Browsing by Subject "Feedforward neural networks"

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    Feedforward neural network based case prediction in Turkish higher courts
    (2022-08-29) Aras, Arda C.; Öztürk, Ceyhun E.; Koç, Aykut
    Thanks to natural language processing (NLP) methods, legal texts can be processed by computers and decision prediction applications can be developed in the legal tech field. Increase in the available data sources in the Turkish legal system provides an opportunity to develop NLP applications as well. In order to develop these applications, the necessary corpora and datasets should be created. In this work, legal case texts from the Turkish Higher Courts that are open to public access and free from personal data are used to develop decision prediction methods. Feedforward neural networks (FFNN) are deployed using word embeddings and the features extracted from texts via the Principal Component Analysis (PCA) algorithm. %85.4 Macro F1 score level is achieved.
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    Long short-term memory for improved transients in neural network adaptive control
    (IEEE, 2023-07-03) İnanç, Emirhan; Gürses, Yiğit; Habboush, Abdullah; Yıldız, Yıldıray
    In this study, we propose a novel adaptive control architecture, which provides dramatically better performance compared to conventional methods. What makes this architecture unique is the synergistic employment of a traditional, Adaptive Neural Network (ANN) controller and a Long Short-Term Memory (LSTM) network. LSTM structures, unlike the standard feed-forward neural networks, take advantage of the dependencies in an input sequence, which helps predict the evolution of an uncertainty. Through a training method we introduced, the LSTM network learns to compensate for the deficiencies of the ANN controller. This substantially improves the transient response by allowing the controller to quickly react to unexpected events. Through careful simulation studies, we demonstrate that this architecture can improve the estimation accuracy on a diverse set of unseen uncertainties. We also provide an analysis of the contributions of the ANN controller and LSTM network, identifying their individual roles in compensating low and high frequency error dynamics. This analysis provides insight into why and how the LSTM augmentation improves the system’s transient response.