static data. Finally, as an application, our method proposes erroneous time-points in the time series data with respect to the optimal learned logic models.
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.
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.
Logical Design and Control of Network in Local Mine Air-Reversing System
Institute of Scientific and Technical Information of China (English)
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.
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.
In-Network Adaptation of Video Streams Using Network Processors
Directory of Open Access Journals (Sweden)
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.
Fluid intelligence and psychosocial outcome: from logical problem solving to social adaptation.
Directory of Open Access Journals (Sweden)
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 based Adaptive Modulation Using Non Data Aided SNR Estimation for OFDM system
Directory of Open Access Journals (Sweden)
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 .
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
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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.
Satisfiability of logic programming based on radial basis function neural networks
Hamadneh, Nawaf; Sathasivam, Saratha; Tilahun, Surafel Luleseged; Choon, Ong Hong
2014-07-01
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.
Okamoto, Satoru; Sato, Takehiro; Yamanaka, Naoaki
2017-01-01
In this paper, flexible and highly reliable metro and access integrated networks with network virtualization and software defined networking technologies will be presented. Logical optical line terminal (L-OLT) technologies and active optical distribution networks (ODNs) are the key to introduce flexibility and high reliability into the metro and access integrated networks. In the Elastic Lambda Aggregation Network (EλAN) project which was started in 2012, a concept of the programmable optical line terminal (P-OLT) has been proposed. A role of the P-OLT is providing multiple network services that have different protocols and quality of service requirements by single OLT box. Accommodated services will be Internet access, mobile front-haul/back-haul, data-center access, and leased line. L-OLTs are configured within the P-OLT box to support the functions required for each network service. Multiple P-OLTs and programmable optical network units (P-ONUs) are connected by the active ODN. Optical access paths which have flexible capacity are set on the ODN to provide network services from L-OLT to logical ONUs (L-ONUs). The L-OLT to L-ONU path on the active ODN provides a logical connection. Therefore, introducing virtualization technologies becomes possible. One example is moving an L-OLT from one P-OLT to another P-OLT like a virtual machine. This movement is called L-OLT migration. The L-OLT migration provides flexible and reliable network functions such as energy saving by aggregating L-OLTs to a limited number of P-OLTs, and network wide optical access path restoration. Other L-OLT virtualization technologies and experimental results will be also discussed in the paper.
A Comparison of Neural Networks and Fuzzy Logic Methods for Process Modeling
Cios, Krzysztof J.; Sala, Dorel M.; Berke, Laszlo
1996-01-01
The goal of this work was to analyze the potential of neural networks and fuzzy logic methods to develop approximate response surfaces as process modeling, that is for mapping of input into output. Structural response was chosen as an example. Each of the many methods surveyed are explained and the results are presented. Future research directions are also discussed.
Quantum Logic Networks for Probabilistic Teleportation of an Arbitrary Three-Particle State
Institute of Scientific and Technical Information of China (English)
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.
Adaptive Interval Type-2 Fuzzy Logic Control for PMSM Drives with a Modified Reference Frame
Chaoui, Hicham
2017-01-10
In this paper, an adaptive interval type-2 fuzzy logic control scheme is proposed for high-performance permanent magnet synchronous machine drives. This strategy combines the power of type-2 fuzzy logic systems with the adaptive control theory to achieve accurate tracking and robustness to higher uncertainties. Unlike other controllers, the proposed strategy does not require electrical transducers and hence, no explicit currents loop regulation is needed, which yields a simplified control scheme. But, this limits the machine\\'s operation range since it results in a higher energy consumption. Therefore, a modified reference frame is also proposed in this paper to decrease the machine\\'s consumption. To better assess the performance of the new reference frame, comparison against its original counterpart is carried-out under the same conditions. Moreover, the stability of the closed-loop control scheme is guaranteed by a Lyapunov theorem. Simulation and experimental results for numerous situations highlight the effectiveness of the proposed controller in standstill, transient, and steady-state conditions.
Fuzzy Logic Control of Adaptive ARQ for Video Distribution over a Bluetooth Wireless Link
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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.
Grüning, André
2011-01-01
Few algorithms for supervised training of spiking neural networks exist that can deal with patterns of multiple spikes, and their computational properties are largely unexplored. We demonstrate in a set of simulations that the ReSuMe learning algorithm can be successfully applied to layered neural networks. Input and output patterns are encoded as spike trains of multiple precisely timed spikes, and the network learns to transform the input trains into target output trains. This is done by combining the ReSuMe learning algorithm with multiplicative scaling of the connections of downstream neurons. We show in particular that layered networks with one hidden layer can learn the basic logical operations, including Exclusive-Or, while networks without hidden layer cannot, mirroring an analogous result for layered networks of rate neurons. While supervised learning in spiking neural networks is not yet fit for technical purposes, exploring computational properties of spiking neural networks advances our understand...
Fuzzy Logic Module of Convolutional Neural Network for Handwritten Digits Recognition
Popko, E. A.; Weinstein, I. A.
2016-08-01
Optical character recognition is one of the important issues in the field of pattern recognition. This paper presents a method for recognizing handwritten digits based on the modeling of convolutional neural network. The integrated fuzzy logic module based on a structural approach was developed. Used system architecture adjusted the output of the neural network to improve quality of symbol identification. It was shown that proposed algorithm was flexible and high recognition rate of 99.23% was achieved.
Network Based Building Lighting Design and Fuzzy Logic via Remote Control
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Cemal YILMAZ
2009-02-01
Full Text Available In this paper, a network based building lighting system is implemented. Profibus-DP network structure is used in the design and Fuzzy Logic Controller (FLC is used on control of the building lighting. Informations received from sensors which measures level of the building illumination is used on FLC and they are transferred to the system by Profibus-DP network. Control of lighting luminaries are made via Profibus-DP network. The illuminance inside the bulding is fitted required level. Energy saving and healthy lighting facilities have been obtained by the design.
Fuzzy-Logic Based Multi-Sensory Quality Evaluation via Communication Network
Institute of Scientific and Technical Information of China (English)
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.
Enzyme-Based Logic Gates and Networks with Output Signals Analyzed by Various Methods.
Katz, Evgeny
2017-07-05
The paper overviews various methods that are used for the analysis of output signals generated by enzyme-based logic systems. The considered methods include optical techniques (optical absorbance, fluorescence spectroscopy, surface plasmon resonance), electrochemical techniques (cyclic voltammetry, potentiometry, impedance spectroscopy, conductivity measurements, use of field effect transistor devices, pH measurements), and various mechanoelectronic methods (using atomic force microscope, quartz crystal microbalance). Although each of the methods is well known for various bioanalytical applications, their use in combination with the biomolecular logic systems is rather new and sometimes not trivial. Many of the discussed methods have been combined with the use of signal-responsive materials to transduce and amplify biomolecular signals generated by the logic operations. Interfacing of biocomputing logic systems with electronics and "smart" signal-responsive materials allows logic operations be extended to actuation functions; for example, stimulating molecular release and switchable features of bioelectronic devices, such as biofuel cells. The purpose of this review article is to emphasize the broad variability of the bioanalytical systems applied for signal transduction in biocomputing processes. All bioanalytical systems discussed in the article are exemplified with specific logic gates and multi-gate networks realized with enzyme-based biocatalytic cascades. © 2017 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim.
A new method for the re-implementation of threshold logic functions with cellular neural networks.
Bénédic, Y; Wira, P; Mercklé, J
2008-08-01
A new strategy is presented for the implementation of threshold logic functions with binary-output Cellular Neural Networks (CNNs). The objective is to optimize the CNNs weights to develop a robust implementation. Hence, the concept of generative set is introduced as a convenient representation of any linearly separable Boolean function. Our analysis of threshold logic functions leads to a complete algorithm that automatically provides an optimized generative set. New weights are deduced and a more robust CNN template assuming the same function can thus be implemented. The strategy is illustrated by a detailed example.
Knox, H. A.; Draelos, T.; Young, C. J.; Lawry, B.; Chael, E. P.; Faust, A.; Peterson, M. G.
2015-12-01
The quality of automatic detections from seismic sensor networks depends on a large number of data processing parameters that interact in complex ways. The largely manual process of identifying effective parameters is painstaking and does not guarantee that the resulting controls are the optimal configuration settings. Yet, achieving superior automatic detection of seismic events is closely related to these parameters. We present an automated sensor tuning (AST) system that learns near-optimal parameter settings for each event type using neuro-dynamic programming (reinforcement learning) trained with historic data. AST learns to test the raw signal against all event-settings and automatically self-tunes to an emerging event in real-time. The overall goal is to reduce the number of missed legitimate event detections and the number of false event detections. Reducing false alarms early in the seismic pipeline processing will have a significant impact on this goal. Applicable both for existing sensor performance boosting and new sensor deployment, this system provides an important new method to automatically tune complex remote sensing systems. Systems tuned in this way will achieve better performance than is currently possible by manual tuning, and with much less time and effort devoted to the tuning process. With ground truth on detections in seismic waveforms from a network of stations, we show that AST increases the probability of detection while decreasing false alarms.
Evolution of a designless nanoparticle network into reconfigurable Boolean logic
Bose, S.K.; Lawrence, C.P.; Liu, Z.; Makarenko, K.S.; Damme, van R.M.J.; Broersma, H.J.; Wiel, van der W.G.
2015-01-01
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 th
Coastal vulnerability assessment using Fuzzy Logic and Bayesian Belief Network approaches
Valentini, Emiliana; Nguyen Xuan, Alessandra; Filipponi, Federico; Taramelli, Andrea
2017-04-01
Natural hazards such as sea surge are threatening low-lying coastal plains. In order to deal with disturbances a deeper understanding of benefits deriving from ecosystem services assessment, management and planning can contribute to enhance the resilience of coastal systems. In this frame assessing current and future vulnerability is a key concern of many Systems Of Systems SOS (social, ecological, institutional) that deals with several challenges like the definition of Essential Variables (EVs) able to synthesize the required information, the assignment of different weight to be attributed to each considered variable, the selection of method for combining the relevant variables. It is widely recognized that ecosystems contribute to human wellbeing and then their conservation increases the resilience capacities and could play a key role in reducing climate related risk and thus physical and economic losses. A way to fully exploit ecosystems potential, i.e. their so called ecopotential (see H2020 EU funded project "ECOPOTENTIAL"), is the Ecosystem based Adaptation (EbA): the use of ecosystem services as part of an adaptation strategy. In order to provide insight in understanding regulating ecosystem services to surge and which variables influence them and to make the best use of available data and information (EO products, in situ data and modelling), we propose a multi-component surge vulnerability assessment, focusing on coastal sandy dunes as natural barriers. The aim is to combine together eco-geomorphological and socio-economic variables with the hazard component on the base of different approaches: 1) Fuzzy Logic; 2) Bayesian Belief Networks (BBN). The Fuzzy Logic approach is very useful to get a spatialized information and it can easily combine variables coming from different sources. It provides information on vulnerability moving along-shore and across-shore (beach-dune transect), highlighting the variability of vulnerability conditions in the spatial
Multi-enzyme logic network architectures for assessing injuries: digital processing of biomarkers.
Halámek, Jan; Bocharova, Vera; Chinnapareddy, Soujanya; Windmiller, Joshua Ray; Strack, Guinevere; Chuang, Min-Chieh; Zhou, Jian; Santhosh, Padmanabhan; Ramirez, Gabriela V; Arugula, Mary A; Wang, Joseph; Katz, Evgeny
2010-12-01
A multi-enzyme biocatalytic cascade processing simultaneously five biomarkers characteristic of traumatic brain injury (TBI) and soft tissue injury (STI) was developed. The system operates as a digital biosensor based on concerted function of 8 Boolean AND logic gates, resulting in the decision about the physiological conditions based on the logic analysis of complex patterns of the biomarkers. The system represents the first example of a multi-step/multi-enzyme biosensor with the built-in logic for the analysis of complex combinations of biochemical inputs. The approach is based on recent advances in enzyme-based biocomputing systems and the present paper demonstrates the potential applicability of biocomputing for developing novel digital biosensor networks.
Directory of Open Access Journals (Sweden)
E.A. Ramadan
2014-09-01
Full Text Available This paper presents an improved adaptive fuzzy logic speed controller for a DC motor, based on field programmable gate array (FPGA hardware implementation. The developed controller includes an adaptive fuzzy logic control (AFLC algorithm, which is designed and verified with a nonlinear model of DC motor. Then, it has been synthesised, functionally verified and implemented using Xilinx Integrated Software Environment (ISE and Spartan-3E FPGA. The performance of this controller has been successfully validated with good tracking results under different operating conditions.
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. Copyright © 2016 Elsevier Inc. All rights reserved.
Coordinated adaptive beamformer over distributed antenna network
Institute of Scientific and Technical Information of China (English)
Liu Desheng; Lu Songtao; Sun Jinping; Wang Jun
2013-01-01
The spatial diversity of distributed network demands the individual filter to accommodate the topology of interference environment.In this paper,a type of distributed adaptive beamformer is proposed to mitigate interference over coordinated antenna arrays network.The proposed approach is formulated as generalized sidelobe canceller (GSC) structure to facilitate the convex combination of neighboring nodes' weights,and then it is solved by unconstrained least mean square (LMS) algorithm due to simplicity.Numerical results show that the robustness and convergence rate of antenna arrays network can be significantly improved in strong interference scenario.And they also clearly illustrate that mixing vector is optimized adaptively and adjusted according to the spatial diversity of the distributed nodes which are placed in different power of received signals to interference ratio (SIR) environments.
Building Toffoli Network for Reversible Logic Synthesis Based on Swapping Bit Strings
Babu, Hafiz Md Hasaan; Islam, Md Rafiqul; Jamal, Lafifa; Ferdaus, Abu Ahmed; Karim, Muhammad Rezaul; Mahmud, Abdullah Al
2010-01-01
In this paper, we have implemented and designed a sorting network for reversible logic circuits synthesis in terms of n*n Toffoli gates. The algorithm presented in this paper constructs a Toffoli Network based on swapping bit strings. Reduction rules are then applied by simple template matching and removing useless gates from the network. Random selection of bit strings and reduction of control inputs are used to minimize both the number of gates and gate width. The method produces near optimal results for up to 3-input 3-output circuits.
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Saifullah Khalid
2016-09-01
Full Text Available Three conventional control constant instantaneous power control, sinusoidal current control, and synchronous reference frame techniques for extracting reference currents for shunt active power filters have been optimized using Fuzzy Logic control and Adaptive Tabu search Algorithm and their performances have been compared. Critical analysis of Comparison of the compensation ability of different control strategies based on THD and speed will be done, and suggestions will be given for the selection of technique to be used. The simulated results using MATLAB model are presented, and they will clearly prove the value of the proposed control method of aircraft shunt APF. The waveforms observed after the application of filter will be having the harmonics within the limits and the power quality will be improved.
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.
Biofuel cell controlled by enzyme logic network--approaching physiologically regulated devices.
Tam, Tsz Kin; Pita, Marcos; Ornatska, Maryna; Katz, Evgeny
2009-09-01
A "smart" biofuel cell switchable ON and OFF upon application of several chemical signals processed by an enzyme logic network was designed. The biocomputing system performing logic operations on the input signals was composed of four enzymes: alcohol dehydrogenase (ADH), amyloglucosidase (AGS), invertase (INV) and glucose dehydrogenase (GDH). These enzymes were activated by different combinations of chemical input signals: NADH, acetaldehyde, maltose and sucrose. The sequence of biochemical reactions catalyzed by the enzymes models a logic network composed of concatenated AND/OR gates. Upon application of specific "successful" patterns of the chemical input signals, the cascade of biochemical reactions resulted in the formation of gluconic acid, thus producing acidic pH in the solution. This resulted in the activation of a pH-sensitive redox-polymer-modified cathode in the biofuel cell, thus, switching ON the entire cell and dramatically increasing its power output. Application of another chemical signal (urea in the presence of urease) resulted in the return to the initial neutral pH value, when the O(2)-reducing cathode and the entire cell are in the mute state. The reversible activation-inactivation of the biofuel cell was controlled by the enzymatic reactions logically processing a number of chemical input signals applied in different combinations. The studied biofuel cell exemplifies a new kind of bioelectronic device where the bioelectronic function is controlled by a biocomputing system. Such devices will provide a new dimension in bioelectronics and biocomputing benefiting from the integration of both concepts.
Fuzzy Logic QoS Dynamic Source Routing for Mobile Ad Hoc Networks
Institute of Scientific and Technical Information of China (English)
ZHANG Xu; CHENG Sheng; FENG Mei-yu; DING Wei
2004-01-01
Considering the characters of dynamic topology and the imprecise state information in mobile ad hoc network,we propose a Fuzzy Logic QoS Dynamic Source Routing (FLQDSR) algorithm based on Dynamic Source Routing (DSR)protocol while adopting fuzzy logic to select the appropriate QoS routing in multiple paths which are searched in parallel.This scheme considers not only the bandwidth and end-to-end delay of routing, but also the cost of the path. On the other hand the merit of using fuzzy logic is that it can be implemented by hardware. This makes the realization of the scheme easier and faster. However our algorithm is based on DSR, the maximal hop count should be less than 10, i.e., the scale of mobile ad hoc network should not be very large. Simulation results show that FLQDSR can tolerate a high degree of information imprecision by adding the fuzzy logic module which integrates the QoS requirements of application and the routing QoS parameters to determine the most qualified one in every node.
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.
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
Directory of Open Access Journals (Sweden)
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.
Directory of Open Access Journals (Sweden)
Abdul Kareem
2012-08-01
Full Text Available This paper presents a novel fuzzy logic based Adaptive Super-twisting Sliding Mode Controller for thecontrol of dynamic uncertain systems. The proposed controller combines the advantages of Second orderSliding Mode Control, Fuzzy Logic Control and Adaptive Control. The reaching conditions, stability androbustness of the system with the proposed controller are guaranteed. In addition, the proposed controlleris well suited for simple design and implementation. The effectiveness of the proposed controller over thefirst order Sliding Mode Fuzzy Logic controller is illustrated by Matlab based simulations performed on aDC-DC Buck converter. Based on this comparison, the proposed controller is shown to obtain the desiredtransient response without causing chattering and error under steady-state conditions. The proposedcontroller is able to give robust performance in terms of rejection to input voltage variations and loadvariations
Understanding Supply Networks from Complex Adaptive Systems
Directory of Open Access Journals (Sweden)
Jamur Johnas Marchi
2014-10-01
Full Text Available This theoretical paper is based on complex adaptive systems (CAS that integrate dynamic and holistic elements, aiming to discuss supply networks as complex systems and their dynamic and co-evolutionary processes. The CAS approach can give clues to understand the dynamic nature and co-evolution of supply networks because it consists of an approach that incorporates systems and complexity. This paper’s overall contribution is to reinforce the theoretical discussion of studies that have addressed supply chain issues, such as CAS.
Quantum logic networks for controlled teleportation of a single particle via W state
Institute of Scientific and Technical Information of China (English)
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.
Quantum Logic Networks for Probabilistic and Controlled Teleportation of Unknown Quantum States
Institute of Scientific and Technical Information of China (English)
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.
Fuzzy Logic Based Anomaly Detection for Embedded Network Security Cyber Sensor
Energy Technology Data Exchange (ETDEWEB)
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.
Network inference via adaptive optimal design
Directory of Open Access Journals (Sweden)
Stigter Johannes D
2012-09-01
Full Text Available Abstract Background Current research in network reverse engineering for genetic or metabolic networks very often does not include a proper experimental and/or input design. In this paper we address this issue in more detail and suggest a method that includes an iterative design of experiments based, on the most recent data that become available. The presented approach allows a reliable reconstruction of the network and addresses an important issue, i.e., the analysis and the propagation of uncertainties as they exist in both the data and in our own knowledge. These two types of uncertainties have their immediate ramifications for the uncertainties in the parameter estimates and, hence, are taken into account from the very beginning of our experimental design. Findings The method is demonstrated for two small networks that include a genetic network for mRNA synthesis and degradation and an oscillatory network describing a molecular network underlying adenosine 3’-5’ cyclic monophosphate (cAMP as observed in populations of Dyctyostelium cells. In both cases a substantial reduction in parameter uncertainty was observed. Extension to larger scale networks is possible but needs a more rigorous parameter estimation algorithm that includes sparsity as a constraint in the optimization procedure. Conclusion We conclude that a careful experiment design very often (but not always pays off in terms of reliability in the inferred network topology. For large scale networks a better parameter estimation algorithm is required that includes sparsity as an additional constraint. These algorithms are available in the literature and can also be used in an adaptive optimal design setting as demonstrated in this paper.
Energy Technology Data Exchange (ETDEWEB)
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.
Lo, Benjamin W Y; Macdonald, R Loch; Baker, Andrew; Levine, Mitchell A H
2013-01-01
The novel clinical prediction approach of Bayesian neural networks with fuzzy logic inferences is created and applied to derive prognostic decision rules in cerebral aneurysmal subarachnoid hemorrhage (aSAH). The approach of Bayesian neural networks with fuzzy logic inferences was applied to data from five trials of Tirilazad for aneurysmal subarachnoid hemorrhage (3551 patients). Bayesian meta-analyses of observational studies on aSAH prognostic factors gave generalizable posterior distributions of population mean log odd ratios (ORs). Similar trends were noted in Bayesian and linear regression ORs. Significant outcome predictors include normal motor response, cerebral infarction, history of myocardial infarction, cerebral edema, history of diabetes mellitus, fever on day 8, prior subarachnoid hemorrhage, admission angiographic vasospasm, neurological grade, intraventricular hemorrhage, ruptured aneurysm size, history of hypertension, vasospasm day, age and mean arterial pressure. Heteroscedasticity was present in the nontransformed dataset. Artificial neural networks found nonlinear relationships with 11 hidden variables in 1 layer, using the multilayer perceptron model. Fuzzy logic decision rules (centroid defuzzification technique) denoted cut-off points for poor prognosis at greater than 2.5 clusters. This aSAH prognostic system makes use of existing knowledge, recognizes unknown areas, incorporates one's clinical reasoning, and compensates for uncertainty in prognostication.
A Neural Network for Generating Adaptive Lessons
Directory of Open Access Journals (Sweden)
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.
Large-scale Networked Multi-axis Control solution using EtherCAT and Soft Logic
Directory of Open Access Journals (Sweden)
Zhiyuan Cheng
2013-09-01
Full Text Available Aiming at the deficiencies of the traditional multi-axis control solution such as complex networked structure, poor clustered-control feature and unsatisfactory engineering practicability, the paper firstly optimized the existing solution in networked fieldbus, controller model, engineering reliability and maintainability. Then it proposed a novel solution combined high speed real-time EtherCAT (Ethernet for control Automation Technology fieldbus with soft logic controller. The new solution took advantage of extraordinary real-time performance of EtherCAT and made good use of powerful clustered-control architecture of soft logic controller. Thus the new solution is concise and effective to solve the Large-scale networked controlling problem of 1100 distributed motors. Compared with the traditional schemes, the engineering practice shows that the novel solution has the advantage of perfect real-time performance, powerful clustered-control capability, flexible and variable networked structure, excellent engineering practicability.The novel solution is worth using for reference in solve similar large-scale networked controlling problems.
Adaptive vertical handoff algorithm in heterogeneous networks
Institute of Scientific and Technical Information of China (English)
XIE Sheng-dong; WU Meng
2007-01-01
The integration of cellular network (CN) and wireless local area network (WLAN) is the trend of the next generation mobile communication systems, and nodes will handoff between the two kinds of networks. The received signal strength (RSS) is the dominant factor consijered when handoff occurs. In order to improve the handoff efficiency, this study proposes an adaptive decision algorithm for vertical handoff on the basis of fast Fourier transform (FFT). The algorithm makes handoff decision after analyzing the signal strength fluctuation which is caused by slow fading through FFT. Simulations show that the algorithm reduces the number of handoff by 35%, shortens the areas influenced by slow fading, and enables the nodes to make full use of WLAN in communication compared with traditional algorithms.
QoS Adaptive Topology Configuration in Synchronous Wireless Sensor Networks
Institute of Scientific and Technical Information of China (English)
YANG Ting; WU Jiaowen; LI Ang; ZHANG Zhidong
2010-01-01
By using hyper-graph theory,this paper proposes a QoS adaptive topology configuration(QATC)algorithm to effectively control large-scale topology and achieve robust data transmitting in synchronous wireless sensor networks.Firstly,a concise hyper-graph model is abstracted to analyze the large-scale and high-connectivity network.Secondly,based on the control theory of biologic "Cell Mergence",a novel self-adaptive topology configuration algorithm is used to build homologous perceptive data logic sub-network for data aggregation.Compared with Flooding,Directed Diffusion,and Energy Aware Directed Diffusion protocols,the simulation proved that QATC algorithm can save more energy,e.g.,about 23.7% in a large size network,and has less delay than the other algorithms.In dynamic experiments,QATC keeps a robust transmitting quality with 10%,20% and 30% random failure nodes.
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.
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.
Logic-based models in systems biology: a predictive and parameter-free network analysis method†
Wynn, Michelle L.; Consul, Nikita; Merajver, Sofia D.
2012-01-01
Highly complex molecular networks, which play fundamental roles in almost all cellular processes, are known to be dysregulated in a number of diseases, most notably in cancer. As a consequence, there is a critical need to develop practical methodologies for constructing and analysing molecular networks at a systems level. Mathematical models built with continuous differential equations are an ideal methodology because they can provide a detailed picture of a network’s dynamics. To be predictive, however, differential equation models require that numerous parameters be known a priori and this information is almost never available. An alternative dynamical approach is the use of discrete logic-based models that can provide a good approximation of the qualitative behaviour of a biochemical system without the burden of a large parameter space. Despite their advantages, there remains significant resistance to the use of logic-based models in biology. Here, we address some common concerns and provide a brief tutorial on the use of logic-based models, which we motivate with biological examples. PMID:23072820
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.
Dissecting the logical types of network control in gene expression profiles
Directory of Open Access Journals (Sweden)
Geertz Marcel
2008-02-01
Full Text Available Abstract Background In the bacterium Escherichia coli the transcriptional regulation of gene expression involves both dedicated regulators binding specific DNA sites with high affinity and also global regulators – abundant DNA architectural proteins of the bacterial nucleoid binding multiple sites with a wide range of affinities and thus modulating the superhelical density of DNA. The first form of transcriptional regulation is predominantly pairwise and specific, representing digitial control, while the second form is (in strength and distribution continuous, representing analog control. Results Here we look at the properties of effective networks derived from significant gene expression changes under variation of the two forms of control and find that upon limitations of one type of control (caused e.g. by mutation of a global DNA architectural factor the other type can compensate for compromised regulation. Mutations of global regulators significantly enhance the digital control, whereas in the presence of global DNA architectural proteins regulation is mostly of the analog type, coupling spatially neighboring genomic loci. Taken together our data suggest that two logically distinct – digital and analog – types of control are balancing each other. Conclusion By revealing two distinct logical types of control, our approach provides basic insights into both the organizational principles of transcriptional regulation and the mechanisms buffering genetic flexibility. We anticipate that the general concept of distinguishing logical types of control will apply to many complex biological networks.
Designing Logical Topology for Wireless Sensor Networks: A Multi-Chain Oriented Approach
Directory of Open Access Journals (Sweden)
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.
Directory of Open Access Journals (Sweden)
Yonghua Wang
2013-01-01
Full Text Available This paper proposes a cooperative spectrum sensing scheme based on trust and fuzzy logic for Cognitive Radio Sensor Networks (CRSN. The CRSN nodes use the T-S fuzzy logic to make local decisions on the presence or absence of the primary users (PU signal, and then use a censoring method to only allow the relatively reliable decisions sent to the fusion center. Utilizing a trust evaluation scheme based on the factors such as local sensing difference, sensing location factors, and sensing channel conditions for each node. Combing the majority rule and the trust values of the nodes, the fusion center makes the final decision. Simulation results show that the proposed scheme could improve the detect probability effectively.
Directory of Open Access Journals (Sweden)
K. Venkata Subbaiah
2010-01-01
Full Text Available The nodes in the mobile ad hoc networks act as router and host, the routing protocol is the primary issue and has to be supported before any applications can be deployed for mobile ad hoc networks. In recent many research protocols are proposed for finding an efficient route between the nodes. But most of the protocol’s that uses conventional techniques in routing; CBRP is a routing protocol that has a hierarchical-based design. This protocol divides the network area into several smaller areas called cluster. We propose a fuzzy logic based cluster head election using energy concept forcluster head routing protocol in MANET’S. Selecting an appropriate cluster head can save power for the whole mobile ad hoc network. Generally, Cluster Head election for mobile ad hoc network is based on the distance to the centroid of a cluster, and the closest one is elected as the cluster head'; or pick a node with the maximum battery capacity as the cluster head. In this paper, we present a cluster head election scheme using fuzzy logic system (FLS for mobile ad hoc networks. Three descriptors are used: distance of a node to the cluster centroid, its remaining battery capacity, and its degree of mobility. The linguistic knowledge of cluster head election based on these three descriptors is obtained from a group of network experts. 27 FLS rules are set up based on the linguistic knowledge. The output of the FLS provides a cluster head possibility, and node with the highest possibility is elected as the cluster head. The performance of fuzzy cluster head selection is evaluated using simulation, and is compared to LEACH protocol with out fuzzy cluster head election procedures and showed the proposed work is efficient than the previous one.
Public Goods Games on Adaptive Coevolutionary Networks
Shapiro, Avi M
2016-01-01
Productive societies feature high levels of cooperation and strong connections between individuals. Public Goods Games (PGGs) are frequently used to study the development of social connections and cooperative behavior in model societies. In such games, contributions to the public good are made only by cooperators, while all players, including defectors, can reap public goods benefits. Classic results of game theory show that mutual defection, as opposed to cooperation, is the Nash Equilibrium of PGGs in well-mixed populations, where each player interacts with all others. In this paper, we explore the coevolutionary dynamics of a low information public goods game on a network without spatial constraints in which players adapt to their environment in order to increase individual payoffs. Players adapt by changing their strategies, either to cooperate or to defect, and by altering their social connections. We find that even if players do not know other players' strategies and connectivity, cooperation can arise ...
Quantitative Adaptive RED in Differentiated Service Networks
Institute of Scientific and Technical Information of China (English)
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.
Social networks as embedded complex adaptive systems.
Benham-Hutchins, Marge; Clancy, Thomas R
2010-09-01
As systems evolve over time, their natural tendency is to become increasingly more complex. Studies in the field of complex systems have generated new perspectives on management in social organizations such as hospitals. Much of this research appears as a natural extension of the cross-disciplinary field of systems theory. This is the 15th in a series of articles applying complex systems science to the traditional management concepts of planning, organizing, directing, coordinating, and controlling. In this article, the authors discuss healthcare social networks as a hierarchy of embedded complex adaptive systems. The authors further examine the use of social network analysis tools as a means to understand complex communication patterns and reduce medical errors.
Energy Technology Data Exchange (ETDEWEB)
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.
Recognition of geometric primitives using logic-program and probabilistic-network reasoning methods
Munck-Fairwood, Roger C.
1992-03-01
This paper addresses the issue of recognition of 3-D objects from a potentially very large database of categories of objects, assuming the data are provided in the form of the edges available from a single monocular view, which indicate the discontinuities in depth and surface orientation. The work is partly inspired by the `Recognition by Components' approach suggested fairly recently by Irving Biederman using `geons,' chosen for their qualitatively distinguishable nonmetric viewpoint-invariant properties. The work is also inspired by Richard Gregory's model of human visual recognition which involves probabilistic reasoning, and the regarding of perception as hypothesis. Further, the interpretation of some data can influence the expectation of other data. A novel attempt is made here to apply two automatic reasoning tools to a sub-task of the general recognition process, viz., the recognition of isolated geons in an idealized image. The tools are logic programming and `belief networks' (causal probabilistic networks). Both the tools have the important property of allowing propagation of information in both directions, i.e., data to hypotheses, and vice-versa. The results to date show good patterns of reasoning consistent with one's intuition and point to the possibility of appropriately `tuning' some feature detectors according to other data received. Future goals include the recognition of geons from real gray-level image data, the extension of the belief network to composite objects, and the use of a reverse-driven image analysis logic program to generate graphics and thereby identify appropriate model constraints.
Exploratory Analysis of the Social Network of Researchers in Inductive Logic Programming
Lavrač, Nada; Grčar, Miha; Fortuna, Blaž; Velardi, Paola
In this chapter, we present selected techniques for social network analysis and text mining and interpret the results of exploratory analysis of the social network of researchers in inductive logic programming (ILP), based on the ILP scientific publications database collected within the ILPnet2 project. Part of the analysis was performed with the Pajek software for large (social) network analyses, where the central entity of the analysis was the author, related to other authors by coauthorship links, weighted by the number of his or her publications registered in the ILPnet2 database. The chapter presents also a novel methodology for topic ontology learning from text documents. The proposed methodology, named OntoTermExtraction (Term Extraction for Ontology learning), is based on OntoGen, a semiautomated tool for topic ontology construction, upgraded by using an advanced terminology extraction tool in an iterative, semiautomated ontology construction process. The approach was successfully used for generating the ontology of topics in Inductive Logic Programming, learned semiautomatically from papers indexed in the ILPnet2 publications database.
A fuzzy logic based clustering strategy for improving vehicular ad-hoc network performance
Indian Academy of Sciences (India)
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
Fuzzy Logic Control Based QoS Management in Wireless Sensor/Actuator Networks
Directory of Open Access Journals (Sweden)
Yu-Chu Tian
2007-12-01
Full Text Available Wireless sensor/actuator networks (WSANs are emerging rapidly as a newgeneration 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. Inparticular, quality-of-service (QoS management in WSANs remains an important issue yetto be investigated. As an attempt in this direction, this paper develops a fuzzy logic controlbased QoS management (FLC-QM scheme for WSANs with constrained resources and indynamic and unpredictable environments. Taking advantage of the feedback controltechnology, this scheme deals with the impact of unpredictable changes in traffic load on theQoS of WSANs. It utilizes a fuzzy logic controller inside each source sensor node to adaptsampling period to the deadline miss ratio associated with data transmission from the sensorto the actuator. The deadline miss ratio is maintained at a pre-determined desired level sothat the required QoS can be achieved. The FLC-QM has the advantages of generality,scalability, and simplicity. Simulation results show that the FLC-QM can provide WSANswith QoS support.
Fuzzy-rule-based Adaptive Resource Control for Information Sharing in P2P Networks
Wu, Zhengping; Wu, Hao
With more and more peer-to-peer (P2P) technologies available for online collaboration and information sharing, people can launch more and more collaborative work in online social networks with friends, colleagues, and even strangers. Without face-to-face interactions, the question of who can be trusted and then share information with becomes a big concern of a user in these online social networks. This paper introduces an adaptive control service using fuzzy logic in preference definition for P2P information sharing control, and designs a novel decision-making mechanism using formal fuzzy rules and reasoning mechanisms adjusting P2P information sharing status following individual users' preferences. Applications of this adaptive control service into different information sharing environments show that this service can provide a convenient and accurate P2P information sharing control for individual users in P2P networks.
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.
Probabilistic Adaptive Anonymous Authentication in Vehicular Networks
Institute of Scientific and Technical Information of China (English)
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.
Guziolowski, Carito; Videla, Santiago; Eduati, Federica; Thiele, Sven; Cokelaer, Thomas; Siegel, Anne; Saez-Rodriguez, Julio
2013-09-15
Logic modeling is a useful tool to study signal transduction across multiple pathways. Logic models can be generated by training a network containing the prior knowledge to phospho-proteomics data. The training can be performed using stochastic optimization procedures, but these are unable to guarantee a global optima or to report the complete family of feasible models. This, however, is essential to provide precise insight in the mechanisms underlaying signal transduction and generate reliable predictions. We propose the use of Answer Set Programming to explore exhaustively the space of feasible logic models. Toward this end, we have developed caspo, an open-source Python package that provides a powerful platform to learn and characterize logic models by leveraging the rich modeling language and solving technologies of Answer Set Programming. We illustrate the usefulness of caspo by revisiting a model of pro-growth and inflammatory pathways in liver cells. We show that, if experimental error is taken into account, there are thousands (11 700) of models compatible with the data. Despite the large number, we can extract structural features from the models, such as links that are always (or never) present or modules that appear in a mutual exclusive fashion. To further characterize this family of models, we investigate the input-output behavior of the models. We find 91 behaviors across the 11 700 models and we suggest new experiments to discriminate among them. Our results underscore the importance of characterizing in a global and exhaustive manner the family of feasible models, with important implications for experimental design. caspo is freely available for download (license GPLv3) and as a web service at http://caspo.genouest.org/. Supplementary materials are available at Bioinformatics online. santiago.videla@irisa.fr.
Directory of Open Access Journals (Sweden)
2009-03-01
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.
Directory of Open Access Journals (Sweden)
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.
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...
ARTIFICIAL NEURAL NETWORK AND FUZZY LOGIC CONTROLLER FOR GTAW MODELING AND CONTROL
Institute of Scientific and Technical Information of China (English)
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.
Efficient quantum computation in a network with probabilistic gates and logical encoding
DEFF Research Database (Denmark)
Borregaard, J.; Sørensen, A. S.; Cirac, J. I.
2017-01-01
An approach to efficient quantum computation with probabilistic gates is proposed and analyzed in both a local and nonlocal setting. It combines heralded gates previously studied for atom or atomlike qubits with logical encoding from linear optical quantum computation in order to perform high......-fidelity quantum gates across a quantum network. The error-detecting properties of the heralded operations ensure high fidelity while the encoding makes it possible to correct for failed attempts such that deterministic and high-quality gates can be achieved. Importantly, this is robust to photon loss, which...... is typically the main obstacle to photonic-based quantum information processing. Overall this approach opens a path toward quantum networks with atomic nodes and photonic links....
Mzenda, Bongile; Gegov, Alexander; Brown, David J; Petrov, Nedyalko
2012-01-01
This study investigates the feasibility of using Artificial Neural Network (ANN) and fuzzy logic based techniques to select treatment margins for dynamically moving targets in the radiotherapy treatment of prostate cancer. The use of data from 15 patients relating error effects to the Tumour Control Probability (TCP) and Normal Tissue Complication Probability (NTCP) radiobiological indices was contrasted against the use of data based on the prostate volume receiving 99% of the prescribed dose (V99%) and the rectum volume receiving more than 60Gy (V60). For the same input data, the results of the ANN were compared to results obtained using a fuzzy system, a fuzzy network and current clinically used statistical techniques. Compared to fuzzy and statistical methods, the ANN derived margins were found to be up to 2 mm larger at small and high input errors and up to 3.5 mm larger at medium input error magnitudes.
Directory of Open Access Journals (Sweden)
José Alonso Borba
2010-04-01
Full Text Available There are problems in Finance and Accounting that can not be easily solved by means of traditional techniques (e.g. bankruptcy prediction and strategies for investing in common stock. In these situations, it is possible to use methods of Artificial Intelligence. This paper analyzes empirical works published in international journals between 2000 and 2007 that present studies about the application of Neural Networks, Fuzzy Logic and Genetic Algorithms to problems in Finance and Accounting. The objective is to identify and quantify the relationships established between the available techniques and the problems studied by the researchers. Analyzing 258 papers, it was noticed that the most used technique is the Artificial Neural Network. The most researched applications are from the field of Finance, especially those related to stock exchanges (forecasting of common stock and indices prices.
Compensation for unmatched uncertainty with adaptive RBF network
African Journals Online (AJOL)
user
radial basis function (RBF) neural networks have showed strong universal approximation ability for unknown ..... w is the ideal constant weight, the ... w with the weight estimation error )(~ twi ..... Gaussian networks for direct adaptive control.
A Fuzzy Logic Based Power Control for Wideband Code Division Multiple Access Wireless Networks
Directory of Open Access Journals (Sweden)
T. Ravichandran
2012-01-01
Full Text Available Problem statement: Resource management is one of the most important engineering issues in 3G systems where multiple traffic classes are supported each being characterized by its required Quality of Service (QoS parameters. Call Admission Control (CAC is one of the resource management functions, which regulates network access to ensure QoS provisioning. Efficient CAC is necessary for the QoS provisioning in WCDMA environment. The effective functioning of WCDMA systems is influenced by the power control utility. Approach: In this study, we propose to design a fuzzy logic based power control for Wideband Code Division Multiple Access Wireless Networks. This proposed technique is aimed at multiple services like voice, video and data for multiclass users. The fuzzy logic technique is used to estimate the optimal admissible users group inclusive of optimum transmitting power level. This technique reduces the interference level and call rejection rate. Results: By simulation results, we demonstrate that the proposed technique achieve reduced energy consumption for a cell with increased throughput. Conclusion: The proposed technique minimizes the power consumption and call rejection rate.
Alomari, Abdullah; Phillips, William; Aslam, Nauman; Comeau, Frank
2017-08-18
Mobile anchor path planning techniques have provided as an alternative option for node localization in wireless sensor networks (WSNs). In such context, path planning is a movement pattern where a mobile anchor node's movement is designed in order to achieve a maximum localization ratio possible with a minimum error rate. Typically, the mobility path planning is designed in advance, which is applicable when the mobile anchor has sufficient sources of energy and time. However, when the mobility movement is restricted or limited, a dynamic path planning design is needed. This paper proposes a novel distributed range-free movement mechanism for mobility-assisted localization in WSNs when the mobile anchor's movement is limited. The designed movement is formed in real-time pattern using a fuzzy-logic approach based on the information received from the network and the nodes' deployment. Our proposed model, Fuzzy-Logic based Path Planning for mobile anchor-assisted Localization in WSNs (FLPPL), offers superior results in several metrics including both localization accuracy and localization ratio in comparison to other similar works.
Semantical Markov Logic Network for Distributed Reasoning in Cyber-Physical Systems
Directory of Open Access Journals (Sweden)
Abdul-Wahid Mohammed
2017-01-01
Full Text Available The challenges associated with developing accurate models for cyber-physical systems are attributable to the intrinsic concurrent and heterogeneous computations of these systems. Even though reasoning based on interconnected domain specific ontologies shows promise in enhancing modularity and joint functionality modelling, it has become necessary to build interoperable cyber-physical systems due to the growing pervasiveness of these systems. In this paper, we propose a semantically oriented distributed reasoning architecture for cyber-physical systems. This model accomplishes reasoning through a combination of heterogeneous models of computation. Using the flexibility of semantic agents as a formal representation for heterogeneous computational platforms, we define autonomous and intelligent agent-based reasoning procedure for distributed cyber-physical systems. Sensor networks underpin the semantic capabilities of this architecture, and semantic reasoning based on Markov logic networks is adopted to address uncertainty in modelling. To illustrate feasibility of this approach, we present a Markov logic based semantic event model for cyber-physical systems and discuss a case study of event handling and processing in a smart home.
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...
Adaptive Filtering Using Recurrent Neural Networks
Parlos, Alexander G.; Menon, Sunil K.; Atiya, Amir F.
2005-01-01
A method for adaptive (or, optionally, nonadaptive) filtering has been developed for estimating the states of complex process systems (e.g., chemical plants, factories, or manufacturing processes at some level of abstraction) from time series of measurements of system inputs and outputs. The method is based partly on the fundamental principles of the Kalman filter and partly on the use of recurrent neural networks. The standard Kalman filter involves an assumption of linearity of the mathematical model used to describe a process system. The extended Kalman filter accommodates a nonlinear process model but still requires linearization about the state estimate. Both the standard and extended Kalman filters involve the often unrealistic assumption that process and measurement noise are zero-mean, Gaussian, and white. In contrast, the present method does not involve any assumptions of linearity of process models or of the nature of process noise; on the contrary, few (if any) assumptions are made about process models, noise models, or the parameters of such models. In this regard, the method can be characterized as one of nonlinear, nonparametric filtering. The method exploits the unique ability of neural networks to approximate nonlinear functions. In a given case, the process model is limited mainly by limitations of the approximation ability of the neural networks chosen for that case. Moreover, despite the lack of assumptions regarding process noise, the method yields minimum- variance filters. In that they do not require statistical models of noise, the neural- network-based state filters of this method are comparable to conventional nonlinear least-squares estimators.
Saez-Rodriguez, Julio; Alexopoulos, Leonidas G; Zhang, Mingsheng; Morris, Melody K; Lauffenburger, Douglas A; Sorger, Peter K
2011-08-15
Substantial effort in recent years has been devoted to constructing and analyzing large-scale gene and protein networks on the basis of "omic" data and literature mining. These interaction graphs provide valuable insight into the topologies of complex biological networks but are rarely context specific and cannot be used to predict the responses of cell signaling proteins to specific ligands or drugs. Conversely, traditional approaches to analyzing cell signaling are narrow in scope and cannot easily make use of network-level data. Here, we combine network analysis and functional experimentation by using a hybrid approach in which graphs are converted into simple mathematical models that can be trained against biochemical data. Specifically, we created Boolean logic models of immediate-early signaling in liver cells by training a literature-based prior knowledge network against biochemical data obtained from primary human hepatocytes and 4 hepatocellular carcinoma cell lines exposed to combinations of cytokines and small-molecule kinase inhibitors. Distinct families of models were recovered for each cell type, and these families clustered topologically into normal and diseased sets.
Adaptive Synchronization in Small-World Dynamical Networks
Institute of Scientific and Technical Information of China (English)
ZOU Yan-li; ZHU Jie; LUO Xiao-shu
2007-01-01
Adaptive synchronization in NW small-world dynamical networks was studied. Firstly, an adaptive synchronization method is presented and explained. Then, it is applied to two different classes of dynamical networks,one is a class-B network, small-world connected R(o)ssler oscillators, the other is a class-A network, small-world connected Chua's circuits. The simulation verifies the validity of the presented method. It also shows that the adaptive synchronization method is robust to the variations of the node systems parameters. So the presented method can be used in networks whose node systems have unknown or time-varying parameters.
Adaptive training of feedforward neural networks by Kalman filtering
Energy Technology Data Exchange (ETDEWEB)
Ciftcioglu, Oe. [Istanbul Technical Univ. (Turkey). Dept. of Electrical Engineering; Tuerkcan, E. [Netherlands Energy Research Foundation (ECN), Petten (Netherlands)
1995-02-01
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.).
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.
Public goods games on adaptive coevolutionary networks
Pichler, Elgar; Shapiro, Avi M.
2017-07-01
Productive societies feature high levels of cooperation and strong connections between individuals. Public Goods Games (PGGs) are frequently used to study the development of social connections and cooperative behavior in model societies. In such games, contributions to the public good are made only by cooperators, while all players, including defectors, reap public goods benefits, which are shares of the contributions amplified by a synergy factor. Classic results of game theory show that mutual defection, as opposed to cooperation, is the Nash Equilibrium of PGGs in well-mixed populations, where each player interacts with all others. In this paper, we explore the coevolutionary dynamics of a low information public goods game on a complex network in which players adapt to their environment in order to increase individual payoffs relative to past payoffs parameterized by greediness. Players adapt by changing their strategies, either to cooperate or to defect, and by altering their social connections. We find that even if players do not know other players' strategies and connectivity, cooperation can arise and persist despite large short-term fluctuations.
Speed Adaptation in Urban Road Network Management
Directory of Open Access Journals (Sweden)
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.
Evolution of Cooperation in Adaptive Social Networks
Segbroeck, Sven Van; Santos, Francisco C.; Traulsen, Arne; Lenaerts, Tom; Pacheco, Jorge M.
Humans are organized in societies, a phenomenon that would never have been possible without the evolution of cooperative behavior. Several mechanisms that foster this evolution have been unraveled over the years, with population structure as a prominent promoter of cooperation. Modern networks of exchange and cooperation are, however, becoming increasingly volatile, and less and less based on long-term stable structure. Here, we address how this change of paradigm aspects the evolution of cooperation. We discuss analytical and numerical models in which individuals can break social ties and create new ones. Interactions are modeled as two-player dilemmas of cooperation. Once a link between two individuals has formed, the productivity of this link is evaluated. Links can be broken off at different rates. This individual capacity of forming new links or severing inconvenient ones can effectively change the nature of the game. We address random formation of new links and local linking rules as well as different individual capacities to maintain social interactions. We conclude by discussing how adaptive social networks can become an important step towards more realistic models of cultural dynamics.
Brain network adaptability across task states.
Directory of Open Access Journals (Sweden)
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.
Gas Turbine Engine Control Design Using Fuzzy Logic and Neural Networks
Directory of Open Access Journals (Sweden)
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.
Mehri, M
2013-04-01
Application of appropriate models to approximate the performance function warrants more precise prediction and helps to make the best decisions in the poultry industry. This study reevaluated the factors affecting hatchability in laying hens from 29 to 56 wk of age. Twenty-eight data lines representing 4 inputs consisting of egg weight, eggshell thickness, egg sphericity, and yolk/albumin ratio and 1 output, hatchability, were obtained from the literature and used to train an artificial neural network (ANN). The prediction ability of ANN was compared with that of fuzzy logic to evaluate the fitness of these 2 methods. The models were compared using R(2), mean absolute deviation (MAD), mean squared error (MSE), mean absolute percentage error (MAPE), and bias. The developed model was used to assess the relative importance of each variable on the hatchability by calculating the variable sensitivity ratio. The statistical evaluations showed that the ANN-based model predicted hatchability more accurately than fuzzy logic. The ANN-based model had a higher determination of coefficient (R(2) = 0.99) and lower residual distribution (MAD = 0.005; MSE = 0.00004; MAPE = 0.732; bias = 0.0012) than fuzzy logic (R(2) = 0.87; MAD = 0.014; MSE = 0.0004; MAPE = 2.095; bias = 0.0046). The sensitivity analysis revealed that the most important variable in the ANN-based model of hatchability was egg weight (variable sensitivity ratio, VSR = 283.11), followed by yolk/albumin ratio (VSR = 113.16), eggshell thickness (VSR = 16.23), and egg sphericity (VSR = 3.63). The results of this research showed that the universal approximation capability of ANN made it a powerful tool to approximate complex functions such as hatchability in the incubation process.
DEFF Research Database (Denmark)
Hu, Junjie; Zecchino, Antonio; Marinelli, Mattia
2016-01-01
either locally or remotely. To evaluate and compare the control performances of the three control logics, all the tests use the same loading profiles. The experimental results indicate that the modified line compensation control can regulate voltage in a safe band in the case of various load......This paper investigates the control logics of an on-load tap-changer (OLTC) transformer by means of an experimental system validation. The experimental low-voltage unbalanced system consists of a decoupled single-phase OLTC transformer, a 75-metre 16 mm2 cable, a controllable single-phase resistive...... 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 Signals Control with Adaptive Fuzzy Controller in Urban Road Network
Institute of Scientific and Technical Information of China (English)
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.
Synchronization of general complex networks via adaptive control schemes
Indian Academy of Sciences (India)
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.
Neural Network Inverse Adaptive Controller Based on Davidon Least Square
Institute of Scientific and Technical Information of China (English)
无
2000-01-01
General neural network inverse adaptive controller haa two flaws: the first is the slow convergence speed; the second is the invalidation to the non-minimum phase system.These defects limit the scope in which the neural network inverse adaptive controller is used.We employ Davidon least squares in training the multi-layer feedforward neural network used in approximating the inverse model of plant to expedite the convergence,and then through constructing the pseudo-plant,a neural network inverse adaptive controller is put forward which is still effective to the nonlinear non-minimum phase system.The simulation results show the validity of this scheme.
Hybrid Method for the Navigation of Mobile Robot Using Fuzzy Logic and Spiking Neural Networks
Directory of Open Access Journals (Sweden)
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.
Knowledge-guided fuzzy logic modeling to infer cellular signaling networks from proteomic data
Liu, Hui; Zhang, Fan; Mishra, Shital Kumar; Zhou, Shuigeng; Zheng, Jie
2016-01-01
Modeling of signaling pathways is crucial for understanding and predicting cellular responses to drug treatments. However, canonical signaling pathways curated from literature are seldom context-specific and thus can hardly predict cell type-specific response to external perturbations; purely data-driven methods also have drawbacks such as limited biological interpretability. Therefore, hybrid methods that can integrate prior knowledge and real data for network inference are highly desirable. In this paper, we propose a knowledge-guided fuzzy logic network model to infer signaling pathways by exploiting both prior knowledge and time-series data. In particular, the dynamic time warping algorithm is employed to measure the goodness of fit between experimental and predicted data, so that our method can model temporally-ordered experimental observations. We evaluated the proposed method on a synthetic dataset and two real phosphoproteomic datasets. The experimental results demonstrate that our model can uncover drug-induced alterations in signaling pathways in cancer cells. Compared with existing hybrid models, our method can model feedback loops so that the dynamical mechanisms of signaling networks can be uncovered from time-series data. By calibrating generic models of signaling pathways against real data, our method supports precise predictions of context-specific anticancer drug effects, which is an important step towards precision medicine. PMID:27774993
Directory of Open Access Journals (Sweden)
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.
Knowledge-guided fuzzy logic modeling to infer cellular signaling networks from proteomic data
Liu, Hui; Zhang, Fan; Mishra, Shital Kumar; Zhou, Shuigeng; Zheng, Jie
2016-10-01
Modeling of signaling pathways is crucial for understanding and predicting cellular responses to drug treatments. However, canonical signaling pathways curated from literature are seldom context-specific and thus can hardly predict cell type-specific response to external perturbations; purely data-driven methods also have drawbacks such as limited biological interpretability. Therefore, hybrid methods that can integrate prior knowledge and real data for network inference are highly desirable. In this paper, we propose a knowledge-guided fuzzy logic network model to infer signaling pathways by exploiting both prior knowledge and time-series data. In particular, the dynamic time warping algorithm is employed to measure the goodness of fit between experimental and predicted data, so that our method can model temporally-ordered experimental observations. We evaluated the proposed method on a synthetic dataset and two real phosphoproteomic datasets. The experimental results demonstrate that our model can uncover drug-induced alterations in signaling pathways in cancer cells. Compared with existing hybrid models, our method can model feedback loops so that the dynamical mechanisms of signaling networks can be uncovered from time-series data. By calibrating generic models of signaling pathways against real data, our method supports precise predictions of context-specific anticancer drug effects, which is an important step towards precision medicine.
Logical Link Control and Channel Scheduling for Multichannel Underwater Sensor Networks
Directory of Open Access Journals (Sweden)
Jun Li
2012-08-01
Full Text Available With recent developments in terrestrial wireless networks and advances in acoustic communications, multichannel technologies have been proposed to be used in underwater networks to increase data transmission rate over bandwidth-limited underwater channels. Due to high bit error rates in underwater networks, an efficient error control technique is critical in the logical link control (LLC sublayer to establish reliable data communications over intrinsically unreliable underwater channels. In this paper, we propose a novel protocol stack architecture featuring cross-layer design of LLC sublayer and more efficient packetto- channel scheduling for multichannel underwater sensor networks. In the proposed stack architecture, a selective-repeat automatic repeat request (SR-ARQ based error control protocol is combined with a dynamic channel scheduling policy at the LLC sublayer. The dynamic channel scheduling policy uses the channel state information provided via cross-layer design. It is demonstrated that the proposed protocol stack architecture leads to more efficient transmission of multiple packets over parallel channels. Simulation studies are conducted to evaluate the packet delay performance of the proposed cross-layer protocol stack architecture with two different scheduling policies: the proposed dynamic channel scheduling and a static channel scheduling. Simulation results show that the dynamic channel scheduling used in the cross-layer protocol stack outperforms the static channel scheduling. It is observed that, when the dynamic channel scheduling is used, the number of parallel channels has only an insignificant impact on the average packet delay. This confirms that underwater sensor networks will benefit from the use of multichannel communications.
Adaptive Mobile Positioning in WCDMA Networks
Directory of Open Access Journals (Sweden)
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.
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...
LTE Adaptation for Mobile Broadband Satellite Networks
Directory of Open Access Journals (Sweden)
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.
I.A. Korthagen (Iris); I.F. van Meerkerk (Ingmar)
2014-01-01
markdownabstract__Abstract__ Although theoretical and empirical work on the democratic legitimacy of governance networks is growing, little attention has been paid to the impact of mediatisation on democracies. Media have their own logic of news-making led by the media’s rules, aims, production rou
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,…
Dynamic multimedia stream adaptation and rate control for heterogeneous networks
Institute of Scientific and Technical Information of China (English)
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.
How adaptation shapes spike rate oscillations in recurrent neuronal networks
Directory of Open Access Journals (Sweden)
Moritz eAugustin
2013-02-01
Full Text Available Neural mass signals from in-vivo recordings often show oscillations with frequencies ranging from <1 Hz to 100 Hz. Fast rhythmic activity in the beta and gamma range can be generated by network based mechanisms such as recurrent synaptic excitation-inhibition loops. Slower oscillations might instead depend on neuronal adaptation currents whose timescales range from tens of milliseconds to seconds. Here we investigate how the dynamics of such adaptation currents contribute to spike rate oscillations and resonance properties in recurrent networks of excitatory and inhibitory neurons. Based on a network of sparsely coupled spiking model neurons with two types of adaptation current and conductance based synapses with heterogeneous strengths and delays we use a mean-field approach to analyze oscillatory network activity. For constant external input, we find that spike-triggered adaptation currents provide a mechanism to generate slow oscillations over a wide range of adaptation timescales as long as recurrent synaptic excitation is sufficiently strong. Faster rhythms occur when recurrent inhibition is slower than excitation and oscillation frequency increases with the strength of inhibition. Adaptation facilitates such network based oscillations for fast synaptic inhibition and leads to decreased frequencies. For oscillatory external input, adaptation currents amplify a narrow band of frequencies and cause phase advances for low frequencies in addition to phase delays at higher frequencies. Our results therefore identify the different key roles of neuronal adaptation dynamics for rhythmogenesis and selective signal propagation in recurrent networks.
Directory of Open Access Journals (Sweden)
Emer Bernal
2017-01-01
Full Text Available In this paper we are presenting a method using fuzzy logic for dynamic parameter adaptation in the imperialist competitive algorithm, which is usually known by its acronym ICA. The ICA algorithm was initially studied in its original form to find out how it works and what parameters have more effect upon its results. Based on this study, several designs of fuzzy systems for dynamic adjustment of the ICA parameters are proposed. The experiments were performed on the basis of solving complex optimization problems, particularly applied to benchmark mathematical functions. A comparison of the original imperialist competitive algorithm and our proposed fuzzy imperialist competitive algorithm was performed. In addition, the fuzzy ICA was compared with another metaheuristic using a statistical test to measure the advantage of the proposed fuzzy approach for dynamic parameter adaptation.
Fuzzy-logic based Q-Learning interference management algorithms in two-tier networks
Xu, Qiang; Xu, Zezhong; Li, Li; Zheng, Yan
2017-10-01
Unloading from macrocell network and enhancing coverage can be realized by deploying femtocells in the indoor scenario. However, the system performance of the two-tier network could be impaired by the co-tier and cross-tier interference. In this paper, a distributed resource allocation scheme is studied when each femtocell base station is self-governed and the resource cannot be assigned centrally through the gateway. A novel Q-Learning interference management scheme is proposed, that is divided into cooperative and independent part. In the cooperative algorithm, the interference information is exchanged between the cell-edge users which are classified by the fuzzy logic in the same cell. Meanwhile, we allocate the orthogonal subchannels to the high-rate cell-edge users to disperse the interference power when the data rate requirement is satisfied. The resource is assigned directly according to the minimum power principle in the independent algorithm. Simulation results are provided to demonstrate the significant performance improvements in terms of the average data rate, interference power and energy efficiency over the cutting-edge resource allocation algorithms.
Maximum Power Point Tracking Using Adaptive Fuzzy Logic control for Photovoltaic System
Directory of Open Access Journals (Sweden)
Anass Ait Laachir
2015-01-01
Full Text Available This work presents an intelligent approach to the improvement and optimization of control performance of a photovoltaic system with maximum power point tracking based on fuzzy logic control. This control was compared with the conventional control based on Perturb &Observe algorithm. The results obtained in Matlab/Simulink under different conditions show a marked improvement in the performance of fuzzy control MPPT of the PV system.
Towards Memristive Dynamic Adaptive Neural Network Arrays
2016-03-17
larger scale spatio-temporal data such as: video and audio classification, autonomous control of robotic systems, and real-time anomaly detection in...Technique for a CMOS/ Nano Memristive Trainable Threshold Gate Array,” IEEE Trans. Circuits Syst., vol. 59, no. 5, pp. 1051–1060, 2012. 6. Z. Abid, A...Memristor- CMOS Hybrid Integrated Circuits for Reconfigurable Logic,” Nano Letters, pp. 3640 –3645, September 2009. 10. J. Rofeh, A. Sodhi, M. Payvand, M
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.
Liquori, Luigi; Cosnard, Michel
2007-01-01
International audience; We propose and discuss foundations for programmable overlay networks and overlay computing systems. Such overlays are built over a large number of distributed computational individuals, virtually organized in colonies, and ruled by a leader (broker) who is elected or imposed by system administrators. Every individual asks the broker to log in the colony by declaring the resources that can be offered (with variable guarantees). Once logged in, an individual can ask the ...
Fuzzy logic of Aristotelian forms
Energy Technology Data Exchange (ETDEWEB)
Perlovsky, L.I. [Nichols Research Corp., Lexington, MA (United States)
1996-12-31
Model-based approaches to pattern recognition and machine vision have been proposed to overcome the exorbitant training requirements of earlier computational paradigms. However, uncertainties in data were found to lead to a combinatorial explosion of the computational complexity. This issue is related here to the roles of a priori knowledge vs. adaptive learning. What is the a-priori knowledge representation that supports learning? I introduce Modeling Field Theory (MFT), a model-based neural network whose adaptive learning is based on a priori models. These models combine deterministic, fuzzy, and statistical aspects to account for a priori knowledge, its fuzzy nature, and data uncertainties. In the process of learning, a priori fuzzy concepts converge to crisp or probabilistic concepts. The MFT is a convergent dynamical system of only linear computational complexity. Fuzzy logic turns out to be essential for reducing the combinatorial complexity to linear one. I will discuss the relationship of the new computational paradigm to two theories due to Aristotle: theory of Forms and logic. While theory of Forms argued that the mind cannot be based on ready-made a priori concepts, Aristotelian logic operated with just such concepts. I discuss an interpretation of MFT suggesting that its fuzzy logic, combining a-priority and adaptivity, implements Aristotelian theory of Forms (theory of mind). Thus, 2300 years after Aristotle, a logic is developed suitable for his theory of mind.
Finding recurrence networks' threshold adaptively for a specific time series
Eroglu, D.; Marwan, N.; Prasad, S.; Kurths, J.
2014-11-01
Recurrence-plot-based recurrence networks are an approach used to analyze time series using a complex networks theory. In both approaches - recurrence plots and recurrence networks -, a threshold to identify recurrent states is required. The selection of the threshold is important in order to avoid bias of the recurrence network results. In this paper, we propose a novel method to choose a recurrence threshold adaptively. We show a comparison between the constant threshold and adaptive threshold cases to study period-chaos and even period-period transitions in the dynamics of a prototypical model system. This novel method is then used to identify climate transitions from a lake sediment record.
Collaborative Trust Networks in Engineering Design Adaptation
DEFF Research Database (Denmark)
Atkinson, Simon Reay; Maier, Anja; Caldwell, Nicholas
2011-01-01
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);...
Directory of Open Access Journals (Sweden)
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.
Collaborative Trust Networks in Engineering Design Adaptation
DEFF Research Database (Denmark)
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...
Global network reorganization during dynamic adaptations of Bacillus subtilis metabolism
DEFF Research Database (Denmark)
Buescher, Joerg Martin; Liebermeister, Wolfram; Jules, Matthieu
2012-01-01
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...
Robust adaptive neural network control with supervisory controller
Institute of Scientific and Technical Information of China (English)
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.
Geographic Routing Using Logical Levels in Wireless Sensor Networks for Sensor Mobility
Directory of Open Access Journals (Sweden)
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.
Wang, Yin-He; Luo, Liang; Fan, Yong-Qing; Zhang, Yun; Liu, Xiao-Ping; Zhang, Si-Ying
2014-03-01
Many practical engineering applications require various types of fuzzy logic systems (FLSs) to design adaptive controllers for nonlinear systems with uncertainties. In this article, we will consider a fundamental theoretical question: is it possible to find a unified adaptive control design method suited to various types of FLSs? In order to solve this problem, we will introduce scalers and saturators at the input and output terminals of FLSs to form the extended FLSs (EFLS). The scalers and saturators have adjustable parameters. By designing the updated laws of these parameters and the estimate values of the fuzzy approximate accuracies, stable adaptive fuzzy controllers can be realised for a class of nonlinear systems with unknown homogeneous drift functions and gains. The proposed design method is only dependent on the outputs of EFLS and the above updated laws, thus increasing its adaptability. The fuzzy control scheme introduced in this article is suitable for all fuzzy systems with or without fuzzy rules. Simulations will also be used to show the validity of the method proposed in this article.
ADAPTIVE GOSSIP BASED PROTOCOL FOR ENERGY EFFICIENT MOBILE ADHOC NETWORK
S. Rajeswari; Venkataramani, Y.
2012-01-01
In Gossip Sleep Protocol, network performance is enhanced based on energy resource. But energy conservation is achieved with the reduced throughput. In this paper, it has been proposed a new Protocol for Mobile Ad hoc Network to achieve reliability with energy conservation. Based on the probability (p) values, the value of sleep nodes is fixed initially. The probability value can be adaptively adjusted by Remote Activated Switch during the transmission process. The adaptiveness of gossiping p...
Adaptive Control for Robotic Manipulators Base on RBF Neural Network
Directory of Open Access Journals (Sweden)
MA Jing
2013-09-01
Full Text Available An adaptive neural network controller is brought forward by the paper to solve trajectory tracking problems of robotic manipulators with uncertainties. The first scheme consists of a PD feedback and a dynamic compensator which is composed by neural network controller and variable structure controller. Neutral network controller is designed to adaptive learn and compensate the unknown uncertainties, variable structure controller is designed to eliminate approach errors of neutral network. The adaptive weight learning algorithm of neural network is designed to ensure online real-time adjustment, offline learning phase is not need; Global asymptotic stability (GAS of system base on Lyapunov theory is analysised to ensure the convergence of the algorithm. The simulation result s show that the kind of the control scheme is effective and has good robustness.
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…
Adaptive control of mobile robots using a neural network.
de Sousa Júnior, C; Hermerly, E M
2001-06-01
A Neural Network - based control approach for mobile robot is proposed. The weight adaptation is made on-line, without previous learning. Several possible situations in robot navigation are considered, including uncertainties in the model and presence of disturbance. Weight adaptation laws are presented as well as simulation results.
Adapting Bayes Network Structures to Non-stationary Domains
DEFF Research Database (Denmark)
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...
Adaptive optimization and control using neural networks
Energy Technology Data Exchange (ETDEWEB)
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.
Terfve, Camille; Cokelaer, Thomas; Henriques, David; MacNamara, Aidan; Goncalves, Emanuel; Morris, Melody K; van Iersel, Martijn; Lauffenburger, Douglas A; Saez-Rodriguez, Julio
2012-10-18
Cells process signals using complex and dynamic networks. Studying how this is performed in a context and cell type specific way is essential to understand signaling both in physiological and diseased situations. Context-specific medium/high throughput proteomic data measured upon perturbation is now relatively easy to obtain but formalisms that can take advantage of these features to build models of signaling are still comparatively scarce. Here we present CellNOptR, an open-source R software package for building predictive logic models of signaling networks by training networks derived from prior knowledge to signaling (typically phosphoproteomic) data. CellNOptR features different logic formalisms, from Boolean models to differential equations, in a common framework. These different logic model representations accommodate state and time values with increasing levels of detail. We provide in addition an interface via Cytoscape (CytoCopteR) to facilitate use and integration with Cytoscape network-based capabilities. Models generated with this pipeline have two key features. First, they are constrained by prior knowledge about the network but trained to data. They are therefore context and cell line specific, which results in enhanced predictive and mechanistic insights. Second, they can be built using different logic formalisms depending on the richness of the available data. Models built with CellNOptR are useful tools to understand how signals are processed by cells and how this is altered in disease. They can be used to predict the effect of perturbations (individual or in combinations), and potentially to engineer therapies that have differential effects/side effects depending on the cell type or context.
Wavelet Neural Networks for Adaptive Equalization
Institute of Scientific and Technical Information of China (English)
JIANGMinghu; DENGBeixing; GIELENGeorges; ZHANGBo
2003-01-01
A structure based on the Wavelet neural networks (WNNs) is proposed for nonlinear channel equalization in a digital communication system. The construction algorithm of the Minimum error probability (MEP) is presented and applied as a performance criterion to update the parameter matrix of wavelet networks. Our experimental results show that performance of the proposed wavelet networks based on equalizer can significantly improve the neural modeling accuracy, perform quite well in compensating the nonlinear distortion introduced by the channel, and outperform the conventional neural networks in signal to noise ratio and channel non-llnearity.
Rate adaptation in ad hoc networks based on pricing
CSIR Research Space (South Africa)
Awuor, F
2011-09-01
Full Text Available to transmit at high power leading to abnormal interference in the network hence degrades network performance (i.e. low data rates, loss of connectivity among others). In this paper, the authors propose rate adaptation based on pricing (RAP) algorithm...
Adaptive synchronization of neural networks with different attractors
Institute of Scientific and Technical Information of China (English)
Zhang Huaguang; Guan Huanxin; Wang Zhanshan
2007-01-01
This paper aims to present an adaptive control scheme for the synchronization of two classes of uncertain neural networks with different attractors. A new sufficient condition for the global synchronization of two kinds of neural networks with different attractors is derived. The proposed control method is efficient and easy to be implemented. Numerical simulation is used to show the effectiveness of the obtained result.
An Adaptive Handover Prediction Scheme for Seamless Mobility Based Wireless Networks
Directory of Open Access Journals (Sweden)
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.
An adaptive holographic implementation of a neural network
Downie, John D.; Hine, Butler P., III; Reid, Max B.
1990-01-01
A holographic implementation for neural networks is proposed and demonstrated as an alternative to the optical matrix-vector multiplier architecture. In comparison, the holographic architecture makes more efficient use of the system space-bandwidth product for certain types of neural networks. The principal network component is a thermoplastic hologram, used to provide both interconnection weights and beam direction. Given the updatable nature of this type of hologram, adaptivity or network learning is possible in the optical system. Two networks with fixed weights are experimentally implemented and verified, and for one of these examples the advantage of the holographic implementation with respect to the matrix-vector processor is demonstrated.
Enhancing the selection of backoff interval using fuzzy logic over wireless Ad Hoc networks.
Ranganathan, Radha; Kannan, Kathiravan
2015-01-01
IEEE 802.11 is the de facto standard for medium access over wireless ad hoc network. The collision avoidance mechanism (i.e., random binary exponential backoff-BEB) of IEEE 802.11 DCF (distributed coordination function) is inefficient and unfair especially under heavy load. In the literature, many algorithms have been proposed to tune the contention window (CW) size. However, these algorithms make every node select its backoff interval between [0, CW] in a random and uniform manner. This randomness is incorporated to avoid collisions among the nodes. But this random backoff interval can change the optimal order and frequency of channel access among competing nodes which results in unfairness and increased delay. In this paper, we propose an algorithm that schedules the medium access in a fair and effective manner. This algorithm enhances IEEE 802.11 DCF with additional level of contention resolution that prioritizes the contending nodes according to its queue length and waiting time. Each node computes its unique backoff interval using fuzzy logic based on the input parameters collected from contending nodes through overhearing. We evaluate our algorithm against IEEE 802.11, GDCF (gentle distributed coordination function) protocols using ns-2.35 simulator and show that our algorithm achieves good performance.
MANCaLog: A Logic for Multi-Attribute Network Cascades (Technical Report)
Shakarian, Paulo; Schroeder, Robert
2013-01-01
The modeling of cascade processes in multi-agent systems in the form of complex networks has in recent years become an important topic of study due to its many applications: the adoption of commercial products, spread of disease, the diffusion of an idea, etc. In this paper, we begin by identifying a desiderata of seven properties that a framework for modeling such processes should satisfy: the ability to represent attributes of both nodes and edges, an explicit representation of time, the ability to represent non-Markovian temporal relationships, representation of uncertain information, the ability to represent competing cascades, allowance of non-monotonic diffusion, and computational tractability. We then present the MANCaLog language, a formalism based on logic programming that satisfies all these desiderata, and focus on algorithms for finding minimal models (from which the outcome of cascades can be obtained) as well as how this formalism can be applied in real world scenarios. We are not aware of any o...
Enhancing the Selection of Backoff Interval Using Fuzzy Logic over Wireless Ad Hoc Networks
Ranganathan, Radha; Kannan, Kathiravan
2015-01-01
IEEE 802.11 is the de facto standard for medium access over wireless ad hoc network. The collision avoidance mechanism (i.e., random binary exponential backoff—BEB) of IEEE 802.11 DCF (distributed coordination function) is inefficient and unfair especially under heavy load. In the literature, many algorithms have been proposed to tune the contention window (CW) size. However, these algorithms make every node select its backoff interval between [0, CW] in a random and uniform manner. This randomness is incorporated to avoid collisions among the nodes. But this random backoff interval can change the optimal order and frequency of channel access among competing nodes which results in unfairness and increased delay. In this paper, we propose an algorithm that schedules the medium access in a fair and effective manner. This algorithm enhances IEEE 802.11 DCF with additional level of contention resolution that prioritizes the contending nodes according to its queue length and waiting time. Each node computes its unique backoff interval using fuzzy logic based on the input parameters collected from contending nodes through overhearing. We evaluate our algorithm against IEEE 802.11, GDCF (gentle distributed coordination function) protocols using ns-2.35 simulator and show that our algorithm achieves good performance. PMID:25879066
Adaptive swarm-based routing in communication networks
Institute of Scientific and Technical Information of China (English)
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
Institute of Scientific and Technical Information of China (English)
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 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
Institute of Scientific and Technical Information of China (English)
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.
Network inference via adaptive optimal design
Stigter, J.D.; Molenaar, J.
2012-01-01
Background Current research in network reverse engineering for genetic or metabolic networks very often does not include a proper experimental and/or input design. In this paper we address this issue in more detail and suggest a method that includes an iterative design of experiments based, on the m
Network Adaptability from WMD Disruption and Cascading Failures
2016-04-01
on the electric grid (or technical problems in a power plant). In this case, some of the network resources will consume electricity from generators...backbone networks due to several reasons among which the primary being the popularity of cloud services, smart devices, video applications, etc. On top of... Network Adaptability from WMD Disruption and Cascading Failures Distribution Statement A. Approved for public release; distribution is unlimited
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.
Directory of Open Access Journals (Sweden)
Kuldeep Singh
2016-05-01
Full Text Available Adaptive modulation is one of the recent technologies used to improve future communication systems. Many adaptive modulation techniques have been developed for the improving the performance of Orthogonal Frequency Division Multiplexing (OFDM system in terms of high data rates and error free delivery of data. But uncertain nature of wireless channel reduces the performance of OFDM system with fixed modulation techniques. In this paper, modified adaptive modulation technique has been proposed which adapts to the nature of communication channel based upon present modulation order, code rate, BER and SNR characterizing uncertain nature of communication channel by using a Fuzzy Inference System which further enhances the performance of OFDM systems in terms of high transmission data rate and error free delivery of data.
TCP-Adaptive in High Speed Long Distance Networks
Directory of Open Access Journals (Sweden)
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
Adaptive impulsive cluster synchronization in community network with nonidentical nodes
Gong, Xiaoli; Gan, Luyining; Wu, Zhaoyan
2016-07-01
In this paper, cluster synchronization in community network with nonidentical nodes is investigated. Through introducing proper adaptive strategy into impulsive control scheme, adaptive impulsive controllers are designed for achieving the cluster synchronization. In this adaptive impulsive control scheme, for any given networks, the impulsive gains can adjust themselves to proper values according to the proposed adaptive strategy when the impulsive intervals are fixed. The impulsive instants can be estimated by solving a sequence of maximum value problems when the impulsive gains are fixed. Both community networks without and with coupling delay are considered. Based on the Lyapunov function method and mathematical analysis technique, two synchronization criteria are derived. Several numerical examples are performed to verify the effectiveness of the derived theoretical results.
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...
Tapoglou, Evdokia; Karatzas, George P.; Trichakis, Ioannis C.; Varouchakis, Emmanouil A.
2014-05-01
The purpose of this study is to examine the use of Artificial Neural Networks (ANN) combined with kriging interpolation method, in order to simulate the hydraulic head both spatially and temporally. Initially, ANNs are used for the temporal simulation of the hydraulic head change. The results of the most appropriate ANNs, determined through a fuzzy logic system, are used as an input for the kriging algorithm where the spatial simulation is conducted. The proposed algorithm is tested in an area located across Isar River in Bayern, Germany and covers an area of approximately 7800 km2. The available data extend to a time period from 1/11/2008 to 31/10/2012 (1460 days) and include the hydraulic head at 64 wells, temperature and rainfall at 7 weather stations and surface water elevation at 5 monitoring stations. One feedforward ANN was trained for each of the 64 wells, where hydraulic head data are available, using a backpropagation algorithm. The most appropriate input parameters for each wells' ANN are determined considering their proximity to the measuring station, as well as their statistical characteristics. For the rainfall, the data for two consecutive time lags for best correlated weather station, as well as a third and fourth input from the second best correlated weather station, are used as an input. The surface water monitoring stations with the three best correlations for each well are also used in every case. Finally, the temperature for the best correlated weather station is used. Two different architectures are considered and the one with the best results is used henceforward. The output of the ANNs corresponds to the hydraulic head change per time step. These predictions are used in the kriging interpolation algorithm. However, not all 64 simulated values should be used. The appropriate neighborhood for each prediction point is constructed based not only on the distance between known and prediction points, but also on the training and testing error of
Directory of Open Access Journals (Sweden)
Jie Jiang
2015-01-01
Full Text Available In recent years, the research of individual wearable physiological monitoring wireless sensor network is in the primary stage. The monitor of physiology and geographical position used in wearable wireless sensor network requires performances such as real time, reliability, and energy balance. According to these requirements, this paper introduces a design of individual wearable wireless sensor network monitoring system; what is more important, based on this background, this paper improves the classical Collection Tree Protocol and puts forward the improved routing protocol F-CTP based on the fuzzy logic routing algorithm. Simulation results illustrate that, with the F-CTP protocol, the sensor node can transmit data to the sink node in real time with higher reliability and the energy of the nodes consumes balance. The sensor node can make full use of network resources reasonably and prolong the network life.
Time-adaptive and history-adaptive multicriterion routing in stochastic, time-dependent networks
DEFF Research Database (Denmark)
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 metho...
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.
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.
Concurrent enhancement of percolation and synchronization in adaptive networks
Eom, Young-Ho; Caldarelli, Guido
2015-01-01
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.
Adaptive Capacity Management in Bluetooth Networks
DEFF Research Database (Denmark)
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......, 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...... of Bluetooth devices is increasing, a larger-scale ad hoc network, scatternet, is formed, as well as the booming of Internet has demanded for large bandwidth and low delay mobile access. This dissertation is to address the capacity management issues in Bluetooth networks. The main goals of the network capacity...
Adaptive computational resource allocation for sensor networks
Institute of Scientific and Technical Information of China (English)
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.
Stiller, S.J.; Meijerink, S.V.
2016-01-01
In the climate adaptation literature, leadership tends to be an understudied factor, although it may be crucial for regional adaptation governance. This article shows how leadership can be usefully conceptualized and operationalized within regional governance networks dealing with climate adaptation
Stability and Adaptation of Neural Networks
1990-11-02
Feature discovery by competitive works.-~ IEEE Trans- Si’st.. Man. Cybern.. vol. SMC-13. pp. 815- learning.- Cogniive Science , vol. 9. pp. 75-112. 1985...include Electronic Engineering Times, the Los Angeles Times, Popular Science , the Economist, and Breakthroughs. As program chairman of the first...feedback neural networks.*’ Science . vol. 235. pp. 1226-1227. Mar. 6. 1987. networks.- submitted for publication. 141 G. A. Carpenter and S. Grossberg
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.
Directory of Open Access Journals (Sweden)
Carlo Penco
2013-03-01
Full Text Available A discussion of The Vienna Circle and the Nordic Countries. Networks and Transformations of Logical Empiricism, edited by Juha Manninen and Friedrich Stadtler, Vienna Circle Institute Yearbook vol.14, Springer, 2010.
Epidemic processes over adaptive state-dependent networks
Ogura, Masaki; Preciado, Victor M.
2016-06-01
In this paper we study the dynamics of epidemic processes taking place in adaptive networks of arbitrary topology. We focus our study on the adaptive susceptible-infected-susceptible (ASIS) model, where healthy individuals are allowed to temporarily cut edges connecting them to infected nodes in order to prevent the spread of the infection. In this paper we derive a closed-form expression for a lower bound on the epidemic threshold of the ASIS model in arbitrary networks with heterogeneous node and edge dynamics. For networks with homogeneous node and edge dynamics, we show that the resulting lower bound is proportional to the epidemic threshold of the standard SIS model over static networks, with a proportionality constant that depends on the adaptation rates. Furthermore, based on our results, we propose an efficient algorithm to optimally tune the adaptation rates in order to eradicate epidemic outbreaks in arbitrary networks. We confirm the tightness of the proposed lower bounds with several numerical simulations and compare our optimal adaptation rates with popular centrality measures.
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.
Dynamics of epidemic diseases on a growing adaptive network
Demirel, Güven; Barter, Edmund; Gross, Thilo
2017-02-01
The study of epidemics on static networks has revealed important effects on disease prevalence of network topological features such as the variance of the degree distribution, i.e. the distribution of the number of neighbors of nodes, and the maximum degree. Here, we analyze an adaptive network where the degree distribution is not independent of epidemics but is shaped through disease-induced dynamics and mortality in a complex interplay. We study the dynamics of a network that grows according to a preferential attachment rule, while nodes are simultaneously removed from the network due to disease-induced mortality. We investigate the prevalence of the disease using individual-based simulations and a heterogeneous node approximation. Our results suggest that in this system in the thermodynamic limit no epidemic thresholds exist, while the interplay between network growth and epidemic spreading leads to exponential networks for any finite rate of infectiousness when the disease persists.
Adaptive synchronization of asymmetric coupled networks with multiple coupling delays
Sun, Weiwei; Hao, Fei; Chen, Xia
2012-05-01
The synchronization problem of asymmetric networks with multiple coupled delays is investigated in this paper. By using Lyapunov stability theory and Lasalle's invariance principle, several synchronization criteria are deduced for both asymmetric networks with and without norm uncertainties. Furthermore, the synchronization problem of a special complex network with each node being a Lurie system is studied. The main results show that the states of all nodes of networks globally asymptotically synchronize to a desired synchronization state by designing suitable adaptive controllers, and these controllers have strong robustness against the uncertain coupling matrixes. Finally, several illustrative examples with numerical simulations are given to show the feasibility and efficiency of theoretical results.
Adaptive mechanism-based congestion control for networked systems
Liu, Zhi; Zhang, Yun; Chen, C. L. Philip
2013-03-01
In order to assure the communication quality in network systems with heavy traffic and limited bandwidth, a new ATRED (adaptive thresholds random early detection) congestion control algorithm is proposed for the congestion avoidance and resource management of network systems. Different to the traditional AQM (active queue management) algorithms, the control parameters of ATRED are not configured statically, but dynamically adjusted by the adaptive mechanism. By integrating with the adaptive strategy, ATRED alleviates the tuning difficulty of RED (random early detection) and shows a better control on the queue management, and achieve a more robust performance than RED under varying network conditions. Furthermore, a dynamic transmission control protocol-AQM control system using ATRED controller is introduced for the systematic analysis. It is proved that the stability of the network system can be guaranteed when the adaptive mechanism is finely designed. Simulation studies show the proposed ATRED algorithm achieves a good performance in varying network environments, which is superior to the RED and Gentle-RED algorithm, and providing more reliable service under varying network conditions.
Adaptive Neural Network Based Control of Noncanonical Nonlinear Systems.
Zhang, Yanjun; Tao, Gang; Chen, Mou
2016-09-01
This paper presents a new study on the adaptive neural network-based control of a class of noncanonical nonlinear systems with large parametric uncertainties. Unlike commonly studied canonical form nonlinear systems whose neural network approximation system models have explicit relative degree structures, which can directly be used to derive parameterized controllers for adaptation, noncanonical form nonlinear systems usually do not have explicit relative degrees, and thus their approximation system models are also in noncanonical forms. It is well-known that the adaptive control of noncanonical form nonlinear systems involves the parameterization of system dynamics. As demonstrated in this paper, it is also the case for noncanonical neural network approximation system models. Effective control of such systems is an open research problem, especially in the presence of uncertain parameters. This paper shows that it is necessary to reparameterize such neural network system models for adaptive control design, and that such reparameterization can be realized using a relative degree formulation, a concept yet to be studied for general neural network system models. This paper then derives the parameterized controllers that guarantee closed-loop stability and asymptotic output tracking for noncanonical form neural network system models. An illustrative example is presented with the simulation results to demonstrate the control design procedure, and to verify the effectiveness of such a new design method.
Explosive Synchronization and Emergence of Assortativity on Adaptive Networks
Institute of Scientific and Technical Information of China (English)
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.
Hayes, Holly; Parchman, Michael L.; Howard, Ray
2012-01-01
Evaluating effective growth and development of a Practice-Based Research Network (PBRN) can be challenging. The purpose of this article is to describe the development of a logic model and how the framework has been used for planning and evaluation in a primary care PBRN. An evaluation team was formed consisting of the PBRN directors, staff and its board members. After the mission and the target audience were determined, facilitated meetings and discussions were held with stakeholders to identify the assumptions, inputs, activities, outputs, outcomes and outcome indicators. The long-term outcomes outlined in the final logic model are two-fold: 1.) Improved health outcomes of patients served by PBRN community clinicians; and 2.) Community clinicians are recognized leaders of quality research projects. The Logic Model proved useful in identifying stakeholder interests and dissemination activities as an area that required more attention in the PBRN. The logic model approach is a useful planning tool and project management resource that increases the probability that the PBRN mission will be successfully implemented. PMID:21900441
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.
Smart handover based on fuzzy logic trend in IEEE802.11 mobile IPv6 networks
Lim, Joanne Mun-Yee
2012-01-01
A properly designed handoff algorithm is essential in reducing the connection quality deterioration when a mobile node moves across the cell boundaries. Therefore, to improve communication quality, we identified three goals in our paper. The first goal is to minimize unnecessary handovers and increase communication quality by reducing misrepresentations of RSSI readings due to multipath and shadow effect with the use of additional parameters. The second goal is to control the handover decisions depending on the users' mobility by utilizing location factors as one of the input parameters in a fuzzy logic handover algorithm. The third goal is to minimize false handover alarms caused by sudden fluctuations of parameters by monitoring the trend of fuzzy logic outputs for a period of time before making handover decision. In this paper, we use RSSI, speed and distance as the input decision criteria of a handover trigger algorithm by means of fuzzy logic. The fuzzy logic output trend is monitored for a period of tim...
Fuzzy logic controller based three-phase shunt active power filter under unbalanced network
Energy Technology Data Exchange (ETDEWEB)
Belaidi, R.; Chikouche, A.; Fathi, M. [Unite de Developpement des Equipements Solaires (Algeria); Haddouche, A.; Guendouz, A. [Universite Badji Mokhtar (Algeria)], E-mail: rachidi3434@yahoo.fr
2011-07-01
In recent years, public awareness of power quality issues in distribution systems has arisen. The photovoltaic interactive shunt active power filter is a system which provides harmonic current damping and reactive power compensation and a fuzzy logic controller was created to adjust the energy storage of the DC voltage; the aim of this paper is to study the performance of the fuzzy logic controller. Simulations were performed using Matlab and Simulink and were analyzed to determine the effectiveness of the system; the instantaneous reactive power theory was utilized. Results showed that the use of the fuzzy logic controller achieves a reduction of the total harmonic distortion of the current from 26.54% to 2.27%. This study demonstrated that the fuzzy logic controller combined with the photovoltaic interactive shunt active power filter helps improve power quality by filtering harmonic currents and compensating reactive power generated by non-linear loads.
Adaptive traffic control systems for urban networks
Directory of Open Access Journals (Sweden)
Radivojević Danilo
2017-01-01
Full Text Available Adaptive traffic control systems represent complex, but powerful tool for improvement of traffic flow conditions in locations or zones where applied. Many traffic agencies, especially those that have a large number of signalized intersections with high variability of the traffic demand, choose to apply some of the adaptive traffic control systems. However, those systems are manufactured and offered by multiple vendors (companies that are competing for the market share. Due to that fact, besides the information available from the vendors themselves, or the information from different studies conducted on different continents, very limited amount of information is available about the details how those systems are operating. The reason for that is the protecting of the intellectual property from plagiarism. The primary goal of this paper is to make a brief analysis of the functionalities, characteristics, abilities and results of the most recognized, but also less known adaptive traffic control systems to the professional public and other persons with interest in this subject.
A candidate multimodal functional genetic network for thermal adaptation
Directory of Open Access Journals (Sweden)
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
QoS-Aware Error Recovery in Wireless Body Sensor Networks Using Adaptive Network Coding
Directory of Open Access Journals (Sweden)
Mohammad Abdur Razzaque
2014-12-01
Full Text Available Wireless body sensor networks (WBSNs for healthcare and medical applications are real-time and life-critical infrastructures, which require a strict guarantee of quality of service (QoS, in terms of latency, error rate and reliability. Considering the criticality of healthcare and medical applications, WBSNs need to fulfill users/applications and the corresponding network’s QoS requirements. For instance, for a real-time application to support on-time data delivery, a WBSN needs to guarantee a constrained delay at the network level. A network coding-based error recovery mechanism is an emerging mechanism that can be used in these systems to support QoS at very low energy, memory and hardware cost. However, in dynamic network environments and user requirements, the original non-adaptive version of network coding fails to support some of the network and user QoS requirements. This work explores the QoS requirements of WBSNs in both perspectives of QoS. Based on these requirements, this paper proposes an adaptive network coding-based, QoS-aware error recovery mechanism for WBSNs. It utilizes network-level and user-/application-level information to make it adaptive in both contexts. Thus, it provides improved QoS support adaptively in terms of reliability, energy efficiency and delay. Simulation results show the potential of the proposed mechanism in terms of adaptability, reliability, real-time data delivery and network lifetime compared to its counterparts.
A multi-objective synthesis methodology for majority/minority logic networks
Sarvaghad-Moghaddam, Moein; Orouji, Ali A.; Houshmand, Monireh
2016-01-01
New technologies such as Quantum-dot Cellular Automata (QCA), Single Electron Tunneling (SET), Tunneling Phase Logic (TPL) and all-spin logic (ASL) devices have been widely advocated in nanotechnology as a response to the physical limits associated with complementary metal oxide semiconductor (CMOS) technology in atomic scales. Some of their peculiar features are their smaller size, higher speed, higher switching frequency, lower power consumption, and higher scale integration. In these techn...
Energy Technology Data Exchange (ETDEWEB)
Carrasquilla, Abel [Universidade Estadual do Norte Fluminense Darcy Ribeiro (UENF), Macae, RJ (Brazil). Lab. de Engenharia e Exploracao de Petroleo]. E-mail: abel@lenep.uenf.br; Silva, Jadir da [Universidade Federal do Rio de Janeiro (UFRJ), RJ (Brazil). Dept. de Geologia; Flexa, Roosevelt [Baker Hughes do Brasil Ltda, Macae, RJ (Brazil)
2008-07-01
In this article, we present a new approach to the automatic identification of lithologies using only well log data, which associates fuzzy logic, neural networks and multivariable statistic methods. Firstly, we chose well log data that represents lithological types, as gamma rays (GR) and density (RHOB), and, immediately, we applied a fuzzy logic algorithm to determine optimal number of clusters. In the following step, a competitive neural network is developed, based on Kohonen's learning rule, where the input layer is composed of two neurons, which represent the same number of used logs. On the other hand, the competitive layer is composed by several neurons, which have the same number of clusters as determined by the fuzzy logic algorithm. Finally, some data bank elements of the lithological types are selected at random to be the discriminate variables, which correspond to the input data of the multigroup discriminate analysis program. In this form, with the application of this methodology, the lithological types were automatically identified throughout the a well of the Namorado Oil Field, Campos Basin, which presented some difficulty in the results, mainly because of geological complexity of this field. (author)
Defining network topologies that can achieve biochemical adaptation.
Ma, Wenzhe; Trusina, Ala; El-Samad, Hana; Lim, Wendell A; Tang, Chao
2009-08-21
Many signaling systems show adaptation-the ability to reset themselves after responding to a stimulus. We computationally searched all possible three-node enzyme network topologies to identify those that could perform adaptation. Only two major core topologies emerge as robust solutions: a negative feedback loop with a buffering node and an incoherent feedforward loop with a proportioner node. Minimal circuits containing these topologies are, within proper regions of parameter space, sufficient to achieve adaptation. More complex circuits that robustly perform adaptation all contain at least one of these topologies at their core. This analysis yields a design table highlighting a finite set of adaptive circuits. Despite the diversity of possible biochemical networks, it may be common to find that only a finite set of core topologies can execute a particular function. These design rules provide a framework for functionally classifying complex natural networks and a manual for engineering networks. For a video summary of this article, see the PaperFlick file with the Supplemental Data available online.
Adaptive network models of collective decision making in swarming systems
Chen, Li; Huepe, Cristián; Gross, Thilo
2016-08-01
We consider a class of adaptive network models where links can only be created or deleted between nodes in different states. These models provide an approximate description of a set of systems where nodes represent agents moving in physical or abstract space, the state of each node represents the agent's heading direction, and links indicate mutual awareness. We show analytically that the adaptive network description captures a phase transition to collective motion in some swarming systems, such as the Vicsek model, and that the properties of this transition are determined by the number of states (discrete heading directions) that can be accessed by each agent.
Adaptive network models of collective decision making in swarming systems
Chen, Li; Gross, Thilo
2015-01-01
We consider a class of adaptive network models where links can only be created or deleted between nodes in different states. These models provide an approximate description of a set of systems where nodes represent agents moving in physical or abstract space, the state of each node represents the agent's heading direction, and links indicate mutual awareness. We show analytically that the adaptive network description captures the phase transition to collective motion in swarming systems and that the properties of this transition are determined by the number of states (discrete heading directions) that can be accessed by each agent.
Radio propagation and adaptive antennas for wireless communication networks
Blaunstein, Nathan
2014-01-01
Explores novel wireless networks beyond 3G, and advanced 4G technologies, such as MIMO, via propagation phenomena and the fundamentals of adapted antenna usage.Explains how adaptive antennas can improve GoS and QoS for any wireless channel, with specific examples and applications in land, aircraft and satellite communications.Introduces new stochastic approach based on several multi-parametric models describing various terrestrial scenarios, which have been experimentally verified in different environmental conditionsNew chapters on fundamentals of wireless networks, cellular and non-cellular,
Direct Adaptive Aircraft Control Using Dynamic Cell Structure Neural Networks
Jorgensen, Charles C.
1997-01-01
A Dynamic Cell Structure (DCS) Neural Network was developed which learns topology representing networks (TRNS) of F-15 aircraft aerodynamic stability and control derivatives. The network is integrated into a direct adaptive tracking controller. The combination produces a robust adaptive architecture capable of handling multiple accident and off- nominal flight scenarios. This paper describes the DCS network and modifications to the parameter estimation procedure. The work represents one step towards an integrated real-time reconfiguration control architecture for rapid prototyping of new aircraft designs. Performance was evaluated using three off-line benchmarks and on-line nonlinear Virtual Reality simulation. Flight control was evaluated under scenarios including differential stabilator lock, soft sensor failure, control and stability derivative variations, and air turbulence.
Study on Adaptive Control with Neural Network Compensation
Institute of Scientific and Technical Information of China (English)
单剑锋; 黄忠华; 崔占忠
2004-01-01
A scheme of adaptive control based on a recurrent neural network with a neural network compensation is presented for a class of nonlinear systems with a nonlinear prefix. The recurrent neural network is used to identify the unknown nonlinear part and compensate the difference between the real output and the identified model output. The identified model of the controlled object consists of a linear model and the neural network. The generalized minimum variance control method is used to identify pareters, which can deal with the problem of adaptive control of systems with unknown nonlinear part, which can not be controlled by traditional methods. Simulation results show that this algorithm has higher precision, faster convergent speed.
Decentralized adaptive synchronization of an uncertain complex delayed dynamical network
Institute of Scientific and Technical Information of China (English)
Weisong ZHONG; Jun ZHAO; Georgi M.DIMIROVSKI
2009-01-01
In this paper,we investigate the locally and globally adaptive synchronization problem for an uncertain complex dynamical network with time-varying coupling delays based on the decentralized control.The coupling terms here are bounded by high-order polynomials with known gains that are ubiquitous in a large class of complex dynamical networks.We generalize the usual technology of searching for an appropriate coordinates transformation to change the network dynamics into a series of decoupled lower-dimensional systems.Several adaptive synchronization criteria are derived by constructing the Lyapunov-Krasovskii functional and Barbalat lemma,and the proposed criteria are simple in form and convenient for the practical engineering design.Numerical simulations illustrated by a nearest-neighbor coupling network verify the effectiveness of the proposed synchronization scheme.
Adaptive Weighted Clustering Algorithm for Mobile Ad-hoc Networks
Directory of Open Access Journals (Sweden)
Adwan Yasin
2016-04-01
Full Text Available In this paper we present a new algorithm for clustering MANET by considering several parameters. This is a new adaptive load balancing technique for clustering out Mobile Ad-hoc Networks (MANET. MANET is special kind of wireless networks where no central management exits and the nodes in the network cooperatively manage itself and maintains connectivity. The algorithm takes into account the local capabilities of each node, the remaining battery power, degree of connectivity and finally the power consumption based on the average distance between nodes and candidate cluster head. The proposed algorithm efficiently decreases the overhead in the network that enhances the overall MANET performance. Reducing the maintenance time of broken routes makes the network more stable, reliable. Saving the power of the nodes also guarantee consistent and reliable network.
Adapting Modeling & SImulation for Network Enabled Operations
2011-03-01
By loosely coupled, we mean the tolerance and encouragement of self- organising informal networks of key individuals who share trust and knowledge...information and authority . Working Towards an Agile Command and Force Structure Firstly, I need to introduce the idea of a number of domains in which the...subordinates. It entails authority , responsibility, and accountability. Authority involves the right and freedom to enforce obedience if necessary
Adaptation of coordination mechanisms to network structures
Directory of Open Access Journals (Sweden)
Herwig Mittermayer
2008-12-01
Full Text Available The coordination efficiency of Supply Chain Management is determined by two opposite poles: benefit from improved planning results and associated coordination cost. The centralization grade, applied coordination mechanisms and IT support have influence on both categories. Therefore three reference types are developed and subsequently detailed in business process models for different network structures. In a simulation study the performance of these organization forms are compared in a process plant network. Coordination benefit is observed if the planning mode is altered by means of a demand planning IT tool. Coordination cost is divided into structural and activity-dependent cost. The activity level rises when reactive planning iterations become necessary as a consequence of inconsistencies among planning levels. Some characteristic influence factors are considered to be a reason for uninfeasible planning. In this study the effect of capacity availability and stochastic machine downtimes is investigated in an uncertain demand situation. Results that if the network runs with high overcapacity, central planning is less likely to increase benefit enough to outweigh associated cost. Otherwise, if capacity constraints are crucial, a central planning mode is recommendable. When also unforeseen machine downtimes are low, the use of sophisticated IT tools is most profitable.
Shaping embodied neural networks for adaptive goal-directed behavior.
Directory of Open Access Journals (Sweden)
Zenas C Chao
2008-03-01
Full Text Available The acts of learning and memory are thought to emerge from the modifications of synaptic connections between neurons, as guided by sensory feedback during behavior. However, much is unknown about how such synaptic processes can sculpt and are sculpted by neuronal population dynamics and an interaction with the environment. Here, we embodied a simulated network, inspired by dissociated cortical neuronal cultures, with an artificial animal (an animat through a sensory-motor loop consisting of structured stimuli, detailed activity metrics incorporating spatial information, and an adaptive training algorithm that takes advantage of spike timing dependent plasticity. By using our design, we demonstrated that the network was capable of learning associations between multiple sensory inputs and motor outputs, and the animat was able to adapt to a new sensory mapping to restore its goal behavior: move toward and stay within a user-defined area. We further showed that successful learning required proper selections of stimuli to encode sensory inputs and a variety of training stimuli with adaptive selection contingent on the animat's behavior. We also found that an individual network had the flexibility to achieve different multi-task goals, and the same goal behavior could be exhibited with different sets of network synaptic strengths. While lacking the characteristic layered structure of in vivo cortical tissue, the biologically inspired simulated networks could tune their activity in behaviorally relevant manners, demonstrating that leaky integrate-and-fire neural networks have an innate ability to process information. This closed-loop hybrid system is a useful tool to study the network properties intermediating synaptic plasticity and behavioral adaptation. The training algorithm provides a stepping stone towards designing future control systems, whether with artificial neural networks or biological animats themselves.
Adaptive synthesis of a wavelet transform using fast neural network
J. Stolarek
2011-01-01
This paper introduces a new method for an adaptive synthesis of a wavelet transform using a fast neural network with a topology based on the lattice structure. The lattice structure and the orthogonal lattice structure are presented and their properties are discussed. A novel method for unsupervised training of the neural network is introduced. The proposed approach is tested by synthesizing new wavelets with an expected energy distribution between low- and high-pass filters. Energy compactio...
Adaptive projective synchronization with different scaling factors in networks
Institute of Scientific and Technical Information of China (English)
Guo Liu-Xiao; Xu Zhen-Yuan; Hu Man-Feng
2008-01-01
We study projective synchronization with different scaling factors (PSDF) in N coupled chaotic systems networks.By using the adaptive linear control,some sufficient criteria for the PSDF in symmetrical and asymmetrical coupled networks are separately given based on the Lyapunov function method and the left eigenvalue theory.Numerical simulations for a generalized chaotic unified system are illustrated to verify the theoretical results.
Adaptive Quality of Transmission Control in Elastic Optical Network
Cai, Xinran
Optical fiber communication is becoming increasingly important due to the burgeoning demand in the internet capacity. However, traditional wavelength division multiplexing (WDM) technique fails to address such demand because of its inefficient spectral utilization. As a result, elastic optical networking (EON) has been under extensive investigation recently. Such network allows sub-wavelength and super-wavelength channel accommodation, and mitigates the stranded bandwidth problem in the WDM network. In addition, elastic optical network is also able to dynamically allocate the spectral resources of the network based on channel conditions and impairments, and adaptively control the quality of transmission of a channel. This application requires two aspects to be investigated: an efficient optical performance monitoring scheme and networking control and management algorithms to reconfigure the network in a dynamic fashion. This thesis focuses on the two aspects discussed above about adaptive QoT control. We demonstrated a supervisory channel method for optical signal to noise ratio (OSNR) and chromatic dispersion (CD) monitoring. In addition, our proof-of-principle testbed experiments show successful impairment aware reconfiguration of the network with modulation format switching (MFS) only and MFS combined with lightpath rerouting (LR) for hundred-GHz QPSK superchannels undergoing time-varying OSNR impairment.
Yeşilkanat, Cafer Mert; Kobya, Yaşar; Taşkın, Halim; Çevik, Uğur
2017-09-01
The aim of this study was to determine spatial risk dispersion of ambient gamma dose rate (AGDR) by using both artificial neural network (ANN) and fuzzy logic (FL) methods, compare the performances of methods, make dose estimations for intermediate stations with no previous measurements and create dose rate risk maps of the study area. In order to determine the dose distribution by using artificial neural networks, two main networks and five different network structures were used; feed forward ANN; Multi-layer perceptron (MLP), Radial basis functional neural network (RBFNN), Quantile regression neural network (QRNN) and recurrent ANN; Jordan networks (JN), Elman networks (EN). In the evaluation of estimation performance obtained for the test data, all models appear to give similar results. According to the cross-validation results obtained for explaining AGDR distribution, Pearson's r coefficients were calculated as 0.94, 0.91, 0.89, 0.91, 0.91 and 0.92 and RMSE values were calculated as 34.78, 43.28, 63.92, 44.86, 46.77 and 37.92 for MLP, RBFNN, QRNN, JN, EN and FL, respectively. In addition, spatial risk maps showing distributions of AGDR of the study area were created by all models and results were compared with geological, topological and soil structure. Copyright © 2017 Elsevier Ltd. All rights reserved.
Bakri, F. A.; Mashor, M. Y.; Sharun, S. M.; Bibi Sarpinah, S. N.; Abu Bakar, Z.
2016-10-01
This study proposes an adaptive fuzzy controller for attitude control system (ACS) of Innovative Satellite (InnoSAT) based on direct action type structure. In order to study new methods used in satellite attitude control, this paper presents three structures of controllers: Fuzzy PI, Fuzzy PD and conventional Fuzzy PID. The objective of this work is to compare the time response and tracking performance among the three different structures of controllers. The parameters of controller were tuned on-line by adjustment mechanism, which was an approach similar to a PID error that could minimize errors between actual and model reference output. This paper also presents a Model References Adaptive Control (MRAC) as a control scheme to control time varying systems where the performance specifications were given in terms of the reference model. All the controllers were tested using InnoSAT system under some operating conditions such as disturbance, varying gain, measurement noise and time delay. In conclusion, among all considered DA-type structures, AFPID controller was observed as the best structure since it outperformed other controllers in most conditions.
Power quality assessment using an adaptive neural network
Energy Technology Data Exchange (ETDEWEB)
Dash, P.K.; Swain, D.P.; Mishra, B.R. [Regional Engineering Coll., Rourkela (India). Dept. of Electrical Engineering; Rahman, S. [Virginia Polytechnic Inst. and State Univ., Blacksburg, VA (United States)
1995-12-31
The paper presents an adaptive neural network approach for the estimation of harmonic components of a power system and the power quality. The neural estimator is based on the use of an adaptive perceptron consisting of a linear adaptive neuron called Adaline. The learning parameters in the proposed algorithm are adjusted to force the error between the actual and desired outputs to satisfy a stable difference error equation. The estimator tracks the Fourier coefficients of the signal data corrupted with noise and decaying dc components very accurately. Adaptive tracking of harmonic components of a power system can easily be done using this algorithm. Several numerical tests have been conducted for the adaptive estimation of harmonic components, total harmonic distortion and power quality of power system signals mixed with noise and decaying dc components.
Social Networking Adapted for Distributed Scientific Collaboration
Karimabadi, Homa
2012-01-01
Share is a social networking site with novel, specially designed feature sets to enable simultaneous remote collaboration and sharing of large data sets among scientists. The site will include not only the standard features found on popular consumer-oriented social networking sites such as Facebook and Myspace, but also a number of powerful tools to extend its functionality to a science collaboration site. A Virtual Observatory is a promising technology for making data accessible from various missions and instruments through a Web browser. Sci-Share augments services provided by Virtual Observatories by enabling distributed collaboration and sharing of downloaded and/or processed data among scientists. This will, in turn, increase science returns from NASA missions. Sci-Share also enables better utilization of NASA s high-performance computing resources by providing an easy and central mechanism to access and share large files on users space or those saved on mass storage. The most common means of remote scientific collaboration today remains the trio of e-mail for electronic communication, FTP for file sharing, and personalized Web sites for dissemination of papers and research results. Each of these tools has well-known limitations. Sci-Share transforms the social networking paradigm into a scientific collaboration environment by offering powerful tools for cooperative discourse and digital content sharing. Sci-Share differentiates itself by serving as an online repository for users digital content with the following unique features: a) Sharing of any file type, any size, from anywhere; b) Creation of projects and groups for controlled sharing; c) Module for sharing files on HPC (High Performance Computing) sites; d) Universal accessibility of staged files as embedded links on other sites (e.g. Facebook) and tools (e.g. e-mail); e) Drag-and-drop transfer of large files, replacing awkward e-mail attachments (and file size limitations); f) Enterprise-level data and
Network on Target: Remotely Configured Adaptive Tactical Networks
2006-06-01
link using FeeeWave Ethernet radio (http://www.freewave.com/FGRHT900.html) connected to the Single Board Computer (SBC) over the network switch. The...Figure 6. Pan-Tilt Units (PTU) The SAOFDM control process is provided by Single Board Computer , which is running Windows XP operating
Favieiro, Gabriela W; Balbinot, Alexandre
2011-01-01
The myoelectric signal is a sign of control of the human body that contains the information of the user's intent to contract a muscle and, therefore, make a move. Studies shows that the Amputees are able to generate standardized myoelectric signals repeatedly before of the intention to perform a certain movement. This paper presents a study that investigates the use of forearm surface electromyography (sEMG) signals for classification of five distinguish movements of the arm using just three pairs of surface electrodes located in strategic places. The classification is done by an adaptive neuro-fuzzy inference system (ANFIS) to process signal features to recognize performed movements. The average accuracy reached for the classification of five motion classes was 86-98% for three subjects.
Adaptive synchronization in an array of asymmetric coupled neural networks
Institute of Scientific and Technical Information of China (English)
Gao Ming; Cui Bao-Tong
2009-01-01
This paper investigates the global synchronization in an array of linearly coupled neural networks with constant and delayed coupling. By a simple combination of adaptive control and linear feedback with the updated laws, some sufficient conditions are derived for global synchronization of the coupled neural networks. The coupling configuration matrix is assumed to be asymmetric, which is more coincident with the realistic network. It is shown that the approaches developed here extend and improve the earlier works. Finally, numerical simulations are presented to demonstrate the effectiveness of the theoretical results.
An Adaptive Neural Network Model for Nonlinear Programming Problems
Institute of Scientific and Technical Information of China (English)
Xiang-sun Zhang; Xin-jian Zhuo; Zhu-jun Jing
2002-01-01
In this paper a canonical neural network with adaptively changing synaptic weights and activation function parameters is presented to solve general nonlinear programming problems. The basic part of the model is a sub-network used to find a solution of quadratic programming problems with simple upper and lower bounds. By sequentially activating the sub-network under the control of an external computer or a special analog or digital processor that adjusts the weights and parameters, one then solves general nonlinear programming problems. Convergence proof and numerical results are given.
Privman, Vladimir; Arugula, Mary A; Halámek, Jan; Pita, Marcos; Katz, Evgeny
2009-04-16
We develop an approach aimed at optimizing the parameters of a network of biochemical logic gates for reduction of the "analog" noise buildup. Experiments for three coupled enzymatic AND gates are reported, illustrating our procedure. Specifically, starch, one of the controlled network inputs, is converted to maltose by beta-amylase. With the use of phosphate (another controlled input), maltose phosphorylase then produces glucose. Finally, nicotinamide adenine dinucleotide (NAD(+)), the third controlled input, is reduced under the action of glucose dehydrogenase to yield the optically detected signal. Network functioning is analyzed by varying selective inputs and fitting standardized few-parameters "response-surface" functions assumed for each gate. This allows a certain probe of the individual gate quality, but primarily yields information on the relative contribution of the gates to noise amplification. The derived information is then used to modify our experimental system to put it in a regime of a less noisy operation.
In-network adaptation of SHVC video in software-defined networks
Awobuluyi, Olatunde; Nightingale, James; Wang, Qi; Alcaraz Calero, Jose Maria; Grecos, Christos
2016-04-01
Software Defined Networks (SDN), when combined with Network Function Virtualization (NFV) represents a paradigm shift in how future networks will behave and be managed. SDN's are expected to provide the underpinning technologies for future innovations such as 5G mobile networks and the Internet of Everything. The SDN architecture offers features that facilitate an abstracted and centralized global network view in which packet forwarding or dropping decisions are based on application flows. Software Defined Networks facilitate a wide range of network management tasks, including the adaptation of real-time video streams as they traverse the network. SHVC, the scalable extension to the recent H.265 standard is a new video encoding standard that supports ultra-high definition video streams with spatial resolutions of up to 7680×4320 and frame rates of 60fps or more. The massive increase in bandwidth required to deliver these U-HD video streams dwarfs the bandwidth requirements of current high definition (HD) video. Such large bandwidth increases pose very significant challenges for network operators. In this paper we go substantially beyond the limited number of existing implementations and proposals for video streaming in SDN's all of which have primarily focused on traffic engineering solutions such as load balancing. By implementing and empirically evaluating an SDN enabled Media Adaptation Network Entity (MANE) we provide a valuable empirical insight into the benefits and limitations of SDN enabled video adaptation for real time video applications. The SDN-MANE is the video adaptation component of our Video Quality Assurance Manager (VQAM) SDN control plane application, which also includes an SDN monitoring component to acquire network metrics and a decision making engine using algorithms to determine the optimum adaptation strategy for any real time video application flow given the current network conditions. Our proposed VQAM application has been implemented and
Fabri, S.; Kadirkamanathan, V.
1996-01-01
The use of composite adaptive laws for control of the affine class of nonlinear systems having unknown dynamics is proposed. These dynamics are approximated by Gaussian radial basis function neural networks whose parameters are updated by a composite law that is driven by both tracking and estimation errors, combining techniques used in direct and indirect adaptive control. This is motivated by the need to improve the speed of convergence of the unknown parameters, hence resulting in a better...
Global Network Reorganization During Dynamic Adaptations of Bacillus subtilis Metabolism
Buescher, Joerg Martin; Liebermeister, Wolfram; Jules, Matthieu; Uhr, Markus; Muntel, Jan; Botella, Eric; Hessling, Bernd; Kleijn, Roelco Jacobus; Le Chat, Ludovic; Lecointe, Francois; Maeder, Ulrike; Nicolas, Pierre; Piersma, Sjouke; Ruegheimer, Frank; Becher, Doerte; Bessieres, Philippe; Bidnenko, Elena; Denham, Emma L.; Dervyn, Etienne; Devine, Kevin M.; Doherty, Geoff; Drulhe, Samuel; Felicori, Liza; Fogg, Mark J.; Goelzer, Anne; Hansen, Annette; Harwood, Colin R.; Hecker, Michael; Hubner, Sebastian; Hultschig, Claus; Jarmer, Hanne; Klipp, Edda; Leduc, Aurelie; Lewis, Peter; Molina, Frank; Noirot, Philippe; Peres, Sabine; Pigeonneau, Nathalie; Pohl, Susanne; Rasmussen, Simon; Rinn, Bernd; Schaffer, Marc; Schnidder, Julian; Schwikowski, Benno; Van Dijl, Jan Maarten; Veiga, Patrick; Walsh, Sean; Wilkinson, Anthony J.; Stelling, Joerg; Aymerich, Stephane; Sauer, Uwe
2012-01-01
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
Global Network Reorganization During Dynamic Adaptations of Bacillus subtilis Metabolism
Buescher, Joerg Martin; Liebermeister, Wolfram; Jules, Matthieu; Uhr, Markus; Muntel, Jan; Botella, Eric; Hessling, Bernd; Kleijn, Roelco Jacobus; Le Chat, Ludovic; Lecointe, Francois; Maeder, Ulrike; Nicolas, Pierre; Piersma, Sjouke; Ruegheimer, Frank; Becher, Doerte; Bessieres, Philippe; Bidnenko, Elena; Denham, Emma L.; Dervyn, Etienne; Devine, Kevin M.; Doherty, Geoff; Drulhe, Samuel; Felicori, Liza; Fogg, Mark J.; Goelzer, Anne; Hansen, Annette; Harwood, Colin R.; Hecker, Michael; Hubner, Sebastian; Hultschig, Claus; Jarmer, Hanne; Klipp, Edda; Leduc, Aurelie; Lewis, Peter; Molina, Frank; Noirot, Philippe; Peres, Sabine; Pigeonneau, Nathalie; Pohl, Susanne; Rasmussen, Simon; Rinn, Bernd; Schaffer, Marc; Schnidder, Julian; Schwikowski, Benno; Van Dijl, Jan Maarten; Veiga, Patrick; Walsh, Sean; Wilkinson, Anthony J.; Stelling, Joerg; Aymerich, Stephane; Sauer, Uwe
2012-01-01
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 mo
Adaptive Multipath Key Reinforcement for Energy Harvesting Wireless Sensor Networks
DEFF Research Database (Denmark)
Di Mauro, Alessio; Dragoni, Nicola
2015-01-01
on the design of the security protocols for such networks, as the nodes have to adapt and optimize their behaviour according to the available energy. Traditional key management schemes do not take energy into account, making them not suitable for EH-WSNs. In this paper we propose a new multipath key...
Dynamic Adaptive Neural Network Arrays: A Neuromorphic Architecture
Energy Technology Data Exchange (ETDEWEB)
Disney, Adam [University of Tennessee (UT); Reynolds, John [University of Tennessee (UT)
2015-01-01
Dynamic Adaptive Neural Network Array (DANNA) is a neuromorphic hardware implementation. It differs from most other neuromorphic projects in that it allows for programmability of structure, and it is trained or designed using evolutionary optimization. This paper describes the DANNA structure, how DANNA is trained using evolutionary optimization, and an application of DANNA to a very simple classification task.
Performance analysis of adaptive scheduling in integrated services UMTS networks
Litjens, Remco; Berg, van den Hans
2002-01-01
For an integrated services UMTS network serving speech and data calls, we propose, evaluate and compare different scheduling schemes, which dynamically adapt the shared data transport channel rates to the varying speech traffic load. within each cell, the assigned data transfer resources are distrib
Adaptive Regularization of Neural Networks Using Conjugate Gradient
DEFF Research Database (Denmark)
Goutte, Cyril; Larsen, Jan
1998-01-01
Andersen et al. (1997) and Larsen et al. (1996, 1997) suggested a regularization scheme which iteratively adapts regularization parameters by minimizing validation error using simple gradient descent. In this contribution we present an improved algorithm based on the conjugate gradient technique........ Numerical experiments with feedforward neural networks successfully demonstrate improved generalization ability and lower computational cost...
Evolving RBF neural networks for adaptive soft-sensor design.
Alexandridis, Alex
2013-12-01
This work presents an adaptive framework for building soft-sensors based on radial basis function (RBF) neural network models. The adaptive fuzzy means algorithm is utilized in order to evolve an RBF network, which approximates the unknown system based on input-output data from it. The methodology gradually builds the RBF network model, based on two separate levels of adaptation: On the first level, the structure of the hidden layer is modified by adding or deleting RBF centers, while on the second level, the synaptic weights are adjusted with the recursive least squares with exponential forgetting algorithm. The proposed approach is tested on two different systems, namely a simulated nonlinear DC Motor and a real industrial reactor. The results show that the produced soft-sensors can be successfully applied to model the two nonlinear systems. A comparison with two different adaptive modeling techniques, namely a dynamic evolving neural-fuzzy inference system (DENFIS) and neural networks trained with online backpropagation, highlights the advantages of the proposed methodology.
Opinion dynamics on a group structured adaptive network
Gargiulo, F
2009-01-01
Many models have been proposed to analyze the evolution of opinion structure due to the interaction of individuals in their social environment. Such models analyze the spreading of ideas both in completely interacting backgrounds and on social networks, where each person has a finite set of interlocutors.Moreover also the investigation on the topological structure of social networks has been object of several analysis, both from the theoretical and the empirical point of view. In this framework a particularly important area of study regards the community structure inside social networks.In this paper we analyze the reciprocal feedback between the opinions of the individuals and the structure of the interpersonal relationships at the level of community structures. For this purpose we define a group based random network and we study how this structure co-evolve with opinion dynamics processes. We observe that the adaptive network structure affects the opinion dynamics process helping the consensus formation. Th...
An Adaptive Replica Allocation Algorithm in Mobile Ad Hoc Networks
Institute of Scientific and Technical Information of China (English)
JingZheng; JinshuSu; KanYang
2004-01-01
In mobile ad hoc networks (MANET), nodes move freely and the distribution of access requests changes dynamically. Replica allocation in such a dynamic environment is a significant challenge. In this paoer, a dynamic adaptive replica allocation algorithm that can adapt to the nodes motion is proposed to minimize the communication cost of object access. When changes occur in the access requests of the object or the network topology, each replica node collects access requests from its neighbors and makes decisions locally to expand replica to neighbors or to relinquish the replica. The algorithm dynamically adapts the replica allocation scheme to a local optimal one. Simulation results show that our algorithms efficiently reduce the communication cost of object access in MANET environment.
Genetic adaptation of the antibacterial human innate immunity network
Directory of Open Access Journals (Sweden)
Lazarus Ross
2011-07-01
Full Text Available Abstract Background Pathogens have represented an important selective force during the adaptation of modern human populations to changing social and other environmental conditions. The evolution of the immune system has therefore been influenced by these pressures. Genomic scans have revealed that immune system is one of the functions enriched with genes under adaptive selection. Results Here, we describe how the innate immune system has responded to these challenges, through the analysis of resequencing data for 132 innate immunity genes in two human populations. Results are interpreted in the context of the functional and interaction networks defined by these genes. Nucleotide diversity is lower in the adaptors and modulators functional classes, and is negatively correlated with the centrality of the proteins within the interaction network. We also produced a list of candidate genes under positive or balancing selection in each population detected by neutrality tests and showed that some functional classes are preferential targets for selection. Conclusions We found evidence that the role of each gene in the network conditions the capacity to evolve or their evolvability: genes at the core of the network are more constrained, while adaptation mostly occurred at particular positions at the network edges. Interestingly, the functional classes containing most of the genes with signatures of balancing selection are involved in autoinflammatory and autoimmune diseases, suggesting a counterbalance between the beneficial and deleterious effects of the immune response.
DEFF Research Database (Denmark)
Nilsson, Jørgen Fischer
A Gentle introduction to logical languages, logical modeling, formal reasoning and computational logic for computer science and software engineering students......A Gentle introduction to logical languages, logical modeling, formal reasoning and computational logic for computer science and software engineering students...
Fuzzy logic-based diversity-controlled self-adaptive differential evolution
Amali, S. Miruna Joe; Baskar, S.
2013-08-01
This article presents a novel method using a fuzzy system (FS) to control the population diversity during the various phases of evolution. A local search is applied at regular intervals on an individual selected at random to aid the population in convergence. This diversity control methodology is applied to vary the crossover rate of self-adaptive differential evolution (SaDE). Three variants of the SaDE algorithm are proposed: (1) diversity-controlled SaDE (DCSaDE); (2) SaDE with local search (SaDE-LS); and (3) diversity-controlled SaDE with local search (DCSaDE-LS). The performance of the proposed algorithms is analysed using a set of unconstrained benchmark functions with respect to average function evaluations, success rate and the mean of the objectives of 30 independent trials. The DCSaDE-LS algorithm had a better success rate for high-dimensional multimodal problems and conserved the number of function evaluations required for most of the problems. It is compared with other popular algorithms and the outcome of the proposed DCSaDE-LS algorithm is validated using non-parametric statistical tests. MATLAB codes for the proposed algorithms may be obtained on request.
Dual adaptive dynamic control of mobile robots using neural networks.
Bugeja, Marvin K; Fabri, Simon G; Camilleri, Liberato
2009-02-01
This paper proposes two novel dual adaptive neural control schemes for the dynamic control of nonholonomic mobile robots. The two schemes are developed in discrete time, and the robot's nonlinear dynamic functions are assumed to be unknown. Gaussian radial basis function and sigmoidal multilayer perceptron neural networks are used for function approximation. In each scheme, the unknown network parameters are estimated stochastically in real time, and no preliminary offline neural network training is used. In contrast to other adaptive techniques hitherto proposed in the literature on mobile robots, the dual control laws presented in this paper do not rely on the heuristic certainty equivalence property but account for the uncertainty in the estimates. This results in a major improvement in tracking performance, despite the plant uncertainty and unmodeled dynamics. Monte Carlo simulation and statistical hypothesis testing are used to illustrate the effectiveness of the two proposed stochastic controllers as applied to the trajectory-tracking problem of a differentially driven wheeled mobile robot.
Adaptive Control of Flexible Redundant Manipulators Using Neural Networks
Institute of Scientific and Technical Information of China (English)
SONG Yimin; LI Jianxin; WANG Shiyu; LIU Jianping
2006-01-01
An investigation on the neural networks based active vibration control of flexible redundant manipulators was conducted.The smart links of the manipulator were synthesized with the flexible links to which were attached piezoceramic actuators and strain gauge sensors.A nonlinear adaptive control strategy named neural networks based indirect adaptive control (NNIAC) was employed to improve the dynamic performance of the manipulator.The mathematical model of the 4-layered dynamic recurrent neural networks (DRNN) was introduced.The neuro-identifier and the neurocontroller featuring the DRNN topology were designed off line so as to enhance the initial robustness of the NNIAC.By adjusting the neuro-identifier and the neuro-controller alternatively,the manipulator was controlled on line for achieving the desired dynamic performance.Finally,a planar 3R redundant manipulator with one smart link was utilized as an illustrative example.The simulation results proved the validity of the control strategy.
Morris, Melody K; Shriver, Zachary; Sasisekharan, Ram; Lauffenburger, Douglas A
2012-01-01
Mathematical models have substantially improved our ability to predict the response of a complex biological system to perturbation, but their use is typically limited by difficulties in specifying model topology and parameter values. Additionally, incorporating entities across different biological scales ranging from molecular to organismal in the same model is not trivial. Here, we present a framework called “querying quantitative logic models” (Q2LM) for building and asking questions of constrained fuzzy logic (cFL) models. cFL is a recently developed modeling formalism that uses logic gates to describe influences among entities, with transfer functions to describe quantitative dependencies. Q2LM does not rely on dedicated data to train the parameters of the transfer functions, and it permits straight-forward incorporation of entities at multiple biological scales. The Q2LM framework can be employed to ask questions such as: Which therapeutic perturbations accomplish a designated goal, and under what environmental conditions will these perturbations be effective? We demonstrate the utility of this framework for generating testable hypotheses in two examples: (i) a intracellular signaling network model; and (ii) a model for pharmacokinetics and pharmacodynamics of cell-cytokine interactions; in the latter, we validate hypotheses concerning molecular design of granulocyte colony stimulating factor. PMID:22125256
DEFF Research Database (Denmark)
Hundebøll, Martin; Pedersen, Morten Videbæk; Roetter, Daniel Enrique Lucani
2014-01-01
This work studies the potential and impact of the FRANC network coding protocol for delivering high quality Dynamic Adaptive Streaming over HTTP (DASH) in wireless networks. Although DASH aims to tailor the video quality rate based on the available throughput to the destination, it relies...
Wen, Guoxing; Chen, C L Philip; Liu, Yan-Jun; Liu, Zhi
2016-10-11
Compared with the existing neural network (NN) or fuzzy logic system (FLS) based adaptive consensus methods, the proposed approach can greatly alleviate the computation burden because it needs only to update a few adaptive parameters online. In the multiagent agreement control, the system uncertainties derive from the unknown nonlinear dynamics are counteracted by employing the adaptive NNs; the state delays are compensated by designing a Lyapunov-Krasovskii functional. Finally, based on Lyapunov stability theory, it is demonstrated that the proposed consensus scheme can steer a multiagent system synchronizing to the predefined reference signals. Two simulation examples, a numerical multiagent system and a practical multimanipulator system, are carried out to further verify and testify the effectiveness of the proposed agreement approach.
Fuzzy logic-based call admission control in 5G cloud radio access networks with preemption
National Research Council Canada - National Science Library
Sigwele, Tshiamo; Pillai, Prashant; Alam, Atm S; Hu, Yim F
2017-01-01
...) with a plethora of applications generating requests to the network. The 5G cellular networks need to cope with such sky-rocketing traffic requests from these devices to avoid network congestion...
Khan, Faiz M; Schmitz, Ulf; Nikolov, Svetoslav; Engelmann, David; Pützer, Brigitte M; Wolkenhauer, Olaf; Vera, Julio
2014-01-01
A decade of successful results indicates that systems biology is the appropriate approach to investigate the regulation of complex biochemical networks involving transcriptional and post-transcriptional regulations. It becomes mandatory when dealing with highly interconnected biochemical networks, composed of hundreds of compounds, or when networks are enriched in non-linear motifs like feedback and feedforward loops. An emerging dilemma is to conciliate models of massive networks and the adequate description of non-linear dynamics in a suitable modeling framework. Boolean networks are an ideal representation of massive networks that are humble in terms of computational complexity and data demand. However, they are inappropriate when dealing with nested feedback/feedforward loops, structural motifs common in biochemical networks. On the other hand, models of ordinary differential equations (ODEs) cope well with these loops, but they require enormous amounts of quantitative data for a full characterization of the model. Here we propose hybrid models, composed of ODE and logical sub-modules, as a strategy to handle large scale, non-linear biochemical networks that include transcriptional and post-transcriptional regulations. We illustrate the construction of this kind of models using as example a regulatory network centered on E2F1, a transcription factor involved in cancer. The hybrid modeling approach proposed is a good compromise between quantitative/qualitative accuracy and scalability when considering large biochemical networks with a small highly interconnected core, and module of transcriptionally regulated genes that are not part of critical regulatory loops. This article is part of a Special Issue entitled: Computational Proteomics, Systems Biology & Clinical Implications. Guest Editor: Yudong Cai.
Network Experiences Lead to the Adaption of a Firm’s Network Competence
Directory of Open Access Journals (Sweden)
Bianka Kühne
2011-12-01
Full Text Available Networks become increasingly important as external sources of innovation for firms. Through networks firms get incontact with different actors with whom they can exchange information and collaborate. A firm’s ability to be asuccessful network actor depends on its network competence. This term can be defined as having the necessaryknowledge, skills and qualifications for networking as well as using them effectively. In this paper we investigate thelink between a firm’s network competence and the benefits resulting from it in a two‐way direction. First, thenetwork competence of the firm facilitates the adoption of information from other network actors which may leadto innovation success. Second the perceived network benefits shall in their turn influence the network competenceof the firm. Consequently, firms will adapt their network strategy corresponding their experiences. The objective ofthis paper is to investigate the dynamics of networking and its influence on the firm’s network competence. For thisexploratory research 3 Belgian networks are examined. In‐depth interviews are used in combination with semistructuredinterview guides to conduct the research. Our results indicate that some firms perceive benefits fromtheir network efforts, for others it is more a burden. Furthermore, in some of our cases we found that positiveexperiences with clear benefits motivate the firm to enhance its network competence. This is illustrated by the factthat collaborations are more frequently initiated, trust is more easily build, firms are more open to communicateinformation and the confidentiality threshold is overcome.
Separation Logic and Concurrency
Bornat, Richard
Concurrent separation logic is a development of Hoare logic adapted to deal with pointers and concurrency. Since its inception, it has been enhanced with a treatment of permissions to enable sharing of data between threads, and a treatment of variables as resource alongside heap cells as resource. An introduction to the logic is given with several examples of proofs, culminating in a treatment of Simpson's 4-slot algorithm, an instance of racy non-blocking concurrency.
Energy Technology Data Exchange (ETDEWEB)
Doellen, U.C. von [Lehrstuhl fuer Regelungssysteme und Steuerungstechnik, Bochum Univ. (Germany); Knof, R. [Noack Entsorgung GmbH, Bochum (Germany); Murmann, C. [VEW AG, Dortmund (Germany). Bereich Gastechnik
1994-12-31
Control and supervision of widespread gas distribution networks requires a great deal of planning and decision making. This task, the so-called gas-dispatching, is determined by an enormous set of various not only technical but also economical boundary conditions. This paper presents a new network control system that was realized for VEW AG one of the most important German gas suppliers. The connection and coordination of formerly isolated subprocesses using fuzzy logic is described, the adaptation of this high-level supervision to the existing process control system leads to remarkable improvement of gas-dispatching. (orig.) [Deutsch] Die Fuehrung und Ueberwachung ausgedehnter Gasversorgungsnetze erfordert einen umfangreichen Planungs- und Entscheidungsablauf. Diese als Gas-Dispatching bezeichnete Aufgabe wird durch eine Vielzahl technischer und wirtschaftlicher Randbedingungen beeinflusst. Der Beitrag stellt eine fuer das Leitungs- und Speichersystem eines der groessten regionalen Gasversorgungsunternehmen der Bundesrepublik, der Vereinigten Elektrizitaetswerke Westfalen AG in Dortmund (VEW), entwickelte Netzregelung vor. Er beschreibt die informatorische Verkopplung bestehender Einzelprozesse mit Hilfe der Fuzzy-Logik. Durch die Integration dieser Koordinationsfunktion in das bestehende Leitsystem wird eine uebergeordnete Optimierung des Gasversorgungsprozesses erreicht. (orig.)
Adaptive Reference Control for Pressure Management in Water Networks
DEFF Research Database (Denmark)
Kallesøe, Carsten; Jensen, Tom Nørgaard; Wisniewski, Rafal
2015-01-01
Water scarcity is an increasing problem worldwide and at the same time a huge amount of water is lost through leakages in the distribution network. It is well known that improved pressure control can lower the leakage problems. In this work water networks with a single pressure actuator and several...... consumers are considered. Under mild assumptions on the consumption pattern and hydraulic resistances of pipes we use properties of the network graph and Kirchhoffs node and mesh laws to show that simple relations exist between the actuator pressure and critical point pressures inside the network....... Subsequently, these relations are exploited in an adaptive reference control scheme for the actuator pressure that ensures constant pressure at the critical points. Numerical experiments underpin the results. © Copyright IEEE - All rights reserved....
Robust adaptive synchronization of chaotic neural networks by slide technique
Institute of Scientific and Technical Information of China (English)
Lou Xu-Yang; Cui Bao-Tong
2008-01-01
In this paper,we focus on the robust adaptive synchronization between two coupled chaotic neural networks with all the parameters unknown and time-varying delay.In order to increase the robustness of the two coupled neural networks,the key idea is that a sliding-mode-type controller is employed.Moreover,without the estimate values of the network unknown parameters taken as an updating object,a new updating object is introduced in the constructing of controller.Using the proposed controller,without any requirements for the boundedness,monotonicity and differentiability of activation functions,and symmetry of connections,the two coupled chaotic neural networks can achieve global robust synchronization no matter what their initial states are.Finally,the numerical simulation validates the effectiveness and feasibility of the proposed technique.
Reliable adaptive multicast protocol in wireless Ad hoc networks
Institute of Scientific and Technical Information of China (English)
Sun Baolin; Li Layuan
2006-01-01
In wireless ad hoc network environments, every link is wireless and every node is mobile. Those features make data lost easily as well as multicasting inefficient and unreliable. Moreover, Efficient and reliable multicast in wireless ad hoc network is a difficult issue. It is a major challenge to transmission delays and packet losses due to link changes of a multicast tree at the provision of high delivery ratio for each packet transmission in wireless ad hoc network environment.In this paper, we propose and evaluate Reliable Adaptive Multicast Protocol (RAMP) based on a relay node concept. Relay nodes are placed along the multicast tree. Data recovery is done between relay nodes. RAMP supports a reliable multicasting suitable for mobile ad hoc network by reducing the number of packet retransmissions. We compare RAMP with SRM (Scalable Reliable Multicast). Simulation results show that the RAMP has high delivery ratio and low end-to-end delay for packet transmission.
SVC VIDEO STREAM ALLOCATION AND ADAPTATION IN HETEROGENEOUS NETWORK
Directory of Open Access Journals (Sweden)
E. A. Pakulova
2016-07-01
Full Text Available The paper deals with video data transmission in format H.264/SVC standard with QoS requirements satisfaction. The Sender-Side Path Scheduling (SSPS algorithm and Sender-Side Video Adaptation (SSVA algorithm were developed. SSPS algorithm gives the possibility to allocate video traffic among several interfaces while SSVA algorithm dynamically changes the quality of video sequence in relation to QoS requirements. It was shown that common usage of two developed algorithms enables to aggregate throughput of access networks, increase parameters of Quality of Experience and decrease losses in comparison with Round Robin algorithm. For evaluation of proposed solution, the set-up was made. The trace files with throughput of existing public networks were used in experiments. Based on this information the throughputs of networks were limited and losses for paths were set. The results of research may be used for study and transmission of video data in heterogeneous wireless networks.
Novel Intrusion Detection using Probabilistic Neural Network and Adaptive Boosting
Tran, Tich Phuoc; Tran, Dat; Nguyen, Cuong Duc
2009-01-01
This article applies Machine Learning techniques to solve Intrusion Detection problems within computer networks. Due to complex and dynamic nature of computer networks and hacking techniques, detecting malicious activities remains a challenging task for security experts, that is, currently available defense systems suffer from low detection capability and high number of false alarms. To overcome such performance limitations, we propose a novel Machine Learning algorithm, namely Boosted Subspace Probabilistic Neural Network (BSPNN), which integrates an adaptive boosting technique and a semi parametric neural network to obtain good tradeoff between accuracy and generality. As the result, learning bias and generalization variance can be significantly minimized. Substantial experiments on KDD 99 intrusion benchmark indicate that our model outperforms other state of the art learning algorithms, with significantly improved detection accuracy, minimal false alarms and relatively small computational complexity.
一种基于模糊逻辑的自适应信标交换算法%An adaptive beacon exchange algorithm based on fuzzy logic
Institute of Scientific and Technical Information of China (English)
李玉龙; 戚云军; 张衡阳
2015-01-01
针对移动无线传感器网络中贪婪地理路由协议采用固定信标周期导致通信暂盲的问题，提出了一种基于模糊逻辑的自适应信标交换算法。该算法以节点移动速度、节点剩余能量和邻居节点的数量作为评价因素，利用模糊逻辑控制机制确定自适应的信标周期，提高了邻居表构建与维护的准确性与实时性，为贪婪地理转发提供了可靠依据。仿真结果表明：该算法有效减少了通信暂盲现象，降低了控制开销和平均端到端时延，提高了分组交付率，适用于对传输可靠性要求高的大规模移动无线传感器网络。%Aiming at problem that temporary communication blindness caused by greedy geographical routing protocol adopts stationary beacon exchange in mobile wireless sensor networks( WSNs),a novel adaptive beacon exchange algorithm is proposed. The algorithm adopts node moving speed,node residual energy,number of neighboring nodes as evaluation factors and confirm adaptive beacon period using fuzzy logic control mechanism. The adaptive beacon exchange algorithm can increase accuracy and realtime of neighbors table construction and maintenance and provide reliable basis for greedy geographical relay. Simulation shows that the proposed algorithm reduce phenomenon of temporary communication blindness,increases packet delivery ratio and reduce average end-to-end delay as well as control overhead,so it is suitable for application of large-scale mobile WSNs,which has high requirement for transmission reliability.
Ullah, Muhammed Zafar
Neural Network and Fuzzy Logic are the two key technologies that have recently received growing attention in solving real world, nonlinear, time variant problems. Because of their learning and/or reasoning capabilities, these techniques do not need a mathematical model of the system, which may be difficult, if not impossible, to obtain for complex systems. One of the major problems in portable or electric vehicle world is secondary cell charging, which shows non-linear characteristics. Portable-electronic equipment, such as notebook computers, cordless and cellular telephones and cordless-electric lawn tools use batteries in increasing numbers. These consumers demand fast charging times, increased battery lifetime and fuel gauge capabilities. All of these demands require that the state-of-charge within a battery be known. Charging secondary cells Fast is a problem, which is difficult to solve using conventional techniques. Charge control is important in fast charging, preventing overcharging and improving battery life. This research work provides a quick and reliable approach to charger design using Neural-Fuzzy technology, which learns the exact battery charging characteristics. Neural-Fuzzy technology is an intelligent combination of neural net with fuzzy logic that learns system behavior by using system input-output data rather than mathematical modeling. The primary objective of this research is to improve the secondary cell charging algorithm and to have faster charging time based on neural network and fuzzy logic technique. Also a new architecture of a controller will be developed for implementing the charging algorithm for the secondary battery.
Effect of Adaptive Delivery Capacity on Networked Traffic Dynamics
Institute of Scientific and Technical Information of China (English)
CAO Xian-Bin; DU Wen-Bo; CHEN Cai-Long; ZHANG Jun
2011-01-01
@@ We introduce an adaptive delivering capacity mechanism into the traffic dynamic model on scale-free networks under shortest path routing strategy and focus on its effect on the network capacity measured by the critical point(Rc) of phase transition from free flow to congestion.Under this mechanism,the total node's delivering capacity is fixed and the allocation of delivering capacity on node i is proportional to niφ,where ni is the queue length of node i and φ is the adjustable parameter.It is found that the network capacity monotonously increases with the increment of φ,but there exists an optimal value of parameter φ leading to the highest transportation efficiency measured by average travelling time(〈T〉).Our work may be helpful for optimal design of networked traffic dynamics.%We introduce an adaptive delivering capacity mechanism into the traffic dynamic model on scale-free networks under shortest path routing strategy and focus on its effect on the network capacity measured by the critical point (Rc) of phase transition from free flow to congestion.Under this mechanism, the total node's delivering capacity is fixed and the allocation of delivering capacity on node i is proportional to niφ, where ni is the queue length of node i and φ is the adjustable parameter.It is found that the network capacity monotonously increases with the increment of φ, but there exists an optimal value of parameter φ leading to the highest transportation efficiency measured by average travelling time (＜T＞).Our work may be helpful for optimal design of networked traffic dynamics.
Adapting Bayes Network Structures to Non-stationary Domains
DEFF Research Database (Denmark)
Nielsen, Søren Holbech; Nielsen, Thomas Dyhre
2006-01-01
When an incremental structural learning method gradually modifies a Bayesian network (BN) structure to fit observations, as they are read from a database, 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 to be gradually constructed as observations of the environment are made. Existing algorithms for incremental learning assume that the samples in the database have been drawn from a single underlying distribution. In this paper we relax this assumption, so that the underlying distribution can change during...
Kong, Fansen; Chen, Ruheng
2004-01-01
A new combined method based on wavelet transformation, fuzzy logic and neuro-networks is proposed for fault diagnosis of a triplex. The failure characteristics of the fluid- and dynamic-end can be divided into wavelet transform in different scales at the same time (in: Jun Zhu et al. (Eds.), Proceedings of an International Conference on Condition Monitoring. National Defense Industry Press, Beijing, 1997, pp. 271-275). Therefore, the characteristic variables can be constructed making use of the coefficients of Edgeworth asymptotic spectrum expansion formula and fuzzified to train the neuro-network to identify the faults of fluid- and dynamic-end of triplex pump in fuzzy domain. Tests indicate that the information of wavelet transformation in scale 2 is related to the meshing state of the gear and the information in scales 4 and 5 is related to the running state of fluid-end. Good agreement between analytical and experimental results has been obtained.
Directory of Open Access Journals (Sweden)
Jiao-Hong Yi
2016-01-01
Full Text Available Probabilistic neural network has successfully solved all kinds of engineering problems in various fields since it is proposed. In probabilistic neural network, Spread has great influence on its performance, and probabilistic neural network will generate bad prediction results if it is improperly selected. It is difficult to select the optimal manually. In this article, a variant of probabilistic neural network with self-adaptive strategy, called self-adaptive probabilistic neural network, is proposed. In self-adaptive probabilistic neural network, Spread can be self-adaptively adjusted and selected and then the best selected Spread is used to guide the self-adaptive probabilistic neural network train and test. In addition, two simplified strategies are incorporated into the proposed self-adaptive probabilistic neural network with the aim of further improving its performance and then two versions of simplified self-adaptive probabilistic neural network (simplified self-adaptive probabilistic neural networks 1 and 2 are proposed. The variants of self-adaptive probabilistic neural networks are further applied to solve the transformer fault diagnosis problem. By comparing them with basic probabilistic neural network, and the traditional back propagation, extreme learning machine, general regression neural network, and self-adaptive extreme learning machine, the results have experimentally proven that self-adaptive probabilistic neural networks have a more accurate prediction and better generalization performance when addressing the transformer fault diagnosis problem.
Adaptation in Food Networks: Theoretical Framework and Empirical Evidences
Directory of Open Access Journals (Sweden)
Gaetano Martino
2013-03-01
Full Text Available The paper concerns the integration in food networks under a governance point of view. We conceptualize the integration processes in terms of the adaptation theory and focus the issues related under a transaction cost economics perspective. We conjecture that the allocation of decisions rights between the parties to a transaction is a key instrument in order to cope with the sources of basic uncertainty in food networks: technological innovation, sustainability strategies, quality and safety objectives. Six case studies are proposed which contribute to corroborate our conjecture. Managerial patters based on a joint decision approach also are documented
Adaptive synchronization of different kinds of chaotic neural networks
Institute of Scientific and Technical Information of China (English)
Huanxin GUAN; Zhanshan WANG; Huaguang ZHANG
2008-01-01
The purpose of the paper is to present an adaptive control method for the synchronization of different classes of chaotic neural networks.A new sufticient condition for the global synchronization of two kinds of chaotic neural networks is derived.The proposed control method is efficient for implementing the synchronization when the parameters of the drive system are different from those of the response system.A numerical example is used to demonstrate the validity of the proposed method and the obtained result.
Effects of Adaptive Wormhole Routing in Event Builder Networks
Moser, R; Branson, J; Brett, A; Cano, E; Carboni, A; Ciganek, M; Cittolin, S; Erhan, S; Gigi, D; Glege, F; Gómez-Reino, Robert; Gulmini, M; Gutiérrez-Mlot, E; Gutleber, J; Jacobs, C; Kim, J C; Klute, M; Lipeles, E; Lopez-Perez, Juan Antonio; Maron, G; Meijers, F; Meschi, E; Murray, S; Oh, A; Orsini, L; Paus, C; Petrucci, A; Pieri, M; Pollet, L; Rácz, A; Sakulin, H; Sani, M; Schieferdecker, P; Schwick, C; Sumorok, K; Suzuki, I; Tsirigkas, D; Varela, J; Bauer, G
2007-01-01
The data acquisition system of the CMS experiment at the Large Hadron Collider features a two-stage event builder, which combines data from about 500 sources into full events at an aggregate throughput of 100 GByte/s. To meet the requirements, several architectures and interconnect technologies have been quantitatively evaluated. Both Gigabit Ethernet and Myrinet networks will be employed during the first run. Nearly full bi-section throughput can be obtained using a custom software driver for Myrinet based on barrel shifter traffic shaping. This paper discusses the use of Myrinet dual-port network interface cards supporting channel bonding to achieve virtual 5GBit/s links with adaptive routing to alleviate the throughput limitations associated with wormhole routing. Adaptive routing is not expected to be suitable for high-throughput event builder applications in high-energy physics. To corroborate this claim, results from the CMS event builder preseries installation at CERN are presented and the problems of ...
Adaptive Media Access Control for Energy Harvesting - Wireless Sensor Networks
DEFF Research Database (Denmark)
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......-changing environmental energy sources. In this paper, we present an improved and extended version of ODMAC and we analyze it by means of an analytical model that can approximate several performance metrics in an arbitrary network topology. The simulations and the analytical experiments show ODMAC's ability to satisfy...... three key properties of EH-WSNs: adaptability of energy consumption, distributed energy-aware load balancing and support for different application-specific requirements....
Adaptive Air-Fuel Ratio Control with MLP Network
Institute of Scientific and Technical Information of China (English)
Shi-Wei Wang; Ding-Li Yu
2005-01-01
This paper presents an application of adaptive neural network model-based predictive control (MPC) to the air-fuel ratio of an engine simulation. A multi-layer perceptron (MLP) neural network is trained using two on-line training algorithms: a back propagation algorithm and a recursive least squares (RLS) algorithm. It is used to model parameter uncertainties in the nonlinear dynamics of internal combustion (IC) engines. Based on the adaptive model, an MPC strategy for controlling air-fuel ratio is realized, and its control performance compared with that of a traditional PI controller.A reduced Hessian method, a newly developed sequential quadratic programming (SQP) method for solving nonlinear programming (NLP) problems, is implemented to speed up nonlinear optimization in the MPC.
ADAPTIVE GOSSIP BASED PROTOCOL FOR ENERGY EFFICIENT MOBILE ADHOC NETWORK
Directory of Open Access Journals (Sweden)
S. Rajeswari
2012-03-01
Full Text Available In Gossip Sleep Protocol, network performance is enhanced based on energy resource. But energy conservation is achieved with the reduced throughput. In this paper, it has been proposed a new Protocol for Mobile Ad hoc Network to achieve reliability with energy conservation. Based on the probability (p values, the value of sleep nodes is fixed initially. The probability value can be adaptively adjusted by Remote Activated Switch during the transmission process. The adaptiveness of gossiping probability is determined by the Packet Delivery Ratio. For performance comparison, we have taken Routing overhead, Packet Delivery Ratio, Number of dropped packets and Energy consumption with the increasing number of forwarding nodes. We used UDP based traffic models to analyze the performance of this protocol. We analyzed TCP based traffic models for average end to end delay. We have used the NS-2 simulator.
Zhao, Hong-Quan; Kasai, Seiya; Shiratori, Yuta; Hashizume, Tamotsu
2009-06-17
A two-bit arithmetic logic unit (ALU) was successfully fabricated on a GaAs-based regular nanowire network with hexagonal topology. This fundamental building block of central processing units can be implemented on a regular nanowire network structure with simple circuit architecture based on graphical representation of logic functions using a binary decision diagram and topology control of the graph. The four-instruction ALU was designed by integrating subgraphs representing each instruction, and the circuitry was implemented by transferring the logical graph structure to a GaAs-based nanowire network formed by electron beam lithography and wet chemical etching. A path switching function was implemented in nodes by Schottky wrap gate control of nanowires. The fabricated circuit integrating 32 node devices exhibits the correct output waveforms at room temperature allowing for threshold voltage variation.
Analytic description of adaptive network topologies in a steady state.
Wieland, Stefan; Nunes, Ana
2015-06-01
In many complex systems, states and interaction structure coevolve towards a dynamic equilibrium. For the adaptive contact process, we obtain approximate expressions for the degree distributions that characterize the interaction network in such active steady states. These distributions are shown to agree quantitatively with simulations except when rewiring is much faster than state update and used to predict and to explain general properties of steady-state topologies. The method generalizes easily to other coevolutionary dynamics.
Adaptive control of system with hysteresis using neural networks
Institute of Scientific and Technical Information of China (English)
Li Chuntao; Tan Yonghong
2006-01-01
An adaptive control scheme is developed for a class of single-input nonlinear systems preceded by unknown hysteresis, which is a non-differentiable and multi-value mapping nonlinearity. The controller based on the three-layer neural network (NN), whose weights are derived from Lyapunov stability analysis, guarantees closed-loop semiglobal stability and convergence of the tracking errors to a small residual set. An example is used to confirm the effectiveness of the proposed control scheme.
Adaptive PID control based on orthogonal endocrine neural networks.
Milovanović, Miroslav B; Antić, Dragan S; Milojković, Marko T; Nikolić, Saša S; Perić, Staniša Lj; Spasić, Miodrag D
2016-12-01
A new intelligent hybrid structure used for online tuning of a PID controller is proposed in this paper. The structure is based on two adaptive neural networks, both with built-in Chebyshev orthogonal polynomials. First substructure network is a regular orthogonal neural network with implemented artificial endocrine factor (OENN), in the form of environmental stimuli, to its weights. It is used for approximation of control signals and for processing system deviation/disturbance signals which are introduced in the form of environmental stimuli. The output values of OENN are used to calculate artificial environmental stimuli (AES), which represent required adaptation measure of a second network-orthogonal endocrine adaptive neuro-fuzzy inference system (OEANFIS). OEANFIS is used to process control, output and error signals of a system and to generate adjustable values of proportional, derivative, and integral parameters, used for online tuning of a PID controller. The developed structure is experimentally tested on a laboratory model of the 3D crane system in terms of analysing tracking performances and deviation signals (error signals) of a payload. OENN-OEANFIS performances are compared with traditional PID and 6 intelligent PID type controllers. Tracking performance comparisons (in transient and steady-state period) showed that the proposed adaptive controller possesses performances within the range of other tested controllers. The main contribution of OENN-OEANFIS structure is significant minimization of deviation signals (17%-79%) compared to other controllers. It is recommended to exploit it when dealing with a highly nonlinear system which operates in the presence of undesirable disturbances.
RATE ADAPTIVE PROTOCOL FOR MULTIRATE IEEE 802.11 NETWORKS
Institute of Scientific and Technical Information of China (English)
Xi Yong; Huang Qingyan; Wei Jibo; Zhao Haitao
2007-01-01
In this paper,a rate adaptive protocol AMARF(Adaptive Multirate Auto Rate Fallback)for multirate IEEE 802.11 networks is proposed.In AMARF,each data rate is assigned a unique success threshold,which is a criterion to judge when to switch a rate to the next higher one,and the success thresholds call be adjusted dynamically in an adaptive manner according to the running conditions,such as packet length and channel parameters.Moreover,the proposed protocol can be implemented by software without any change to the current IEEE 802.11 standards.Simulation result shows that AMARF yields significantly higher throughput than other existing schemes including ARF and its variants,in various running conditions.
Sparse gamma rhythms arising through clustering in adapting neuronal networks.
Directory of Open Access Journals (Sweden)
Zachary P Kilpatrick
2011-11-01
Full Text Available Gamma rhythms (30-100 Hz are an extensively studied synchronous brain state responsible for a number of sensory, memory, and motor processes. Experimental evidence suggests that fast-spiking interneurons are responsible for carrying the high frequency components of the rhythm, while regular-spiking pyramidal neurons fire sparsely. We propose that a combination of spike frequency adaptation and global inhibition may be responsible for this behavior. Excitatory neurons form several clusters that fire every few cycles of the fast oscillation. This is first shown in a detailed biophysical network model and then analyzed thoroughly in an idealized model. We exploit the fact that the timescale of adaptation is much slower than that of the other variables. Singular perturbation theory is used to derive an approximate periodic solution for a single spiking unit. This is then used to predict the relationship between the number of clusters arising spontaneously in the network as it relates to the adaptation time constant. We compare this to a complementary analysis that employs a weak coupling assumption to predict the first Fourier mode to destabilize from the incoherent state of an associated phase model as the external noise is reduced. Both approaches predict the same scaling of cluster number with respect to the adaptation time constant, which is corroborated in numerical simulations of the full system. Thus, we develop several testable predictions regarding the formation and characteristics of gamma rhythms with sparsely firing excitatory neurons.
Adaptive comanagement of a marine protected area network in Fiji.
Weeks, Rebecca; Jupiter, Stacy D
2013-12-01
Adaptive management of natural resources is an iterative process of decision making whereby management strategies are progressively changed or adjusted in response to new information. Despite an increasing focus on the need for adaptive conservation strategies, there remain few applied examples. We describe the 9-year process of adaptive comanagement of a marine protected area network in Kubulau District, Fiji. In 2011, a review of protected area boundaries and management rules was motivated by the need to enhance management effectiveness and the desire to improve resilience to climate change. Through a series of consultations, with the Wildlife Conservation Society providing scientific input to community decision making, the network of marine protected areas was reconfigured so as to maximize resilience and compliance. Factors identified as contributing to this outcome include well-defined resource-access rights; community respect for a flexible system of customary governance; long-term commitment and presence of comanagement partners; supportive policy environment for comanagement; synthesis of traditional management approaches with systematic monitoring; and district-wide coordination, which provided a broader spatial context for adaptive-management decision making. Co-Manejo Adaptativo de una Red de Áreas Marinas Protegidas en Fiyi. © 2013 The Authors. Conservation Biology published by Wiley Periodicals, Inc., on behalf of the Society for Conservation Biology.
Kenney, Michael; Horgan, John; Horne, Cale; Vining, Peter; Carley, Kathleen M; Bigrigg, Michael W; Bloom, Mia; Braddock, Kurt
2013-09-01
Social networks are said to facilitate learning and adaptation by providing the connections through which network nodes (or agents) share information and experience. Yet, our understanding of how this process unfolds in real-world networks remains underdeveloped. This paper explores this gap through a case study of al-Muhajiroun, an activist network that continues to call for the establishment of an Islamic state in Britain despite being formally outlawed by British authorities. Drawing on organisation theory and social network analysis, we formulate three hypotheses regarding the learning capacity and social network properties of al-Muhajiroun (AM) and its successor groups. We then test these hypotheses using mixed methods. Our methods combine quantitative analysis of three agent-based networks in AM measured for structural properties that facilitate learning, including connectedness, betweenness centrality and eigenvector centrality, with qualitative analysis of interviews with AM activists focusing organisational adaptation and learning. The results of these analyses confirm that al-Muhajiroun activists respond to government pressure by changing their operations, including creating new platforms under different names and adjusting leadership roles among movement veterans to accommodate their spiritual leader's unwelcome exodus to Lebanon. Simple as they are effective, these adaptations have allowed al-Muhajiroun and its successor groups to continue their activism in an increasingly hostile environment. Copyright © 2012 Elsevier Ltd and The Ergonomics Society. All rights reserved.
Adaptive Influence Maximization in Social Networks: Why Commit when You can Adapt?
Vaswani, Sharan; Lakshmanan, Laks V. S.
2016-01-01
Most previous work on influence maximization in social networks is limited to the non-adaptive setting in which the marketer is supposed to select all of the seed users, to give free samples or discounts to, up front. A disadvantage of this setting is that the marketer is forced to select all the seeds based solely on a diffusion model. If some of the selected seeds do not perform well, there is no opportunity to course-correct. A more practical setting is the adaptive setting in which the ma...
LAMAN: Load Adaptable MAC for Ad Hoc Networks
Directory of Open Access Journals (Sweden)
Realp Marc
2005-01-01
Full Text Available In mobile ad hoc radio networks, mechanisms on how to access the radio channel are extremely important in order to improve network efficiency. In this paper, the load adaptable medium access control for ad hoc networks (LAMAN protocol is described. LAMAN is a novel decentralized multipacket MAC protocol designed following a cross-layer approach. Basically, this protocol is a hybrid CDMA-TDMA-based protocol that aims at throughput maximization in multipacket communication environments by efficiently combining contention and conflict-free protocol components. Such combination of components is used to adapt the nodes' access priority to changes on the traffic load while, at the same time, accounting for the multipacket reception (MPR capability of the receivers. A theoretical analysis of the system is developed presenting closed expressions of network throughput and packet delay. By simulations the validity of our analysis is shown and the performances of a LAMAN-based system and an Aloha-CDMA-based one are compared.
Chen, Chunfeng; Liu, Hua; Fan, Ge
2005-02-01
In this paper we consider the problem of designing a network of optical cross-connects(OXCs) to provide end-to-end lightpath services to label switched routers (LSRs). Like some previous work, we select the number of OXCs as our objective. Compared with the previous studies, we take into account the fault-tolerant characteristic of logical topology. First of all, using a Prufer number randomly generated, we generate a tree. By adding some edges to the tree, we can obtain a physical topology which consists of a certain number of OXCs and fiber links connecting OXCs. It is notable that we for the first time limit the number of layers of the tree produced according to the method mentioned above. Then we design the logical topologies based on the physical topologies mentioned above. In principle, we will select the shortest path in addition to some consideration on the load balancing of links and the limitation owing to the SRLG. Notably, we implement the routing algorithm for the nodes in increasing order of the degree of the nodes. With regarding to the problem of the wavelength assignment, we adopt the heuristic algorithm of the graph coloring commonly used. It is clear our problem is computationally intractable especially when the scale of the network is large. We adopt the taboo search algorithm to find the near optimal solution to our objective. We present numerical results for up to 1000 LSRs and for a wide range of system parameters such as the number of wavelengths supported by each fiber link and traffic. The results indicate that it is possible to build large-scale optical networks with rich connectivity in a cost-effective manner, using relatively few but properly dimensioned OXCs.
A Bayesian regularized artificial neural network for adaptive optics forecasting
Sun, Zhi; Chen, Ying; Li, Xinyang; Qin, Xiaolin; Wang, Huiyong
2017-01-01
Real-time adaptive optics is a technology for enhancing the resolution of ground-based optical telescopes and overcoming the disturbance of atmospheric turbulence. The performance of the system is limited by delay errors induced by the servo system and photoelectrons noise of wavefront sensor. In order to cut these delay errors, this paper proposes a novel model to forecast the future control voltages of the deformable mirror. The predictive model is constructed by a multi-layered back propagation network with Bayesian regularization (BRBP). For the purpose of parallel computation and less disturbance, we adopt a number of sub-BP neural networks to substitute the whole network. The Bayesian regularized network assigns a probability to the network weights, allowing the network to automatically and optimally penalize excessively complex models. The simulation results show that the BRBP introduces smaller mean absolute percentage error (MAPE) and mean square errors (MSE) than other typical algorithms. Meanwhile, real data analysis results show that the BRBP model has strong generalization capability and parallelism.
Analysis of Fuzzy Logic Based Intrusion Detection Systems in Mobile Ad Hoc Networks
Directory of Open Access Journals (Sweden)
A. Chaudhary
2014-01-01
Full Text Available Due to the advancement in wireless technologies, many of new paradigms have opened for communications. Among these technologies, mobile ad hoc networks play a prominent role for providing communication in many areas because of its independent nature of predefined infrastructure. But in terms of security, these networks are more vulnerable than the conventional networks because firewall and gateway based security mechanisms cannot be applied on it. That’s why intrusion detection systems are used as keystone in these networks. Many number of intrusion detection systems have been discovered to handle the uncertain activity in mobile ad hoc networks. This paper emphasized on proposed fuzzy based intrusion detection systems in mobile ad hoc networks and presented their effectiveness to identify the intrusions. This paper also examines the drawbacks of fuzzy based intrusion detection systems and discussed the future directions in the field of intrusion detection for mobile ad hoc networks.
Network and adaptive system of systems modeling and analysis.
Energy Technology Data Exchange (ETDEWEB)
Lawton, Craig R.; Campbell, James E. Dr. (.; .); Anderson, Dennis James; Eddy, John P.
2007-05-01
This report documents the results of an LDRD program entitled ''Network and Adaptive System of Systems Modeling and Analysis'' that was conducted during FY 2005 and FY 2006. The purpose of this study was to determine and implement ways to incorporate network communications modeling into existing System of Systems (SoS) modeling capabilities. Current SoS modeling, particularly for the Future Combat Systems (FCS) program, is conducted under the assumption that communication between the various systems is always possible and occurs instantaneously. A more realistic representation of these communications allows for better, more accurate simulation results. The current approach to meeting this objective has been to use existing capabilities to model network hardware reliability and adding capabilities to use that information to model the impact on the sustainment supply chain and operational availability.
Adaptive Decision-Making Scheme for Cognitive Radio Networks
Alqerm, Ismail
2014-05-01
Radio resource management becomes an important aspect of the current wireless networks because of spectrum scarcity and applications heterogeneity. Cognitive radio is a potential candidate for resource management because of its capability to satisfy the growing wireless demand and improve network efficiency. Decision-making is the main function of the radio resources management process as it determines the radio parameters that control the use of these resources. In this paper, we propose an adaptive decision-making scheme (ADMS) for radio resources management of different types of network applications including: power consuming, emergency, multimedia, and spectrum sharing. ADMS exploits genetic algorithm (GA) as an optimization tool for decision-making. It consists of the several objective functions for the decision-making process such as minimizing power consumption, packet error rate (PER), delay, and interference. On the other hand, maximizing throughput and spectral efficiency. Simulation results and test bed evaluation demonstrate ADMS functionality and efficiency.
Adaptive topology evolution in information-sharing social networks
Chen, Duanbing; Lu, Linyuan; Medo, Matus; Zhang, Yi-Cheng; Zhou, Tao
2011-01-01
The advent of Internet and World Wide Web has led to unprecedent growth of the information available. People usually face the information overload by following a limited number of sources which best fit their interests. In order to get the picture it is important to address issues like who people do follow and how they search for better information sources. In this work we conduct an empirical analysis on different on-line social networking sites, and draw inspiration from its results to present different source selection strategies in an adaptive model for social recommendation. We show that local search rules which enhance the typical topological features of real social communities give rise to network configurations that are globally optimal. Hence these abstract rules help to create networks which are both effective in information diffusion and people friendly.
Adaptive Conflict-Free Optimization of Rule Sets for Network Security Packet Filtering Devices
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Andrea Baiocchi
2015-01-01
Full Text Available Packet filtering and processing rules management in firewalls and security gateways has become commonplace in increasingly complex networks. On one side there is a need to maintain the logic of high level policies, which requires administrators to implement and update a large amount of filtering rules while keeping them conflict-free, that is, avoiding security inconsistencies. On the other side, traffic adaptive optimization of large rule lists is useful for general purpose computers used as filtering devices, without specific designed hardware, to face growing link speeds and to harden filtering devices against DoS and DDoS attacks. Our work joins the two issues in an innovative way and defines a traffic adaptive algorithm to find conflict-free optimized rule sets, by relying on information gathered with traffic logs. The proposed approach suits current technology architectures and exploits available features, like traffic log databases, to minimize the impact of ACO development on the packet filtering devices. We demonstrate the benefit entailed by the proposed algorithm through measurements on a test bed made up of real-life, commercial packet filtering devices.
Senthil Kumar, A R; Goyal, Manish Kumar; Ojha, C S P; Singh, R D; Swamee, P K
2013-01-01
The prediction of streamflow is required in many activities associated with the planning and operation of the components of a water resources system. Soft computing techniques have proven to be an efficient alternative to traditional methods for modelling qualitative and quantitative water resource variables such as streamflow, etc. The focus of this paper is to present the development of models using multiple linear regression (MLR), artificial neural network (ANN), fuzzy logic and decision tree algorithms such as M5 and REPTree for predicting the streamflow at Kasol located at the upstream of Bhakra reservoir in Sutlej basin in northern India. The input vector to the various models using different algorithms was derived considering statistical properties such as auto-correlation function, partial auto-correlation and cross-correlation function of the time series. It was found that REPtree model performed well compared to other soft computing techniques such as MLR, ANN, fuzzy logic, and M5P investigated in this study and the results of the REPTree model indicate that the entire range of streamflow values were simulated fairly well. The performance of the naïve persistence model was compared with other models and the requirement of the development of the naïve persistence model was also analysed by persistence index.
Neural network payload estimation for adaptive robot control.
Leahy, M R; Johnson, M A; Rogers, S K
1991-01-01
A concept is proposed for utilizing artificial neural networks to enhance the high-speed tracking accuracy of robotic manipulators. Tracking accuracy is a function of the controller's ability to compensate for disturbances produced by dynamical interactions between the links. A model-based control algorithm uses a nominal model of those dynamical interactions to reduce the disturbances. The problem is how to provide accurate dynamics information to the controller in the presence of payload uncertainty and modeling error. Neural network payload estimation uses a series of artificial neural networks to recognize the payload variation associated with a degradation in tracking performance. The network outputs are combined with a knowledge of nominal dynamics to produce a computationally efficient direct form of adaptive control. The concept is validated through experimentation and analysis on the first three links of a PUMA-560 manipulator. A multilayer perceptron architecture with two hidden layers is used. Integration of the principles of neural network pattern recognition and model-based control produces a tracking algorithm with enhanced robustness to incomplete dynamic information. Tracking efficacy and applicability to robust control algorithms are discussed.
Self-Adaptive Genetic Algorithm for LTE Backhaul Network
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Li Li
2014-03-01
Full Text Available Mobile communication evolution from 2G, 3G to LTE shows a broadband and IP-oriented trend and the architecture of LTE backhaul network turns to be flat. In order to fit these new features, layer 3 routing technology has to be adopted in backhaul network and needs to be modified to fit it. In this paper, a new algorithm, named Self-Adaptive Genetic Algorithm (SAGA, is proposed to meet the demand of providing a highly efficient and QoS guaranteed routing scheme for LTE backhaul network. It can be used in Open Shortest Path First protocol (OSPF as the core path selection algorithm. It is based on traditional genetic algorithm(GA but improves the population initialization process in it as well as proposes a new fitness calculation function for it. Simulation verifies it can balance not only the traffic of network but also the load of MME pools, which improves the utility efficiency of the whole network.
Rescue of endemic states in interconnected networks with adaptive coupling
Vazquez, F; Miguel, M San
2015-01-01
We study the Susceptible-Infected-Susceptible model of epidemic spreading on two layers of networks interconnected by adaptive links, which are rewired at random to avoid contacts between infected and susceptible nodes at the interlayer. We find that the rewiring reduces the effective connectivity for the transmission of the disease between layers, and may even totally decouple the networks. Weak endemic states, in which the epidemics spreads only if the two layers are interconnected, show a transition from the endemic to the healthy phase when the rewiring overcomes a threshold value that depends on the infection rate, the strength of the coupling and the mean connectivity of the networks. In the strong endemic scenario, in which the epidemics is able to spread on each separate network, the prevalence in each layer decreases when increasing the rewiring, arriving to single network values only in the limit of infinitely fast rewiring. We also find that finite-size effects are amplified by the rewiring, as the...
An Adaptive Power Efficient Packet Scheduling Algorithm for Wimax Networks
Prasad, R Murali
2010-01-01
Admission control schemes and scheduling algorithms are designed to offer QoS services in 802.16/802.16e networks and a number of studies have investigated these issues. But the channel condition and priority of traffic classes are very rarely considered in the existing scheduling algorithms. Although a number of energy saving mechanisms have been proposed for the IEEE 802.16e, to minimize the power consumption of IEEE 802.16e mobile stations with multiple real-time connections has not yet been investigated. Moreover, they mainly consider non real- time connections in IEEE 802.16e networks. In this paper, we propose to design an adaptive power efficient packet scheduling algorithm that provides a minimum fair allocation of the channel bandwidth for each packet flow and additionally minimizes the power consumption. In the adaptive scheduling algorithm, packets are transmitted as per allotted slots from different priority of traffic classes adaptively, depending on the channel condition. Suppose if the buffer s...
ADAPTIVE SERVICE PROVISIONING FOR MOBILE AD HOC NETWORKS
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Cynthia Jayapal
2010-09-01
Full Text Available Providing efficient and scalable service provisioning in Mobile Ad Hoc Network (MANET is a big research challenge. In adaptive service provisioning mechanism an adaptive election procedure is used to select a coordinator node. The role of a service coordinator is crucial in any distributed directory based service provisioning scheme. The existing coordinator election schemes use either the nodeID or a hash function to choose the coordinator. In these schemes, the leader changes are more frequent due to node mobility. We propose an adaptive scheme that makes use of an eligibility factor that is calculated based on the distance to the zone center, remaining battery power and average speed to elect a core node that change according to the network dynamics. We also retain the node with the second highest priority as a backup node. Our algorithm is compared with the existing solution by simulation and the result shows that the core node selected by us is more stable and hence reduces the number of handoffs. This in turn improves the service delivery performance by increasing the packet delivery ratio and decreasing the delay, the overhead and the forwarding cost.
Directory of Open Access Journals (Sweden)
A. Daeinabi
2013-08-01
Full Text Available The Intercell Interference (ICI problem is one of the main challenges in Long Term Evolution (LTEdownlink system. In order to deal with the ICI problem, this paper proposes a joint resource block andtransmit power allocation scheme in LTE downlink networks. The proposed scheme is implemented in threephases: (1 the priority of users is calculated based on interference level, Quality of Service (QoS andHead of Line (HoL delay;(2 users in each cell are scheduled on the specified subbands based on theirpriority; and (3 eNodeBs dynamically control the transmit power using a fuzzy logic system andexchanging messages to each other. Simulation results demonstrate that the proposed priority schemeoutperforms the existing Reuse Factor one (RF1 and Soft Frequency Reuse (SFR schemes in terms of cellthroughput, cell edge user throughput, delay and interference level.
Directory of Open Access Journals (Sweden)
Elakhdar Benyoussef
2015-02-01
Full Text Available This paper presents a direct torque control is applied for salient-pole double star synchronous machine without mechanical speed and stator flux linkage sensors. The estimation is performed using the extended Kalman filter known by it is ability to process noisy discrete measurements. Two control approaches using fuzzy logic DTC, and neural network DTC are proposed and compared. The validity of the proposed controls scheme is verified by simulation tests of a double star synchronous machine. The stator flux, torque, and speed are determined and compared in the above techniques. Simulation results presented in this paper highlight the improvements produced by the proposed control method based on the extended Kalman filter under various operation conditions.
Directory of Open Access Journals (Sweden)
José Raúl Castro
2016-02-01
Full Text Available This paper presents an efficient algorithm to solve the multi-objective (MO voltage control problem in distribution networks. The proposed algorithm minimizes the following three objectives: voltage variation on pilot buses, reactive power production ratio deviation, and generator voltage deviation. This work leverages two optimization techniques: fuzzy logic to find the optimum value of the reactive power of the distributed generation (DG and Pareto optimization to find the optimal value of the pilot bus voltage so that this produces lower losses under the constraints that the voltage remains within established limits. Variable loads and DGs are taken into account in this paper. The algorithm is tested on an IEEE 13-node test feeder and the results show the effectiveness of the proposed model.
A Logically Centralized Approach for Control and Management of Large Computer Networks
Iqbal, Hammad A.
2012-01-01
Management of large enterprise and Internet service provider networks is a complex, error-prone, and costly challenge. It is widely accepted that the key contributors to this complexity are the bundling of control and data forwarding in traditional routers and the use of fully distributed protocols for network control. To address these…
An adaptive blind watermarking scheme utilizing neural network for synchronization
Institute of Scientific and Technical Information of China (English)
WU Jian-zhen; XIE Jian-ying; YANG Yu-pu
2007-01-01
An important problem constraining the practical implementation of robust watermarking technology is the low robustness of existing algorithms against geometrical distortions. An adaptive blind watermarking scheme utilizing neural network for synchronization is proposed in this paper,which allows to recover watermark even if the image has been subjected to generalized geometrical transforms. Through classification of image's brightness, texture and contrast sensitivity utilizing fuzzy clustering theory and human visual system, more robust watermark is adaptively embedded in DWT domain. In order to register rotation, scaling and translation parameters, feedforward neural network is utilized to learn image geometric pattern represented by six combined low order image moments. The distortion can be inverted after determining the affine distortion applied to the image and watermark can be extracted in a standard way without original image. It only needs a trained neural network. Experimental results demonstrate its advantages over previous method in terms of computational effectiveness and parameter estimation accuracy. It can embed more robust watermark under certain visual distance, and effectively resist JPEG compression, noise and geometric attacks.
Data-Adaptive Detection of Transient Deformation in GNSS Networks
Calais, E.; Walwer, D.; Ghil, M.
2014-12-01
Dense Global Navigation Satellite System (GNSS) networks have recently been developed in actively deforming regions and elsewhere. Their operation is leading to a rapidly increasing amount of data, and position time series are now routinely provided by several high-quality services. These networks often capture transient-deformation features of geophysical origin that are difficult to separate from the background noise or from seasonal residuals in the time series. In addition, because of the very large number of stations now available, it has become impossible to systematically inspect each time series and visually compare them at all neighboring sites. In order to overcome these limitations, we adapt Multichannel Singular Spectrum Analysis (M-SSA), a method derived from the analysis of dynamical systems, to the spatial and temporal analysis of GNSS position time series in dense networks. We show that this data-adaptive method — previously applied to climate, bio-medical and macro-economic indicators — allows us to extract spatio-temporal features of geophysical interest from GPS time series without a priori knowledge of the system's dynamics or of the data set's noise characteristics. We illustrate our results with examples from seasonal signals in Alaska and from micro-inflation/deflation episodes at an Aleutian-arc volcano.
S.Prayla Shyry; Ramachandran, V.
2011-01-01
Selfish overlay routing is the technique whereby the sender of the packet can specify the route that the packet should take through the network. Selfish overlay routing allow end users to select routes in an egocentic fashion to optimize their own performance without considering the system wide criteria which in turn cause performance degradation .The main concept behind the selfish overlay network is whenever there is a link failure the overlay nodes in the network will route the packet to t...
DSTN (Distributed Sleep Transistor Network) for Low Power Programmable Logic array Design
Singla, Pradeep; Malik, Naveen Kr; 10.5120/7004-9563
2012-01-01
With the high demand of the portable electronic products, Low- power design of VLSI circuits & Power dissipation has been recognized as a challenging technology in the recent years. PLA (Programming logic array) is one of the important off shelf part in the industrial application. This paper describes the new design of PLA using power gating structure sleep transistor at circuit level implementation for the low power applications. The important part of the power gating design i.e. header and footer switch selection is also describes in the paper. The simulating results of the proposed architecture of the new PLA is shown and compared with the conventional PLA. This paper clearly shows the optimization in the reduction of power dissipation in the new design implementation of the PLA. The transient response of the power gates structure of PLA is also illustrate in the paper by using TINA-PRO software.
Sensor Activation and Radius Adaptation (SARA) in Heterogeneous Sensor Networks
Bartolini, Novella; la Porta, Thomas; Petrioli, Chiara; Silvestri, Simone
2010-01-01
In this paper we address the problem of prolonging the lifetime of wireless sensor networks (WSNs) deployed to monitor an area of interest. In this scenario, a helpful approach is to reduce coverage redundancy and therefore the energy expenditure due to coverage. We introduce the first algorithm which reduces coverage redundancy by means of Sensor Activation and sensing Radius Adaptation (SARA)in a general applicative scenario with two classes of devices: sensors that can adapt their sensing range (adjustable sensors) and sensors that cannot (fixed sensors). In particular, SARA activates only a subset of all the available sensors and reduces the sensing range of the adjustable sensors that have been activated. In doing so, SARA also takes possible heterogeneous coverage capabilities of sensors belonging to the same class into account. It specifically addresses device heterogeneity by modeling the coverage problem in the Laguerre geometry through Voronoi-Laguerre diagrams. SARA executes quickly and is guarante...
Spatial Path Following for AUVs Using Adaptive Neural Network Controllers
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Jiajia Zhou
2013-01-01
Full Text Available The spatial path following control problem of autonomous underwater vehicles (AUVs is addressed in this paper. In order to realize AUVs’ spatial path following control under systemic variations and ocean current, three adaptive neural network controllers which are based on the Lyapunov stability theorem are introduced to estimate uncertain parameters of the vehicle’s model and unknown current disturbances. These controllers are designed to guarantee that all the error states in the path following system are asymptotically stable. Simulation results demonstrated that the proposed controller was effective in reducing the path following error and was robust against the disturbances caused by vehicle's uncertainty and ocean currents.
ADAPTATIVE IMAGE WATERMARKING SCHEME BASED ON NEURAL NETWORK
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BASSEL SOLAIMANE
2011-01-01
Full Text Available Digital image watermarking has been proposed as a method to enhance medical data security, confidentiality and integrity. Medical image watermarking requires extreme care when embedding additional data, given their importance to clinical diagnosis, treatment, and research. In this paper, a novel image watermarking approach based on the human visual system (HVS model and neural network technique is proposed. The watermark was inserted into the middle frequency coefficients of the cover image’s blocked DCT based transform domain. In order to make the watermark stronger and less susceptible to different types of attacks, it is essential to find the maximum amount of interested watermark before the watermark becomes visible. In this paper, neural networks are used to implement an automated system of creating maximum-strength watermarks. The experimental results show that such method can survive of common image processing operations and has good adaptability for automated watermark embedding.
Strategic tradeoffs in competitor dynamics on adaptive networks
Hébert-Dufresne, Laurent; Noël, Pierre-André; Young, Jean-Gabriel; Libby, Eric
2016-01-01
Non-linear competitor dynamics have been studied on several non-trivial but static network structures. We consider a general model on adaptive networks and interpret the resulting structure as a signature of competitor strategies. We combine the voter model with a directed stochastic block model to encode how a strategy targets competitors (i.e., an aggressive strategy) or its own type (i.e., a defensive strategy). We solve the dynamics in particular cases with tradeoffs between aggressiveness and defensiveness. These tradeoffs yield interesting behaviors such as long transient dynamics, sensitive dependence to initial conditions, and non-transitive dynamics. Not only are such results reminiscent of classic voting paradoxes but they also translate to a dynamical view of political campaign strategies. Finally, while in a two competitor system there exists an optimal strategy that balances aggressiveness and defensiveness, three competitor systems have no such solution. The introduction of extreme strategies ca...
Ultra Low Energy FDSOI Asynchronous Reconfiguration Network for Adaptive Circuits
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Soundous Chairat
2017-05-01
Full Text Available This paper introduces a plug-and-play on-chip asynchronous communication network aimed at the dynamic reconfiguration of a low-power adaptive circuit such as an internet of things (IoT system. By using a separate communication network, we can address both digital and analog blocks at a lower configuration cost, increasing the overall system power efficiency. As reconfiguration only occurs according to specific events and has to be automatically in stand-by most of the time, our design is fully asynchronous using handshake protocols. The paper presents the circuit’s architecture, performance results, and an example of the reconfiguration of frequency locked loops (FLL to validate our work. We obtain an overall energy per bit of 0.07 pJ/bit for one stage, in a 28 nm Fully Depleted Silicon On Insulator (FDSOI technology at 0.6 V and a 1.1 ns/bit latency per stage.
LOAD AWARE ADAPTIVE BACKBONE SYNTHESIS IN WIRELESS MESH NETWORKS
Institute of Scientific and Technical Information of China (English)
Yuan Yuan; Zheng Baoyu
2009-01-01
Wireless Mesh Networks (WMNs) are envisioned to support the wired backbone with a wireless Backbone Networks (BNet) for providing internet connectivity to large-scale areas.With a wide range of internet-oriented applications with different Quality of Service (QoS) requirement,the large-scale WMNs should have good scalability and large bandwidth.In this paper,a Load Aware Adaptive Backbone Synthesis (LAABS) algorithm is proposed to automatically balance the traffic flow in the WMNs.The BNet will dynamically split into smaller size or merge into bigger one according to statistic load information of Backbone Nodes (BNs).Simulation results show LAABS generates moderate BNet size and converges quickly,thus providing scalable and stable BNet to facilitate traffic flow.
A POINT OF VIEW ON THE LOGIC MODELLING OF THE FINANCIAL NETWORK
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Mihail DIMITRIU
2014-03-01
Full Text Available The identification and solving of the different problems that confront us presently, particularly due to the process of globalization, requires a more complex approach of the financial domain. We hereby undertake to bring clarifications and proposals for a more profound approach of the analytical aspects of the network-type models. We thus identify the elements of a financial network which bestow upon it its character if specificity, such as knots, instruments, operations, interconnections, interactions, determinants and flows. We also identify some defining characteristics of the financial network, such as its credibility, representativeness, complexity, efficacy, extensiveness, intensiveness, connectivity, integrability and establishment. Finally, we describe a mechanism of transformation of the financial flows within a network knot, using the concept of interface. We mention that, to a significant extent, the present paper was expounded at the International Conference Financial and Monetary Economics FME 2013 for Financial and Monetary Research, 25 October 2013.
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Y Indu
2016-04-01
Full Text Available Wireless sensor networks monitor the environment with various types of sensors. Environment in its broader terms can be the geographic environment or it can be our human body. One such type of network is Wearable and Implantable Body Sensor Network (WIBSN. This paper focuses on processing of data generated from WIBSN. WIBSN includes a network of sensors that generate different type of values. This paper treats each sensor as a dimension in the whole dataset. In this case, data may have both continuous and discrete values. Hence; proposed work can be applicable for both of those data values. By identifying nature of the sensor data model, underlying similarity or dissimilarity measure is selected. A novel Crisp clustering technique is used to simulate the proposed work.
Analysis of microseismicity using fuzzy logic and fractals for fracture network characterization
Aminzadeh, F.; Ayatollahy Tafti, T.; Maity, D.; Boyle, K.; Sahimi, M.; Sammis, C. G.
2010-12-01
The area where microseismic events occur may be correlated with the fracture network at a geothermal field. For an Enhanced Geothermal System (EGS) reservoir, an extensive fracture network with a large aerial distribution is required. Pore-pressure increase, temperature changes, volume change due to fluid withdrawal/injection and chemical alteration of fracture surfaces are all mechanisms that may explain microseismic events at a geothermal field. If these mechanisms are operative, any fuzzy cluster of the microseismic events should represent a connected fracture network. Drilling new EGS wells (both injection and production wells) in these locations may facilitate the creation of an EGS reservoir. In this article we use the fuzzy clustering technique to find the location and characteristics of fracture networks in the Geysers geothermal field. We also show that the centers of these fuzzy clusters move in time, which may represent fracture propagation or fluid movement within the fracture network. Furthermore, analyzing the distribution of fuzzy hypocenters and quantifying their fractal structure helps us to develop an accurate fracture map for the reservoir. Combining the fuzzy clustering results with the fractal analysis allows us to better understand the mechanisms for fracture stimulation and better characterize the evolution of the fracture network. We also show how micro-earthquake date collected in different time periods can be correlated with drastic changes in the distribution of active fractures resulting from injection, production or other transient events.
Adaptive Local Information Transfer in Random Boolean Networks.
Haruna, Taichi
2017-01-01
Living systems such as gene regulatory networks and neuronal networks have been supposed to work close to dynamical criticality, where their information-processing ability is optimal at the whole-system level. We investigate how this global information-processing optimality is related to the local information transfer at each individual-unit level. In particular, we introduce an internal adjustment process of the local information transfer and examine whether the former can emerge from the latter. We propose an adaptive random Boolean network model in which each unit rewires its incoming arcs from other units to balance stability of its information processing based on the measurement of the local information transfer pattern. First, we show numerically that random Boolean networks can self-organize toward near dynamical criticality in our model. Second, the proposed model is analyzed by a mean-field theory. We recognize that the rewiring rule has a bootstrapping feature. The stationary indegree distribution is calculated semi-analytically and is shown to be close to dynamical criticality in a broad range of model parameter values.
Strategic tradeoffs in competitor dynamics on adaptive networks.
Hébert-Dufresne, Laurent; Allard, Antoine; Noël, Pierre-André; Young, Jean-Gabriel; Libby, Eric
2017-08-08
Recent empirical work highlights the heterogeneity of social competitions such as political campaigns: proponents of some ideologies seek debate and conversation, others create echo chambers. While symmetric and static network structure is typically used as a substrate to study such competitor dynamics, network structure can instead be interpreted as a signature of the competitor strategies, yielding competition dynamics on adaptive networks. Here we demonstrate that tradeoffs between aggressiveness and defensiveness (i.e., targeting adversaries vs. targeting like-minded individuals) creates paradoxical behaviour such as non-transitive dynamics. And while there is an optimal strategy in a two competitor system, three competitor systems have no such solution; the introduction of extreme strategies can easily affect the outcome of a competition, even if the extreme strategies have no chance of winning. Not only are these results reminiscent of classic paradoxical results from evolutionary game theory, but the structure of social networks created by our model can be mapped to particular forms of payoff matrices. Consequently, social structure can act as a measurable metric for social games which in turn allows us to provide a game theoretical perspective on online political debates.
Adaptive control of call acceptance in WCDMA network
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Milan Manojle Šunjevarić
2013-10-01
Full Text Available In this paper, an overview of the algorithms for access control in mobile wireless networks is presented. A review of adaptive control methods of accepting a call in WCDMA networks is discussed, based on the overview of the algorithms used for this purpose, and their comparison. Appropriate comments and conculsions in comparison with the basic characteristics of these algorithms are given. The OVSF codes are explained as well as how the allocation method influences the capacity and probability of blocking.. Introduction We are witnessing a steady increase in the number of demands placed upon modern wireless networks. New applications and an increasing number of users as well as user activities growth in recent years reinforce the need for an efficient use of the spectrum and its proper distribution among different applications and classes of services. Besides humans, the last few years saw different computers, machines, applications, and, in the future, many other devices, RFID applications, and finally networked objects, as a new kind of wireless networks "users". Because of the exceptional rise in the number of users, the demands placed upon modern wireless networks are becoming larger, and spectrum management plays an important role. For these reasons, choosing an appropriate call admission control algorithm is of great importance. Multiple access and resource management in wireless networks Radio resource management of mobile networks is a set of algorithms to manage the use of radio resources with the aim is to maximize the total capacity of wireless systems with equal distribution of resources to users. Management of radio resources in cellular networks is usually located in the base station controller, the base station and the mobile terminal, and is based on decisions made on appropriate measurement and feedback. It is often defined as the maximum volume of traffic load that the system can provide for some of the requirements for the
Research on PGNAA adaptive analysis method with BP neural network
Peng, Ke-Xin; Yang, Jian-Bo; Tuo, Xian-Guo; Du, Hua; Zhang, Rui-Xue
2016-11-01
A new approach method to dealing with the puzzle of spectral analysis in prompt gamma neutron activation analysis (PGNAA) is developed and demonstrated. It consists of utilizing BP neural network to PGNAA energy spectrum analysis which is based on Monte Carlo (MC) simulation, the main tasks which we will accomplish as follows: (1) Completing the MC simulation of PGNAA spectrum library, we respectively set mass fractions of element Si, Ca, Fe from 0.00 to 0.45 with a step of 0.05 and each sample is simulated using MCNP. (2) Establishing the BP model of adaptive quantitative analysis of PGNAA energy spectrum, we calculate peak areas of eight characteristic gamma rays that respectively correspond to eight elements in each individual of 1000 samples and that of the standard sample. (3) Verifying the viability of quantitative analysis of the adaptive algorithm where 68 samples were used successively. Results show that the precision when using neural network to calculate the content of each element is significantly higher than the MCLLS.
Energy Technology Data Exchange (ETDEWEB)
Moeller, M. P.; Urbanik, II, T.; Desrosiers, A. E.
1982-03-01
This paper describes the methodology and application of the computer model CLEAR (Calculates Logical Evacuation And Response) which estimates the time required for a specific population density and distribution to evacuate an area using a specific transportation network. The CLEAR model simulates vehicle departure and movement on a transportation network according to the conditions and consequences of traffic flow. These include handling vehicles at intersecting road segments, calculating the velocity of travel on a road segment as a function of its vehicle density, and accounting for the delay of vehicles in traffic queues. The program also models the distribution of times required by individuals to prepare for an evacuation. In order to test its accuracy, the CLEAR model was used to estimate evacuatlon tlmes for the emergency planning zone surrounding the Beaver Valley Nuclear Power Plant. The Beaver Valley site was selected because evacuation time estimates had previously been prepared by the licensee, Duquesne Light, as well as by the Federal Emergency Management Agency and the Pennsylvania Emergency Management Agency. A lack of documentation prevented a detailed comparison of the estimates based on the CLEAR model and those obtained by Duquesne Light. However, the CLEAR model results compared favorably with the estimates prepared by the other two agencies.
Wei, Duo; Bodenreider, Olivier
2010-01-01
To investigate errors identified in SNOMED CT by human reviewers with help from the Abstraction Network methodology and examine why they had escaped detection by the Description Logic (DL) classifier. Case study; Two examples of errors are presented in detail (one missing IS-A relation and one duplicate concept). After correction, SNOMED CT is reclassified to ensure that no new inconsistency was introduced. DL-based auditing techniques built in terminology development environments ensure the logical consistency of the terminology. However, complementary approaches are needed for identifying and addressing other types of errors.
Nugamesh Mutter, Kussay; Mat Jafri, Mohd Zubir; Abdul Aziz, Azlan
2010-05-01
Many researches are conducted to improve Hopfield Neural Network (HNN) performance especially for speed and memory capacity in different approaches. However, there is still a significant scope of developing HNN using Optical Logic Gates. We propose here a new model of HNN based on all-optical XNOR logic gates for real time color image recognition. Firstly, we improved HNN toward optimum learning and converging operations. We considered each unipolar image as a set of small blocks of 3-pixels as vectors for HNN. This enables to save large number of images in the net with best reaching into global minima, and because there are only eight fixed states of weights so that only single iteration performed to construct a vector with stable state at minimum energy. HNN is useless in dealing with data not in bipolar representation. Therefore, HNN failed to work with color images. In RGB bands each represents different values of brightness, for d-bit RGB image it is simply consists of d-layers of unipolar. Each layer is as a single unipolar image for HNN. In addition, the weight matrices with stability of unity at the diagonal perform clear converging in comparison with no self-connecting architecture. Synchronously, each matrix-matrix multiplication operation would run optically in the second part, since we propose an array of all-optical XOR gates, which uses Mach-Zehnder Interferometer (MZI) for neurons setup and a controlling system to distribute timely signals with inverting to achieve XNOR function. The primary operation and simulation of the proposal HNN is demonstrated.
A DYNAMIC APPROACH FOR RATE ADAPTATION IN MOBILE ADHOC NETWORKS
Directory of Open Access Journals (Sweden)
Suganya Subramaniam
2013-01-01
Full Text Available A Mobile Ad hoc Network (MANET is a collection of mobile nodes with no fixed infrastructure. The absence of central authorization facility in dynamic and distributed environment affects the optimal utilization of resources like, throughput, power and bandwidth. Rate adaptation is the key technique to optimize the resource throughput. Some recently proposed rate adaptations use Request to Send/Clear to Send (RTS/CTS to suppress the collision effect by differentiating collisions from channel errors. This study presents a methodology to detect the misbehavior of nodes in MANET and proposed the new dynamic algorithm for rate adaptation which in turn can improve the throughput. The proposed approach is implemented in the distributed stipulating architecture with core and access routers. This method does not require additional probing overhead incurred by RTS/CTS exchanges and may be practically deployed without change in firmware. The collision and channel error occurrence will be detected by core router and intimated to the access router to choose alternate route and retain the current rate for transmission. The extensive simulation results demonstrate the effectiveness of proposed method by comparing with existing approaches.
A Fuzzy Logic Approach to Beaconing for Vehicular Ad hoc Networks
Ghafoor, Kayhan Zrar; Bakar, Kamalrulnizam Abu; Eenennaam, van Martijn; Khokhar, Rashid Hafeez; Gonzalez, Alberto J.
2011-01-01
Vehicular Ad Hoc Network (VANET) is an emerging field of technology that allows vehicles to communicate together in the absence of fixed infrastructure. The basic premise of VANET is that in order for a vehicle to detect other vehicles in the vicinity. This cognizance, awareness of other vehicles, c
A Fuzzy Logic Approach to Beaconing for Vehicular Ad hoc Networks
Ghafoor, Kayhan Zrar; Bakar, Kamalrulnizam Abu; van Eenennaam, Martijn; Khokhar, Rashid Hafeez; Gonzalez, Alberto J.
Vehicular Ad Hoc Network (VANET) is an emerging field of technology that allows vehicles to communicate together in the absence of fixed infrastructure. The basic premise of VANET is that in order for a vehicle to detect other vehicles in the vicinity. This cognizance, awareness of other vehicles,
Smullyan, Raymond
2008-01-01
This book features a unique approach to the teaching of mathematical logic by putting it in the context of the puzzles and paradoxes of common language and rational thought. It serves as a bridge from the author's puzzle books to his technical writing in the fascinating field of mathematical logic. Using the logic of lying and truth-telling, the author introduces the readers to informal reasoning preparing them for the formal study of symbolic logic, from propositional logic to first-order logic, a subject that has many important applications to philosophy, mathematics, and computer science. T
Directory of Open Access Journals (Sweden)
Xueling Jiang
2014-01-01
Full Text Available The problem of adaptive asymptotical synchronization is discussed for the stochastic complex dynamical networks with time-delay and Markovian switching. By applying the stochastic analysis approach and the M-matrix method for stochastic complex networks, several sufficient conditions to ensure adaptive asymptotical synchronization for stochastic complex networks are derived. Through the adaptive feedback control techniques, some suitable parameters update laws are obtained. Simulation result is provided to substantiate the effectiveness and characteristics of the proposed approach.
Combining fuzzy logic and eigenvector centrality measure in social network analysis
Parand, Fereshteh-Azadi; Rahimi, Hossein; Gorzin, Mohsen
2016-10-01
The rapid growth of social networks use has made a great platform to present different services, increasing beneficiary of services and business profit. Therefore considering different levels of member activities in these networks, finding highly active members who can have the influence on the choice and the role of other members of the community is one the most important and challenging issues in recent years. These nodes that usually have a high number of relations with a lot of quality interactions are called influential nodes. There are various types of methods and measures presented to find these nodes. Among all the measures, centrality is the one that identifies various types of influential nodes in a network. Here we define four different factors which affect the strength of a relationship. A fuzzy inference system calculates the strength of each relation, creates a crisp matrix in which the corresponding elements identify the strength of each relation, and using this matrix eigenvector measure calculates the most influential node. Applying our suggested method resulted in choosing a more realistic central node with consideration of the strength of all friendships.
Fuzzy-Logic Based Distributed Energy-Efficient Clustering Algorithm for Wireless Sensor Networks
Zhang, Ying; Wang, Jun; Han, Dezhi; Wu, Huafeng; Zhou, Rundong
2017-01-01
Due to the high-energy efficiency and scalability, the clustering routing algorithm has been widely used in wireless sensor networks (WSNs). In order to gather information more efficiently, each sensor node transmits data to its Cluster Head (CH) to which it belongs, by multi-hop communication. However, the multi-hop communication in the cluster brings the problem of excessive energy consumption of the relay nodes which are closer to the CH. These nodes’ energy will be consumed more quickly than the farther nodes, which brings the negative influence on load balance for the whole networks. Therefore, we propose an energy-efficient distributed clustering algorithm based on fuzzy approach with non-uniform distribution (EEDCF). During CHs’ election, we take nodes’ energies, nodes’ degree and neighbor nodes’ residual energies into consideration as the input parameters. In addition, we take advantage of Takagi, Sugeno and Kang (TSK) fuzzy model instead of traditional method as our inference system to guarantee the quantitative analysis more reasonable. In our scheme, each sensor node calculates the probability of being as CH with the help of fuzzy inference system in a distributed way. The experimental results indicate EEDCF algorithm is better than some current representative methods in aspects of data transmission, energy consumption and lifetime of networks. PMID:28671641
Fuzzy-Logic Based Distributed Energy-Efficient Clustering Algorithm for Wireless Sensor Networks.
Zhang, Ying; Wang, Jun; Han, Dezhi; Wu, Huafeng; Zhou, Rundong
2017-07-03
Due to the high-energy efficiency and scalability, the clustering routing algorithm has been widely used in wireless sensor networks (WSNs). In order to gather information more efficiently, each sensor node transmits data to its Cluster Head (CH) to which it belongs, by multi-hop communication. However, the multi-hop communication in the cluster brings the problem of excessive energy consumption of the relay nodes which are closer to the CH. These nodes' energy will be consumed more quickly than the farther nodes, which brings the negative influence on load balance for the whole networks. Therefore, we propose an energy-efficient distributed clustering algorithm based on fuzzy approach with non-uniform distribution (EEDCF). During CHs' election, we take nodes' energies, nodes' degree and neighbor nodes' residual energies into consideration as the input parameters. In addition, we take advantage of Takagi, Sugeno and Kang (TSK) fuzzy model instead of traditional method as our inference system to guarantee the quantitative analysis more reasonable. In our scheme, each sensor node calculates the probability of being as CH with the help of fuzzy inference system in a distributed way. The experimental results indicate EEDCF algorithm is better than some current representative methods in aspects of data transmission, energy consumption and lifetime of networks.
Institute of Scientific and Technical Information of China (English)
马博军; 方勇纯; 肖潇
2007-01-01
In this paper, a switching logic-based adaptive robust control is proposed for a class of nonlinearly parameterized systems (NPS). Specifically, the controller mainly consists of a robust type term to address the system uncertainty, and a switching logic tuning mechanism to update the involved control gain. The constructed controller achieves a global uniformly ultimate boundedness (GUUB) result for the system errors, and simulation results are included to demonstrate the effectiveness of the control law.
DEFF Research Database (Denmark)
Pretolani, Daniele; Nielsen, Lars Relund; Andersen, Kim Allan;
We compare two different models for multicriterion routing in stochastic time-dependent networks: the classic "time-adaptive'' route choice and the more flexible "history-adaptive'' route choice. We point out some interesting properties of the sets of efficient solutions ("strategies'') found...... under the two models. We also suggest possible directions for improving computational techniques....
The cis-regulatory logic of the mammalian photoreceptor transcriptional network.
Directory of Open Access Journals (Sweden)
Timothy H-C Hsiau
Full Text Available The photoreceptor cells of the retina are subject to a greater number of genetic diseases than any other cell type in the human body. The majority of more than 120 cloned human blindness genes are highly expressed in photoreceptors. In order to establish an integrative framework in which to understand these diseases, we have undertaken an experimental and computational analysis of the network controlled by the mammalian photoreceptor transcription factors, Crx, Nrl, and Nr2e3. Using microarray and in situ hybridization datasets we have produced a model of this network which contains over 600 genes, including numerous retinal disease loci as well as previously uncharacterized photoreceptor transcription factors. To elucidate the connectivity of this network, we devised a computational algorithm to identify the photoreceptor-specific cis-regulatory elements (CREs mediating the interactions between these transcription factors and their target genes. In vivo validation of our computational predictions resulted in the discovery of 19 novel photoreceptor-specific CREs near retinal disease genes. Examination of these CREs permitted the definition of a simple cis-regulatory grammar rule associated with high-level expression. To test the generality of this rule, we used an expanded form of it as a selection filter to evolve photoreceptor CREs from random DNA sequences in silico. When fused to fluorescent reporters, these evolved CREs drove strong, photoreceptor-specific expression in vivo. This study represents the first systematic identification and in vivo validation of CREs in a mammalian neuronal cell type and lays the groundwork for a systems biology of photoreceptor transcriptional regulation.
Mobilization and Adaptation of a Rural Cradle-to-Career Network
Zuckerman, Sarah J.
2016-01-01
This case study explored the development of a rural cradle-to-career network with a dual focus on the initial mobilization of network members and subsequent adaptations made to maintain mobilization, while meeting local needs. Data sources included interviews with network members, observations of meetings, and documentary evidence. Network-based…
Skeleton-supported stochastic networks of organic memristive devices: Adaptations and learning
Energy Technology Data Exchange (ETDEWEB)
Erokhina, Svetlana; Sorokin, Vladimir [IFMB, Kazan Federal University, Kremliovskaya str. 18, 420008, Kazan (Russian Federation); Erokhin, Victor, E-mail: victor.erokhin@fis.unipr.it [IFMB, Kazan Federal University, Kremliovskaya str. 18, 420008, Kazan (Russian Federation); CNR-IMEM, Parco delle Scienze 37/A, 43124, Parma Italy (Italy)
2015-02-15
Stochastic networks of memristive devices were fabricated using a sponge as a skeleton material. Cyclic voltage-current characteristics, measured on the network, revealed properties, similar to the organic memristive device with deterministic architecture. Application of the external training resulted in the adaptation of the network electrical properties. The system revealed an improved stability with respect to the networks, composed from polymer fibers.
Fuzzy logic congestion control in IEEE 802.11 wireless local area networks: A performance evaluation
CSIR Research Space (South Africa)
Nyirenda, CN
2007-09-01
Full Text Available networks because they are Clement .N. Nyirenda is with the Meraka Institute, Centre for Scientific and Industrial Research, Pretoria, South Africa (+27 72 1404564; e-mail: nyirendac@ ieee.org). Dawoud S. Dawoud is with the Radio Access Technologies... heavily depends on the frame payload size. When only frames are sent, the maximum throughput on the wireless channel on the wireless channel can drop below 1Mbps even at a data rate of 11 Mbps. At present, an IEEE working group (IEEE802.11n...
Adaptive Probabilistic Broadcasting over Dense Wireless Ad Hoc Networks
Directory of Open Access Journals (Sweden)
Victor Gau
2010-01-01
Full Text Available We propose an idle probability-based broadcasting method, iPro, which employs an adaptive probabilistic mechanism to improve performance of data broadcasting over dense wireless ad hoc networks. In multisource one-hop broadcast scenarios, the modeling and simulation results of the proposed iPro are shown to significantly outperform the standard IEEE 802.11 under saturated condition. Moreover, the results also show that without estimating the number of competing nodes and changing the contention window size, the performance of the proposed iPro can still approach the theoretical bound. We further apply iPro to multihop broadcasting scenarios, and the experiment results show that within the same elapsed time after the broadcasting, the proposed iPro has significantly higher Packet-Delivery Ratios (PDR than traditional methods.
Efficient community-based control strategies in adaptive networks
Yang, Hui; Zhang, Hai-Feng
2012-01-01
Most researches on adaptive networks mainly concentrate on the properties of steady state, but neglect transient dynamics. In this study, we pay attention to the emergence of community structures in transient process and the effects of community-based control strategies on epidemic spreading. First, by normalizing modularity $Q$, we investigate the evolution of community structures during the transient process, and find that very strong community structures are induced by rewiring mechanism in the early stage of epidemic spreading, which remarkably delays the outbreaks of epidemic. Then we study the effects of control strategies started from different stages on the prevalence. Both immunization and quarantine strategies indicate that it is not "the earlier, the better" for the implementing of control measures. And the optimal control effect is obtained if control measures can be efficiently implemented in the period of strong community structure. For immunization strategy, immunizing the S nodes on SI links a...
A recurrent neural network for adaptive beamforming and array correction.
Che, Hangjun; Li, Chuandong; He, Xing; Huang, Tingwen
2016-08-01
In this paper, a recurrent neural network (RNN) is proposed for solving adaptive beamforming problem. In order to minimize sidelobe interference, the problem is described as a convex optimization problem based on linear array model. RNN is designed to optimize system's weight values in the feasible region which is derived from arrays' state and plane wave's information. The new algorithm is proven to be stable and converge to optimal solution in the sense of Lyapunov. So as to verify new algorithm's performance, we apply it to beamforming under array mismatch situation. Comparing with other optimization algorithms, simulations suggest that RNN has strong ability to search for exact solutions under the condition of large scale constraints.
Fuzzy adaptive learning control network with sigmoid membership function
Institute of Scientific and Technical Information of China (English)
无
2007-01-01
To get simpler operation in modified fuzzy adaptive learning control network (FALCON) in some engineering application, sigmoid nonlinear function is employed as a substitute of traditional Gaussian membership function. For making the modified FALCON learning more efficient and stable, a simulated annealing (SA) learning coefficient is introduced into learning algorithm. At first, the basic concepts and main advantages of FALCON were briefly reviewed. Subsequently, the topological structure and nodes operation were illustrated; the gradient-descent learning algorithm with SA learning coefficient was derived;and the distinctions between the archetype and the modification were analyzed. Eventually, the significance and worthiness of the modified FALCON were validated by its application to probability prediction of anode effect in aluminium electrolysis cells.
Kleene, Stephen Cole
2002-01-01
Undergraduate students with no prior instruction in mathematical logic will benefit from this multi-part text. Part I offers an elementary but thorough overview of mathematical logic of 1st order. Part II introduces some of the newer ideas and the more profound results of logical research in the 20th century. 1967 edition.
Energy Technology Data Exchange (ETDEWEB)
Chertkov, Michael [Los Alamos National Laboratory
2012-07-24
The goal of the DTRA project is to develop a mathematical framework that will provide the fundamental understanding of network survivability, algorithms for detecting/inferring pre-cursors of abnormal network behaviors, and methods for network adaptability and self-healing from cascading failures.
Energy Technology Data Exchange (ETDEWEB)
Chertkov, Michael [Los Alamos National Laboratory
2012-07-24
The goal of the DTRA project is to develop a mathematical framework that will provide the fundamental understanding of network survivability, algorithms for detecting/inferring pre-cursors of abnormal network behaviors, and methods for network adaptability and self-healing from cascading failures.
Scalable Lunar Surface Networks and Adaptive Orbit Access Project
National Aeronautics and Space Administration — Innovative network architecture, protocols, and algorithms are proposed for both lunar surface networks and orbit access networks. Firstly, an overlaying...
Huang, Yanyan; Ran, Xiang; Lin, Youhui; Ren, Jinsong; Qu, Xiaogang
2015-04-22
Based on enzymatic reactions-triggered changes of pH values and biocomputing, a novel and multistage interconnection biological network with multiple easy-detectable signal outputs has been developed. Compared with traditional chemical computing, the enzyme-based biological system could overcome the interference between reactions or the incompatibility of individual computing gates and offer a unique opportunity to assemble multicomponent/multifunctional logic circuitries. Our system included four enzyme inputs: β-galactosidase (β-gal), glucose oxidase (GOx), esterase (Est) and urease (Ur). With the assistance of two signal transducers (gold nanoparticles and acid-base indicators) or pH meter, the outputs of the biological network could be conveniently read by the naked eyes. In contrast to current methods, the approach present here could realize cost-effective, label-free and colorimetric logic operations without complicated instrument. By designing a series of Boolean logic operations, we could logically make judgment of the compositions of the samples on the basis of visual output signals. Our work offered a promising paradigm for future biological computing technology and might be highly useful in future intelligent diagnostics, prodrug activation, smart drug delivery, process control, and electronic applications. Copyright © 2015 Elsevier B.V. All rights reserved.
An integrated architecture of adaptive neural network control for dynamic systems
Energy Technology Data Exchange (ETDEWEB)
Ke, Liu; Tokar, R.; Mcvey, B.
1994-07-01
In this study, an integrated neural network control architecture for nonlinear dynamic systems is presented. Most of the recent emphasis in the neural network control field has no error feedback as the control input which rises the adaptation problem. The integrated architecture in this paper combines feed forward control and error feedback adaptive control using neural networks. The paper reveals the different internal functionality of these two kinds of neural network controllers for certain input styles, e.g., state feedback and error feedback. Feed forward neural network controllers with state feedback establish fixed control mappings which can not adapt when model uncertainties present. With error feedbacks, neural network controllers learn the slopes or the gains respecting to the error feedbacks, which are error driven adaptive control systems. The results demonstrate that the two kinds of control scheme can be combined to realize their individual advantages. Testing with disturbances added to the plant shows good tracking and adaptation.
Yanushkevich, Svetlana N
2008-01-01
Preface Design Process and Technology Theory of logic design Analysis and synthesis Implementation technologies Predictable technologies Contemporary CAD of logic networks Number Systems Positional numbers Counting in a positional number system Basic arithmetic operations in various number systems Binary arithmetic Radix-complement representations Techniques for conversion of numbers in various radices Overflow Residue arithmetic Other binary codes Redundancy and reliability Graphical Data Structures Graphs in discrete devices and systems design Basic definitions T
Adaptive Neural Network Nonparametric Identifier With Normalized Learning Laws.
Chairez, Isaac
2016-04-05
This paper addresses the design of a normalized convergent learning law for neural networks (NNs) with continuous dynamics. The NN is used here to obtain a nonparametric model for uncertain systems described by a set of ordinary differential equations. The source of uncertainties is the presence of some external perturbations and poor knowledge of the nonlinear function describing the system dynamics. A new adaptive algorithm based on normalized algorithms was used to adjust the weights of the NN. The adaptive algorithm was derived by means of a nonstandard logarithmic Lyapunov function (LLF). Two identifiers were designed using two variations of LLFs leading to a normalized learning law for the first identifier and a variable gain normalized learning law. In the case of the second identifier, the inclusion of normalized learning laws yields to reduce the size of the convergence region obtained as solution of the practical stability analysis. On the other hand, the velocity of convergence for the learning laws depends on the norm of errors in inverse form. This fact avoids the peaking transient behavior in the time evolution of weights that accelerates the convergence of identification error. A numerical example demonstrates the improvements achieved by the algorithm introduced in this paper compared with classical schemes with no-normalized continuous learning methods. A comparison of the identification performance achieved by the no-normalized identifier and the ones developed in this paper shows the benefits of the learning law proposed in this paper.
Energy Technology Data Exchange (ETDEWEB)
Uberti, Rafael Carvalho; Santos, Ricardo Souza; Plucenio, Agustinho [Santa Catarina Univ., Florianopolis, SC (Brazil). Dept. de Automacao e Sistemas]. E-mail: uberti, rsantos, plucenio@das.ufsc.br
2003-07-01
The majority of the oil industry processes are characterized by a nonlinear behavior. For this class of systems traditional controllers based on a linear feedback structure with constant gains cannot achieve a good performance in a wide range of operation, rejecting disturbances. Then, it is necessary to apply new design of control strategies. The proposal of this work is to apply an adaptive controller adopting a multiple model structure using elements of fuzzy logic, allowing a smooth transition between different regions of operation. The proposed approach is based on Field bus Networks, due to the increased utilization of this technology in the oil and gas industry. To clarify the application a controller is designed for a level control problem of an educational plant based on Foundation Field bus. (author)
Albert, Réka; Thakar, Juilee
2014-01-01
The biomolecules inside or near cells form a complex interacting system. Cellular phenotypes and behaviors arise from the totality of interactions among the components of this system. A fruitful way of modeling interacting biomolecular systems is by network-based dynamic models that characterize each component by a state variable, and describe the change in the state variables due to the interactions in the system. Dynamic models can capture the stable state patterns of this interacting system and can connect them to different cell fates or behaviors. A Boolean or logic model characterizes each biomolecule by a binary state variable that relates the abundance of that molecule to a threshold abundance necessary for downstream processes. The regulation of this state variable is described in a parameter free manner, making Boolean modeling a practical choice for systems whose kinetic parameters have not been determined. Boolean models integrate the body of knowledge regarding the components and interactions of biomolecular systems, and capture the system's dynamic repertoire, for example the existence of multiple cell fates. These models were used for a variety of systems and led to important insights and predictions. Boolean models serve as an efficient exploratory model, a guide for follow-up experiments, and as a foundation for more quantitative models.
Discrete rate and variable power adaptation for underlay cognitive networks
Abdallah, Mohamed M.
2010-01-01
We consider the problem of maximizing the average spectral efficiency of a secondary link in underlay cognitive networks. In particular, we consider the network setting whereby the secondary transmitter employs discrete rate and variable power adaptation under the constraints of maximum average transmit power and maximum average interference power allowed at the primary receiver due to the existence of an interference link between the secondary transmitter and the primary receiver. We first find the optimal discrete rates assuming a predetermined partitioning of the signal-to-noise ratio (SNR) of both the secondary and interference links. We then present an iterative algorithm for finding a suboptimal partitioning of the SNR of the interference link assuming a fixed partitioning of the SNR of secondary link selected for the case where no interference link exists. Our numerical results show that the average spectral efficiency attained by using the iterative algorithm is close to that achieved by the computationally extensive exhaustive search method for the case of Rayleigh fading channels. In addition, our simulations show that selecting the optimal partitioning of the SNR of the secondary link assuming no interference link exists still achieves the maximum average spectral efficiency for the case where the average interference constraint is considered. © 2010 IEEE.
Disruption and adaptation of urban transport networks from flooding
Directory of Open Access Journals (Sweden)
Pregnolato Maria
2016-01-01
Full Text Available Transport infrastructure networks are increasingly vulnerable to disruption from extreme rainfall events due to increasing surface water runoff from urbanization and changes in climate. Impacts from such disruptions typically extend far beyond the flood footprint, because of the interconnection and spatial extent of modern infrastructure. An integrated flood risk assessment couples high resolution information on depth and velocity from the CityCAT urban flood model with empirical analysis of vehicle speeds in different depths of flood water, to perturb a transport accessibility model and determine the impact of a given event on journey times across the urban area. A case study in Newcastle-upon-Tyne (UK shows that even minor flooding associate with a 1 in 10 year event can cause traffic disruptions of nearly half an hour. Two adaptation scenarios are subsequently tested (i hardening (i.e. flood protection a single major junction, (ii introduction of green roofs across all buildings. Both options have benefits in terms of reduced disruption, but for a 1 in 200 year event greening all roofs in the city provided only three times the benefit of protecting one critical road junction, highlighting the importance of understanding network attributes such as capacity and flows.
Development of quantum-based adaptive neuro-fuzzy networks.
Kim, Sung-Suk; Kwak, Keun-Chang
2010-02-01
In this study, we are concerned with a method for constructing quantum-based adaptive neuro-fuzzy networks (QANFNs) with a Takagi-Sugeno-Kang (TSK) fuzzy type based on the fuzzy granulation from a given input-output data set. For this purpose, we developed a systematic approach in producing automatic fuzzy rules based on fuzzy subtractive quantum clustering. This clustering technique is not only an extension of ideas inherent to scale-space and support-vector clustering but also represents an effective prototype that exhibits certain characteristics of the target system to be modeled from the fuzzy subtractive method. Furthermore, we developed linear-regression QANFN (LR-QANFN) as an incremental model to deal with localized nonlinearities of the system, so that all modeling discrepancies can be compensated. After adopting the construction of the linear regression as the first global model, we refined it through a series of local fuzzy if-then rules in order to capture the remaining localized characteristics. The experimental results revealed that the proposed QANFN and LR-QANFN yielded a better performance in comparison with radial basis function networks and the linguistic model obtained in previous literature for an automobile mile-per-gallon prediction, Boston Housing data, and a coagulant dosing process in a water purification plant.
AQM Algorithm with Adaptive Reference Queue Threshold for Communication Networks
Directory of Open Access Journals (Sweden)
Liming Chen
2012-09-01
Full Text Available Nowadays, congestion in communication networks has been more intractable than ever before due to the explosive growth of network scale and multimedia traffic. Active queue management (AQM algorithms had been proposed to alleviate congestion to improve quality of service (QoS, but existing algorithms often suffer from some flaws in one aspect or another. In this paper, a novel AQM algorithm with adaptive reference queue threshold (ARTAQM is proposed of which the main innovative contributions are recounted as follows. First, traffic is predicted to calculate the packet loss ratio (PLR and the traffic rate based on traffic prediction algorithm. Second, by means of periodical measurements, a weighted PLR is obtained to dynamically adjust packet dropping probability in ARTAQM algorithm. Third, ARTAQM algorithm runs in both coarse and fine granularities. In coarse granularity, the mismatch of the predicted traffic rate and link capacity can adjusts the reference queue length in every period, while in fine granularity, reference queue remains fixed and the instantaneous queue is adjusted packet by packet in one period. Simulation results indicate that ARTAQM algorithm not only maintains stable queue and fast response speed, but has lower PLR and higher link utilization as well.
时滞对逻辑网络优化控制的影响%The Effect of Time Delay on the Optimization Control of Logical Networks
Institute of Scientific and Technical Information of China (English)
杨萌; 李睿; 楚天广
2012-01-01
分析了包含两种个体的逻辑网络的时滞优化控制.其中第1种被称为机器的个体策略是固定的;第2种被称为人的个体具有自适应性,即能根据系统的整体状态做出策略的调整.以矩阵的半张量积作为逻辑分析的工具,分析了在状态时滞和输入时滞的影响下使人的收益最大的最优控制问题.理论分析显示状态时滞导致最优控制策略的周期长度增加,但人的最大收益值不会改变.最后提出仿真算法,其数值结果与理论结果一致.%This paper investigates the optimal control with time delay of the logical network that consists of two kinds of agents called machine and human, respectively. The former has a fixed strategy whereas the latter can adaptively modify its strategy according to the state of the system. By means of the semi-tensor product of matrix technique* we solve the optimization problem of maximizing the payoff of human in the presence of time delay in states and inputs. The theoretical analysis indicates that the period of the optimal control strategy becomes longer due to the delay in states, but the maximum payoff does not change. We also propose a simulation algorithm. The numerical results thus obtained are in good agreement with the theoretical results.
Suppressing Halo-chaos for Intense Ion Beamby Neural Network Adaptation Control Strategy
Institute of Scientific and Technical Information of China (English)
FANGJin-qing; LUOXiao-shu; WENGJia-qiang; ZHULun-wu
2003-01-01
Neural network has some advantages of adaptation, learn-self, self-organization and suitable for high-dimension for various applications in many fields, especially among them the feed-forward back-propagating neural network self-adaptation method is suitable for control of nonlinear systems.
Tapoglou, Evdokia; Karatzas, George P.; Trichakis, Ioannis C.; Varouchakis, Emmanouil A.
2015-04-01
The purpose of this study is to evaluate the uncertainty, using various methodologies, in a combined Artificial Neural Network (ANN) - Fuzzy logic - Kriging system, which can simulate spatially and temporally the hydraulic head in an aquifer. This system uses ANNs for the temporal prediction of hydraulic head in various locations, one ANN for every location with available data, and Kriging for the spatial interpolation of ANN's results. A fuzzy logic is used for the interconnection of these two methodologies. The full description of the initial system and its functionality can be found in Tapoglou et al. (2014). Two methodologies were used for the calculation of uncertainty for the implementation of the algorithm in a study area. First, the uncertainty of Kriging parameters was examined using a Bayesian bootstrap methodology. In this case the variogram is calculated first using the traditional methodology of Ordinary Kriging. Using the parameters derived and the covariance function of the model, the covariance matrix is constructed. A common method for testing a statistical model is the use of artificial data. Normal random numbers generation is the first step in this procedure and by multiplying them by the decomposed covariance matrix, correlated random numbers (sample set) can be calculated. These random values are then fitted into a variogram and the value in an unknown location is estimated using Kriging. The distribution of the simulated values using the Kriging of different correlated random values can be used in order to derive the prediction intervals of the process. In this study 500 variograms were constructed for every time step and prediction point, using the method described above, and their results are presented as the 95th and 5th percentile of the predictions. The second methodology involved the uncertainty of ANNs training. In this case, for all the data points 300 different trainings were implemented having different training datasets each time
Adaptive output regulation and circuit realization for a class of attenuated coupled networks
Jin, Xiao-Zheng; Park, Ju H.
2015-09-01
In this paper, an adaptive regulation method for couplings and its physical implementation are presented to deal with the problem of output synchronization of networks. The networks are supposed to suffer from a fault described by network attenuation. For the sake of eliminating the adverse impact of network attenuation, a self-regulating network is introduced by adjusting coupling strength based on adaptive technique. By using the Lyapunov stability theory for a synchronization error system, asymptotic output synchronization of the overall networks can be established for the attenuated couplings even without any control input. Moreover, based on the adaptive regulation strategy, an approach for application of knowledge of electricity is proposed to physically realize the self-regulating networks. Finally, numerical simulations on a Rössler oscillator network are given to illustrate the effectiveness of the derived results.
A Markov Logic Network Based Sentence Sentimental Analysis Method%基于马尔科夫逻辑网的句子情感分析方法
Institute of Scientific and Technical Information of China (English)
杨立公; 汤世平; 朱俭
2013-01-01
提出一种基于马尔科夫逻辑网的句子情感分析方法.与深度学习方法相结合实现跨领域的知识迁移,同时采用马尔科夫逻辑网将句子的上下文信息与其它情感特征相结合实现句子情感分析.在COAE评测数据上的实验结果表明,该方法与SVM分类方法相比,准确率达到70.02％,并且在跨领域的情感分析任务中也得到了较好的结果.%A new method for sentence sentimental analysis based on Markov logic network is proposed.With the combination of Markov logic network and deep learning methods,it could realize the crossdomain knowledge migration.By the function of Markov logic network that could combine discourse information with other sentiment features of sentence,the proposed method could also realize the sentence sentiment orientated analysis.Experimental results on COAE data show that,compared with SVM method,this method could improve the precision considerably and achieve the high precision for implementing cross-domain sentimental analysis task.
Adaptive categorization of ART networks in robot behavior learning using game-theoretic formulation.
Fung, Wai-keung; Liu, Yun-hui
2003-12-01
Adaptive Resonance Theory (ART) networks are employed in robot behavior learning. Two of the difficulties in online robot behavior learning, namely, (1) exponential memory increases with time, (2) difficulty for operators to specify learning tasks accuracy and control learning attention before learning. In order to remedy the aforementioned difficulties, an adaptive categorization mechanism is introduced in ART networks for perceptual and action patterns categorization in this paper. A game-theoretic formulation of adaptive categorization for ART networks is proposed for vigilance parameter adaptation for category size control on the categories formed. The proposed vigilance parameter update rule can help improving categorization performance in the aspect of category number stability and solve the problem of selecting initial vigilance parameter prior to pattern categorization in traditional ART networks. Behavior learning using physical robot is conducted to demonstrate the effectiveness of the proposed adaptive categorization mechanism in ART networks.
Directory of Open Access Journals (Sweden)
Camilo Caraveo
2017-07-01
Full Text Available Fuzzy logic is a soft computing technique that has been very successful in recent years when it is used as a complement to improve meta-heuristic optimization. In this paper, we present a new variant of the bio-inspired optimization algorithm based on the self-defense mechanisms of plants in the nature. The optimization algorithm proposed in this work is based on the predator-prey model originally presented by Lotka and Volterra, where two populations interact with each other and the objective is to maintain a balance. The system of predator-prey equations use four variables (α, β, λ, δ and the values of these variables are very important since they are in charge of maintaining a balance between the pair of equations. In this work, we propose the use of Type-2 fuzzy logic for the dynamic adaptation of the variables of the system. This time a fuzzy controller is in charge of finding the optimal values for the model variables, the use of this technique will allow the algorithm to have a higher performance and accuracy in the exploration of the values.
Optimal Control Problem of Feeding Adaptations of Daphnia and Neural Network Simulation
Kmet', Tibor; Kmet'ov, Mria
2010-09-01
A neural network based optimal control synthesis is presented for solving optimal control problems with control and state constraints and open final time. The optimal control problem is transcribed into nonlinear programming problem, which is implemented with adaptive critic neural network [9] and recurrent neural network for solving nonlinear proprojection equations [10]. The proposed simulation methods is illustrated by the optimal control problem of feeding adaptation of filter feeders of Daphnia. Results show that adaptive critic based systematic approach and neural network solving of nonlinear equations hold promise for obtaining the optimal control with control and state constraints and open final time.
Xuguang, Guan; Yintang, Yang; Zhangming, Zhu; Duan, Zhou
2010-08-01
To improve two shortcomings of conventional network-on-chips, i.e. low utilization rate in channels between routers and excessive interconnection lines, this paper proposes a full asynchronous self-adaptive bi-directional transmission channel. It can utilize interconnection lines and register resources with high efficiency, and dynamically detect the data transmission state between routers through a direction regulator, which controls the sequencer to automatically adjust the transmission direction of the bi-directional channel, so as to provide a flexible data transmission environment. Null convention logic units are used to make the circuit quasi-delay insensitive and highly robust. The proposed bi-directional transmission channel is implemented based on SMIC 0.18 μm standard CMOS technology. Post-layout simulation results demonstrate that this self-adaptive bi-directional channel has better performance on throughput, transmission flexibility and channel bandwidth utilization compared to a conventional single direction channel. Moreover, the proposed channel can save interconnection lines up to 30% and can provide twice the bandwidth resources of a single direction transmission channel. The proposed channel can apply to an on-chip network which has limited resources of registers and interconnection lines.
An OCP Compliant Network Adapter for GALS-based SoC Design Using the MANGO Network-on-Chip
DEFF Research Database (Denmark)
Bjerregaard, Tobias; Mahadevan, Shankar; Olsen, Rasmus Grøndahl
2005-01-01
decouples communication and computation, providing memory-mapped OCP transactions based on primitive message-passing services of the network. Also, it facilitates GALS-type systems, by adapting to the clockless network. This helps leverage a modular SoC design flow. We evaluate performance and cost of 0......The demand for IP reuse and system level scalability in System-on-Chip (SoC) designs is growing. Network-onchip (NoC) constitutes a viable solution space to emerging SoC design challenges. In this paper we describe an OCP compliant network adapter (NA) architecture for the MANGO NoC. The NA...
On the Influence of Informed Agents on Learning and Adaptation over Networks
Tu, Sheng-Yuan
2012-01-01
Adaptive networks consist of a collection of agents with adaptation and learning abilities. The agents interact with each other on a local level and diffuse information across the network through their collaborations. In this work, we consider two types of agents: informed agents and uninformed agents. The former receive new data regularly and perform consultation and in-network tasks, while the latter do not collect data and only participate in the consultation tasks. We examine the performance of adaptive networks as a function of the proportion of informed agents and their distribution in space. The results reveal some interesting and surprising trade-offs between convergence rate and mean-square performance. In particular, among other results, it is shown that the performance of adaptive networks does not necessarily improve with a larger proportion of informed agents. Instead, it is established that the larger the proportion of informed agents is, the faster the convergence rate of the network becomes al...
Adaptive synchronization of neural networks with time-varying delay and distributed delay
Wang, Kai; Teng, Zhidong; Jiang, Haijun
2008-01-01
In this paper, the adaptive synchronization of neural networks with time-varying delay and distributed delay is discussed. Based on the LaSalle invariant principle of functional differential equations and the adaptive feedback control technique, some sufficient conditions for adaptive synchronization of such a system are obtained. Finally, a numerical example is given to show the effectiveness of the proposed synchronization method.
Adaptive fuzzy-neural-network control for maglev transportation system.
Wai, Rong-Jong; Lee, Jeng-Dao
2008-01-01
A magnetic-levitation (maglev) transportation system including levitation and propulsion control is a subject of considerable scientific interest because of highly nonlinear and unstable behaviors. In this paper, the dynamic model of a maglev transportation system including levitated electromagnets and a propulsive linear induction motor (LIM) based on the concepts of mechanical geometry and motion dynamics is developed first. Then, a model-based sliding-mode control (SMC) strategy is introduced. In order to alleviate chattering phenomena caused by the inappropriate selection of uncertainty bound, a simple bound estimation algorithm is embedded in the SMC strategy to form an adaptive sliding-mode control (ASMC) scheme. However, this estimation algorithm is always a positive value so that tracking errors introduced by any uncertainty will cause the estimated bound increase even to infinity with time. Therefore, it further designs an adaptive fuzzy-neural-network control (AFNNC) scheme by imitating the SMC strategy for the maglev transportation system. In the model-free AFNNC, online learning algorithms are designed to cope with the problem of chattering phenomena caused by the sign action in SMC design, and to ensure the stability of the controlled system without the requirement of auxiliary compensated controllers despite the existence of uncertainties. The outputs of the AFNNC scheme can be directly supplied to the electromagnets and LIM without complicated control transformations for relaxing strict constrains in conventional model-based control methodologies. The effectiveness of the proposed control schemes for the maglev transportation system is verified by numerical simulations, and the superiority of the AFNNC scheme is indicated in comparison with the SMC and ASMC strategies.
Adaptive synchronization of two nonlinearly coupled complex dynamical networks with delayed coupling
Zheng, Song; Wang, Shuguo; Dong, Gaogao; Bi, Qinsheng
2012-01-01
This paper investigates the adaptive synchronization between two nonlinearly delay-coupled complex networks with the bidirectional actions and nonidentical topological structures. Based on LaSalle's invariance principle, some criteria for the synchronization between two coupled complex networks are achieved via adaptive control. To validate the proposed methods, the unified chaotic system as the nodes of the networks are analyzed in detail, and numerical simulations are given to illustrate the theoretical results.
Azzali, F.; Ghazali, O.; Omar, M. H.
2017-08-01
The design of next generation networks in various technologies under the “Anywhere, Anytime” paradigm offers seamless connectivity across different coverage. A conventional algorithm such as RSSThreshold algorithm, that only uses the received strength signal (RSS) as a metric, will decrease handover performance regarding handover latency, delay, packet loss, and handover failure probability. Moreover, the RSS-based algorithm is only suitable for horizontal handover decision to examine the quality of service (QoS) compared to the vertical handover decision in advanced technologies. In the next generation network, vertical handover can be started based on the user’s convenience or choice rather than connectivity reasons. This study proposes a vertical handover decision algorithm that uses a Fuzzy Logic (FL) algorithm, to increase QoS performance in heterogeneous vehicular ad-hoc networks (VANET). The study uses network simulator 2.29 (NS 2.29) along with the mobility traffic network and generator to implement simulation scenarios and topologies. This helps the simulation to achieve a realistic VANET mobility scenario. The required analysis on the performance of QoS in the vertical handover can thus be conducted. The proposed Fuzzy Logic algorithm shows improvement over the conventional algorithm (RSSThreshold) in the average percentage of handover QoS whereby it achieves 20%, 21% and 13% improvement on handover latency, delay, and packet loss respectively. This is achieved through triggering a process in layer two and three that enhances the handover performance.
Energy Technology Data Exchange (ETDEWEB)
Huang, Yanyan; Ran, Xiang; Lin, Youhui [Laboratory of Chemical Biology, Division of Biological Inorganic Chemistry, State Key Laboratory of Rare Earth Resource Utilization, Changchun Institute of Applied Chemistry, Chinese Academy of Sciences, Changchun, Jilin 130022 (China); Graduate School of University of Chinese Academy of Sciences, Beijing 100039 (China); Ren, Jinsong, E-mail: jren@ciac.ac.cn [Laboratory of Chemical Biology, Division of Biological Inorganic Chemistry, State Key Laboratory of Rare Earth Resource Utilization, Changchun Institute of Applied Chemistry, Chinese Academy of Sciences, Changchun, Jilin 130022 (China); Qu, Xiaogang [Laboratory of Chemical Biology, Division of Biological Inorganic Chemistry, State Key Laboratory of Rare Earth Resource Utilization, Changchun Institute of Applied Chemistry, Chinese Academy of Sciences, Changchun, Jilin 130022 (China)
2015-04-22
Highlights: • A colorimetric and multistage biological network has been developed. • This system was on the basis of the enzyme-regulated changes of pH values. • This enzyme-based system could assemble large biological circuit. • Two signal transducers (DNA/AuNPs and acid–base indicators) were used. • The compositions of samples could be detected through visual output signals. - Abstract: Based on enzymatic reactions-triggered changes of pH values and biocomputing, a novel and multistage interconnection biological network with multiple easy-detectable signal outputs has been developed. Compared with traditional chemical computing, the enzyme-based biological system could overcome the interference between reactions or the incompatibility of individual computing gates and offer a unique opportunity to assemble multicomponent/multifunctional logic circuitries. Our system included four enzyme inputs: β-galactosidase (β-gal), glucose oxidase (GOx), esterase (Est) and urease (Ur). With the assistance of two signal transducers (gold nanoparticles and acid–base indicators) or pH meter, the outputs of the biological network could be conveniently read by the naked eyes. In contrast to current methods, the approach present here could realize cost-effective, label-free and colorimetric logic operations without complicated instrument. By designing a series of Boolean logic operations, we could logically make judgment of the compositions of the samples on the basis of visual output signals. Our work offered a promising paradigm for future biological computing technology and might be highly useful in future intelligent diagnostics, prodrug activation, smart drug delivery, process control, and electronic applications.
Hypertext classification based on Markov logic networks%基于Markov逻辑网的超文本分类
Institute of Scientific and Technical Information of China (English)
张玉芳; 孔润; 田源; 熊忠阳
2011-01-01
In traditional supervised learning tasks,the labeled entities are related to each other in complex ways and their labels are not independent.For example,in hypertext classification,the labels of linked pages are highly correlated.A standard approach is to classify each entity independently,ignoring the correlations between them.We use a statistically relational learning model,Markov logic networks,in hypertext classification in order to solve this problem.Our experiments prove that this model has better performance than k-nearest neighbor does in hypertext classification and the correlations between the entities benefit for the performance as well.%在传统的监督学习任务中,实体被认为是独立同分布的.然而,现实世界中实体之间通过复杂的方式相互关联.例如在超文本分类中,具有链接关系的页面之间高度相关.标准的分类方法是忽略实体之间的联系,对每个实体单独分类.本文将Markov逻辑网应用到超文本分类中,旨在改善这一问题.实验结果显示了采用Markov逻辑网模型要比采用K最邻近节点算法的分类效果好;同时将实体之间存在的联系用于学习和推理对于分类也有一定的贡献.
Directory of Open Access Journals (Sweden)
Schumann Andrew
2016-03-01
Full Text Available The paper considers main features of two groups of logics for biological devices, called Physarum Chips, based on the plasmodium. Let us recall that the plasmodium is a single cell with many diploid nuclei. It propagates networks by growing pseudopodia to connect scattered nutrients (pieces of food. As a result, we deal with a kind of computing. The first group of logics for Physarum Chips formalizes the plasmodium behaviour under conditions of nutrient-poor substrate. This group can be defined as standard storage modification machines. The second group of logics for Physarum Chips covers the plasmodium computing under conditions of nutrient-rich substrate. In this case the plasmodium behaves in a massively parallel manner and propagates in all possible directions. The logics of the second group are unconventional and deal with non-well-founded data such as infinite streams.
Adaptive autonomous Communications Routing Optimizer for Network Efficiency Management Project
National Aeronautics and Space Administration — Maximizing network efficiency for NASA's Space Networking resources is a large, complex, distributed problem, requiring substantial collaboration. We propose the...
Distributed reinforcement learning for adaptive and robust network intrusion response
Malialis, Kleanthis; Devlin, Sam; Kudenko, Daniel
2015-07-01
Distributed denial of service (DDoS) attacks constitute a rapidly evolving threat in the current Internet. Multiagent Router Throttling is a novel approach to defend against DDoS attacks where multiple reinforcement learning agents are installed on a set of routers and learn to rate-limit or throttle traffic towards a victim server. The focus of this paper is on online learning and scalability. We propose an approach that incorporates task decomposition, team rewards and a form of reward shaping called difference rewards. One of the novel characteristics of the proposed system is that it provides a decentralised coordinated response to the DDoS problem, thus being resilient to DDoS attacks themselves. The proposed system learns remarkably fast, thus being suitable for online learning. Furthermore, its scalability is successfully demonstrated in experiments involving 1000 learning agents. We compare our approach against a baseline and a popular state-of-the-art throttling technique from the network security literature and show that the proposed approach is more effective, adaptive to sophisticated attack rate dynamics and robust to agent failures.
Resource pooling for frameless network architecture with adaptive resource allocation
Institute of Scientific and Technical Information of China (English)
XU XiaoDong; WANG Da; TAO XiaoFeng; SVENSSON Tommy
2013-01-01
The system capacity for future mobile communication needs to be increased to fulfill the emerging requirements of mobile services and innumerable applications. The cellular topology has for long been regarded as the most promising way to provide the required increase in capacity. However with the emerging densification of cell deployments, the traditional cellular structure limits the efficiency of the resource, and the coordination between different types of base stations is more complicated and entails heavy cost. Consequently, this study proposes frameless network architecture （FNA） to release the cell boundaries, enabling the topology needed to implement the FNA resource allocation strategy. This strategy is based on resource pooling incorporating a new resource dimension-antenna/antenna array. Within this architecture, an adaptive resource allocation method based on genetic algorithm is proposed to find the optimal solution for the multi-dimensional resource allocation problem. Maximum throughput and proportional fair resource allocation criteria are considered. The simulation results show that the proposed architecture and resource allocation method can achieve performance gains for both criteria with a relatively low complexity compared to existing schemes.
Xenomic networks variability and adaptation traits in wood decaying fungi.
Morel, Mélanie; Meux, Edgar; Mathieu, Yann; Thuillier, Anne; Chibani, Kamel; Harvengt, Luc; Jacquot, Jean-Pierre; Gelhaye, Eric
2013-05-01
Fungal degradation of wood is mainly restricted to basidiomycetes, these organisms having developed complex oxidative and hydrolytic enzymatic systems. Besides these systems, wood-decaying fungi possess intracellular networks allowing them to deal with the myriad of potential toxic compounds resulting at least in part from wood degradation but also more generally from recalcitrant organic matter degradation. The members of the detoxification pathways constitute the xenome. Generally, they belong to multigenic families such as the cytochrome P450 monooxygenases and the glutathione transferases. Taking advantage of the recent release of numerous genomes of basidiomycetes, we show here that these multigenic families are extended and functionally related in wood-decaying fungi. Furthermore, we postulate that these rapidly evolving multigenic families could reflect the adaptation of these fungi to the diversity of their substrate and provide keys to understand their ecology. This is of particular importance for white biotechnology, this xenome being a putative target for improving degradation properties of these fungi in biomass valorization purposes.
Design of artificial genetic regulatory networks with multiple delayed adaptive responses
Kaluza, Pablo
2016-01-01
Genetic regulatory networks with adaptive responses are widely studied in biology. Usually, models consisting only of a few nodes have been considered. They present one input receptor for activation and one output node where the adaptive response is computed. In this work, we design genetic regulatory networks with many receptors and many output nodes able to produce delayed adaptive responses. This design is performed by using an evolutionary algorithm of mutations and selections that minimizes an error function defined by the adaptive response in signal shapes. We present several examples of network constructions with a predefined required set of adaptive delayed responses. We show that an output node can have different kinds of responses as a function of the activated receptor. Additionally, complex network structures are presented since processing nodes can be involved in several input-output pathways.
Tugué, Tosiyuki; Slaman, Theodore
1989-01-01
These proceedings include the papers presented at the logic meeting held at the Research Institute for Mathematical Sciences, Kyoto University, in the summer of 1987. The meeting mainly covered the current research in various areas of mathematical logic and its applications in Japan. Several lectures were also presented by logicians from other countries, who visited Japan in the summer of 1987.
Construction of a new adaptive wavelet network and its learning algorithm
Institute of Scientific and Technical Information of China (English)
无
2001-01-01
A new adaptive learning algorithm for constructing and training wavelet networks is proposed based on the time-frequency localization properties of wavelet frames and the adaptive projection algorithm. The exponential convergence of the adaptive projection algorithm in finite-dimensional Hilbert spaces is constructively proved, with exponential decay ratios given with high accuracy. The learning algorithm can sufficiently utilize the time-frequency information contained in the training data, iteratively determines the number of the hidden layer nodes and the weights of wavelet networks, and solves the problem of structure optimization of wavelet networks. The algorithm is simple and efficient, as illustrated by examples of signal representation and denoising.
Photovoltaic Power Prediction Based on Scene Simulation Knowledge Mining and Adaptive Neural Network
Directory of Open Access Journals (Sweden)
Dongxiao Niu
2013-01-01
Full Text Available Influenced by light, temperature, atmospheric pressure, and some other random factors, photovoltaic power has characteristics of volatility and intermittent. Accurately forecasting photovoltaic power can effectively improve security and stability of power grid system. The paper comprehensively analyzes influence of light intensity, day type, temperature, and season on photovoltaic power. According to the proposed scene simulation knowledge mining (SSKM technique, the influencing factors are clustered and fused into prediction model. Combining adaptive algorithm with neural network, adaptive neural network prediction model is established. Actual numerical example verifies the effectiveness and applicability of the proposed photovoltaic power prediction model based on scene simulation knowledge mining and adaptive neural network.
DEFF Research Database (Denmark)
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 ...
Directory of Open Access Journals (Sweden)
R. Lasri
2013-01-01
Full Text Available The main objective of this paper is to prove the great advantage that brings our novel approach to the intelligent control area. A set of various types of intelligent controllers have been designed to control the temperature of a room in a real-time control process in order to compare the obtained results with each other. Through a training board that allows us to control the temperature, all the used algorithms should present their best performances in this control process; therefore, our self-organized and online adaptive fuzzy logic controller (FLC will be required to present great improvements in the control task and a real high control performance. Simulation results can show clearly that the new approach presented and tested in this work is very efficient. Thus, our adaptive and self-organizing FLC presents the best accuracy compared with the remaining used controllers, and, besides that, it can guarantee an important reduction of the power consumption during the control process.
Current understanding of the formation and adaptation of metabolic systems based on network theory.
Takemoto, Kazuhiro
2012-07-12
Formation and adaptation of metabolic networks has been a long-standing question in biology. With recent developments in biotechnology and bioinformatics, the understanding of metabolism is progressively becoming clearer from a network perspective. This review introduces the comprehensive metabolic world that has been revealed by a wide range of data analyses and theoretical studies; in particular, it illustrates the role of evolutionary events, such as gene duplication and horizontal gene transfer, and environmental factors, such as nutrient availability and growth conditions, in evolution of the metabolic network. Furthermore, the mathematical models for the formation and adaptation of metabolic networks have also been described, according to the current understanding from a perspective of metabolic networks. These recent findings are helpful in not only understanding the formation of metabolic networks and their adaptation, but also metabolic engineering.
Directory of Open Access Journals (Sweden)
Yong Jin
2014-02-01
Full Text Available The data delivery over wireless links with QoS-guarantee is a big challenge because of the unreliable and dynamic characteristics of wireless sensor networks, as well as QoS diversity requirements of applications. In this paper, we propose an adaptive cooperative Forward Error Correction algorithm based on network coding, in the hope quality of experience could be satisfied on receivers with high quality. The algorithm, based on wireless link and distance, adjusts the RS coder parameter and selects the optimal relay nodes. On the other hand, we combine the channel coding and network coding technology at the data link layer to fulfil the requirements of QoS diversity. Both mathematical analysis and NS simulation results demonstrate the proposed mechanism is superior to the traditional FEC and cooperative FEC alone at the reliability, real time performance and energy efficiency. In addition, the proposed mechanism can significantly improve quality of media streaming, in terms of playable frame rate on the receiving side.
Adaptation of mobile ad-hoc network protocols for sensor networks to vehicle control applications
Sato, Kenya; Matsui, Yosuke; Koita, Takahiro
2005-12-01
As sensor network applications to monitor and control the physical environment from remote locations, a mobile ad-hoc network (MANET) has been the focus of many recent research and development efforts. A MANET, autonomous system of mobile hosts, is characterized by multi-hop wireless links, absence of any cellular infrastructure, and frequent host mobility. Many kinds of routing protocols for ad-hoc network have been proposed and still actively updated, because each application has different characteristics and requirements. Since the current studies show it is almost impossible to design an efficient routing protocol to be adapted for all kinds of applications. We, therefore, have focused a certain application, inter-vehicle communication for ITS (Intelligent Transport Systems), to evaluate the routing protocols. In our experiment, we defined several traffic flow models for inter-vehicle communication applications. By using simulation, we evaluated end-to-end delay and throughput performance of data transmission for inter-vehicle communications with the existing routing protocols. The result confirms the feasibility of using some routing protocols for inter-vehicle communication services.
Skeleton-supported stochastic networks of organic memristive devices: Adaptations and learning
Directory of Open Access Journals (Sweden)
Svetlana Erokhina
2015-02-01
Full Text Available Stochastic networks of memristive devices were fabricated using a sponge as a skeleton material. Cyclic voltage-current characteristics, measured on the network, revealed properties, similar to the organic memristive device with deterministic architecture. Application of the external training resulted in the adaptation of the network electrical properties. The system revealed an improved stability with respect to the networks, composed from polymer fibers.
Energy Technology Data Exchange (ETDEWEB)
Xu Yuhua, E-mail: yuhuaxu2004@163.co [College of Information Science and Technology, Donghua University, Shanghai 201620 (China) and Department of Maths, Yunyang Teacher' s College, Hubei 442000 (China); Zhou Wuneng, E-mail: wnzhou@163.co [College of Information Science and Technology, Donghua University, Shanghai 201620 (China); Fang Jian' an [College of Information Science and Technology, Donghua University, Shanghai 201620 (China); Lu Hongqian [Shandong Institute of Light Industry, Shandong Jinan 250353 (China)
2009-12-28
This Letter proposes an approach to identify the topological structure and unknown parameters for uncertain general complex networks simultaneously. By designing effective adaptive controllers, we achieve synchronization between two complex networks. The unknown network topological structure and system parameters of uncertain general complex dynamical networks are identified simultaneously in the process of synchronization. Several useful criteria for synchronization are given. Finally, an illustrative example is presented to demonstrate the application of the theoretical results.
Breast image feature learning with adaptive deconvolutional networks
Jamieson, Andrew R.; Drukker, Karen; Giger, Maryellen L.
2012-03-01
Feature extraction is a critical component of medical image analysis. Many computer-aided diagnosis approaches employ hand-designed, heuristic lesion extracted features. An alternative approach is to learn features directly from images. In this preliminary study, we explored the use of Adaptive Deconvolutional Networks (ADN) for learning high-level features in diagnostic breast mass lesion images with potential application to computer-aided diagnosis (CADx) and content-based image retrieval (CBIR). ADNs (Zeiler, et. al., 2011), are recently-proposed unsupervised, generative hierarchical models that decompose images via convolution sparse coding and max pooling. We trained the ADNs to learn multiple layers of representation for two breast image data sets on two different modalities (739 full field digital mammography (FFDM) and 2393 ultrasound images). Feature map calculations were accelerated by use of GPUs. Following Zeiler et. al., we applied the Spatial Pyramid Matching (SPM) kernel (Lazebnik, et. al., 2006) on the inferred feature maps and combined this with a linear support vector machine (SVM) classifier for the task of binary classification between cancer and non-cancer breast mass lesions. Non-linear, local structure preserving dimension reduction, Elastic Embedding (Carreira-Perpiñán, 2010), was then used to visualize the SPM kernel output in 2D and qualitatively inspect image relationships learned. Performance was found to be competitive with current CADx schemes that use human-designed features, e.g., achieving a 0.632+ bootstrap AUC (by case) of 0.83 [0.78, 0.89] for an ultrasound image set (1125 cases).
Adaptive robotic control driven by a versatile spiking cerebellar network.
Casellato, Claudia; Antonietti, Alberto; Garrido, Jesus A; Carrillo, Richard R; Luque, Niceto R; Ros, Eduardo; Pedrocchi, Alessandra; D'Angelo, Egidio
2014-01-01
The cerebellum is involved in a large number of different neural processes, especially in associative learning and in fine motor control. To develop a comprehensive theory of sensorimotor learning and control, it is crucial to determine the neural basis of coding and plasticity embedded into the cerebellar neural circuit and how they are translated into behavioral outcomes in learning paradigms. Learning has to be inferred from the interaction of an embodied system with its real environment, and the same cerebellar principles derived from cell physiology have to be able to drive a variety of tasks of different nature, calling for complex timing and movement patterns. We have coupled a realistic cerebellar spiking neural network (SNN) with a real robot and challenged it in multiple diverse sensorimotor tasks. Encoding and decoding strategies based on neuronal firing rates were applied. Adaptive motor control protocols with acquisition and extinction phases have been designed and tested, including an associative Pavlovian task (Eye blinking classical conditioning), a vestibulo-ocular task and a perturbed arm reaching task operating in closed-loop. The SNN processed in real-time mossy fiber inputs as arbitrary contextual signals, irrespective of whether they conveyed a tone, a vestibular stimulus or the position of a limb. A bidirectional long-term plasticity rule implemented at parallel fibers-Purkinje cell synapses modulated the output activity in the deep cerebellar nuclei. In all tasks, the neurorobot learned to adjust timing and gain of the motor responses by tuning its output discharge. It succeeded in reproducing how human biological systems acquire, extinguish and express knowledge of a noisy and changing world. By varying stimuli and perturbations patterns, real-time control robustness and generalizability were validated. The implicit spiking dynamics of the cerebellar model fulfill timing, prediction and learning functions.
Adaptive robotic control driven by a versatile spiking cerebellar network.
Directory of Open Access Journals (Sweden)
Claudia Casellato
Full Text Available The cerebellum is involved in a large number of different neural processes, especially in associative learning and in fine motor control. To develop a comprehensive theory of sensorimotor learning and control, it is crucial to determine the neural basis of coding and plasticity embedded into the cerebellar neural circuit and how they are translated into behavioral outcomes in learning paradigms. Learning has to be inferred from the interaction of an embodied system with its real environment, and the same cerebellar principles derived from cell physiology have to be able to drive a variety of tasks of different nature, calling for complex timing and movement patterns. We have coupled a realistic cerebellar spiking neural network (SNN with a real robot and challenged it in multiple diverse sensorimotor tasks. Encoding and decoding strategies based on neuronal firing rates were applied. Adaptive motor control protocols with acquisition and extinction phases have been designed and tested, including an associative Pavlovian task (Eye blinking classical conditioning, a vestibulo-ocular task and a perturbed arm reaching task operating in closed-loop. The SNN processed in real-time mossy fiber inputs as arbitrary contextual signals, irrespective of whether they conveyed a tone, a vestibular stimulus or the position of a limb. A bidirectional long-term plasticity rule implemented at parallel fibers-Purkinje cell synapses modulated the output activity in the deep cerebellar nuclei. In all tasks, the neurorobot learned to adjust timing and gain of the motor responses by tuning its output discharge. It succeeded in reproducing how human biological systems acquire, extinguish and express knowledge of a noisy and changing world. By varying stimuli and perturbations patterns, real-time control robustness and generalizability were validated. The implicit spiking dynamics of the cerebellar model fulfill timing, prediction and learning functions.
Automated interpretation of LIBS spectra using a fuzzy logic inference engine.
Hatch, Jeremy J; McJunkin, Timothy R; Hanson, Cynthia; Scott, Jill R
2012-03-01
Automated interpretation of laser-induced breakdown spectroscopy (LIBS) data is necessary due to the plethora of spectra that can be acquired in a relatively short time. However, traditional chemometric and artificial neural network methods that have been employed are not always transparent to a skilled user. A fuzzy logic approach to data interpretation has now been adapted to LIBS spectral interpretation. Fuzzy logic inference rules were developed using methodology that includes data mining methods and operator expertise to differentiate between various copper-containing and stainless steel alloys as well as unknowns. Results using the fuzzy logic inference engine indicate a high degree of confidence in spectral assignment.
Institute of Scientific and Technical Information of China (English)
HUANG Chih-wei; HWANG Jenq-neng
2006-01-01
With the rapid growth of wireless broadband technologies, such as WLAN and WiMAX, quality streaming video contents are available through portable devices anytime, anywhere. The layered multicast system using scalable video codecs has been proposed as an efficient architecture for video dissemination taking account of user and link diversities. However, in the wired/wireless combined best-effort based heterogeneous IP networks which provide more fluctuation in available bandwidth and end-to-end delay, the performance of streaming systems has been greatly degraded due to frequent packet loss, resulting from either wired congestion or wireless fading/shadowing. In this paper, we present a real-time embedded packet train probing scheme for estimating end-to-end available bandwidth so as to accomplish effective congestion and error control. This is facilitated by effective classification of packet loss sources, delay trend detection algorithm and flexible transmission rate of packets. Under the proper wireless channel modelling and estimation, our layered structure can allow appropriate subscription of video layers and adaptively insert necessary amount of forward error correction (FEC) packets so as to achieve QoS optimized system for scalable video multicasting.
Adaptive localization and tracking of objects in a sensor network
2014-01-01
[ANGLÈS] Wireless Sensor Networks (WSNs) are used to monitor physical or environmental conditions, and to pass their data through the network to a central location. These networks have applications in diverse areas including environmental, health monitoring, home automation or military. The devices that form the network have limited resources, such as power and computational capacity.\\par This thesis focus on the localization and tracking problem, presenting a method that can be used with obj...
Exploring Educational and Cultural Adaptation through Social Networking Sites
Ryan, Sherry D.; Magro, Michael J.; Sharp, Jason H.
2011-01-01
Social networking sites have seen tremendous growth and are widely used around the world. Nevertheless, the use of social networking sites in educational contexts is an under explored area. This paper uses a qualitative methodology, autoethnography, to investigate how social networking sites, specifically Facebook[TM], can help first semester…
Dynamic adaptable overlay networks for personalised service delivery
Mathieu, B.; Stiemerling, M.; Soveri, M.C.; Galis, A.; Jean, K.; Ocampo, R.; Lai, Z.; Kampmann, M.; Tariq, M.A.; Balos, K.; Ahmed, O.K.; Busropan, B.J.; Prins, M.J.
2007-01-01
Overlay Networks have been designed as a promising solution to deliver new services via the use of intermediate nodes, acting as proxies or relays. This concept enables to hide the heterogeneity and variability of the underlying networks. In the Ambient Networks (ANs) project, the objectives are to
Character Recognition Using Novel Optoelectronic Neural Network
1993-04-01
17 2.3.7. Learning rule ................................................................... 18 3. ADALINE ... ADALINE neuron and linear separability which provides a justification for multilayer networks. The MADALINE (many ADALINE ) multi layer network is also...element used In many neural networks (Figure 3.1). The ADALINE functions as an adaptive threshold logic element. In digital Implementation, an input
Energy Technology Data Exchange (ETDEWEB)
Matthew Andrews; Spyridon Antonakopoulos; Steve Fortune; Andrea Francini; Lisa Zhang
2011-07-12
This Concept Definition Study focused on developing a scientific understanding of methods to reduce energy consumption in data networks using rate adaptation. Rate adaptation is a collection of techniques that reduce energy consumption when traffic is light, and only require full energy when traffic is at full provisioned capacity. Rate adaptation is a very promising technique for saving energy: modern data networks are typically operated at average rates well below capacity, but network equipment has not yet been designed to incorporate rate adaptation. The Study concerns packet-switching equipment, routers and switches; such equipment forms the backbone of the modern Internet. The focus of the study is on algorithms and protocols that can be implemented in software or firmware to exploit hardware power-control mechanisms. Hardware power-control mechanisms are widely used in the computer industry, and are beginning to be available for networking equipment as well. Network equipment has different performance requirements than computer equipment because of the very fast rate of packet arrival; hence novel power-control algorithms are required for networking. This study resulted in five published papers, one internal report, and two patent applications, documented below. The specific technical accomplishments are the following: • A model for the power consumption of switching equipment used in service-provider telecommunication networks as a function of operating state, and measured power-consumption values for typical current equipment. • An algorithm for use in a router that adapts packet processing rate and hence power consumption to traffic load while maintaining performance guarantees on delay and throughput. • An algorithm that performs network-wide traffic routing with the objective of minimizing energy consumption, assuming that routers have less-than-ideal rate adaptivity. • An estimate of the potential energy savings in service-provider networks
2014-10-03
that must be woven into proofs of security statements. 03-10-2014 Memorandum Report Logic System-on-a-Chip Distributed systems 9888 ASDR&EAssistant...can be removed without damaging the logic. For all propositional letters p, E1. p ⊃ [r] p From now on, a distributed logic contains at least the...a ∈ x iff 〈h〉 ∈ x. These same definitions work for the canonical relation R for r : h y k where now a ∈ MA(k), [r] a, 〈r〉 a ∈ MA(h), x ∈ CF(h), and
Mobilization and Adaptation of a Rural Cradle-to-Career Network
Directory of Open Access Journals (Sweden)
Sarah J. Zuckerman
2016-10-01
Full Text Available This case study explored the development of a rural cradle-to-career network with a dual focus on the initial mobilization of network members and subsequent adaptations made to maintain mobilization, while meeting local needs. Data sources included interviews with network members, observations of meetings, and documentary evidence. Network-based social capital facilitated mobilization. Where networks were absent and where distrust and different values were evident, mobilization faltered. Three network adaptations were discovered: Special rural community organizing strategies, district-level action planning, and a theory of action focused on out-of-school factors. All three were attributable to the composition of mobilized stakeholders and this network’s rural social geography. These findings illuminate the importance of social geography in the development and advancement of rural cradle-to-career networks.
Ma, Chuang; Zhang, Hai-Feng
2016-01-01
So far, many network-structure-based link prediction methods have been proposed. However, these traditional methods were proposed by highlighting one or two structural features of networks, and then use the methods to implement link prediction in different networks. In many cases, the performance is not ideal since each network has its unique underlying structural features. In this article, by analyzing different real networks, we find that the structural features of different networks are remarkably different. In particular, even in the same networks, their inner structural features are utterly different. Inspired by these facts, an \\emph{adaptive} link prediction method is proposed to incorporate multiple structural features from the perspective of combination optimization. In the model, the weight of each structural feature is \\emph{adaptively } determined by logistic regression but not be artificially given in advance. According to our experimental results, we find that the logistic regression based link ...
NEURAL NETWORKS CONTROL OF THE HYBRID POWER UNIT BASED ON THE METHOD OF ADAPTIVE CRITICS
Directory of Open Access Journals (Sweden)
S. Serikov
2012-01-01
Full Text Available The formal statement of the optimization problem of hybrid vehicle power unit control is given. Its solving by neural networks method application on the basis of adaptive critic is considered.
Adaptive RBF Neural Network Control for Three-Phase Active Power Filter
Directory of Open Access Journals (Sweden)
Juntao Fei
2013-05-01
Full Text Available Abstract An adaptive radial basis function (RBF neural network control system for three-phase active power filter (APF is proposed to eliminate harmonics. Compensation current is generated to track command current so as to eliminate the harmonic current of non-linear load and improve the quality of the power system. The asymptotical stability of the APF system can be guaranteed with the proposed adaptive neural network strategy. The parameters of the neural network can be adaptively updated to achieve the desired tracking task. The simulation results demonstrate good performance, for example showing small current tracking error, reduced total harmonic distortion (THD, improved accuracy and strong robustness in the presence of parameters variation and nonlinear load. It is shown that the adaptive RBF neural network control system for three-phase APF gives better control than hysteresis control.
On the Interaction of Adaptive Video Streaming with Content-Centric Networking
Grandl, Reinhard; Kai SU; Westphal, Cedric
2013-01-01
Two main trends in today's internet are of major interest for video streaming services: most content delivery platforms coincide towards using adaptive video streaming over HTTP and new network architectures allowing caching at intermediate points within the network. We investigate one of the most popular streaming service in terms of rate adaptation and opportunistic caching. Our experimental study shows that the streaming client's rate selection trajectory, i.e., the set of selected segment...
Che, Y.; Li, R. X.; Han, C. X.; Wang, J.; Cui, S. G.; Deng, B.; Wei, X.
2012-08-01
This paper presents an adaptive lag synchronization based method for simultaneous identification of topology and parameters of uncertain general complex dynamical networks with and without time delays. Based on Lyapunov stability theorem and LaSalle's invariance principle, an adaptive controller is designed to realize lag synchronization between drive and response systems, meanwhile, identification criteria of network topology and system parameters are obtained. Numerical simulations illustrate the effectiveness of the proposed method.
An Adaptive WLAN Interference Mitigation Scheme for ZigBee Sensor Networks
Jo Woon Chong; Chae Ho Cho; Ho Young Hwang; Dan Keun Sung
2015-01-01
We propose an adaptive interference avoidance scheme that enhances the performance of ZigBee networks by adapting ZigBees' transmissions to measured wireless local area network (WLAN) interference. Our proposed algorithm is based on a stochastic analysis of ZigBee operation that is interfered with by WLAN transmission, given ZigBee and WLAN channels are overlaid in the industrial, scientific, and medical (ISM) band. We assume that WLAN devices have higher transmission power than ZigBee device...
Adaptive Neural Network Dynamic Inversion with Prescribed Performance for Aircraft Flight Control
Wendong Gai; Honglun Wang; Jing Zhang; Yuxia Li
2013-01-01
An adaptive neural network dynamic inversion with prescribed performance method is proposed for aircraft flight control. The aircraft nonlinear attitude angle model is analyzed. And we propose a new attitude angle controller design method based on prescribed performance which describes the convergence rate and overshoot of the tracking error. Then the model error is compensated by the adaptive neural network. Subsequently, the system stability is analyzed in detail. Finally, the proposed meth...
Adaptive control of chaotic systems based on a single layer neural network
Energy Technology Data Exchange (ETDEWEB)
Shen Liqun [Space Control and Inertia Technology Research Center, Harbin Institute of Technology, Harbin 150001 (China)], E-mail: liqunshen@gmail.com; Wang Mao [Space Control and Inertia Technology Research Center, Harbin Institute of Technology, Harbin 150001 (China)
2007-08-27
This Letter presents an adaptive neural network control method for the chaos control problem. Based on a single layer neural network, the dynamic about the unstable fixed period point of the chaotic system can be adaptively identified without detailed information about the chaotic system. And the controlled chaotic system can be stabilized on the unstable fixed period orbit. Simulation results of Henon map and Lorenz system verify the effectiveness of the proposed control method.
Directory of Open Access Journals (Sweden)
Chih-Hong Lin
2012-03-01
Full Text Available The permanent magnet synchronous motor (PMSM is suitable for high-performance servo applications and has been used widely for the industrial robots, computer-numerically-controlled (CNC machine tools and elevators. The control performance of the actual PMSM drive system depends on many parameters, such as parameter variations, external load disturbance, and friction force. Their relationships are complex and the actual PMSM drive system has the properties of nonlinear uncertainty and time-varying characteristics. It is difficult to establish an accurate model for the nonlinear uncertainty and time-varying characteristics of the actual PMSM drive system Therefore, an adaptive recurrent neural network uncertainty observer (ARNNUO based integral backstepping control system is developed to overcome this problem in this paper. The proposed control strategy is based on integral backstepping control combined with RNN uncertainty observer to estimate the required lumped uncertainty. An adaptive rule of the RNN uncertainty observer is employed to on-line adjust the weights of sigmoidal functions by using the gradient descent method and the backpropagation algorithm in according to Lyapunov function. This ARNNUO has the on-line learning ability to respond to the system’s nonlinear and time-varying behaviors. Experimental results are executed to show the control performance of the proposed control scheme.
Institute of Scientific and Technical Information of China (English)
FANG Jin-Qing; LUO Xiao-Shu; HUANG Guo-Xian
2006-01-01
Subject of the halo-chaos control in beam transport networks (channels) has become a key concerned issue for many important applications of high-current proton beam since 1990'. In this paper, the magnetic field adaptive control based on the neuralnetwork with time-delayed feedback is proposed for suppressing beam halo-chaos in the beam transport network with periodic focusing channels. The envelope radius of high-current proton beam is controlled to reach the matched beam radius by suitably selecting the control structure and parameter of the neural network, adjusting the delayed-time and control coefficient of the neural network.
Schreiter, Juerg; Ramacher, Ulrich; Heittmann, Arne; Matolin, Daniel; Schuffny, Rene
2004-05-01
We present a cellular pulse coupled neural network with adaptive weights and its analog VLSI implementation. The neural network operates on a scalar image feature, such as grey scale or the output of a spatial filter. It detects segments and marks them with synchronous pulses of the corresponding neurons. The network consists of integrate-and-fire neurons, which are coupled to their nearest neighbors via adaptive synaptic weights. Adaptation follows either one of two empirical rules. Both rules lead to spike grouping in wave like patterns. This synchronous activity binds groups of neurons and labels the corresponding image segments. Applications of the network also include feature preserving noise removal, image smoothing, and detection of bright and dark spots. The adaptation rules are insensitive for parameter deviations, mismatch and non-ideal approximation of the implied functions. That makes an analog VLSI implementation feasible. Simulations showed no significant differences in the synchronization properties between networks using the ideal adaptation rules and networks resembling implementation properties such as randomly distributed parameters and roughly implemented adaptation functions. A prototype is currently being designed and fabricated using an Infineon 130nm technology. It comprises a 128 × 128 neuron array, analog image memory, and an address event representation pulse output.
Yang, Wei; Wu, Gang; Wang, Haifeng
2010-01-01
Relay selection enhances the performance of the cooperative networks by selecting the links with higher capacity. Meanwhile link adaptation improves the spectral efficiency of wireless data-centric networks through adapting the modulation and coding schemes (MCS) to the current link condition. In this paper, relay selection is combined with link adaptation for distributed beamforming in a two-hop regenerative cooperative system. A novel signaling mechanism and related optimal algorithms are proposed for joint relay selection and link adaptation. In the proposed scheme, there is no need to feedback the relay selection results to each relay. Instead, by broadcasting the link adaptation results from the destination, each relay will automatically understand whether it is selected or not. The lower and upper bounds of the throughput of the proposed scheme are derived. The analysis and simulation results indicate that the proposed scheme provides synergistic gains compared to the pure relay selection and link adapt...
A Model for Evaluating Sharing Policies for Network-assisted HTTP Adaptive Streaming
Kleinrouweler, J.W.M.; Cabrero Barros, S.; Mei, R.D. van der; Cesar Garcia, P.S.
2016-01-01
HTTP adaptive streaming (HAS) has become the dominant technology for streaming video over the Internet. It gained popularity because of its ability to adapt the video quality to the current network conditions and other appealing properties such as usage of off-the-shelf HTTP servers and easy firewal
Lengyel, Florian
2012-01-01
We define Denial Logic DL, a system of justification logic that models an agent whose justified beliefs are false, who cannot avow his own propositional attitudes and who can believe contradictions but not tautologies of classical propositional logic. Using Artemov's natural semantics for justification logic JL, in which justifications are interpreted as sets of formulas, we provide an inductive construction of models of DL, and prove soundness and completeness results for DL. Some logical notions developed for JL, such as constant specifications and the internalization property, are inconsistent with DL. This leads us to define negative constant specifications for DL, which can be used to model agents with justified false beliefs. Denial logic can therefore be relevant to philosophical skepticism. We use DL with what we call coherent negative constant specifications to model a Putnamian brain in a vat with the justified false belief that it is not a brain in a vat, and derive a model of JL in which "I am a b...
2009-04-01
form, function, and logic is derived from Gilles Deleuze and Felix Guattari, A Thousand Plateaus; Capitalism and Schizophreni, (Minneapolis: University...February 8, 2009]. Deleuze , Gilles and Felix Guattari. A Thousand Plateaus; Capitalism and Schizophrenia. Minneapolis: University of Minnesota Press, 1987
Directory of Open Access Journals (Sweden)
A.M. Ibrahim
2016-09-01
Full Text Available This paper presents an adaptive protection coordination scheme for optimal coordination of DOCRs in interconnected power networks with the impact of DG, the used coordination technique is the Artificial Bee Colony (ABC. The scheme adapts to system changes; new relays settings are obtained as generation-level or system-topology changes. The developed adaptive scheme is applied on the IEEE 30-bus test system for both single- and multi-DG existence where results are shown and discussed.
Donges, Jonathan; Lucht, Wolfgang; Wiedermann, Marc; Heitzig, Jobst; Kurths, Jürgen
2015-04-01
In the anthropocene, the rise of global social and economic networks with ever increasing connectivity and speed of interactions, e.g., the internet or global financial markets, is a key challenge for sustainable development. The spread of opinions, values or technologies on these networks, in conjunction with the coevolution of the network structures themselves, underlies nexuses of current concern such as anthropogenic climate change, biodiversity loss or global land use change. To isolate and quantitatively study the effects and implications of network dynamics for sustainable development, we propose an agent-based model of information flow on adaptive networks between myopic harvesters that exploit private renewable resources. In this conceptual model of a network of socio-ecological systems, information on management practices flows between agents via boundedly rational imitation depending on the state of the resource stocks involved in an interaction. Agents can also adapt the structure of their social network locally by preferentially connecting to culturally similar agents with identical management practices and, at the same time, disconnecting from culturally dissimilar agents. Investigating in detail the statistical mechanics of this model, we find that an increasing rate of information flow through faster imitation dynamics or growing density of network connectivity leads to a marked increase in the likelihood of environmental resource collapse. However, we show that an optimal rate of social network adaptation can mitigate this negative effect without loss of social cohesion through network fragmentation. Our results highlight that seemingly immaterial network dynamics of spreading opinions or values can be of large relevance for the sustainable management of socio-ecological systems and suggest smartly conservative network adaptation as a strategy for mitigating environmental collapse. Hence, facing the great acceleration, these network dynamics should
Adaptive Sampling for WSAN Control Applications Using Artificial Neural Networks
2012-01-01
Wireless sensor actuator networks are becoming a solution for control applications. Reliable data transmission and real time constraints are the most significant challenges. Control applications will have some Quality of Service (QoS) requirements from the sensor network, such as minimum delay and guaranteed delivery of packets. We investigate variable sampling method to mitigate the effects of time delays in wireless networked control systems using an observer based control system model. Our...
Adaptive Resource Allocation and Internet Traffic Engineering on Data Network
Directory of Open Access Journals (Sweden)
Hatim Hussein
2015-02-01
Full Text Available This research paper describes the issues of bandwid th allocation, optimum capacity allocation, network operational cost reduction, and improve Int ernet user experience. Traffic engineering (TE is used to manipulate network traffic to achie ve certain requirements and meets certain needs. TE becomes one of the most important buildin g blocks in the design of the Internet backbone infrastructure. Research objective: effici ent allocation of bandwidth across multiple paths. Optimum path selection. Minimize network tra ffic delays and maximize bandwidth utilization over multiple network paths. The bandwi dth allocation is performed proportionally over multiple paths based on the path capacity.
Burken, John J.
2005-01-01
This viewgraph presentation covers the following topics: 1) Brief explanation of Generation II Flight Program; 2) Motivation for Neural Network Adaptive Systems; 3) Past/ Current/ Future IFCS programs; 4) Dynamic Inverse Controller with Explicit Model; 5) Types of Neural Networks Investigated; and 6) Brief example
Directory of Open Access Journals (Sweden)
Joshua Rodewald
2016-10-01
Full Text Available Supply networks existing today in many industries can behave as complex adaptive systems making them more difficult to analyze and assess. Being able to fully understand both the complex static and dynamic structures of a complex adaptive supply network (CASN are key to being able to make more informed management decisions and prioritize resources and production throughout the network. Previous efforts to model and analyze CASN have been impeded by the complex, dynamic nature of the systems. However, drawing from other complex adaptive systems sciences, information theory provides a model-free methodology removing many of those barriers, especially concerning complex network structure and dynamics. With minimal information about the network nodes, transfer entropy can be used to reverse engineer the network structure while local transfer entropy can be used to analyze the network structure’s dynamics. Both simulated and real-world networks were analyzed using this methodology. Applying the methodology to CASNs allows the practitioner to capitalize on observations from the highly multidisciplinary field of information theory which provides insights into CASN’s self-organization, emergence, stability/instability, and distributed computation. This not only provides managers with a more thorough understanding of a system’s structure and dynamics for management purposes, but also opens up research opportunities into eventual strategies to monitor and manage emergence and adaption within the environment.
Towards adaptive security for convergent wireless sensor networks in beyond 3G environments
DEFF Research Database (Denmark)
Mitseva, Anelia; Aivaloglou, Efthimia; Marchitti, Maria-Antonietta
2010-01-01
The integration of wireless sensor networks with different network systems gives rise to many research challenges to ensure security, privacy and trust in the overall architecture. The main contribution of this paper is a generic security, privacy and trust framework providing context-aware adapt...
DEFF Research Database (Denmark)
Braüner, Torben
2011-01-01
Intuitionistic hybrid logic is hybrid modal logic over an intuitionistic logic basis instead of a classical logical basis. In this short paper we introduce intuitionistic hybrid logic and we give a survey of work in the area.......Intuitionistic hybrid logic is hybrid modal logic over an intuitionistic logic basis instead of a classical logical basis. In this short paper we introduce intuitionistic hybrid logic and we give a survey of work in the area....
2014-09-17
AFRL-OSR-VA-TR-2014-0255 ADAPTIVE PIEZOELECTRIC CIRCUITRY SENSOR NETWORK KON-WELL WANG MICHIGAN UNIV ANN ARBOR Final Report 09/17/2014 DISTRIBUTION A...by ANSI Std. Z39.18 09-09-2014 Final Performance Report 06-01-2011 - 05-31-2014 Adaptive Piezoelectric Circuitry Sensor Network with High-Frequency...approach. Specifically, we propose to create a new concept of adaptive high-frequency piezoelectric self-sensing interrogation by means of tunable
Directory of Open Access Journals (Sweden)
Tat-Bao-Thien Nguyen
2014-01-01
Full Text Available In this paper, based on fuzzy neural networks, we develop an adaptive sliding mode controller for chaos suppression and tracking control in a chaotic permanent magnet synchronous motor (PMSM drive system. The proposed controller consists of two parts. The first is an adaptive sliding mode controller which employs a fuzzy neural network to estimate the unknown nonlinear models for constructing the sliding mode controller. The second is a compensational controller which adaptively compensates estimation errors. For stability analysis, the Lyapunov synthesis approach is used to ensure the stability of controlled systems. Finally, simulation results are provided to verify the validity and superiority of the proposed method.
Institute of Scientific and Technical Information of China (English)
Zu Yun-Xiao; Zhou Jie
2012-01-01
Multi-user cognitive radio network resource allocation based on the adaptive niche immune genetic algorithm is proposed,and a fitness function is provided.Simulations are conducted using the adaptive niche immune genetic algorithm,the simulated annealing algorithm,the quantum genetic algorithm and the simple genetic algorithm,respectively.The results show that the adaptive niche immune genetic algorithm performs better than the other three algorithms in terms of the multi-user cognitive radio network resource allocation,and has quick convergence speed and strong global searching capability,which effectively reduces the system power consumption and bit error rate.
An Adaptive Scheme for Neighbor Discovery in Mobile Ad Hoc Networks
Institute of Scientific and Technical Information of China (English)
无
2007-01-01
The neighbor knowledge in mobile ad hoc networks is important information. However, the accuracy of neighbor knowledge is paid in terms of energy consumption. In traditional schemes for neighbor discovery, a mobile node uses fixed period to send HELLO messages to notify its existence. An adaptive scheme was proposed.The objective is that when mobile nodes are distributed sparsely or move slowly, fewer HELLO messages are needed to achieve reasonable accuracy, while in a mutable network where nodes are dense or move quickly, they can adaptively send more HELLO messages to ensure the accuracy. Simulation results show that the adaptive scheme achieves the objective and performs effectively.