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Journal articles on the topic 'Perceptrons'

Author: Grafiati

Published: 4 June 2021

Last updated: 25 July 2025

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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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2

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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3

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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4

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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5

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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6

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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7

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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8

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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9

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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10

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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11

Legenstein, Robert, and Wolfgang Maass. "On the Classification Capability of Sign-Constrained Perceptrons." Neural Computation 20, no. 1 (2008): 288–309. http://dx.doi.org/10.1162/neco.2008.20.1.288.

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The perceptron (also referred to as McCulloch-Pitts neuron, or linear threshold gate) is commonly used as a simplified model for the discrimination and learning capability of a biological neuron. Criteria that tell us when a perceptron can implement (or learn to implement) all possible dichotomies over a given set of input patterns are well known, but only for the idealized case, where one assumes that the sign of a synaptic weight can be switched during learning. We present in this letter an analysis of the classification capability of the biologically more realistic model of a sign-constrain

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12

Lyuu, Yuh-Dauh, and Igor Rivin. "Tight Bounds on Transition to Perfect Generalization in Perceptrons." Neural Computation 4, no. 6 (1992): 854–62. http://dx.doi.org/10.1162/neco.1992.4.6.854.

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“Sudden” transition to perfect generalization in binary perceptrons is investigated. Building on recent theoretical works of Gardner and Derrida (1989) and Baum and Lyuu (1991), we show the following: for α > αc = 1.44797 …, if α n examples are drawn from the uniform distribution on {+1, −1}n and classified according to a target perceptron wt ∈ {+1, −1}n as positive if wt · x ≥ 0 and negative otherwise, then the expected number of nontarget perceptrons consistent with the examples is 2−⊖(√n); the same number, however, grows exponentially 2⊖(n) if α ≤ αc. Numerical calculations for n up to 1

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13

Majer, P., A. Engel, and A. Zippelius. "Perceptrons above saturation." Journal of Physics A: Mathematical and General 26, no. 24 (1993): 7405–16. http://dx.doi.org/10.1088/0305-4470/26/24/015.

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14

Silva, Francisco, Mikel Sanz, João Seixas, Enrique Solano, and Yasser Omar. "Perceptrons from memristors." Neural Networks 122 (February 2020): 273–78. http://dx.doi.org/10.1016/j.neunet.2019.10.013.

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15

Ou, Jun, Yujian Li, and Chuanhui Shan. "Two-dimensional perceptrons." Soft Computing 24, no. 5 (2019): 3355–64. http://dx.doi.org/10.1007/s00500-019-04098-w.

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16

Cerella, John. "Pigeons and perceptrons." Pattern Recognition 19, no. 6 (1986): 431–38. http://dx.doi.org/10.1016/0031-3203(86)90041-5.

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17

Navia-Vázquez, A. "Support vector perceptrons." Neurocomputing 70, no. 4-6 (2007): 1089–95. http://dx.doi.org/10.1016/j.neucom.2006.08.001.

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18

Kiranyaz, Serkan, Turker Ince, Alexandros Iosifidis, and Moncef Gabbouj. "Progressive Operational Perceptrons." Neurocomputing 224 (February 2017): 142–54. http://dx.doi.org/10.1016/j.neucom.2016.10.044.

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19

Belue, Lisa M., Kenneth W. Bauer, and Dennis W. Ruck. "Selecting Optimal Experiments for Multiple Output Multilayer Perceptrons." Neural Computation 9, no. 1 (1997): 161–83. http://dx.doi.org/10.1162/neco.1997.9.1.161.

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Where should a researcher conduct experiments to provide training data for a multilayer perceptron? This question is investigated, and a statistical method for selecting optimal experimental design points for multiple output multilayer perceptrons is introduced. Multiple class discrimination problems are examined using a framework in which the multilayer perceptron is viewed as a multivariate nonlinear regression model. Following a Bayesian formulation for the case where the variance-covariance matrix of the responses is unknown, a selection criterion is developed. This criterion is based on t

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20

Yu, Xin, Mian Xie, Li Xia Tang, and Chen Yu Li. "Learning Algorithm for Fuzzy Perceptron with Max-Product Composition." Applied Mechanics and Materials 687-691 (November 2014): 1359–62. http://dx.doi.org/10.4028/www.scientific.net/amm.687-691.1359.

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Fuzzy neural networks is a powerful computational model, which integrates fuzzy systems with neural networks, and fuzzy perceptron is a kind of this neural networks. In this paper, a learning algorithm is proposed for a fuzzy perceptron with max-product composition, and the topological structure of this fuzzy perceptron is the same as conventional linear perceptrons. The inner operations involved in the working process of this fuzzy perceptron are based on the max-product logical operations rather than conventional multiplication and summation etc. To illustrate the finite convergence of propo

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21

Nadal, J. P., and N. Parga. "Duality Between Learning Machines: A Bridge Between Supervised and Unsupervised Learning." Neural Computation 6, no. 3 (1994): 491–508. http://dx.doi.org/10.1162/neco.1994.6.3.491.

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We exhibit a duality between two perceptrons that allows us to compare the theoretical analysis of supervised and unsupervised learning tasks. The first perceptron has one output and is asked to learn a classification of p patterns. The second (dual) perceptron has p outputs and is asked to transmit as much information as possible on a distribution of inputs. We show in particular that the maximum information that can be stored in the couplings for the supervised learning task is equal to the maximum information that can be transmitted by the dual perceptron.

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22

Bridgeman, Bruce. "The dynamical model is a Perceptron." Behavioral and Brain Sciences 21, no. 5 (1998): 631–32. http://dx.doi.org/10.1017/s0140525x98251739.

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Van Gelder's example of a dynamical model is a Perceptron. The similarity of dynamical models and Perceptrons in turn exemplifies the close relationship between dynamical and algorithmic models. Both are models, not literal descriptions of brains. The brain states of standard modeling are better conceived as processes in the dynamical sense, but algorithmic models remain useful.

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23

Zwietering, P. J., E. H. L. Aarts, and J. Wessels. "EXACT CLASSIFICATION WITH TWO-LAYERED PERCEPTRONS." International Journal of Neural Systems 03, no. 02 (1992): 143–56. http://dx.doi.org/10.1142/s0129065792000127.

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We study the capabilities of two-layered perceptrons for classifying exactly a given subset. Both necessary and sufficient conditions are derived for subsets to be exactly classifiable with two-layered perceptrons that use the hard-limiting response function. The necessary conditions can be viewed as generalizations of the linear-separability condition of one-layered perceptrons and confirm the conjecture that the capabilities of two-layered perceptrons are more limited than those of three-layered perceptrons. The sufficient conditions show that the capabilities of two-layered perceptrons exte

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24

Kirdin, Alexander, Sergey Sidorov, and Nikolai Zolotykh. "Rosenblatt’s First Theorem and Frugality of Deep Learning." Entropy 24, no. 11 (2022): 1635. http://dx.doi.org/10.3390/e24111635.

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The Rosenblatt’s first theorem about the omnipotence of shallow networks states that elementary perceptrons can solve any classification problem if there are no discrepancies in the training set. Minsky and Papert considered elementary perceptrons with restrictions on the neural inputs: a bounded number of connections or a relatively small diameter of the receptive field for each neuron at the hidden layer. They proved that under these constraints, an elementary perceptron cannot solve some problems, such as the connectivity of input images or the parity of pixels in them. In this note, we dem

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25

LIANG, XUN, and SHAOWEI XIA. "METHODS OF TRAINING AND CONSTRUCTING MULTILAYER PERCEPTRONS WITH ARBITRARY PATTERN SETS." International Journal of Neural Systems 06, no. 03 (1995): 233–47. http://dx.doi.org/10.1142/s0129065795000172.

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This paper presents two compensation methods for multilayer perceptrons (MLPs) which are very difficult to train by traditional Back Propagation (BP) methods. For MLPs trapped in local minima, compensating methods can correct the wrong outputs one by one using constructing techniques until all outputs are right, so that the MLPs can skip from the local minima to the global minima. A hidden neuron is added as compensation for a binary input three-layer perceptron trapped in a local minimum; and one or two hidden neurons are added as compensation for a real input three-layer perceptron. For a pe

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26

Bylander, Tom. "Learning Linear Threshold Approximations Using Perceptrons." Neural Computation 7, no. 2 (1995): 370–79. http://dx.doi.org/10.1162/neco.1995.7.2.370.

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We demonstrate sufficient conditions for polynomial learnability of suboptimal linear threshold functions using perceptrons. The central result is as follows. Suppose there exists a vector w*, of n weights (including the threshold) with “accuracy” 1 − α, “average error” η, and “balancing separation” σ, i.e., with probability 1 − α, w* correctly classifies an example x; over examples incorrectly classified by w*, the expected value of |w* · x| is η (source of inaccuracy does not matter); and over a certain portion of correctly classified examples, the expected value of |w* · x| is σ. Then, with

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27

Podolskii, V. V. "Perceptrons of large weight." Problems of Information Transmission 45, no. 1 (2009): 46–53. http://dx.doi.org/10.1134/s0032946009010062.

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28

Bunzmann, C., M. Biehl, and R. Urbanczik. "Efficiently Learning Multilayer Perceptrons." Physical Review Letters 86, no. 10 (2001): 2166–69. http://dx.doi.org/10.1103/physrevlett.86.2166.

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29

Watkin, T. L. H., and A. Rau. "Selecting examples for perceptrons." Journal of Physics A: Mathematical and General 25, no. 1 (1992): 113–21. http://dx.doi.org/10.1088/0305-4470/25/1/016.

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30

Kerlirzin, P., and F. Vallet. "Robustness in Multilayer Perceptrons." Neural Computation 5, no. 3 (1993): 473–82. http://dx.doi.org/10.1162/neco.1993.5.3.473.

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In this paper, we study the robustness of multilayer networks versus the destruction of neurons. We show that the classical backpropagation algorithm does not lead to optimal robustness and we propose a modified algorithm that improves this capability.

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31

Kuzuoglu, M., and K. Leblebicioglu. "Infinite-dimensional multilayer perceptrons." IEEE Transactions on Neural Networks 7, no. 4 (1996): 889–96. http://dx.doi.org/10.1109/72.508932.

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32

Charalampidis, Dimitrios, and Barry Muldrey. "Clustering using multilayer perceptrons." Nonlinear Analysis: Theory, Methods & Applications 71, no. 12 (2009): e2807-e2813. http://dx.doi.org/10.1016/j.na.2009.06.064.

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33

Murthy, C. "Multilayer perceptrons and fractals." Information Sciences 112, no. 1-4 (1998): 137–50. http://dx.doi.org/10.1016/s0020-0255(98)10028-2.

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34

Xiang, Xuyan, Yingchun Deng, and Xiangqun Yang. "Second order spiking perceptrons." Soft Computing 13, no. 12 (2009): 1219–30. http://dx.doi.org/10.1007/s00500-009-0415-3.

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35

Tattersall, G. D., S. Foster, and R. D. Johnston. "Single-layer lookup perceptrons." IEE Proceedings F Radar and Signal Processing 138, no. 1 (1991): 46. http://dx.doi.org/10.1049/ip-f-2.1991.0008.

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36

Hamid, Danish, Syed Sajid Ullah, Jawaid Iqbal, Saddam Hussain, Ch Anwar ul Hassan, and Fazlullah Umar. "A Machine Learning in Binary and Multiclassification Results on Imbalanced Heart Disease Data Stream." Journal of Sensors 2022 (September 20, 2022): 1–13. http://dx.doi.org/10.1155/2022/8400622.

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In medical filed, predicting the occurrence of heart diseases is a significant piece of work. Millions of healthcare-related complexities that have remained unsolved up until now can be greatly simplified with the help of machine learning. The proposed study is concerned with the cardiac disease diagnosis decision support system. An OpenML repository data stream with 1 million instances of heart disease and 14 features is used for this study. After applying to preprocess and feature engineering techniques, machine learning approaches like random forest, decision trees, gradient boosted trees,

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37

Hush, D. R., B. Horne, and J. M. Salas. "Error surfaces for multilayer perceptrons." IEEE Transactions on Systems, Man, and Cybernetics 22, no. 5 (1992): 1152–61. http://dx.doi.org/10.1109/21.179853.

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38

Murphy, O. J. "Nearest neighbor pattern classification perceptrons." Proceedings of the IEEE 78, no. 10 (1990): 1595–98. http://dx.doi.org/10.1109/5.58344.

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39

Raudys, Sarunas, and Aistis Raudys. "Pairwise Costs in Multiclass Perceptrons." IEEE Transactions on Pattern Analysis and Machine Intelligence 32, no. 7 (2010): 1324–28. http://dx.doi.org/10.1109/tpami.2010.72.

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40

Kurchan, J., and E. Domany. "Critical capacity of constrained perceptrons." Journal of Physics A: Mathematical and General 23, no. 16 (1990): L847—L852. http://dx.doi.org/10.1088/0305-4470/23/16/013.

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41

Engel, A., D. Malzahn, and I. Kanter. "Storage properties of correlated perceptrons." Philosophical Magazine B 77, no. 5 (1998): 1507–13. http://dx.doi.org/10.1080/13642819808205042.

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42

Coelho, A. L. V., and F. O. França. "Enhancing perceptrons with contrastive biclusters." Electronics Letters 52, no. 24 (2016): 1974–76. http://dx.doi.org/10.1049/el.2016.3067.

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43

Schietse, J., M. Bouten, and C. van den Broeck. "Training Binary Perceptrons by Clipping." Europhysics Letters (EPL) 32, no. 3 (1995): 279–84. http://dx.doi.org/10.1209/0295-5075/32/3/015.

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44

Wiegerinck, W. A. J. J., and A. C. C. Coolen. "The connections of large perceptrons." Journal of Physics A: Mathematical and General 26, no. 11 (1993): 2535–48. http://dx.doi.org/10.1088/0305-4470/26/11/007.

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45

Malzahn, D., A. Engel, and I. Kanter. "Storage capacity of correlated perceptrons." Physical Review E 55, no. 6 (1997): 7369–78. http://dx.doi.org/10.1103/physreve.55.7369.

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46

Alpaydin, E., and M. I. Jordan. "Local linear perceptrons for classification." IEEE Transactions on Neural Networks 7, no. 3 (1996): 788–94. http://dx.doi.org/10.1109/72.501737.

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47

Verma, B. "Fast training of multilayer perceptrons." IEEE Transactions on Neural Networks 8, no. 6 (1997): 1314–20. http://dx.doi.org/10.1109/72.641454.

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48

Tran, Dat Thanh, Serkan Kiranyaz, Moncef Gabbouj, and Alexandros Iosifidis. "Progressive Operational Perceptrons with Memory." Neurocomputing 379 (February 2020): 172–81. http://dx.doi.org/10.1016/j.neucom.2019.10.079.

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49

Apolloni, B., and G. Ronchini. "Dynamic sizing of multilayer perceptrons." Biological Cybernetics 71, no. 1 (1994): 49–63. http://dx.doi.org/10.1007/bf00198911.

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50

Watkin, Timothy L. H., and Albrecht Rau. "Learning unlearnable problems with perceptrons." Physical Review A 45, no. 6 (1992): 4102–10. http://dx.doi.org/10.1103/physreva.45.4102.

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