Abstract
Neural networks are the mathematical models that process information or any signal based on biological neuron. The structure of these networks is very complex as it consists of interconnected neurons that make problem-solving less complicated. Since artificial neural networks (ANN) can be used in various applications specifically in the field of computer science and electronics; the researchers are designing artificial neural networks to find solutions to problems like recognition of pattern, optimization, prediction, associative memory, and control. This chapter gives the basic knowledge of artificial neural networks, its general architecture, and various categories. The chapter focuses on different models, their mathematical proof, and applications in real life. It also covers the detailed information about the use of ANN in different sectors.
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References
P. Kumar, P. Sharma, Artificial neural networks-a study. Int. J. Emerg. Eng. Res. Technol. 2(2), 143–148 (2014)
S. O. Haykin (ed.), Neural networks and learning machines, 3rd edn. (Prentice Hall, New Jersey, 2008)
S.W. Smith, Neural networks (and more!), in The Scientist and Engineer’s Guide to Digital Signal Processing, (California Technical Publishing, 1999)
https://www.fon.hum.uva.nl/praat/manual/Feedforward_neural_networks_1_1__The_learning_ phase.html. Accessed 22 August 2020
https://www.fon.hum.uva.nl/praat/manual/Feedforward_neural_networks_1_2__The_ classification_pha.html. Accessed 22 August 2020
R. Sigit, E. Triyana, M. Rochmad, Cataract detection using single layer perceptron based on smartphone, in 2019 3rd International Conference on Informatics and Computational Sciences (ICICoS), (Semarang, Indonesia, 2019), pp. 1–6. https://doi.org/10.1109/ICICoS48119.2019.8982445
M.D. Akhtar, A. Kumar, D. Ghosal, A. Ekbal, P. Bhattacharyya, A Multilayer Perceptron based Ensemble Technique for Fine-grained Financial Sentiment Analysis (2017), pp. 540–546. https://doi.org/10.18653/v1/D17-1057
R. Cheruku, D. Edla, V. Kuppili, Diabetes classification using radial basis function network by combining cluster validity index and BAT optimization with novel fitness function. Int. J. Comput. Intell. Syst. 10, 247 (2017). https://doi.org/10.2991/ijcis.2017.10.1.17
K.L. Du, M.N.S. Swamy, Recurrent neural networks, in Neural Networks and Statistical Learning, (Springer, London, 2019). https://doi.org/10.1007/978-1-4471-7452-3_12
A. Rodan, P. Tino, Minimum complexity echo state network. IEEE Trans. Neural Netw. 22(1), 131–144 (2011)
V. Tynchenko, V. Tynchenko, V. Bukhtoyarov, V. Kukartsev, V. Kukartsev, D. Eremeev, Application of Kohonen self-organizing maps to the analysis of enterprises’ employees certification results, in IOP Conference Series: Materials Science and Engineering, vol. 537, (2019), p. 042010. https://doi.org/10.1088/1757-899X/537/4/042010
M. Dahiya, S. Gill, Detection of rogue access point in WLAN using Hopfield Neural Network. Int. J. Electr. Comp. Eng. 7, 1060–1070 (2017). https://doi.org/10.11591/ijece.v7i2.pp1060-1070
Q. Hong, Y. Li, X. Wang, Memristive continuous Hopfield neural network circuit for image restoration. Neural Comput. Appl. (2019). https://doi.org/10.1007/s00521-019-04305-7
X. Dang, X. Tang, Z. Hao, J. Ren, Discrete Hopfield neural network based indoor Wi-Fi localization using CSI. EURASIP. J. Wireless Comm. Netw., 2020 (2020). https://doi.org/10.1186/s13638-020-01692-7
S. Grossberg, Recurrent neural networks. Scholarpedia. 8, 1888 (2013). https://doi.org/10.4249/scholarpedia.1888
G. Araújo, C. Ralha, Using ART1 neural networks for clustering computer forensics documents. Int. J. Forensic Comp. Sci. 7, 24–41 (2012). https://doi.org/10.5769/J201201003
H. Jinbing, Y. Yunping, Fault data diagnosis of energy consumption equipment for urban rail transit based on ART2 model, in 2017 International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery (CyberC), (Nanjing, 2017), pp. 177–181. https://doi.org/10.1109/CyberC.2017.29
L. Zhang, W. Meng, A. Chen, M. Mei, Y. Liu, Application of LSTM neural network for urban road diseases trend forecasting, in 2018 IEEE International Conference on Big Data (Big Data), (Seattle, WA, USA, 2018), pp. 4176–4181. https://doi.org/10.1109/BigData.2018.8622480
Yanfeng He, Yali Liu, Shuai Shao, Xuhang Zhao, Guojun Liu, Xiangji Kong, Lu Liu, Application of CNN-LSTM in gradual changing fault diagnosis of rod pumping system. Math. Prob. Eng., vol. 2019, 4203821, 9, 2019. https://doi.org/10.1155/2019/4203821.
X. Zhang, Y. LeCun, Text Understanding from Scratch. arXiv:1502.01710v5 [cs.LG], (2016)
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Katal, A., Singh, N. (2022). Artificial Neural Network: Models, Applications, and Challenges. In: Tomar, R., Hina, M.D., Zitouni, R., Ramdane-Cherif, A. (eds) Innovative Trends in Computational Intelligence. EAI/Springer Innovations in Communication and Computing. Springer, Cham. https://doi.org/10.1007/978-3-030-78284-9_11
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