Artificial Neural Network Model and Its Application in Signal Processing
Asian Journal of Advanced Research and Reports,
Page 1-8
DOI:
10.9734/ajarr/2023/v17i1459
Abstract
The human brain is a powerful image and pattern recognition processor, and its basic processing element is neurons. Synapses are weighted interconnections between neurons, allowing learning and communication between neurons. Artificial neural network (ANN) is an information processing system established by simulating the structure and logical thinking mode of human brain. The uniqueness of ANN is that it is nonlinear and trained to complete processing tasks in a way similar to human brain learning. It is particularly suitable for processing signals sent by various sensors, signals sent by communication devices, and other signals that are difficult to identify. This paper introduces the origin, types and research progress of neural networks, and summarizes the application research progress of neural networks in the field of signal processing. This paper introduces the origin, types and research progress of ANN, and summarizes the application research progress of ANN in the field of signal processing.
Keywords:
- Signal processing
- artificial neural network
- BP neural network
- CNN neural network
- RBF neural network
How to Cite
Pan, X., Wang, Y., & Qi, Y. (2023). Artificial Neural Network Model and Its Application in Signal Processing. Asian Journal of Advanced Research and Reports, 17(1), 1-8. https://doi.org/10.9734/ajarr/2023/v17i1459
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Schmidt-Nielsen A, Everett SS. A conversational test for comparing voice systems using working two-way communication links [J]. IEEE Transactions on Acoustics, Speech, and Signal Processing. 2003;30(6): 853-863.
WILLARD P. WEBSTER PH.D. Artificial Neural Networks and Their Application to Weapons, Naval Engineers Journal. 1991; 103(3):46-59.
Malmgren BA, Nordlund U. Application of artificial neural networks to chemostratigraphy, Paleoceanography. 1996;11(4):505– 512
Gao J, Tembine H. Distributed Mean-Field-Type Filter for Vehicle Tracking, in American Control Conference (ACC), Seattle, USA; 2017.
Pham CC, Jeon JW. Robust Object Proposals Re-ranking for Object Detection in Autonomous Driving Using Convolutional Neural Networks [J]. Signal Processing Image Communication, 2017; 53:110-122.
Cococcioni M , Ruffaldi E , Saponara S . Exploiting Posit Arithmetic for Deep Neural Networks in Autonomous Driving Applications[C]// IEEE Automotive. IEEE; 2018.
Wang H, Chen Y. Application of Artificial Neural Networks in Chemical Process Control. Asian Journal of Research in Computer Science. 2022;14(1): 22-37.
Zhao N, Lu J. Review of Neural Network Algorithm and its Application in Temperature Control of Distillation Tower, Journal of Engineering Research and Reports, 2021;20(4):50-61.
Zheng Z, Qi Y. Study on the Simulation Control of Neural Network Algorithm in Thermally Coupled Distillation. Asian Journal of Research in Computer Science. 2021;10(3):53-64.
Mo R, Wang H. Application of Neural Network Algorithm in Optimal Control of Ethylene Distillation Tower, Asian Journal of Research in Computer Science. 2021;9 (2):19-29.
Aizenberg I, Aizenberg N, Hiltner J. Cellular neural networks and computational intelligence in medical image processing [J]. Image & Vision Computing. 2001;19(4):177-183.
Gao J, Shi F. A Rotation and Scale Invariant Approach for Dense Wide Baseline Matching. Intelligent Computing Theory - 10th International Conference, ICIC (1) 2014;345-356.
Nasrabadi NM, Katsaggelos AK. Applications of Artificial Neural Networks in Image Processing. Proceedings of SPIE - The International Society for Optical Engineering. 1996;9.
Khan MA, Tembine H, Vasilakos AV. Evolutionary coalitional games: design and challenges in wireless networks. IEEE Wireless Commun. 2012;19(2):50-56.
Mahalingam N, Kumar D. Neural networks for signal processing applications: ECG classification [J]. Australas Phys Eng Sci Med. 1997;20(3):147-151.
McCulloch WS, Pitts W. A logical calculus of the ideas immanent in nervous activity, Bulletin of Mathematical Biophysics. 1943; 5:115-133.
Hebb DO，The Organization of Behavior: A Neuropsychological Theory[M]. Lawrence Erlbaum Associates, New Jersey; 1949.
Widrow B. Adaptive ` Adaline' Neuron Using Chemical ` Memistors, Stanford Electronics Laboratories Technical Report, No. 1553-2; 1960.
Rumelhart DE, Hinton GE, Williams RJ. Learning representations by back-propagation errors, Naturem. 1986;323: 53–536.
Vapnik V．Statistical learning theory,Wiley, New York. 1998;3.
Hinton GE, Salakhutdinov RR. Reducing the Dimensionality of Data with Neural Networks [J]. Science. 2006;313(5786): 504.
Ghaleb FA, Zainal A, Rassam MA, Mohammed F. An effective misbehavior detection model using artificial neural network for vehicular ad hoc network applications, 2017 IEEE Conference on Application, Information and Network Security (AINS), Miri. 2017;13-18.
Qi Y, Zheng Z. Neural Network Algorithm and Its Application in Supercritical Extraction Process, Asian Journal of Chemical Sciences. 2021;9(1): 19-28.
Moody J, Darken C. Fast Learning Locally-tuned Processing Units. Neural Computation. 1989; 1:281-294.
Du MX, Wang YW, Zhang XZ. Optimal Design of Centrifugal Pump Based on RBF Neural Network and Genetic Algorithm. Journal of China Three Gorges University，Natural Sciences. 2020;42(04):88-93. (In Chinese)
Yue L, Nan LY, Bing L, Biao WC. Research on the Correlation Between Physical Examination Indexes and TCM Constitutions Using the RBF Neural Network [J]. Digital Chinese Medicine. 2020;3(1):11-19.
Le Cun Y, Bottou L, Bengio Y and Haffner P. Gradient-Based Learning Applied to Document Recognition. Proceedings of the IEEE. 1998;86(11):2278-2324.
Lin M，Chen Q，Yan S. Network in network. Ar Xiv preprint ar-Xiv:1312. 4400; 2013.
Szegedy C, Liu W, Jia Y, et al. Going Deeper with Convolutions. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Boston, MA. 2015;1-9.
Gao J, Tembine H. Distributionally robust games: Wasserstein metric. International Joint Conference on Neural Networks (IJCNN), Rio de Janeiro, Brazil; 2018.
Gatys LA, Ecker AS, Bethge M. Image style transfer using convolutional neural networks. In 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). 2016;2414-2423.
Gao J, Chakraborty D, Tembine H, Olaleye O. Nonparallel emotional speech conversion. INTERSPEECH 2019, Graz, Austria; 2019.
Scanlon VC, Sanders T. Essentials of Anatomy and Physiology, 5th Edition, F.A. Davis Co, Philadelphia, oCLC: ocm68694088; 2007.
Wagner GS, Strauss DG. Marriott’s Practical Electrocardiography, 12th Edition, Lippincott Williams & Wilkins, Philadelphia, PA; 2013.
Morabito M, Macerata A, Taddei A, et al. QRS morphological classification using artificial neural networks[C]// Computers in Cardiology. IEEE; 1991.
Chromik J, Pirl L, Beilharz J, Arnrich B, Polze A. Certainty in QRS detection with artificial neural networks, Biomedical Signal Processing and Control, 2021, 68: 102628.
Clifford GD, Silva I, Moody B, et al. The PhysioNet/computing in cardiology challenge 2015: reducing false arrhythmia alarms in the ICU, in: 2015 Computing in Cardiology Conference (CinC), IEEE, Nice, France. 2015;273–276.
Rai H M, Trivedi A, Shukla S. ECG signal processing for abnormalities detection using multi-resolution wavelet transform and Artificial Neural Network classifier, Measurement Journal of the International Measurement Confederation. 2013;46(9): 3238-3246.
Xing WL, He XW. Applications of artificial neural networks on signal processing of piezoelectric crystal sensors, Sensors and Actuators B: Chemical. 2000;66(1–3):272-276.
Suah FBM, Ahmad M, Tai MN. Applications of artificial neural network on signal processing of optical fibre pH sensor based on bromophenol blue doped with sol–gel film, Sensors and Actuators B: Chemical, 2003;90(1–3): 182-188.
Zhou Y，Hong J，Zhang X，Zhao P. Application of HHT and Elman Neural Network in Vibration Signal Processing for Centrifugal Pump Failure，Fluid Machinery. 2007;35(5):21-24.
Fang S, Qu Z, Huan Y. Magnetic Signal Processing With Artificial Neutral Network And Adaptive Filter, Marine Electric & Electronic Engineering. 2009;29(10):47-49.
Gao J, Tembine H. Correlative Mean-Field Filter for Sequential and Spatial Data Processing, in the Proceedings of IEEE International Conference on Computer as a Tool (EUROCON), Ohrid, Macedonia; 2017.
Alapuranen P, Schroeder J. Complex artificial neural network with applications to wireless communications, Digital Signal Processing. 2021;118:103194.
Igwe KC, Oyedum OD. Aibinu A.M, Ajewole M.O, Moses AS. Application of artificial neural network modeling techniques to signal strength computation, Heliyon. 2021;7:e06047.
Balabanova I, Kostadinova S, Georgiev G Recognition of Noises and Noise Speech Signals by Artificial Neural Networks, 2021 International Conference on Biomedical Innovations and Applications (BIA). 2022; 119-122.
Valtl J, Mendez J, Mauro G, Cabrera A, Issakov V. Investigation for the Need of Traditional Data-Preprocessing when Applying Artificial Neural Networks to FMCW-Radar Data, 2022 29th International Conference on Systems, Signals and Image Processing (IWSSIP). 2022;1-4.
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