Search results for "Stochastic neural network"

showing 5 items of 5 documents

Finite-time boundedness for uncertain discrete neural networks with time-delays and Markovian jumps

2014

This paper is concerned with stochastic finite-time boundedness analysis for a class of uncertain discrete-time neural networks with Markovian jump parameters and time-delays. The concepts of stochastic finite-time stability and stochastic finite-time boundedness are first given for neural networks. Then, applying the Lyapunov approach and the linear matrix inequality technique, sufficient criteria on stochastic finite-time boundedness are provided for the class of nominal or uncertain discrete-time neural networks with Markovian jump parameters and time-delays. It is shown that the derived conditions are characterized in terms of the solution to these linear matrix inequalities. Finally, n…

Lyapunov functionDiscrete-time systems; Linear matrix inequalities; Markovian jump systems; Neural networks; Stochastic finite-time boundedness; Artificial Intelligence; Computer Science Applications1707 Computer Vision and Pattern Recognition; Cognitive NeuroscienceArtificial neural networkMarkov chainStochastic processCognitive NeuroscienceMarkovian jump systemsLinear matrix inequalitiesLinear matrix inequalityComputer Science Applications1707 Computer Vision and Pattern RecognitionComputer Science Applicationssymbols.namesakeDiscrete time and continuous timeArtificial IntelligenceDiscrete-time systemssymbolsCalculusApplied mathematicsStochastic neural networkJump processNeural networksStochastic finite-time boundednessMathematics
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Robust linear quadratic mean-field games in crowd-seeking social networks.

2013

We consider a social network where opinions evolve following a stochastic averaging process under the influence of adversarial disturbances. We provide a robust mean-field game model in the spirit of H∞-optimal control, establish existence of a mean-field equilibrium, and analyze its stochastic stability.

Stochastic controlContinuous-time stochastic processMathematical optimizationSocial networkStochastic processbusiness.industryControl (management)mean field gamesRobust controlStochastic neural networkbusinessGame theoryMathematical economicsMathematics
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Diffusive neural network

2002

Abstract A non-connectionist model of a neuronal network based on passive diffusion of neurotransmitters is presented as an alternative to hard-wired artificial neural networks. Classic thermodynamical approach shows that the diffusive network is capable of exhibiting asymptotic stability and a dynamics resembling that of a chaotic system. Basic computational capabilities of the net are discussed based on the equivalence with a Turing machine. The model offers a way to represent mass-sustained brain functions in terms of recurrent behaviors in the phase space.

Theoretical computer scienceQuantitative Biology::Neurons and CognitionArtificial neural networkComputer scienceCognitive NeuroscienceChaoticTopologyComputer Science ApplicationsTuring machinesymbols.namesakeRecurrent neural networkExponential stabilityArtificial IntelligencePhase spacesymbolsBiological neural networkStochastic neural networkNeurocomputing
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A novel strategy for solving the stochastic point location problem using a hierarchical searching scheme

2014

Stochastic point location (SPL) deals with the problem of a learning mechanism (LM) determining the optimal point on the line when the only input it receives are stochastic signals about the direction in which it should move. One can differentiate the SPL from the traditional class of optimization problems by the fact that the former considers the case where the directional information, for example, as inferred from an Oracle (which possibly computes the derivatives), suffices to achieve the optimization-without actually explicitly computing any derivatives. The SPL can be described in terms of a LM (algorithm) attempting to locate a point on a line. The LM interacts with a random environme…

Continuous-time stochastic processMathematical optimizationOptimization problemControlled random walkTime reversibilityDiscretized learning02 engineering and technologyTime reversibilityLearning automataStochastic-point problem0202 electrical engineering electronic engineering information engineeringElectrical and Electronic EngineeringStochastic neural networkMathematicsBinary treeLearning automata020206 networking & telecommunicationsRandom walkComputer Science ApplicationsHuman-Computer InteractionControl and Systems Engineering020201 artificial intelligence & image processingStochastic optimizationSoftwareInformation Systems
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The Random Neural Network Model for the On-line Multicast Problem

2005

In this paper we propose the adoption of the Random Neural Network Model for the solution of the dynamic version of the Steiner Tree Problem in Networks (SPN). The Random Neural Network (RNN) is adopted as a heuristic capable of improving solutions achieved by previously proposed dynamic algorithms. We adapt the RNN model in order to map the network characteristics during a multicast transmission. The proposed methodology is validated by means of extensive experiments.

Multicast transmissionMulticastHeuristic (computer science)Computer sciencebusiness.industryDistributed computingComputer Science::Neural and Evolutionary ComputationSteiner tree problemRandom neural networksymbols.namesakeProbabilistic neural networkLine (geometry)symbolsArtificial intelligenceStochastic neural networkbusiness
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