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Journal articles on the topic 'Disordered Systems and Neural Networks'

Author: Grafiati

Published: 4 June 2021

Last updated: 13 February 2022

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1

Iesari, Fabio, Hiroyuki Setoyama, and Toshihiro Okajima. "Extracting Local Symmetry of Mono-Atomic Systems from Extended X-ray Absorption Fine Structure Using Deep Neural Networks." Symmetry 13, no. 6 (June 15, 2021): 1070. http://dx.doi.org/10.3390/sym13061070.

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In recent years, neural networks have become a new method for the analysis of extended X-ray absorption fine structure data. Due to its sensitivity to local structure, X-ray absorption spectroscopy is often used to study disordered systems and one of its more interesting property is the sensitivity not only to pair distribution function, but also to three-body distribution, which contains information on the local symmetry. In this study, by considering the case of Ni, we show that by using neural networks, it is possible to obtain not only the radial distribution function, but also the bond angle distribution between the first nearest-neighbors. Additionally, by adding appropriate configurations in the dataset used for training, we show that the neural network is able to analyze also data from disordered phases (liquid and undercooled state), detecting small changes in the local ordering compatible with results obtained through other methods.

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2

Madkhali, Marwah M. M., Conor D. Rankine, and Thomas J. Penfold. "Enhancing the analysis of disorder in X-ray absorption spectra: application of deep neural networks to T-jump-X-ray probe experiments." Physical Chemistry Chemical Physics 23, no. 15 (2021): 9259–69. http://dx.doi.org/10.1039/d0cp06244h.

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3

BERNARDES, AMÉRICO T., and HANS J. HERRMANN. "A SIMPLE MODEL WITH STRONG ASYMMETRIC COUPLINGS." International Journal of Modern Physics C 04, no. 04 (August 1993): 765–74. http://dx.doi.org/10.1142/s012918319300063x.

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In this paper an Ising model on a random lattice with strongly asymmetric couplings inspired by neural networks is studied. We investigate the phase space structure and find evidence for an ultrametric, "multivalley" structure as observed in disordered magnetic systems. We have calculated the size of the basins of attraction.

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4

MEDOFF, D. R. "Neural Networks: Neural Systems I: Neural Systems I." American Journal of Psychiatry 157, no. 6 (June 1, 2000): 877. http://dx.doi.org/10.1176/appi.ajp.157.6.877.

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5

MEDOFF, DEBORAH, and HENRY HOLCOMB. "Neural Networks: Neural Systems II." American Journal of Psychiatry 157, no. 8 (August 2000): 1212. http://dx.doi.org/10.1176/appi.ajp.157.8.1212.

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6

KLESSE, ROCHUS, and MARCUS METZLER. "MODELING DISORDERED QUANTUM SYSTEMS WITH DYNAMICAL NETWORKS." International Journal of Modern Physics C 10, no. 04 (June 1999): 577–606. http://dx.doi.org/10.1142/s0129183199000449.

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It is the purpose of the present article to show that so-called network models, originally designed to describe static properties of disordered electronic systems, can be easily generalized to quantum-dynamical models, which then allow for an investigation of dynamical and spectral aspects. This concept is exemplified by the Chalker–Coddington model for the quantum Hall effect and a three-dimensional generalization of it. We simulate phase coherent diffusion of wave packets and consider spatial and spectral correlations of network eigenstates as well as the distribution of (quasi-)energy levels. Apart from that, it is demonstrated how network models can be used to determine two-point conductances. Our numerical calculations for the three-dimensional model at the Metal-Insulator transition point delivers, among others, an anomalous diffusion exponent of η=3-D2=1.7±0.1. The methods presented here in detail have been used partially in earlier work.

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7

Wang, Jun. "Artificial neural networks versus natural neural networks." Decision Support Systems 11, no. 5 (June 1994): 415–29. http://dx.doi.org/10.1016/0167-9236(94)90016-7.

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8

Antsaklis, P. J. "Neural networks for control systems." IEEE Transactions on Neural Networks 1, no. 2 (June 1990): 242–44. http://dx.doi.org/10.1109/72.80237.

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9

Godjevac, Jelena, and Nigel Steele. "Fuzzy Systems and Neural Networks." Intelligent Automation & Soft Computing 4, no. 1 (January 1998): 27–37. http://dx.doi.org/10.1080/10798587.1998.10750719.

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10

Kosko, Bart, and John C. Burgess. "Neural Networks and Fuzzy Systems." Journal of the Acoustical Society of America 103, no. 6 (June 1998): 3131. http://dx.doi.org/10.1121/1.423096.

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11

Virgil Negoita, Constantin. "Neural Networks as Fuzzy Systems." Kybernetes 23, no. 3 (April 1, 1994): 7–9. http://dx.doi.org/10.1108/03684929410059000.

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Any fuzzy system is a knowledge‐based system which implies an inference engine. Proposes neural networks as a means of performing the inference. Using the Theorem of Representation proposes an encoding scheme that allows the neural network to be trained to perform modus ponens.

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12

Zambelli, Stefano. "Neural networks and fuzzy systems." Journal of Economic Dynamics and Control 17, no. 3 (May 1993): 523–29. http://dx.doi.org/10.1016/0165-1889(93)90010-p.

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13

Looney, Carl G. "Neural networks as expert systems." Expert Systems with Applications 6, no. 2 (April 1993): 129–36. http://dx.doi.org/10.1016/0957-4174(93)90003-o.

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14

Narendra, Kumpati S., and Kannan Parthasarathy. "Neural networks and dynamical systems." International Journal of Approximate Reasoning 6, no. 2 (February 1992): 109–31. http://dx.doi.org/10.1016/0888-613x(92)90014-q.

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15

Dvorak, V. "Neural networks and fuzzy systems." Knowledge-Based Systems 6, no. 3 (September 1993): 179. http://dx.doi.org/10.1016/0950-7051(93)90043-s.

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16

HOY, R. R. "Invertebrate Neural Systems: Model Neural Networks and Behavior." Science 232, no. 4747 (April 11, 1986): 272. http://dx.doi.org/10.1126/science.232.4747.272.

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17

Tafti, Mohammed H. A. "Neural networks." ACM SIGMIS Database: the DATABASE for Advances in Information Systems 23, no. 1 (March 1992): 51–54. http://dx.doi.org/10.1145/134347.134361.

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18

Newton, S. "Introducing neural networks." Knowledge-Based Systems 7, no. 1 (March 1994): 57. http://dx.doi.org/10.1016/0950-7051(94)90016-7.

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19

Veselý, A., and D. Brechlerová. "Neural networks in intrusion detection systems." Agricultural Economics (Zemědělská ekonomika) 50, No. 1 (February 24, 2012): 35–40. http://dx.doi.org/10.17221/5164-agricecon.

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Security of an information system is its very important property, especially today, when computers are interconnected via internet. Because no system can be absolutely secure, the timely and accurate detection of intrusions is necessary. For this purpose, Intrusion Detection Systems (IDS) were designed. There are two basic models of IDS: misuse IDS and anomaly IDS. Misuse systems detect intrusions by looking for activity that corresponds to the known signatures of intrusions or vulnerabilities. Anomaly systems detect intrusions by searching for an abnormal system activity. Most IDS commercial tools are misuse systems with rule-based expert system structure. However, these techniques are less successful when attack characteristics vary from built-in signatures. Artificial neural networks offer the potential to resolve these problems. As far as anomaly systems are concerned, it is very difficult to build them, because it is difficult to define the normal and abnormal behaviour of a system. Also for building anomaly system, neural networks can be used, because they can learn to discriminate the normal and abnormal behaviour of a system from examples. Therefore, they offer a promising technique for building anomaly systems. This paper presents an overview of the applicability of neural networks in building intrusion systems and discusses advantages and drawbacks of neural network technology.

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20

Dumitrache, I., Th Borangiu, A. Popescu, and Gh Musca. "Neural Networks in Intelligent Manufactuirng Systems." IFAC Proceedings Volumes 27, no. 4 (June 1994): 17–25. http://dx.doi.org/10.1016/s1474-6670(17)45994-5.

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21

Tomsovic, Kevin. "Neural networks applications in power systems." International Journal of Electrical Power & Energy Systems 19, no. 6 (August 1997): 419. http://dx.doi.org/10.1016/s0142-0615(97)00013-6.

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22

Fairhurst, Michael. "Neural Networks for Control and Systems." IEE Review 38, no. 9 (1992): 318. http://dx.doi.org/10.1049/ir:19920141.

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23

Nedjah, Nadia, and Luiza de Macedo Mourelle. "Neural networks in intelligent systems design." Neurocomputing 72, no. 10-12 (June 2009): 2153. http://dx.doi.org/10.1016/j.neucom.2009.02.001.

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24

Atlas, L. E., and Y. Suzuki. "Digital systems for artificial neural networks." IEEE Circuits and Devices Magazine 5, no. 6 (November 1989): 20–24. http://dx.doi.org/10.1109/101.41879.

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25

Klemenov, V. V., and E. V. Novikov. "Neural networks in adaptive optical systems." Journal of Optical Technology 67, no. 2 (February 1, 2000): 94. http://dx.doi.org/10.1364/jot.67.000094.

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26

Stark, Jaroslav. "Iterated Function Systems as neural networks." Neural Networks 4, no. 5 (January 1991): 679–90. http://dx.doi.org/10.1016/0893-6080(91)90021-v.

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27

Kentala, E., I. Pyykkö, Y. Auramo, and M. Juhola. "Neural Networks in Neurotologic Expert Systems." Acta Oto-Laryngologica 117, sup529 (January 1997): 127–29. http://dx.doi.org/10.3109/00016489709124102.

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28

Hanzalek, Zdenek. "Neural networks for control and systems." Control Engineering Practice 3, no. 7 (July 1995): 1051. http://dx.doi.org/10.1016/0967-0661(95)90003-9.

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29

Seth, Anil K. "Causal networks in simulated neural systems." Cognitive Neurodynamics 2, no. 1 (October 20, 2007): 49–64. http://dx.doi.org/10.1007/s11571-007-9031-z.

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30

Ryczko, Kevin, Kyle Mills, Iryna Luchak, Christa Homenick, and Isaac Tamblyn. "Convolutional neural networks for atomistic systems." Computational Materials Science 149 (June 2018): 134–42. http://dx.doi.org/10.1016/j.commatsci.2018.03.005.

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31

Sunar, Mehmet. "Artificial neural networks for thermopiezoelectric systems." International Journal of Energy Research 23, no. 13 (October 25, 1999): 1123–31. http://dx.doi.org/10.1002/(sici)1099-114x(19991025)23:13<1123::aid-er542>3.0.co;2-f.

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32

Turluev, Rizvan, and Laura Hadjieva. "Neural Networks in Corporate Governance Systems." SHS Web of Conferences 93 (2021): 03016. http://dx.doi.org/10.1051/shsconf/20219303016.

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Every year, the interest in solving more complex problems is growing, due to automation, the need for communication processes in intelligent systems. One of the promising directions for solving this problem is based on the use of artificial neural networks and neurocomputers, as the most progressive in relation to corporate governance problems.

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33

Warwick, K. "Cultured neural networks." Proceedings of the Institution of Mechanical Engineers, Part I: Journal of Systems and Control Engineering 224, no. 2 (January 7, 2010): 109–11. http://dx.doi.org/10.1243/09596518jsce916.

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34

Blake, J. "The implementation of fuzzy systems, neural networks and fuzzy neural networks using FPGAs." Information Sciences 112, no. 1-4 (December 1998): 151–68. http://dx.doi.org/10.1016/s0020-0255(98)10029-4.

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35

Aggarwal, R., and Yonghua Song. "Artificial neural networks in power systems. Part 2: Types of artificial neural networks." Power Engineering Journal 12, no. 1 (February 1, 1998): 41–47. http://dx.doi.org/10.1049/pe:19980110.

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36

Simons, Robert, and J. G. Taylor. "Neural Networks." Journal of the Operational Research Society 47, no. 4 (April 1996): 596. http://dx.doi.org/10.2307/3010740.

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37

Simons, Robert. "Neural Networks." Journal of the Operational Research Society 47, no. 4 (April 1996): 596–97. http://dx.doi.org/10.1057/jors.1996.70.

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38

BÄKER, M., T. KALKREUTER, G. MACK, and M. SPEH. "NEURAL MULTIGRID METHODS FOR GAUGE THEORIES AND OTHER DISORDERED SYSTEMS." International Journal of Modern Physics C 04, no. 02 (April 1993): 239–47. http://dx.doi.org/10.1142/s0129183193000252.

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We present evidence that multigrid works for wave equations in disordered systems, e.g. in the presence of gauge fields, no matter how strong the disorder, but one needs to introduce a "neural computations" point of view into large scale simulations: First, the system must learn how to do the simulations efficiently, then do the simulation (fast). The method can also be used to provide smooth interpolation kernels which are needed in multigrid Monte Carlo updates.

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39

Yoon, Youngohc, and Lynn Peterson. "Artificial neural networks." ACM SIGMIS Database: the DATABASE for Advances in Information Systems 23, no. 1 (March 1992): 55–57. http://dx.doi.org/10.1145/134347.134362.

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40

Yan, Le, Jean-Philippe Bouchaud, and Matthieu Wyart. "Edge mode amplification in disordered elastic networks." Soft Matter 13, no. 34 (2017): 5795–801. http://dx.doi.org/10.1039/c7sm00475c.

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41

Aleksander, Igor. "Neural networks: current applications." Knowledge-Based Systems 6, no. 3 (September 1993): 180–81. http://dx.doi.org/10.1016/0950-7051(93)90045-u.

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42

Dalhoum, Abdel Latif Abu, and Mohammed Al-Rawi. "High-Order Neural Networks are Equivalent to Ordinary Neural Networks." Modern Applied Science 13, no. 2 (January 27, 2019): 228. http://dx.doi.org/10.5539/mas.v13n2p228.

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Equivalence of computational systems can assist in obtaining abstract systems, and thus enable better understanding of issues related their design and performance. For more than four decades, artificial neural networks have been used in many scientific applications to solve classification problems as well as other problems. Since the time of their introduction, multilayer feedforward neural network referred as Ordinary Neural Network (ONN), that contains only summation activation (Sigma) neurons, and multilayer feedforward High-order Neural Network (HONN), that contains Sigma neurons, and product activation (Pi) neurons, have been treated in the literature as different entities. In this work, we studied whether HONNs are mathematically equivalent to ONNs. We have proved that every HONN could be converted to some equivalent ONN. In most cases, one just needs to modify the neuronal transfer function of the Pi neuron to convert it to a Sigma neuron. The theorems that we have derived clearly show that the original HONN and its corresponding equivalent ONN would give exactly the same output, which means; they can both be used to perform exactly the same functionality. We also derived equivalence theorems for several other non-standard neural networks, for example, recurrent HONNs and HONNs with translated multiplicative neurons. This work rejects the hypothesis that HONNs and ONNs are different entities, a conclusion that might initiate a new research frontier in artificial neural network research.

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43

Liu, G. P. "NEURAL-LEARNING CONTROL OF NONLINEAR SYSTEMS USING VARIABLE NEURAL NETWORKS." IFAC Proceedings Volumes 35, no. 1 (2002): 265–70. http://dx.doi.org/10.3182/20020721-6-es-1901.00697.

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44

Gao, X. Z., and S. J. Ovaska. "Neural networks-based approximation of fuzzy systems." Integrated Computer-Aided Engineering 10, no. 4 (September 9, 2003): 319–31. http://dx.doi.org/10.3233/ica-2003-10403.

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45

Hanaoka, Kyohei. "Deep Neural Networks for Multicomponent Molecular Systems." ACS Omega 5, no. 33 (August 10, 2020): 21042–53. http://dx.doi.org/10.1021/acsomega.0c02599.

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46

Shu, Huailin, and Youguo Pi. "PID neural networks for time-delay systems." Computers & Chemical Engineering 24, no. 2-7 (July 2000): 859–62. http://dx.doi.org/10.1016/s0098-1354(00)00340-9.

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47

Vega, M. P., E. L. Lima, and J. C. Pinto. "Modeling Lumped-Distributed Systems Using Neural Networks." IFAC Proceedings Volumes 33, no. 10 (June 2000): 803–8. http://dx.doi.org/10.1016/s1474-6670(17)38638-x.

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48

Bojanowska, Agnieszka. "Application of neural networks in CRM systems." ITM Web of Conferences 15 (2017): 04001. http://dx.doi.org/10.1051/itmconf/20171504001.

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49

Cichocki, A., and A. Bargiela. "Neural networks for solving linear inequality systems." Parallel Computing 22, no. 11 (January 1997): 1455–75. http://dx.doi.org/10.1016/s0167-8191(96)00065-8.

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50

Lee, Soo-Young, Jim Austine, and Rhee Man Kil. "Building information systems based on neural networks." Neurocomputing 35, no. 1-4 (November 2000): 1–2. http://dx.doi.org/10.1016/s0925-2312(00)00297-6.

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