Global ETD Search
Search theses and dissertations gathered from participating repositories worldwide. Every result links back to the library that holds it. No account is needed.
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Showing 1 to 11 of 11 for “"Stochastic learning"”.
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Synthesis of stochastic learning automata.
… interest has developed in the field of stochastic learning automata theory and, consequently, the application areas for learning systems. In control engineering, they are viewed as a means to implement optimal adaptive controllers for situations where little or no a priori information on …
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Asynchronous stochastic learning curve effects in a large scale production system
… can be attributed to the effects of asynchronous stochastic learning curves (ASLC) among all partners. There is no known prior research that provides an analytical model of the synchronized performance among partners in a large scale production system (LSPS) with these ASLC effects.In large scale …
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Stochastic Learning Feedback Hybrid Automata for Dynamic Power Management in Embedded Systems
… energy in a given system model. Secondly, stochastic control is added to the automata model, whose control strategy is learnt dynamically using stochastic learning automata (SLA). Several linear and non-linear feedback algorithms are incorporated in the final Stochastic Learning Hybrid …
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Improving algorithmic performance using stochastic learning rates: In-expectation and almost-surely stochastic approximation, and online learning, results and applications
Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2024-09-16 without embargo terms
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Channel-predictive link layer ARQ protocols in wireless networks
… Some ideas have been proposed such as stochastic learning automaton based ARQ, and channel probing based ARQ. However, these algorithms do not attempt to estimate the channel's existing condition. Instead, the retransmission decision is made according to a simple feedback, on whether …
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Stochastic optimization algorithms for adaptive modulation in software defined radio
… into mobile devices. In this thesis we present stochastic learning algorithms for adaptive modulation design. The algorithms presented allow for adaptive modulation system design in-dependent of error correction coding and modulation constellation requirements. In real time, the performance of …
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Incorporating Memory and Learning Mechanisms Into Meta-RaPS
… It is proposed that incorporating memory and learning mechanisms into Meta-RaPS, which is currently classified as a memoryless metaheuristic, can help the algorithm produce higher quality results.</p> <p>The proposed Meta-RaPS versions were created by taking different perspectives of learning. …
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Intelligent Navigation of Autonomous Vehicles in an Automated Highway System: Learning Methods and Interacting Vehicles Approach
… an artificial intelligence technique called stochastic learning automata to design an intelligent vehicle path controller. Using the information obtained by on-board sensors and local communication modules, two automata are capable of learning the best possible (lateral and longitudinal) …
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Learning-based adaptive design for dynamic spectrum access in cognitive radio networks.
… networks. The main objective is designing online learning and access policies which maximize the total throughput of the secondary users in a cognitive radio network. As the first approach, we consider the auction-based formulation in design of dynamic spectrum access mechanisms where it is …
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Information, Learning and Incentive Design for Urban Transportation Networks
… (navigation apps) on the strategic behavior and learning processes of travelers in uncertain networks; 2) Market mechanism design for efficient carpooling and toll pricing in the presence of autonomous driving technology; 3) Security analysis and resource allocation for robustness under random or …
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On the resolution of misspecification in stochastic optimization, variational inequality, and game-theoretic problems
… parameter is a solution to a suitably defined (stochastic) learning problem based on having access to a set of samples. Practical approaches in resolving such a set of coupled problems have been either sequential or direct variational approaches. In the case of the former, this entails the …