Adaptive Networks Theory Models And Applications Pdf
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- Predicting collapse of adaptive networked systems without knowing the network
- An Adaptive Network Model to Simulate Consensus Formation Driven by Social Identity Recognition
- Artificial neural network
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It seems that you're in Germany. We have a dedicated site for Germany. With adaptive, complex networks, the evolution of the network topology and the dynamical processes on the network are equally important and often fundamentally entangled.
Predicting collapse of adaptive networked systems without knowing the network
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A complex adaptive system is a system that is complex in that it is a dynamic network of interactions , but the behavior of the ensemble may not be predictable according to the behavior of the components. It is adaptive in that the individual and collective behavior mutate and self-organize corresponding to the change-initiating micro-event or collection of events. The study of complex adaptive systems, a subset of nonlinear dynamical systems ,  is an interdisciplinary matter that attempts to blend insights from the natural and social sciences to develop system-level models and insights that allow for heterogeneous agents , phase transition , and emergent behavior. The term complex adaptive systems , or complexity science , is often used to describe the loosely organized academic field that has grown up around the study of such systems. Complexity science is not a single theory—it encompasses more than one theoretical framework and is interdisciplinary, seeking the answers to some fundamental questions about living , adaptable, changeable systems. Complex adaptive systems may adopt hard or softer approaches. Softer theories use natural language and narratives that may be imprecise, and agents are subjects having both tangible and intangible properties.
An Adaptive Network Model to Simulate Consensus Formation Driven by Social Identity Recognition
Skip to search form Skip to main content You are currently offline. Some features of the site may not work correctly. DOI: Sutton and A. Sutton , A. Many adaptive neural network theories are based on neuronlike adaptive elements that can behave as single unit analogs of associative conditioning. Expand Abstract.
Request PDF | On Jan 1, , Thilo Gross and others published Adaptive Networks, Theory, Models and Applications | Find, read and cite all the research you.
Artificial neural network
Collective intelligence Collective action Self-organized criticality Herd mentality Phase transition Agent-based modelling Synchronization Ant colony optimization Particle swarm optimization Swarm behaviour. Evolutionary computation Genetic algorithms Genetic programming Artificial life Machine learning Evolutionary developmental biology Artificial intelligence Evolutionary robotics. Reaction—diffusion systems Partial differential equations Dissipative structures Percolation Cellular automata Spatial ecology Self-replication.
Models of the consensus of the individual state in social systems have been the subject of recent research studies in the physics literature. We investigate how network structures coevolve with the individual state under the framework of social identity theory. Also, we propose an adaptive network model to achieve state consensus or local structural adjustment of individuals by evaluating the homogeneity among them. Specifically, the similarity threshold significantly affects the evolution of the network with different initial conditions, and thus there emerges obvious community structure and polarization. More importantly, there exists a critical point of phase transition, at which the network may evolve into a significant community structure and state-consistent group.
Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the National Science Foundation. The rapidly growing complex network science has presented novel approaches to complex systems modeling that were not fully foreseen even in a few decades ago. Interestingly, complex network science has traditionally addressed either "dynamics on networks" state transition on a network with a fixed topology or "dynamics of networks" topological transformation of a network with no dynamic state changes almost separately. In many real-world complex biological and social networks, however, these two dynamics interact with each other and often coevolve over the same time scales. Modeling and predicting state-topology coevolution is now recognized as one of the most significant challenges in complex network science. The goals of this NSF-funded project were to establish a generalized modeling framework that could effectively describe state-topology coevolution of complex adaptive networks and to develop computational methods for automatic discovery of dynamical rules that best capture both state transition and topological transformation in empirical data. To achieve these goals, graph rewriting systems were used as a means of unified representation of state transition and topological transformation.
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