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Gu X, Han J, Shen Q, Angelov PP. Autonomous learning for fuzzy systems: a review. Artif Intell Rev 2022. [DOI: 10.1007/s10462-022-10355-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
Abstract
AbstractAs one of the three pillars in computational intelligence, fuzzy systems are a powerful mathematical tool widely used for modelling nonlinear problems with uncertainties. Fuzzy systems take the form of linguistic IF-THEN fuzzy rules that are easy to understand for human. In this sense, fuzzy inference mechanisms have been developed to mimic human reasoning and decision-making. From a data analytic perspective, fuzzy systems provide an effective solution to build precise predictive models from imprecise data with great transparency and interpretability, thus facilitating a wide range of real-world applications. This paper presents a systematic review of modern methods for autonomously learning fuzzy systems from data, with an emphasis on the structure and parameter learning schemes of mainstream evolving, evolutionary, reinforcement learning-based fuzzy systems. The main purpose of this paper is to introduce the underlying concepts, underpinning methodologies, as well as outstanding performances of the state-of-the-art methods. It serves as a one-stop guide for readers learning the representative methodologies and foundations of fuzzy systems or who desire to apply fuzzy-based autonomous learning in other scientific disciplines and applied fields.
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Alves KSTR, Pestana de Aguiar E. A novel rule-based evolving Fuzzy System applied to the thermal modeling of power transformers. Appl Soft Comput 2021. [DOI: 10.1016/j.asoc.2021.107764] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
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Leite D, Škrjanc I, Gomide F. An overview on evolving systems and learning from stream data. EVOLVING SYSTEMS 2020. [DOI: 10.1007/s12530-020-09334-5] [Citation(s) in RCA: 25] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
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Lucas F, Costa P, Batalha R, Leite D, Škrjanc I. Fault detection in smart grids with time-varying distributed generation using wavelet energy and evolving neural networks. EVOLVING SYSTEMS 2020. [DOI: 10.1007/s12530-020-09328-3] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
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Silva S, Costa P, Santana M, Leite D. Evolving neuro-fuzzy network for real-time high impedance fault detection and classification. Neural Comput Appl 2018. [DOI: 10.1007/s00521-018-3789-2] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/28/2022]
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Ngoc Son N, Anh HPH, Thanh Nam N. Robot manipulator identification based on adaptive multiple-input and multiple-output neural model optimized by advanced differential evolution algorithm. INT J ADV ROBOT SYST 2016. [DOI: 10.1177/1729881416677695] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022] Open
Abstract
This article proposes a novel advanced differential evolution method which combines the differential evolution with the modified back-propagation algorithm. This new proposed approach is applied to train an adaptive enhanced neural model for approximating the inverse model of the industrial robot arm. Experimental results demonstrate that the proposed modeling procedure using the new identification approach obtains better convergence and more precision than the traditional back-propagation method or the lonely differential evolution approach. Furthermore, the inverse model of the industrial robot arm using the adaptive enhanced neural model performs outstanding results.
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Affiliation(s)
- Nguyen Ngoc Son
- Faculty of Electronics Engineering, Industrial University of Ho Chi Minh City, Ho Chi Minh City, Vietnam
| | - Ho Pham Huy Anh
- Faculty of Electrical Electronics Engineering, Ho Chi Minh City University of Technology – Vietnam National University-Ho Chi Minh City, Ho Chi Minh City, Vietnam
| | - Nguyen Thanh Nam
- DCSELAB, Ho Chi Minh City University of Technology – Vietnam National University-Ho Chi Minh City, Ho Chi Minh City, Vietnam
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Toloue SF, Akbarzadeh MR, Akbarzadeh A, Jalaeian-F M. Position tracking of a 3-PSP parallel robot using dynamic growing interval type-2 fuzzy neural control. Appl Soft Comput 2015. [DOI: 10.1016/j.asoc.2015.07.015] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
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Romero Ugalde HM, Carmona JC, Reyes-Reyes J, Alvarado VM, Mantilla J. Computational cost improvement of neural network models in black box nonlinear system identification. Neurocomputing 2015. [DOI: 10.1016/j.neucom.2015.04.022] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
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Jamil M, Sharma SK, Singh R. Fault detection and classification in electrical power transmission system using artificial neural network. SPRINGERPLUS 2015; 4:334. [PMID: 26180754 PMCID: PMC4496419 DOI: 10.1186/s40064-015-1080-x] [Citation(s) in RCA: 86] [Impact Index Per Article: 9.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/30/2014] [Accepted: 06/03/2015] [Indexed: 11/30/2022]
Abstract
This paper focuses on the detection and classification of the faults on electrical power transmission line using artificial neural networks. The three phase currents and voltages of
one end are taken as inputs in the proposed scheme. The feed forward neural network along with back propagation algorithm has been employed for detection and classification of the fault for analysis of each of the three phases involved in the process. A detailed analysis with varying number of hidden layers has been performed to validate the choice of the neural network. The simulation results concluded that the present method based on the neural network is efficient in detecting and classifying the faults on transmission lines with satisfactory performances. The different faults are simulated with different parameters to check the versatility of the method. The proposed method can be extended to the Distribution network of the Power System. The various simulations and analysis of signals is done in the MATLAB® environment.
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Affiliation(s)
- Majid Jamil
- Department of Electrical Engineering, Faculty of Engineering, Jamia Millia Islamia, New Delhi, 110025 India
| | - Sanjeev Kumar Sharma
- Department of Electrical Engineering, Faculty of Engineering, Jamia Millia Islamia, New Delhi, 110025 India
| | - Rajveer Singh
- Department of Electrical Engineering, Faculty of Engineering, Jamia Millia Islamia, New Delhi, 110025 India
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Intelligent exponential sliding-mode control with uncertainty estimator for antilock braking systems. Neural Comput Appl 2015. [DOI: 10.1007/s00521-015-1946-4] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
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Dehghan SAM, Danesh M, Sheikholeslam F, Zekri M. Adaptive force–environment estimator for manipulators based on adaptive wavelet neural network. Appl Soft Comput 2015. [DOI: 10.1016/j.asoc.2014.12.021] [Citation(s) in RCA: 16] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/24/2022]
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Liu Z, Chen C, Zhang Y, Chen CLP. Adaptive neural control for dual-arm coordination of humanoid robot with unknown nonlinearities in output mechanism. IEEE TRANSACTIONS ON CYBERNETICS 2015; 45:521-532. [PMID: 24968367 DOI: 10.1109/tcyb.2014.2329931] [Citation(s) in RCA: 29] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/03/2023]
Abstract
To achieve an excellent dual-arm coordination of the humanoid robot, it is essential to deal with the nonlinearities existing in the system dynamics. The literatures so far on the humanoid robot control have a common assumption that the problem of output hysteresis could be ignored. However, in the practical applications, the output hysteresis is widely spread; and its existing limits the motion/force performances of the robotic system. In this paper, an adaptive neural control scheme, which takes the unknown output hysteresis and computational efficiency into account, is presented and investigated. In the controller design, the prior knowledge of system dynamics is assumed to be unknown. The motion error is guaranteed to converge to a small neighborhood of the origin by Lyapunov's stability theory. Simultaneously, the internal force is kept bounded and its error can be made arbitrarily small.
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Aloraini A. Penalized ensemble feature selection methods for hidden associations in time series environments case study: equities companies in Saudi Stock Exchange Market. EVOLVING SYSTEMS 2014. [DOI: 10.1007/s12530-014-9124-y] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
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Nirmal J, Zaveri M, Patnaik S, Kachare P. Voice conversion using General Regression Neural Network. Appl Soft Comput 2014. [DOI: 10.1016/j.asoc.2014.06.040] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
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Zhang H, Tang Y, Liu X. Batch gradient training method with smoothing $$\boldsymbol{\ell}_{\bf 0}$$ ℓ 0 regularization for feedforward neural networks. Neural Comput Appl 2014. [DOI: 10.1007/s00521-014-1730-x] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
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Romero Ugalde HM, Carmona JC, Reyes-Reyes J, Alvarado VM, Corbier C. Balanced simplicity–accuracy neural network model families for system identification. Neural Comput Appl 2014. [DOI: 10.1007/s00521-014-1716-8] [Citation(s) in RCA: 25] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
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A meta-cognitive interval type-2 fuzzy inference system and its projection based learning algorithm. EVOLVING SYSTEMS 2013. [DOI: 10.1007/s12530-013-9102-9] [Citation(s) in RCA: 29] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/25/2022]
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