SURVEYING ARTIFICIAL GLANDS IN ENDOCRINE NEURAL NETWORKS APPLIED IN CONTROL SYSTEMS

Miroslav Milovanović, Jianxun Cui, Jelena Petrović, Anđela Đorđević, Saša S. Nikolić

DOI Number
https://doi.org/10.22190/FUACR240325006M
First page
063
Last page
076

Abstract


In this paper, an effort would be made to provide a review of current state of the development of artificial glands within endocrine neural networks. The main goal is to systematize the approaches of building the glands, to offer mathematical apparatus behind them, and to describe control logics enabling smooth work and efficient synergy between the glands and traditional neural networks. In the final phase, this work will offer recommendations for selecting optimal gland profile in accordance with a specific use case.

Keywords

Endocrine neural network, artificial gland, control systems, environmental stimulus, disturbance processing.

Full Text:

PDF

References


M. T. Hagan, H. B. Demuth, O. D. Jesús, “An introduction to the use of neural networks in control systems,”, International Journal of Robust and Nonlinear Control: IFAC‐Affiliated Journal, vol. 12 no 11, pp. 959-985, 2002, doi: 10.1002/rnc.727

S. A. Emami, P. Castaldi, A. Banazadeh, “Neural network-based flight control systems: Present and future,” Annual Reviews in Control, vol. 53, pp. 97-137, 2022, doi: 10.1016/j.arcontrol.2022.04.006

I. Eski, A. Temürlenk, “Design of neural network-based control systems for active steering system”, Nonlinear dynamics, vol. 73, pp. 1443-1454, 2013, doi: 10.1007/s11071-013-0875-y

J. Dunfied, M. Tarbouchi, G. Labonte, “Neural network based control of a four rotor helicopter”, In 2004 IEEE International

Conference on Industrial Technology, 2004. IEEE ICIT'04, Hammamet, Tunisia, vol. 3, pp. 1543-1548, 2004, doi: 10.1109/ICIT.2004.1490796

V. K. Ojha, A. Abraham, V. Snášel, “Metaheuristic design of feedforward neural networks: A review of two decades of research,” Engineering Applications of Artificial Intelligence, vol. 60, pp. 97-116, 2017, doi: 10.1016/j.engappai.2017.01.013

M. B. Milovanović, D. S. Antić, M. T. Milojković, S. S. Nikolić, S. Lj. Perić, M. D. Spasić, “Adaptive PID Control Based on Orthogonal Endocrine Neural Networks,” Neural Networks, vol. 84, pp. 80–90, Print ISSN: 0893-6080, Publisher: Elsevier, December 2016, doi: 10.1016/j.neunet.2016.08.012.

B. Warsito, R. Santoso, R., Suparti, H. Yasin, “Cascade forward neural network for time series prediction,” Journal of Physics: Conference Series, vol. 1025, p. 012097, IOP Publishing, May 2018,, doi: 10.1088/1742-6596/1025/1/012097

R. Qamar, B. A. Zardari, “Artificial Neural Networks: An Overview,” Mesopotamian Journal of Computer Science, pp. 130-139, 2023, doi: 10.58496/MJCSC/2023/014.

K. K. Dutta, S. Poornima, R. Sharma, D. Nair, P.G. Ploeger, “Applications of recurrent neural network: Overview and case studies”, Recurrent Neural Networks, pp. 23-41, CRC press, 2022, doi: 10.1201/9781003307822-3

A. K. Tyagi, A. Abraham, Recurrent neural networks: Concepts and applications, CRC Press, 2022, doi: 10.1201/9781003307822

K. O'shea, R. Nash, “An introduction to convolutional neural networks,” arXiv preprint, 2015. [Online]. Available: https://arxiv.org/abs/1511.08458, doi: 10.48550/arXiv.1511.08458.

S. Albawi, T. A. Mohammed, S. Al-Zawi, “Understanding of a convolutional neural network,“ 2017 international conference on engineering and technology (ICET), Antalya, Turkey, pp. 1-6, August 2017, doi: 10.1109/ICEngTechnol.2017.8308186

A. Dhillon, G. K. Verma, “Convolutional neural network: a review of models, methodologies and applications to object detection,” Progress in Artificial Intelligence, vol. 9 no. 2, pp. 85-112, 2020, doi: 10.1007/s13748-019-00203-0

M. M. Taye, “Theoretical understanding of convolutional neural network: Concepts, architectures, applications, future directions,” Computation, vol. 11 no. 3, p. 52, 2023, doi: 10.3390/computation11030052

Z. Li, F. Liu, W. Yang, S. Peng, J. Zhou, “A survey of convolutional neural networks: analysis, applications, and prospects,” IEEE transactions on neural networks and learning systems, vol. 33 no. 12, pp. 6999-7019, 2021, doi: 10.1109/TNNLS.2021.3084827.

H. Chen, J. Chen, J. Ding, “Data evaluation and enhancement for quality improvement of machine learning,” IEEE Transactions on Reliability, vol. 70, no. 2, pp. 831-847, 2021, doi: 10.1109/TR.2021.3070863

D. Chen, J. Wang, F. Zou, W. Yuan, W. Hou, “Time series prediction with improved neuro-endocrine model,” Neural Computing and Applications, vol. 24, no. 6, pp. 1465-1475, 2014, doi: 10.1007/s00521-013-1373-3.

R. C. Moioli, P. A. Vargas, P. Husbands, “A multiple hormone approach to the homeostatic control of conflicting behaviours in an autonomous mobile robot,” 2009 IEEE Congress on Evolutionary Computation, Trondheim, Norway, pp. 47-54, IEEE, May 2009, doi: 10.1109/CEC.2009.4982929.

M. B. Milovanović, D. S. Antić, M. T. Milojković, M. D. Spasić, "Adaptive Control of Nonlinear MIMO System With Orthogonal Endocrine Intelligent Controller," IEEE Transactions on Cybernetics, vol. 52, no. 2, pp. 1221-1232, Feb. 2022, doi: 10.1109/TCYB.2020.2998505.

M. Milovanović, D. Antić, M. Rajić, P. Milosavljević, A. Pavlović, C. Fragassa, “Wood Resource Management Using an Endocrine NARX Neural Network,” European Journal of Wood and Wood Products, vol. 76, no. 2, pp. 687–697, Print ISSN: 0018-3768, Publisher: Springer Berlin Heidelberg, March 2018, doi: 10.1007/s00107-017-1223-6.

C. Sauze, M. Neal, “Artificial endocrine controller for power management in robotic systems,” IEEE transactions on neural networks and learning systems, vol. 24 no. 12, pp. 1973-1985, 2013, doi: 10.1109/TNNLS.2013.2271094.

S. Lj. Perić, D. S. Antić, M. B. Milovanović, D. B. Mitić, M. T. Milojković, S. S. Nikolić, “Quasi-Sliding Mode Control with Orthogonal Endocrine Neural Network-Based Estimator Applied in Anti-Lock Braking System,” IEEE/ASME Transactions on Mechatronics, vol. 21, no. 2, pp. 754–764, Print ISSN: 1083-4435, Publisher: IEEE, April 2016, doi: 10.1109/TMECH.2015.2492682.

M. Milovanović, D. Antić, M. Milojković, S. S. Nikolić, M. Spasić, S. Perić, “Time Series Forecasting with Orthogonal Endocrine Neural Network Based on Postsynaptic Potentials,” Journal of Dynamic Systems, Measurement, and Control, vol. 139, no. 4, pp. 041006-1÷041006-9, DS-15-1656, Print ISSN: 0022-0434, Publisher: American Society of Mechanical Engineers, April 2017, doi: 10.1115/1.4035090.

P. Teerakittikul, G. Tempesti, A. M. Tyrrell, “Artificial hormone network for adaptive robot in a dynamic environment,” In 2012 NASA/ESA Conference on Adaptive Hardware and Systems (AHS), Erlangen, Germany, pp. 129-136, IEEE, June 2012, doi: 10.1109/AHS.2012.6268640.

J. Timmis, M. Neal, J. Thorniley, “An adaptive neuro-endocrine system for robotic systems,” 2009 IEEE Workshop on Robotic Intelligence in Informationally Structured Space, Nashville, TN, USA, pp. 129-136, IEEE, March 2009, doi: 10.1109/RIISS.2009.4937917.

M. Milovanović, A. Oarcea, S. Nikolić, A. Djordjević, M. Spasić, “An approach to networking a new type of artificial orthogonal glands within orthogonal endocrine neural networks,” Applied Sciences, vol. 12 no. 11, p. 5372, 2022, doi: 10.3390/app12115372.

J. Timmis, L. Murray, M. Neal, “A Neural-Endocrine Architecture for Foraging in Swarm Robotic Systems,” González, J.R., Pelta, D.A., Cruz, C., Terrazas, G., Krasnogor, N. (eds) Nature Inspired Cooperative Strategies for Optimization (NICSO 2010), Studies in Computational Intelligence, vol. 284, Springer, Berlin, Heidelberg, 2010, doi: 10.1007/978-3-642-12538-6_27




DOI: https://doi.org/10.22190/FUACR240325006M

Refbacks

  • There are currently no refbacks.


Print ISSN: 1820-6417
Online ISSN: 1820-6425