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Relevant bibliographies by topics / Perceptrons

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Academic literature on the topic 'Perceptrons'

Author: Grafiati

Published: 4 June 2021

Last updated: 25 July 2025

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Journal articles on the topic "Perceptrons"

1

TOH, H. S. "WEIGHT CONFIGURATIONS OF TRAINED PERCEPTRONS." International Journal of Neural Systems 04, no. 03 (1993): 231–46. http://dx.doi.org/10.1142/s0129065793000195.

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We strive to predict the function mapping and rules performed by a trained perceptron from studying the weights. We derive a few properties of the trained weights and show how the perceptron's representation of knowledge, rules and functions depend on these properties. Two types of perceptrons are studied — one case with continuous inputs and one hidden layer, the other a simple binary classifier with boolean inputs and no hidden units.

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BLATT, MARCELO, EYTAN DOMANY, and IDO KANTER. "ON THE EQUIVALENCE OF TWO-LAYERED PERCEPTRONS WITH BINARY NEURONS." International Journal of Neural Systems 06, no. 03 (1995): 225–31. http://dx.doi.org/10.1142/s0129065795000160.

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We consider two-layered perceptrons consisting of N binary input units, K binary hidden units and one binary output unit, in the limit N≫K≥1. We prove that the weights of a regular irreducible network are uniquely determined by its input-output map up to some obvious global symmetries. A network is regular if its K weight vectors from the input layer to the K hidden units are linearly independent. A (single layered) perceptron is said to be irreducible if its output depends on every one of its input units; and a two-layered perceptron is irreducible if the K+1 perceptrons that constitute such

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KUKOLJ, DRAGAN D., MIROSLAVA T. BERKO-PUSIC, and BRANISLAV ATLAGIC. "Experimental design of supervisory control functions based on multilayer perceptrons." Artificial Intelligence for Engineering Design, Analysis and Manufacturing 15, no. 5 (2001): 425–31. http://dx.doi.org/10.1017/s0890060401155058.

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This article presents the results of research concerning possibilities of applying multilayer perceptron type of neural network for fault diagnosis, state estimation, and prediction in the gas pipeline transmission network. The influence of several factors on accuracy of the multilayer perceptron was considered. The emphasis was put on the multilayer perceptrons' function as a state estimator. The choice of the most informative features, the amount and sampling period of training data sets, as well as different configurations of multilayer perceptrons were analyzed.

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Elizalde, E., and S. Gomez. "Multistate perceptrons: learning rule and perceptron of maximal stability." Journal of Physics A: Mathematical and General 25, no. 19 (1992): 5039–45. http://dx.doi.org/10.1088/0305-4470/25/19/016.

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Racca, Robert. "Can periodic perceptrons replace multi-layer perceptrons?" Pattern Recognition Letters 21, no. 12 (2000): 1019–25. http://dx.doi.org/10.1016/s0167-8655(00)00057-x.

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Cannas, Sergio A. "Arithmetic Perceptrons." Neural Computation 7, no. 1 (1995): 173–81. http://dx.doi.org/10.1162/neco.1995.7.1.173.

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A feedforward layered neural network (perceptron) with one hidden layer, which adds two N-bit binary numbers is constructed. The set of synaptic strengths and thresholds is obtained exactly for different architectures of the network and for arbitrary N. These structures can be easily generalized to perform more complicated arithmetic operations (like subtraction).

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Falkowski, Bernd-Jürgen. "Probabilistic perceptrons." Neural Networks 8, no. 4 (1995): 513–23. http://dx.doi.org/10.1016/0893-6080(94)00107-w.

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Falkowski, Bernd-Jürgen. "Perceptrons revisited." Information Processing Letters 36, no. 4 (1990): 207–13. http://dx.doi.org/10.1016/0020-0190(90)90075-9.

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Lewenstein, M. "Quantum Perceptrons." Journal of Modern Optics 41, no. 12 (1994): 2491–501. http://dx.doi.org/10.1080/09500349414552331.

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Rowcliffe, P., Jianfeng Feng, and H. Buxton. "Spiking perceptrons." IEEE Transactions on Neural Networks 17, no. 3 (2006): 803–7. http://dx.doi.org/10.1109/tnn.2006.873274.

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Dissertations / Theses on the topic "Perceptrons"

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Kallin, Westin Lena. "Preprocessing perceptrons." Doctoral thesis, Umeå : Univ, 2004. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-234.

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Filho, Osame Kinouchi. "Generalização ótima em perceptrons." Universidade de São Paulo, 1992. http://www.teses.usp.br/teses/disponiveis/54/54131/tde-07042015-165731/.

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O perceptron tem sido estudado no contexto da física estatística desde o trabalho seminal de Gardner e Derrida sobre o espaço de aclopamentos desta rede neural simples. Recentemente, Opper e Haussler calcularam via método de réplicas, o desempenho ótimo teórico do perceptron na aprendizagem de uma regra a partir de exemplos (generalização). Neste trabalho encontramos a curva de desempenho ótimo após a primeira apresentação dos exemplos (primeiro passo da dinâmica de aprendizagem). No limite de grande número de exemplos encontramos que o erro de generalização é apenas duas vezes maior que o err

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Adharapurapu, Ratnasri Krishna. "Convergence properties of perceptrons." CSUSB ScholarWorks, 1995. https://scholarworks.lib.csusb.edu/etd-project/1034.

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Friess, Thilo-Thomas. "Perceptrons in kernel feature spaces." Thesis, University of Sheffield, 2000. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.327730.

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Zhao, Lenny. "Uncertainty prediction with multi-layer perceptrons." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 2000. http://www.collectionscanada.ca/obj/s4/f2/dsk1/tape3/PQDD_0018/MQ55733.pdf.

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Mourao, Kira Margaret Thom. "Learning action representations using kernel perceptrons." Thesis, University of Edinburgh, 2012. http://hdl.handle.net/1842/7717.

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Action representation is fundamental to many aspects of cognition, including language. Theories of situated cognition suggest that the form of such representation is distinctively determined by grounding in the real world. This thesis tackles the question of how to ground action representations, and proposes an approach for learning action models in noisy, partially observable domains, using deictic representations and kernel perceptrons. Agents operating in real-world settings often require domain models to support planning and decision-making. To operate effectively in the world, an agent mu

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Black, Michael David. "Applying perceptrons to speculation in computer architecture." College Park, Md. : University of Maryland, 2007. http://hdl.handle.net/1903/6725.

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Thesis (Ph. D.) -- University of Maryland, College Park, 2007.<br>Thesis research directed by: Electrical Engineering. Title from t.p. of PDF. Includes bibliographical references. Published by UMI Dissertation Services, Ann Arbor, Mich. Also available in paper.

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Cairns, Graham Andrew. "Learning with analogue VLSI multi-layer perceptrons." Thesis, University of Oxford, 1995. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.296901.

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Grandvalet, Yves. "Injection de bruit dans les perceptrons multicouches." Compiègne, 1995. http://www.theses.fr/1995COMPD802.

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Lors de l'estimation d'une fonction de régression, la sélection d'un modèle est une étape clef. Elle détermine la complexité du modèle généralisant au mieux les données, i. E. Minimisant l'erreur en prédiction. Dans les perceptons multicouches, la complexité peut être réglée en modifiant l'architecture du réseau. Mais il est également possible de la contrôler à architecture fixée. Les méthodes employées consistent à ajouter au critère d'erreur, explicitement ou non, un terme pénalisant la complexité de la solution. La notion de paramètres effectifs supplante alors celle de paramètres. Parmi ce

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Octavian, Stan. "New recursive algorithms for training feedforward multilayer perceptrons." Diss., Georgia Institute of Technology, 1999. http://hdl.handle.net/1853/13534.

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Books on the topic "Perceptrons"

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Murty, M. N., and Rashmi Raghava. Support Vector Machines and Perceptrons. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-41063-0.

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Minsky, Marvin Lee. Perceptrons: An Introduction to Computational Geometry. MIT Press, 1988.

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Gorelik, A. L. Sovremennoe sostoi͡a︡nie problemy raspoznavanii͡a︡: Nekotorye aspekty. "Radio i svi͡a︡zʹ", 1985.

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Bielecki, Andrzej. Models of Neurons and Perceptrons: Selected Problems and Challenges. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-319-90140-4.

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Niemiro, Wojciech. Statystyczne własnosci metody minimalizacji perceptronowej funkcji kryterialnej w dyskryminacji liniowej. In-t Biocybernetyki i Inżynierii Biomedycznej, 2000.

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Ma, Zhe. Explanation by general rules extracted from trained multi-layer perceptrons. University of Sheffield, Dept. of Automatic Control & Systems Engineering, 1996.

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International Conference on Information Acquisition (2004 Hefei Shi, China). ICIA 2004: Proceedings of 2004 International Conference on Information Acquisition : June 21-25, 2004, Hefei, China. Edited by Mei Tao and International Association of Information Acquisition. IEEE, 2004.

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Peeling, S. M. Experiments in isolated digit recognition using the multi-layer perceptron. HMSO, 1987.

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9

P, Banks Stephen. Can Perceptrons find Lyapunov functions?: An algorithmic approach to systems stability. University of Sheffield, Dept. of Control Engineering, 1989.

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Glaz, A. B. Parametricheskai͡a︡ i strukturnai͡a︡ adaptat͡s︡ii͡a︡ reshai͡u︡shchikh pravil v zadachakh raspoznavanii͡a︡. "Zinatne", 1988.

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Book chapters on the topic "Perceptrons"

1

Du, Ke-Lin, and M. N. S. Swamy. "Perceptrons." In Neural Networks and Statistical Learning. Springer London, 2013. http://dx.doi.org/10.1007/978-1-4471-5571-3_3.

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Nauck, Detlef, Frank Klawonn, and Rudolf Kruse. "Perceptrons." In Neuronale Netze und Fuzzy-Systeme. Vieweg+Teubner Verlag, 1994. http://dx.doi.org/10.1007/978-3-322-85993-8_4.

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Du, Ke-Lin, and M. N. S. Swamy. "Perceptrons." In Neural Networks and Statistical Learning. Springer London, 2019. http://dx.doi.org/10.1007/978-1-4471-7452-3_4.

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Silaparasetty, Vinita. "Perceptrons." In Deep Learning Projects Using TensorFlow 2. Apress, 2020. http://dx.doi.org/10.1007/978-1-4842-5802-6_2.

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Peters, H. J. M. "Perceptrons." In Lecture Notes in Computer Science. Springer Berlin Heidelberg, 1995. http://dx.doi.org/10.1007/bfb0027023.

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Picton, Phil. "Perceptrons." In Introduction to Neural Networks. Macmillan Education UK, 1994. http://dx.doi.org/10.1007/978-1-349-13530-1_3.

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Nauck, Detlef, Frank Klawonn, and Rudolf Kruse. "Perceptrons." In Neuronale Netze und Fuzzy-Systeme. Vieweg+Teubner Verlag, 1996. http://dx.doi.org/10.1007/978-3-663-10898-6_4.

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Nauck, Detlef, Frank Klawonn, and Rudolf Kruse. "Multilayer-Perceptrons." In Neuronale Netze und Fuzzy-Systeme. Vieweg+Teubner Verlag, 1994. http://dx.doi.org/10.1007/978-3-322-85993-8_6.

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García, Daniel, Ana González, and José R. Dorronsoro. "Convex Perceptrons." In Intelligent Data Engineering and Automated Learning – IDEAL 2006. Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11875581_70.

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Kruse, Rudolf, Christian Borgelt, Christian Braune, Sanaz Mostaghim, and Matthias Steinbrecher. "Multilayer Perceptrons." In Texts in Computer Science. Springer London, 2016. http://dx.doi.org/10.1007/978-1-4471-7296-3_5.

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Conference papers on the topic "Perceptrons"

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Walker, Markus, Hayk Amirkhanian, Marco F. Huber, and Uwe D. Hanebeck. "Trustworthy Bayesian Perceptrons." In 2024 27th International Conference on Information Fusion (FUSION). IEEE, 2024. http://dx.doi.org/10.23919/fusion59988.2024.10706490.

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Carrinho, Pedro, Oscar Ferraz, João Dinis Ferreira, Yann Falevoz, Vitor Silva, and Gabriel Falcao. "Processing Multi-Layer Perceptrons In-Memory." In 2024 IEEE Workshop on Signal Processing Systems (SiPS). IEEE, 2024. https://doi.org/10.1109/sips62058.2024.00010.

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Echamsi, Safaa, Essadik Belouafi, Aziza El Bakali Kassimi, Abdelhakim Alali, Abdelaziz Bouroumi, and Asmae Guennoun. "Optimizing Training Hyperparameters for Multilayer Perceptrons in Deep Learning." In 2025 5th International Conference on Innovative Research in Applied Science, Engineering and Technology (IRASET). IEEE, 2025. https://doi.org/10.1109/iraset64571.2025.11008006.

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Saromo, Daniel, Elizabeth Villota, and Edwin Villanueva. "Auto-Rotating Perceptrons." In LatinX in AI at Neural Information Processing Systems Conference 2019. Journal of LatinX in AI Research, 2019. http://dx.doi.org/10.52591/lxai2019120826.

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This paper proposes an improved design of the perceptron unit to mitigate the vanishing gradient problem. This nuisance appears when training deep multilayer perceptron networks with bounded activation functions. The new neuron design, named auto-rotating perceptron (ARP), has a mechanism to ensure that the node always operates in the dynamic region of the activation function, by avoiding saturation of the perceptron. The proposed method does not change the inference structure learned at each neuron. We test the effect of using ARP units in some network architectures which use the sigmoid acti

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Bueno, Felipe Roberto, and Peter Sussner. "FUZZY MORPHOLOGICAL PERCEPTRONS AND HYBRID FUZZY MORPHOLOGICAL/LINEAR PERCEPTRONS." In The 11th International FLINS Conference (FLINS 2014). WORLD SCIENTIFIC, 2014. http://dx.doi.org/10.1142/9789814619998_0120.

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Xiang, Xuyan, Yingchun Deng, and Xiangqun Yang. "Spike-Rate Perceptrons." In 2008 Fourth International Conference on Natural Computation. IEEE, 2008. http://dx.doi.org/10.1109/icnc.2008.556.

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Vucetic, Slobodan, Vladimir Coric, and Zhuang Wang. "Compressed Kernel Perceptrons." In 2009 Data Compression Conference (DCC). IEEE, 2009. http://dx.doi.org/10.1109/dcc.2009.75.

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Ou, Jun, and Yujian Li. "Two-Dimensional Perceptrons." In the 2018 2nd International Conference. ACM Press, 2018. http://dx.doi.org/10.1145/3297156.3297213.

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McDonnell, John R., and Donald E. Waagen. "Evolving recurrent perceptrons." In Optical Engineering and Photonics in Aerospace Sensing, edited by Dennis W. Ruck. SPIE, 1993. http://dx.doi.org/10.1117/12.152634.

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Kou-Yuan Huang. "Sequential classification by perceptrons and application to net pruning of multilayer perceptron." In Proceedings of 1994 IEEE International Conference on Neural Networks (ICNN'94). IEEE, 1994. http://dx.doi.org/10.1109/icnn.1994.374226.

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Reports on the topic "Perceptrons"

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Chen, B., T. Hickling, M. Krnjajic, et al. Multi-Layer Perceptrons and Support Vector Machines for Detection Problems with Low False Alarm Requirements: an Eight-Month Progress Report. Office of Scientific and Technical Information (OSTI), 2007. http://dx.doi.org/10.2172/922310.

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Vurkaç, Mehmet. Prestructuring Multilayer Perceptrons based on Information-Theoretic Modeling of a Partido-Alto-based Grammar for Afro-Brazilian Music: Enhanced Generalization and Principles of Parsimony, including an Investigation of Statistical Paradigms. Portland State University Library, 2000. http://dx.doi.org/10.15760/etd.384.

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Raychev, Nikolay. Mathematical foundations of neural networks. Implementing a perceptron from scratch. Web of Open Science, 2020. http://dx.doi.org/10.37686/nsr.v1i1.74.

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Ramakrishnan, Aravind, Fangyu Liu, Angeli Jayme, and Imad Al-Qadi. Prediction of Pavement Damage under Truck Platoons Utilizing a Combined Finite Element and Artificial Intelligence Model. Illinois Center for Transportation, 2024. https://doi.org/10.36501/0197-9191/24-030.

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For robust pavement design, accurate damage computation is essential, especially for loading scenarios such as truck platoons. Studies have developed a framework to compute pavement distresses as function of lateral position, spacing, and market-penetration level of truck platoons. The established framework uses a robust 3D pavement model, along with the AASHTOWare Mechanistic–Empirical Pavement Design Guidelines (MEPDG) transfer functions to compute pavement distresses. However, transfer functions include high variability and lack physical significance. Therefore, as an improvement to effecti

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Kirichek, Galina, Vladyslav Harkusha, Artur Timenko, and Nataliia Kulykovska. System for detecting network anomalies using a hybrid of an uncontrolled and controlled neural network. [б. в.], 2020. http://dx.doi.org/10.31812/123456789/3743.

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In this article realization method of attacks and anomalies detection with the use of training of ordinary and attacking packages, respectively. The method that was used to teach an attack on is a combination of an uncontrollable and controlled neural network. In an uncontrolled network, attacks are classified in smaller categories, taking into account their features and using the self- organized map. To manage clusters, a neural network based on back-propagation method used. We use PyBrain as the main framework for designing, developing and learning perceptron data. This framework has a suffi

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Alwan, Iktimal, Dennis D. Spencer, and Rafeed Alkawadri. Comparison of Machine Learning Algorithms in Sensorimotor Functional Mapping. Progress in Neurobiology, 2023. http://dx.doi.org/10.60124/j.pneuro.2023.30.03.

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Objective: To compare the performance of popular machine learning algorithms (ML) in mapping the sensorimotor cortex (SM) and identifying the anterior lip of the central sulcus (CS). Methods: We evaluated support vector machines (SVMs), random forest (RF), decision trees (DT), single layer perceptron (SLP), and multilayer perceptron (MLP) against standard logistic regression (LR) to identify the SM cortex employing validated features from six-minute of NREM sleep icEEG data and applying standard common hyperparameters and 10-fold cross-validation. Each algorithm was tested using vetted feature

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Guzmán Paredes, Yomaira. Redes neuronales artificiales. Ediciones Universidad Cooperativa de Colombia, 2023. https://doi.org/10.16925/gcgp.113.

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A través de esta guía el estudiante profundizará en conceptos sobre redes neuronales artificiales como algoritmo de inteligencia computacional para la predicción. Esta guía práctica describe cómo aplicar el concepto de una red perceptrón mediante el uso de Python en un ejercicio. Las redes neuronales artificiales son algoritmos bioinspirados en el comportamiento de una neurona biológica, a partir de ellas podemos realizar clasificación. En esta oportunidad está enfocado en un ejercicio enfocado a evaluar la admisión de un estudiante a la Universidad Cooperativa de Colombia. Una vez haya finali

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Rivera-Casillas, Peter, and Ian Dettwiller. Neural Ordinary Differential Equations for rotorcraft aerodynamics. Engineer Research and Development Center (U.S.), 2024. http://dx.doi.org/10.21079/11681/48420.

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High-fidelity computational simulations of aerodynamics and structural dynamics on rotorcraft are essential for helicopter design, testing, and evaluation. These simulations usually entail a high computational cost even with modern high-performance computing resources. Reduced order models can significantly reduce the computational cost of simulating rotor revolutions. However, reduced order models are less accurate than traditional numerical modeling approaches, making them unsuitable for research and design purposes. This study explores the use of a new modified Neural Ordinary Differential

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Ogunbire, Abimbola, Panick Kalambay, Hardik Gajera, and Srinivas Pulugurtha. Deep Learning, Machine Learning, or Statistical Models for Weather-related Crash Severity Prediction. Mineta Transportation Institute, 2023. http://dx.doi.org/10.31979/mti.2023.2320.

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Nearly 5,000 people are killed and more than 418,000 are injured in weather-related traffic incidents each year. Assessments of the effectiveness of statistical models applied to crash severity prediction compared to machine learning (ML) and deep learning techniques (DL) help researchers and practitioners know what models are most effective under specific conditions. Given the class imbalance in crash data, the synthetic minority over-sampling technique for nominal (SMOTE-N) data was employed to generate synthetic samples for the minority class. The ordered logit model (OLM) and the ordered p

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Arhin, Stephen, Babin Manandhar, Hamdiat Baba Adam, and Adam Gatiba. Predicting Bus Travel Times in Washington, DC Using Artificial Neural Networks (ANNs). Mineta Transportation Institute, 2021. http://dx.doi.org/10.31979/mti.2021.1943.

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Washington, DC is ranked second among cities in terms of highest public transit commuters in the United States, with approximately 9% of the working population using the Washington Metropolitan Area Transit Authority (WMATA) Metrobuses to commute. Deducing accurate travel times of these metrobuses is an important task for transit authorities to provide reliable service to its patrons. This study, using Artificial Neural Networks (ANN), developed prediction models for transit buses to assist decision-makers to improve service quality and patronage. For this study, we used six months of Automati

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