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Thematische Bibliographien / Neural networks

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Auswahl der wissenschaftlichen Literatur zum Thema „Neural networks“

Autor: Grafiati

Veröffentlicht am 4. Juni 2021

Zuletzt aktualisiert am 25. Juli 2025

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Zeitschriftenartikel zum Thema "Neural networks"

1

Navghare, Tukaram, Aniket Muley, and Vinayak Jadhav. "Siamese Neural Networks for Kinship Prediction: A Deep Convolutional Neural Network Approach." Indian Journal Of Science And Technology 17, no. 4 (2024): 352–58. http://dx.doi.org/10.17485/ijst/v17i4.3018.

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N, Vikram. "Artificial Neural Networks." International Journal of Research Publication and Reviews 4, no. 4 (2023): 4308–9. http://dx.doi.org/10.55248/gengpi.4.423.37858.

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O. H. Abdelwahed, O. H. Abdelwahed, and M. El-Sayed Wahed. "Optimizing Single Layer Cellular Neural Network Simulator using Simulated Annealing Technique with Neural Networks." Indian Journal of Applied Research 3, no. 6 (2011): 91–94. http://dx.doi.org/10.15373/2249555x/june2013/31.

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Perfetti, R. "A neural network to design neural networks." IEEE Transactions on Circuits and Systems 38, no. 9 (1991): 1099–103. http://dx.doi.org/10.1109/31.83884.

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AVeselý. "Neural networks in data mining." Agricultural Economics (Zemědělská ekonomika) 49, No. 9 (2012): 427–31. http://dx.doi.org/10.17221/5427-agricecon.

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To posses relevant information is an inevitable condition for successful enterprising in modern business. Information could be parted to data and knowledge. How to gather, store and retrieve data is studied in database theory. In the knowledge engineering, there is in the centre of interest the knowledge and methods of its formalization and gaining are studied. Knowledge could be gained from experts, specialists in the area of interest, or it can be gained by induction from sets of data. Automatic induction of knowledge from data sets, usually stored in large databases, is called data mining.

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Zengguo Sun, Zengguo Sun, Guodong Zhao Zengguo Sun, Rafał Scherer Guodong Zhao, Wei Wei Rafał Scherer, and Marcin Woźniak Wei Wei. "Overview of Capsule Neural Networks." 網際網路技術學刊 23, no. 1 (2022): 033–44. http://dx.doi.org/10.53106/160792642022012301004.

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<p>As a vector transmission network structure, the capsule neural network has been one of the research hotspots in deep learning since it was proposed in 2017. In this paper, the latest research progress of capsule networks is analyzed and summarized. Firstly, we summarize the shortcomings of convolutional neural networks and introduce the basic concept of capsule network. Secondly, we analyze and summarize the improvements in the dynamic routing mechanism and network structure of the capsule network in recent years and the combination of the capsule network with other network structures

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J, Joselin, Dinesh T, and Ashiq M. "A Review on Neural Networks." International Journal of Trend in Scientific Research and Development Volume-2, Issue-6 (2018): 565–69. http://dx.doi.org/10.31142/ijtsrd18461.

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Alle, Kailash. "Sentiment Analysis Using Neural Networks." International Journal of Science and Research (IJSR) 7, no. 12 (2018): 1604–8. http://dx.doi.org/10.21275/sr24716104045.

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Gumen, O., I. Selina, and D. Міz. "NEURAL NETWORKS. COMPUTER VISUAL RECOGNITION." Modern problems of modeling, no. 26 (June 13, 2024): 95–99. https://doi.org/10.33842/2313125x-2024-26-95-99.

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Ziroyan, M. A., E. A. Tusova, A. S. Hovakimian, and S. G. Sargsyan. "Neural networks apparatus in biometrics." Contemporary problems of social work 1, no. 2 (2015): 129–37. http://dx.doi.org/10.17922/2412-5466-2015-1-2-129-137.

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Dissertationen zum Thema "Neural networks"

1

Patterson, Raymond A. "Hybrid Neural networks and network design." Connect to resource, 1995. http://rave.ohiolink.edu/etdc/view.cgi?acc%5Fnum=osu1262707683.

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Rastogi, Preeti. "Assessing Wireless Network Dependability Using Neural Networks." Ohio University / OhioLINK, 2005. http://rave.ohiolink.edu/etdc/view?acc_num=ohiou1129134364.

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Chambers, Mark Andrew. "Queuing network construction using artificial neural networks /." The Ohio State University, 2000. http://rave.ohiolink.edu/etdc/view?acc_num=osu1488193665234291.

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Dunn, Nathan A. "A Novel Neural Network Analysis Method Applied to Biological Neural Networks." Thesis, view abstract or download file of text, 2006. http://proquest.umi.com/pqdweb?did=1251892251&sid=2&Fmt=2&clientId=11238&RQT=309&VName=PQD.

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Thesis (Ph. D.)--University of Oregon, 2006.<br>Typescript. Includes vita and abstract. Includes bibliographical references (leaves 122- 131). Also available for download via the World Wide Web; free to University of Oregon users.

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Dong, Yue. "Higher Order Neural Networks and Neural Networks for Stream Learning." Thesis, Université d'Ottawa / University of Ottawa, 2017. http://hdl.handle.net/10393/35731.

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The goal of this thesis is to explore some variations of neural networks. The thesis is mainly split into two parts: a variation of the shaping functions in neural networks and a variation of learning rules in neural networks. In the first part, we mainly investigate polynomial perceptrons - a perceptron with a polynomial shaping function instead of a linear one. We prove the polynomial perceptron convergence theorem and illustrate the notion by showing that a higher order perceptron can learn the XOR function through empirical experiments with implementation. In the second part, we propose

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Xu, Shuxiang, University of Western Sydney, and of Informatics Science and Technology Faculty. "Neuron-adaptive neural network models and applications." THESIS_FIST_XXX_Xu_S.xml, 1999. http://handle.uws.edu.au:8081/1959.7/275.

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Artificial Neural Networks have been widely probed by worldwide researchers to cope with the problems such as function approximation and data simulation. This thesis deals with Feed-forward Neural Networks (FNN's) with a new neuron activation function called Neuron-adaptive Activation Function (NAF), and Feed-forward Higher Order Neural Networks (HONN's) with this new neuron activation function. We have designed a new neural network model, the Neuron-Adaptive Neural Network (NANN), and mathematically proved that one NANN can approximate any piecewise continuous function to any desired accuracy

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Allen, T. J. "Optoelectronic neural networks." Thesis, University of Nottingham, 1996. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.362900.

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Sloan, Cooper Stokes. "Neural bus networks." Thesis, Massachusetts Institute of Technology, 2018. http://hdl.handle.net/1721.1/119711.

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Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2018.<br>This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.<br>Cataloged from student-submitted PDF version of thesis.<br>Includes bibliographical references (pages 65-68).<br>Bus schedules are unreliable, leaving passengers waiting and increasing commute times. This problem can be solved by modeling the traffic network, and delivering predicted arrival times to passengers. Research att

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Boychenko, I. V., and G. I. Litvinenko. "Artificial neural networks." Thesis, Вид-во СумДУ, 2009. http://essuir.sumdu.edu.ua/handle/123456789/17044.

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Landry, Kenneth D. "Evolutionary neural networks." Thesis, Virginia Polytechnic Institute and State University, 1988. http://hdl.handle.net/10919/51904.

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To create neural networks that work, one needs to specify a structure and the interconnection weights between each pair of connected computing elements. The structure of a network can be selected by the designer depending on the application, although the selection of interconnection weights is a much larger problem. Algorithms have been developed to alter the weights slightly in order to produce the desired results. Learning algorithms such as Hebb's rule, the Delta rule and error propagation have been used, with success, to learn the appropriate weights. The major objection to this class of a

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Bücher zum Thema "Neural networks"

1

Dominique, Valentin, and Edelman Betty, eds. Neural networks. Sage Publications, 1999.

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Abdi, Hervé, Dominique Valentin, and Betty Edelman. Neural Networks. SAGE Publications, Inc., 1999. http://dx.doi.org/10.4135/9781412985277.

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Davalo, Eric, and Patrick Naïm. Neural Networks. Macmillan Education UK, 1991. http://dx.doi.org/10.1007/978-1-349-12312-4.

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Müller, Berndt, and Joachim Reinhardt. Neural Networks. Springer Berlin Heidelberg, 1990. http://dx.doi.org/10.1007/978-3-642-97239-3.

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Rojas, Raúl. Neural Networks. Springer Berlin Heidelberg, 1996. http://dx.doi.org/10.1007/978-3-642-61068-4.

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Müller, Berndt, Joachim Reinhardt, and Michael T. Strickland. Neural Networks. Springer Berlin Heidelberg, 1995. http://dx.doi.org/10.1007/978-3-642-57760-4.

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Almeida, Luis B., and Christian J. Wellekens, eds. Neural Networks. Springer Berlin Heidelberg, 1990. http://dx.doi.org/10.1007/3-540-52255-7.

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1931-, Taylor John, and UNICOM Seminars, eds. Neural networks. A. Waller, 1995.

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Salem, Fathi M. Recurrent Neural Networks. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-89929-5.

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Tyagi, Amit Kumar, and Ajith Abraham. Recurrent Neural Networks. CRC Press, 2022. http://dx.doi.org/10.1201/9781003307822.

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Buchteile zum Thema "Neural networks"

1

Bile, Alessandro. "Introduction to Neural Networks: Biological Neural Network." In Solitonic Neural Networks. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-48655-5_1.

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Müller, Berndt, Joachim Reinhardt, and Michael T. Strickland. "Cybernetic Networks." In Neural Networks. Springer Berlin Heidelberg, 1995. http://dx.doi.org/10.1007/978-3-642-57760-4_5.

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Rojas, Raúl. "Associative Networks." In Neural Networks. Springer Berlin Heidelberg, 1996. http://dx.doi.org/10.1007/978-3-642-61068-4_12.

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Rojas, Raúl. "Stochastic Networks." In Neural Networks. Springer Berlin Heidelberg, 1996. http://dx.doi.org/10.1007/978-3-642-61068-4_14.

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Rojas, Raúl. "Kohonen Networks." In Neural Networks. Springer Berlin Heidelberg, 1996. http://dx.doi.org/10.1007/978-3-642-61068-4_15.

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Müller, Berndt, and Joachim Reinhardt. "Cybernetic Networks." In Neural Networks. Springer Berlin Heidelberg, 1990. http://dx.doi.org/10.1007/978-3-642-97239-3_5.

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Wüthrich, Mario V., and Michael Merz. "Recurrent Neural Networks." In Springer Actuarial. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-12409-9_8.

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AbstractThis chapter considers recurrent neural (RN) networks. These are special network architectures that are useful for time-series modeling, e.g., applied to time-series forecasting. We study the most popular RN networks which are the long short-term memory (LSTM) networks and the gated recurrent unit (GRU) networks. We apply these networks to mortality forecasting.

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Hardy, Yorick, and Willi-Hans Steeb. "Neural Networks." In Classical and Quantum Computing. Birkhäuser Basel, 2001. http://dx.doi.org/10.1007/978-3-0348-8366-5_14.

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Bungay, Henry R. "Neural Networks." In Environmental Systems Engineering. Springer US, 1998. http://dx.doi.org/10.1007/978-1-4615-5507-0_7.

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Cios, Krzysztof J., Witold Pedrycz, and Roman W. Swiniarski. "Neural Networks." In Data Mining Methods for Knowledge Discovery. Springer US, 1998. http://dx.doi.org/10.1007/978-1-4615-5589-6_7.

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Konferenzberichte zum Thema "Neural networks"

1

Wright, Logan. "Physical Neural Networks Based on Multimode Optical Waves." In Integrated Photonics Research, Silicon and Nanophotonics. Optica Publishing Group, 2024. https://doi.org/10.1364/iprsn.2024.iw2b.4.

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Physical neural networks provide a way to realize neural network calculations by leveraging the controllable computations physical systems natively perform. I present an example on-chip physical neural network based on arbitrarily controllable multimode wave propagation. Full-text article not available; see video presentation

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Kleiner, Matan, Lior Michaeli, and Tomer Michaeli. "Coherence Awareness in Diffractive Neural Networks." In CLEO: Fundamental Science. Optica Publishing Group, 2024. http://dx.doi.org/10.1364/cleo_fs.2024.fw4q.5.

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We demonstrate the significant i nfluence of th e il lumination co herence on diffractive networks, and propose a framework for network optimization with any prescribed degree of spatial and temporal coherence. We analyze performance for varied coherence properties.

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Yang, Zhun, Adam Ishay, and Joohyung Lee. "NeurASP: Embracing Neural Networks into Answer Set Programming." In Twenty-Ninth International Joint Conference on Artificial Intelligence and Seventeenth Pacific Rim International Conference on Artificial Intelligence {IJCAI-PRICAI-20}. International Joint Conferences on Artificial Intelligence Organization, 2020. http://dx.doi.org/10.24963/ijcai.2020/243.

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We present NeurASP, a simple extension of answer set programs by embracing neural networks. By treating the neural network output as the probability distribution over atomic facts in answer set programs, NeurASP provides a simple and effective way to integrate sub-symbolic and symbolic computation. We demonstrate how NeurASP can make use of a pre-trained neural network in symbolic computation and how it can improve the neural network's perception result by applying symbolic reasoning in answer set programming. Also, NeurASP can make use of ASP rules to train a neural network better so that a n

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Shi, Weijia, Andy Shih, Adnan Darwiche, and Arthur Choi. "On Tractable Representations of Binary Neural Networks." In 17th International Conference on Principles of Knowledge Representation and Reasoning {KR-2020}. International Joint Conferences on Artificial Intelligence Organization, 2020. http://dx.doi.org/10.24963/kr.2020/91.

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We consider the compilation of a binary neural network’s decision function into tractable representations such as Ordered Binary Decision Diagrams (OBDDs) and Sentential Decision Diagrams (SDDs). Obtaining this function as an OBDD/SDD facilitates the explanation and formal veriﬁcation of a neural network’s behavior. First, we consider the task of verifying the robustness of a neural network, and show how we can compute the expected robustness of a neural network, given an OBDD/SDD representation of it. Next, we consider a more efﬁcient approach for compiling neural networks, based on a pseudo-

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Najmon, Joel C., and Andres Tovar. "Comparing Derivatives of Neural Networks for Regression." In ASME 2023 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2023. http://dx.doi.org/10.1115/detc2023-117571.

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Abstract In the past decades, neural networks have rapidly grown in popularity as a way to model complex non-linear relationships. The computational efficiently and flexibility of neural networks has made them popular for machine learning-based optimization methods. As such the derivative of a neural network’s output is required for gradient-based optimization algorithms. Recently, there have been several works towards improving derivatives of neural network targets, however there is yet to be done a comparative study on the different derivation methods for the derivative of a neural network’s

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Ozcan, Neyir. "New results for global stability of neutral-type delayed neural networks." In The 11th International Conference on Integrated Modeling and Analysis in Applied Control and Automation. CAL-TEK srl, 2018. http://dx.doi.org/10.46354/i3m.2018.imaaca.004.

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"This paper deals with the stability analysis of the class of neutral-type neural networks with constant time delay. By using a suitable Lyapunov functional, some delay independent sufficient conditions are derived, which ensure the global asymptotic stability of the equilibrium point for this this class of neutral-type neural networks with time delays with respect to the Lipschitz activation functions. The presented stability results rely on checking some certain properties of matrices. Therefore, it is easy to verify the validation of the constraint conditions on the network parameters of ne

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Benmaghnia, Hanane, Matthieu Martel, and Yassamine Seladji. "Fixed-Point Code Synthesis for Neural Networks." In 6th International Conference on Artificial Intelligence, Soft Computing and Applications (AISCA 2022). Academy and Industry Research Collaboration Center (AIRCC), 2022. http://dx.doi.org/10.5121/csit.2022.120202.

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Over the last few years, neural networks have started penetrating safety critical systems to take decisions in robots, rockets, autonomous driving car, etc. A problem is that these critical systems often have limited computing resources. Often, they use the fixed-point arithmetic for its many advantages (rapidity, compatibility with small memory devices.) In this article, a new technique is introduced to tune the formats (precision) of already trained neural networks using fixed-point arithmetic, which can be implemented using integer operations only. The new optimized neural network computes

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Adewusi, S. A., and B. O. Al-Bedoor. "Detection of Propagating Cracks in Rotors Using Neural Networks." In ASME 2002 Pressure Vessels and Piping Conference. ASMEDC, 2002. http://dx.doi.org/10.1115/pvp2002-1518.

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This paper presents the application of neural networks for rotor cracks detection. The basic working principles of neural networks are presented. Experimental vibration signals of rotors with and without a propagating crack were used to train the Multi-layer Feed-forward Neural Networks using back-propagation algorithm. The trained neural networks were tested with other set of vibration data. A simple two-layer feed-forward neural network with two neurons in the input layer and one neuron in the output layer trained with the signals of a cracked rotor and a normal rotor without a crack was fou

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Pryor, Connor, Charles Dickens, Eriq Augustine, Alon Albalak, William Yang Wang, and Lise Getoor. "NeuPSL: Neural Probabilistic Soft Logic." In Thirty-Second International Joint Conference on Artificial Intelligence {IJCAI-23}. International Joint Conferences on Artificial Intelligence Organization, 2023. http://dx.doi.org/10.24963/ijcai.2023/461.

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In this paper, we introduce Neural Probabilistic Soft Logic (NeuPSL), a novel neuro-symbolic (NeSy) framework that unites state-of-the-art symbolic reasoning with the low-level perception of deep neural networks. To model the boundary between neural and symbolic representations, we propose a family of energy-based models, NeSy Energy-Based Models, and show that they are general enough to include NeuPSL and many other NeSy approaches. Using this framework, we show how to seamlessly integrate neural and symbolic parameter learning and inference in NeuPSL. Through an extensive empirical evaluatio

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Owechko, Yuri. "Self-Pumped Optical Neural Networks." In Optical Computing. Optica Publishing Group, 1989. http://dx.doi.org/10.1364/optcomp.1989.md4.

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Neural network models for artificial intelligence offer an approach fundamentally different from conventional symbolic approaches, but the merits of the two paradigms cannot be fairly compared until neural network models with large numbers of ”neurons” are implemented. Despite the attractiveness of neural networks for computing applications which involve adaptation and learning, most of the published demonstrations of neural network technology have involved relatively small numbers of ”neurons”. One reason for this is the poor match between conventional electronic serial or coarse-grained mult

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Berichte der Organisationen zum Thema "Neural networks"

1

Smith, Patrick I. Neural Networks. Office of Scientific and Technical Information (OSTI), 2003. http://dx.doi.org/10.2172/815740.

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Johnson, John L., and C. C. Sung. Neural Networks. Defense Technical Information Center, 1990. http://dx.doi.org/10.21236/ada222110.

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Tarasenko, Andrii O., Yuriy V. Yakimov, and Vladimir N. Soloviev. Convolutional neural networks for image classification. [б. в.], 2020. http://dx.doi.org/10.31812/123456789/3682.

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This paper shows the theoretical basis for the creation of convolutional neural networks for image classification and their application in practice. To achieve the goal, the main types of neural networks were considered, starting from the structure of a simple neuron to the convolutional multilayer network necessary for the solution of this problem. It shows the stages of the structure of training data, the training cycle of the network, as well as calculations of errors in recognition at the stage of training and verification. At the end of the work the results of network training, calculatio

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Holder, Nanette S. Introduction to Neural Networks. Defense Technical Information Center, 1992. http://dx.doi.org/10.21236/ada248258.

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Abu-Mostafa, Yaser S., and Amir F. Atiya. Theory of Neural Networks. Defense Technical Information Center, 1991. http://dx.doi.org/10.21236/ada253187.

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Alltop, W. O. Piecewise Linear Neural Networks. Defense Technical Information Center, 1992. http://dx.doi.org/10.21236/ada265031.

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Wiggins, Vince L., Larry T. Looper, and Sheree K. Engquist. Neural Networks: A Primer. Defense Technical Information Center, 1991. http://dx.doi.org/10.21236/ada235920.

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Yu, Haichao, Haoxiang Li, Honghui Shi, Thomas S. Huang, and Gang Hua. Any-Precision Deep Neural Networks. Web of Open Science, 2020. http://dx.doi.org/10.37686/ejai.v1i1.82.

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We present Any-Precision Deep Neural Networks (Any- Precision DNNs), which are trained with a new method that empowers learned DNNs to be flexible in any numerical precision during inference. The same model in runtime can be flexibly and directly set to different bit-width, by trun- cating the least significant bits, to support dynamic speed and accuracy trade-off. When all layers are set to low- bits, we show that the model achieved accuracy compara- ble to dedicated models trained at the same precision. This nice property facilitates flexible deployment of deep learn- ing models in real-worl

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Keller, P. E. Artificial neural networks in medicine. Office of Scientific and Technical Information (OSTI), 1994. http://dx.doi.org/10.2172/10162484.

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Johnson, M. A., G. Kendall, P. J. Cote, and L. V. Meisel. Neural Networks in Seizure Diagnosis. Defense Technical Information Center, 1995. http://dx.doi.org/10.21236/ada295629.

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