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 12 of 12 for “"Learning automata"”.
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Synthesis of stochastic learning automata.
… 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 the plant …
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Theory and application of learning automata.
Although the theoretical performance of many learning automata has been considered, the practical operation of these automata has received far less attention. This work starts with the construction of two action Tsetlin and Krylov automata. The performance of these automata has been measured in …
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Adaptive control of communication networks using learning automata.
… network routing procedures, based on distributed learning automata concepts for circuit and packet switched networks. For this application, the learning automaton is shown to be an ideal adaptive control mechanism, with simple feedback and updating strategies which allow extremely practical …
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Distributed learning automata based data dissemination in swarm robotic systems
… delivery latency while consuming minimal energy. Learning automata are a form of Reinforcement Learning that is computationally inexpensive and can adapt to a dynamic environment. This combination allows for lightweight decision making based on the current topology of the network. We present two …
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Multi-criteria decision making using reinforcement learning and its application to food, energy, and water systems (FEWS) problem
… model is devised using reinforcement learning to carry out multi-criteria optimization problems. Learning automata algorithm is used to identify an optimal solution in the presence of single and multiple environments (criteria) using pareto optimality. The application of this model is …
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Stochastic Learning Feedback Hybrid Automata for Dynamic Power Management in Embedded Systems
… 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 Automata (SLHA) model. Simulation-based …
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Digital image compression.
… images with low detail. The second scheme uses a learning automata to predict the probability distribution of the grey levels of an image related to its spatial context and position. An optimal reward/punishment function is proposed such that the automata converges to its steady state within 4000 …
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A learning-based scheme to optimise a cognitive handoff
… strategies. From the noted results, machine-learning techniques have paved a direction for radio protocols to achieve better levels of performance. With their definition, efficient learning practices and the use of effective spectrum sharing methods necessitate the development of better …
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Intelligent Navigation of Autonomous Vehicles in an Automated Highway System: Learning Methods and Interacting Vehicles Approach
… 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) actions to …
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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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Data-Driven Pipeline for Learning Discrete behavioural Models of Cyber-Physical Systems
… discrete-event behavioural models called automata, while minimizing the need for ground truth about the data or the system that generated the logs. The pipeline consists of two recurring steps. In the discretization stage, high-dimensional time series data are converted into event traces …