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Results 1 - 10
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73,582
Discrete Dynamical Networks and their Attractor Basins
- Complexity International
, 1998
"... A key notion in the study of network dynamics is that state-space is connected into basins of attraction. Convergence in attractor basins correlates with order-complexity-chaos measures on space-time patterns. A network's \memory", its ability to categorize, is provided by the con- guratio ..."
Abstract
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Cited by 27 (0 self)
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A key notion in the study of network dynamics is that state-space is connected into basins of attraction. Convergence in attractor basins correlates with order-complexity-chaos measures on space-time patterns. A network's \memory", its ability to categorize, is provided by the con
Research Article Robust Impulsive Synchronization of Discrete Dynamical Networks
"... We aim to study robust impulsive synchronization problem for uncertain discrete dynamical net-works. For the discrete dynamical networks with unknown but bounded network coupling, we will design some robust impulsive controllers which ensure that the state of a discrete dynamical network asymptotica ..."
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We aim to study robust impulsive synchronization problem for uncertain discrete dynamical net-works. For the discrete dynamical networks with unknown but bounded network coupling, we will design some robust impulsive controllers which ensure that the state of a discrete dynamical network
Discrete dynamical networks, basins of attraction, and content addressable memory
"... A key notion underlying the collective behaviour of discrete dynamical networks is that state-space is organized into a number of basins of attraction, connecting states according to their transitions, and summing up the network’s global dynamics[4, 5]. Discrete dynamical networks consist of a set o ..."
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A key notion underlying the collective behaviour of discrete dynamical networks is that state-space is organized into a number of basins of attraction, connecting states according to their transitions, and summing up the network’s global dynamics[4, 5]. Discrete dynamical networks consist of a set
Dynamic Bayesian Networks: Representation, Inference and Learning
, 2002
"... Modelling sequential data is important in many areas of science and engineering. Hidden Markov models (HMMs) and Kalman filter models (KFMs) are popular for this because they are simple and flexible. For example, HMMs have been used for speech recognition and bio-sequence analysis, and KFMs have bee ..."
Abstract
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Cited by 770 (3 self)
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been used for problems ranging from tracking planes and missiles to predicting the economy. However, HMMs
and KFMs are limited in their “expressive power”. Dynamic Bayesian Networks (DBNs) generalize HMMs by allowing the state space to be represented in factored form, instead of as a single discrete
Discrete Dynamics Lab: Tools for investigating cellular automata and discrete dynamical networks”, (updated for multi-value
- Artificial Life Models in Software”, eds. A.Adamatzky and M.Komosinski
, 2004
"... DDLab is interactive graphics software for creating, visualizing, and analyzing many aspects of Cellular Automata, Random Boolean Net-works, and Discrete Dynamical Networks in general, and studying their behavior, both from the time-series perspective – space-time patterns, and from the state-space ..."
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Cited by 4 (1 self)
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DDLab is interactive graphics software for creating, visualizing, and analyzing many aspects of Cellular Automata, Random Boolean Net-works, and Discrete Dynamical Networks in general, and studying their behavior, both from the time-series perspective – space-time patterns, and from the state
Ant algorithms for discrete optimization
- ARTIFICIAL LIFE
, 1999
"... This article presents an overview of recent work on ant algorithms, that is, algorithms for discrete optimization that took inspiration from the observation of ant colonies’ foraging behavior, and introduces the ant colony optimization (ACO) metaheuristic. In the first part of the article the basic ..."
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Cited by 489 (42 self)
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This article presents an overview of recent work on ant algorithms, that is, algorithms for discrete optimization that took inspiration from the observation of ant colonies’ foraging behavior, and introduces the ant colony optimization (ACO) metaheuristic. In the first part of the article the basic
Dynamic source routing in ad hoc wireless networks
- Mobile Computing
, 1996
"... An ad hoc network is a collection of wireless mobile hosts forming a temporary network without the aid of any established infrastructure or centralized administration. In such an environment, it may be necessary for one mobile host to enlist the aid of other hosts in forwarding a packet to its desti ..."
Abstract
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Cited by 3108 (31 self)
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destination, due to the limited range of each mobile host’s wireless transmissions. This paper presents a protocol for routing in ad hoc networks that uses dynamic source routing. The protocol adapts quickly to routing changes when host movement is frequent, yet requires little or no overhead during periods
Alliances and networks
"... This paper introduces a social network perspective to the study of strategic alliances. It extends prior research, which has primarily considered alliances as dyadic exchanges and paid less attention to the fact that key precursors, processes, and outcomes associated with alliances can be defined an ..."
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Cited by 833 (6 self)
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and shaped in important ways by the social networks within which most firms are embedded. It identifies five key issues for the study of alliances: (1) the formation of alliances, (2) the choice of governance structure, (3) the dynamic evolution of alliances, (4) the performance of alliances, and (5
Consensus Problems in Networks of Agents with Switching Topology and Time-Delays
, 2003
"... In this paper, we discuss consensus problems for a network of dynamic agents with fixed and switching topologies. We analyze three cases: i) networks with switching topology and no time-delays, ii) networks with fixed topology and communication time-delays, and iii) max-consensus problems (or leader ..."
Abstract
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Cited by 1112 (21 self)
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In this paper, we discuss consensus problems for a network of dynamic agents with fixed and switching topologies. We analyze three cases: i) networks with switching topology and no time-delays, ii) networks with fixed topology and communication time-delays, and iii) max-consensus problems (or
Dynamic Fine-grained Localization in Ad-Hoc Networks of Sensors
- PROCEEDINGS OF THE SEVENTH ANNUAL INTERNATIONAL CONFERENCE ON MOBILE COMPUTING AND NETWORKING, MOBICOM 2001
, 2001
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