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A Hybrid System of Hidden Markov Models and Recurrent Neural Networks for Learning Deterministic Finite State Automata
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/></div></noscript> <!-- /Yandex.Metrika counter --> <!-- Matomo --> <!-- End Matomo Code --> <title>A Hybrid System of Hidden Markov Models and Recurrent Neural Networks for Learning Deterministic Finite State Automata</title> <meta name="description" content="A Hybrid System of Hidden Markov Models and Recurrent Neural Networks for Learning Deterministic Finite State Automata"> <meta name="keywords" content="hybrid systems, hidden markov models, recurrent neural networks, deterministic finite state automata"> <meta name="viewport" content="width=device-width, initial-scale=1, minimum-scale=1, maximum-scale=1, user-scalable=no"> <meta charset="utf-8"> <link href="https://cdn.waset.org/favicon.ico" type="image/x-icon" rel="shortcut icon"> <link href="https://cdn.waset.org/static/plugins/bootstrap-4.2.1/css/bootstrap.min.css" rel="stylesheet"> <link href="https://cdn.waset.org/static/plugins/fontawesome/css/all.min.css" rel="stylesheet"> <link 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Rallabandi</a>, <a href="https://publications.waset.org/abstracts/search?q=Kailash%20C.%20Patidar"> Kailash C. Patidar</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, we present an optimization technique or a learning algorithm using the hybrid architecture by combining the most popular sequence recognition models such as Recurrent Neural Networks (RNNs) and Hidden Markov models (HMMs). In order to improve the sequence or pattern recognition/ classification performance by applying a hybrid/neural symbolic approach, a gradient descent learning algorithm is developed using the Real Time Recurrent Learning of Recurrent Neural Network for processing the knowledge represented in trained Hidden Markov Models. The developed hybrid algorithm is implemented on automata theory as a sample test beds and the performance of the designed algorithm is demonstrated and evaluated on learning the deterministic finite state automata. <iframe src="https://publications.waset.org/abstracts/37759.pdf" style="width:100%; height:400px;" frameborder="0"></iframe> <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=hybrid%20systems" title="hybrid systems">hybrid systems</a>, <a href="https://publications.waset.org/abstracts/search?q=hidden%20markov%20models" title=" hidden markov models"> hidden markov models</a>, <a href="https://publications.waset.org/abstracts/search?q=recurrent%20neural%20networks" title=" recurrent neural networks"> recurrent neural networks</a>, <a href="https://publications.waset.org/abstracts/search?q=deterministic%20finite%20state%20automata" title=" deterministic finite state automata"> deterministic finite state automata</a> </p> 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