Relevant bibliographies by topics / Multi-layer neural networks

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers

Academic literature on the topic 'Multi-layer neural networks'

Author: Grafiati
Published: 3 June 2025
Last updated: 31 July 2025

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Journal articles on the topic "Multi-layer neural networks"

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Ban, Jung-Chao, and Chih-Hung Chang. "The layer effect on multi-layer cellular neural networks." Applied Mathematics Letters 26, no. 7 (2013): 706–9. http://dx.doi.org/10.1016/j.aml.2013.01.013.

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Scherer, Magdalena. "Multi-layer neural networks for sales forecasting." Journal of Applied Mathematics and Computational Mechanics 17, no. 1 (2018): 61–68. http://dx.doi.org/10.17512/jamcm.2018.1.06.

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Ban, Jung-Chao, and Chih-Hung Chang. "Hausdorff Dimension of Multi-Layer Neural Networks." Advances in Pure Mathematics 03, no. 09 (2013): 9–14. http://dx.doi.org/10.4236/apm.2013.39a1002.

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Ban, Jung-Chao, and Chih-Hung Chang. "Diamond in multi-layer cellular neural networks." Applied Mathematics and Computation 222 (October 2013): 1–12. http://dx.doi.org/10.1016/j.amc.2013.07.010.

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Redwan, Renas M. "Neural networks and Sigmoid Activation Function in Multi-Layer Networks." Qubahan Academic Journal 1, no. 2 (2020): 29–43. http://dx.doi.org/10.48161/qaj.v1n2a11.

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Back propagation neural networks are known for computing the problems that cannot easily be computed (huge datasets analysis or training) in artificial neural networks. The main idea of this paper is to implement XOR logic gate by ANNs using back propagation neural networks for back propagation of errors, and sigmoid activation function. This neural networks to map non-linear threshold gate. The non-linear used to classify binary inputs ( ) and passing it through hidden layer for computing and ( ), after computing errors by ( ) the weights and thetas ( ) are changing according to errors. Sigmo
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YEN, GARY, and HAIMING LU. "HIERARCHICAL GENETIC ALGORITHM FOR NEAR-OPTIMAL FEEDFORWARD NEURAL NETWORK DESIGN." International Journal of Neural Systems 12, no. 01 (2002): 31–43. http://dx.doi.org/10.1142/s0129065702001023.

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In this paper, we propose a genetic algorithm based design procedure for a multi-layer feed-forward neural network. A hierarchical genetic algorithm is used to evolve both the neural network's topology and weighting parameters. Compared with traditional genetic algorithm based designs for neural networks, the hierarchical approach addresses several deficiencies, including a feasibility check highlighted in literature. A multi-objective cost function is used herein to optimize the performance and topology of the evolved neural network simultaneously. In the prediction of Mackey–Glass chaotic ti
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TÖRÖK, LEVENTE, and TAMÁS ROSKA. "STABILITY OF MULTI-LAYER CELLULAR NEURAL/NONLINEAR NETWORKS." International Journal of Bifurcation and Chaos 14, no. 10 (2004): 3567–86. http://dx.doi.org/10.1142/s0218127404011582.

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We have found a formalism that lets us present generalizations of several stability theorems (see Chua & Roska, 1990; Chua & Wu, 1992; Gilli, 1993; Forti, 2002] on Multi-Layer Cellular Neural/Nonlinear Networks (MLCNN) formerly claimed for Single-Layer Cellular Neural/Nonlinear Networks (CNN). The theorems were selected with special regard to usefulness in engineering applications. Hence, in contrast to many works considering stability on recurrent neural networks, the criteria of the new theorems have clear indications that are easy to verify directly on the template values. Proofs of
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Nguyen, Tan Loc, and Yonggwan Won. "Sleep snoring detection using multi-layer neural networks." Bio-Medical Materials and Engineering 26, s1 (2015): S1749—S1755. http://dx.doi.org/10.3233/bme-151475.

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Svozil, Daniel, Vladimír Kvasnicka, and Jir̂í Pospichal. "Introduction to multi-layer feed-forward neural networks." Chemometrics and Intelligent Laboratory Systems 39, no. 1 (1997): 43–62. http://dx.doi.org/10.1016/s0169-7439(97)00061-0.

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Ban, Jung-Chao, and Chih-Hung Chang. "The learning problem of multi-layer neural networks." Neural Networks 46 (October 2013): 116–23. http://dx.doi.org/10.1016/j.neunet.2013.05.006.

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Dissertations / Theses on the topic "Multi-layer neural networks"

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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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Ahmed, Zulfiqar. "An hybrid architecture for multi-layer feed-forward neural networks." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1999. http://www.collectionscanada.ca/obj/s4/f2/dsk1/tape8/PQDD_0009/MQ52500.pdf.

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Tombs, Jonathan Noel. "Multi-layer neural networks and their implementation in analogue VLSI." Thesis, University of Oxford, 1992. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.334293.

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Zheng, Gonghui. "Design and evaluation of a multi-output-layer perceptron." Thesis, University of Ulster, 1996. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.338195.

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Setyawati, Bina R. "Multi-layer feed forward neural networks for foreign exchange time series forecasting." Morgantown, W. Va. : [West Virginia University Libraries], 2005. https://eidr.wvu.edu/etd/documentdata.eTD?documentid=4180.

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Thesis (Ph. D.)--West Virginia University, 2005.<br>Title from document title page. Document formatted into pages; contains xii, 185 p. : ill. (some col.). Includes abstract. Includes bibliographical references (p. 140-146).
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Bulbuller, Gokhan. "Recognition of in-ear microphone speech data using multi-layer neural networks." Thesis, Monterey, Calif. : Springfield, Va. : Naval Postgraduate School ; Available from National Technical Information Service, 2006. http://library.nps.navy.mil/uhtbin/hyperion/06Mar%5FBulbuller.pdf.

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Thesis (M.S. in Electrical Engineering)--Naval Postgraduate School, March 2006.<br>"March 2006." Thesis Advisor(s): Monique P. Fargues, Ravi Vaidyanathan. Includes bibliographical references (p. 159-162). Also available online.
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Wang, Hao. "A new scheme for training ReLU-based multi-layer feedforward neural networks." Thesis, KTH, Skolan för datavetenskap och kommunikation (CSC), 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-217384.

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A new scheme for training Rectified Linear Unit (ReLU) based feedforward neural networks is examined in this thesis. The project starts with the row-by-row updating strategy designed for Single-hidden Layer Feedforward neural Networks (SLFNs). This strategy exploits the properties held by ReLUs and optimizes each row in the input weight matrix individually, under the common optimization scheme. Then the Direct Updating Strategy (DUS), which has two different versions: Vector-Based Method (VBM) and Matrix-Based Method (MBM), is proposed to optimize the input weight matrix as a whole. Finally DU
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Penny, William Douglas. "The storage, training and generalization properties of multi-layer logical neural networks." Thesis, Brunel University, 1993. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.331996.

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McGarry, Kenneth J. "Rule extraction and knowledge transfer from radial basis function neural networks." Thesis, University of Sunderland, 2002. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.391744.

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Krasniewicz, Jan A. "The application and analysis of genetic algorithms to discover topological free parameters in multi-layer perceptions." Thesis, Birmingham City University, 2000. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.367474.

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Books on the topic "Multi-layer neural networks"

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Shepherd, Adrian J. Second-order methods for neural networks: Fast and reliable training methods for multi-layer perceptrons. Springer, 1997.

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Penny, William Douglas. The storage, training and generalization properties of multi-layer logical neural networks. Brunel University, 1993.

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Dissertation: Autonomous Construction of Multi Layer Perceptron Neural Networks. Storming Media, 1997.

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Shepherd, Adrian J. Second-Order Methods for Neural Networks: Fast and Reliable Training Methods for Multi-Layer Perceptrons. Springer, 2014.

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Shepherd, Adrian J. Second-Order Methods for Neural Networks: Fast and Reliable Training Methods for Multi-Layer Perceptrons. Springer London, Limited, 2012.

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Book chapters on the topic "Multi-layer neural networks"

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Davalo, Eric, and Patrick Naïm. "Multi-layer Neural Networks." In Neural Networks. Macmillan Education UK, 1991. http://dx.doi.org/10.1007/978-1-349-12312-4_3.

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Shepherd, Adrian J. "Multi-Layer Perceptron Training." In Second-Order Methods for Neural Networks. Springer London, 1997. http://dx.doi.org/10.1007/978-1-4471-0953-2_1.

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Lee, Tsu-Chang. "Multi-Layer Feed-Forward Networks." In Structure Level Adaptation for Artificial Neural Networks. Springer US, 1991. http://dx.doi.org/10.1007/978-1-4615-3954-4_3.

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Widrow, Bernard, and Edward P. Katz. "Backpropagation for Multi-layer Neural Networks." In Springer Series on Bio- and Neurosystems. Springer Nature Switzerland, 2025. https://doi.org/10.1007/978-3-031-80939-2_6.

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Yang, Shuzhong, Siwei Luo, and Jianyu Li. "Building Multi-layer Small World Neural Network." In Advances in Neural Networks - ISNN 2006. Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11759966_102.

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Eleuteri, Antonio, Roberto Tagliaferri, and Leopoldo Milano. "Divergence Projections for Variable Selection in Multi–layer Perceptron Networks." In Neural Nets. Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/978-3-540-45216-4_32.

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Suresh, Sundaram, Narasimhan Sundararajan, and Ramasamy Savitha. "Fully Complex-valued Multi Layer Perceptron Networks." In Supervised Learning with Complex-valued Neural Networks. Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-29491-4_2.

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Pérez-Miñana, Elena, Peter Ross, and John Hallam. "Multi-layer perceptron design using Delaunay triangulations." In Fuzzy Logic, Neural Networks, and Evolutionary Computation. Springer Berlin Heidelberg, 1996. http://dx.doi.org/10.1007/3-540-61988-7_22.

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Barber, David, Peter Sollich, and David Saad. "Finite Size Effects in On-Line Learning of Multi-Layer Neural Networks." In Mathematics of Neural Networks. Springer US, 1997. http://dx.doi.org/10.1007/978-1-4615-6099-9_11.

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Sollich, Peter. "Query Learning for Maximum Information Gain in a Multi-Layer Neural Network." In Mathematics of Neural Networks. Springer US, 1997. http://dx.doi.org/10.1007/978-1-4615-6099-9_59.

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Conference papers on the topic "Multi-layer neural networks"

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Zou, Hanyi, Huifang Xu, Qingchao Kong, Yilin Cao, and Wenji Mao. "Generating Relevant Article Comments via Variational Multi-Layer Fusion." In 2024 International Joint Conference on Neural Networks (IJCNN). IEEE, 2024. http://dx.doi.org/10.1109/ijcnn60899.2024.10650383.

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Sreenivasan, Prashanth, and Yogesh H. Kulkarni. "Computing Midcurve with Multi-Layer and Convolutional Neural Networks." In 2025 International Conference on Computational, Communication and Information Technology (ICCCIT). IEEE, 2025. https://doi.org/10.1109/icccit62592.2025.10928152.

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Ruiz, José Luis López, Ángeles Verdejo Espinosa, and Macarena Espinilla Estévez. "Federated Learning Methodology for Indoor Location Systems in Multi-Layer Architecture." In 2024 International Joint Conference on Neural Networks (IJCNN). IEEE, 2024. http://dx.doi.org/10.1109/ijcnn60899.2024.10651450.

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Han, Chaoqi, Bingcai Chen, Chanjuan Liu, et al. "SlideMLP: A Pure Multi-layer Perceptrons Method For Medical Image Segmentation." In 2024 International Joint Conference on Neural Networks (IJCNN). IEEE, 2024. http://dx.doi.org/10.1109/ijcnn60899.2024.10651002.

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Liu, Ziao, Xuyang Li, Lin Zhao, Zhongquan Gao, and Yu Zheng. "Industrial Surface Defect Detection via Multi-scale Mask Cross-layer Fusion Network." In 2024 International Joint Conference on Neural Networks (IJCNN). IEEE, 2024. http://dx.doi.org/10.1109/ijcnn60899.2024.10651547.

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Du, Ruizhong, Yidan Li, Mingyue Li, Jinjia Peng, Yuting Zhu, and Caixia Ma. "Multi-attribute Semantic Adversarial Attack Based on Cross-layer Interpolation for Face Recognition." In 2024 International Joint Conference on Neural Networks (IJCNN). IEEE, 2024. http://dx.doi.org/10.1109/ijcnn60899.2024.10650828.

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Zhang, Tianhua, and Pianran Guo. "Track Finding for Multi-Layer Silicon Pixel Detectors with GNN." In 2025 5th International Conference on Neural Networks, Information and Communication Engineering (NNICE). IEEE, 2025. https://doi.org/10.1109/nnice64954.2025.11064706.

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Fujino, Kazushi, Keiki Takadama, and Hiroyuki Sato. "Multi-layer Cortical Learning Algorithm for Forecasting Time-series Data with Smoothly Changing Variation Patterns." In 2024 International Joint Conference on Neural Networks (IJCNN). IEEE, 2024. http://dx.doi.org/10.1109/ijcnn60899.2024.10650432.

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Sun, Mengyuan. "Music Melody Extraction Algorithm Based on Multi-Layer Perceptron Neural Network." In 2024 4th International Conference on Mobile Networks and Wireless Communications (ICMNWC). IEEE, 2024. https://doi.org/10.1109/icmnwc63764.2024.10871981.

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Zhang, Pei, Lihua Zhou, Lizhen Wang, Hongmei Chen, and Qing Xiao. "Multi-masks and Bi-spaces Reconstruction based Single-Layer Auto-encoder for Heterogeneous Graph Representation Learning." In 2024 International Joint Conference on Neural Networks (IJCNN). IEEE, 2024. http://dx.doi.org/10.1109/ijcnn60899.2024.10650586.

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