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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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Nasiri H, Ebadzadeh MM. MFRFNN: Multi-Functional Recurrent Fuzzy Neural Network for Chaotic Time Series Prediction. Neurocomputing 2022. [DOI: 10.1016/j.neucom.2022.08.032] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/15/2022]
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3
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Recursive least mean dual p-power solution to the generalization of evolving fuzzy system under multiple noises. Inf Sci (N Y) 2022. [DOI: 10.1016/j.ins.2022.07.090] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022]
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Kafiyan-Safari M, Rouhani M. Adaptive one-pass passive-aggressive radial basis function for classification problems. Neurocomputing 2022. [DOI: 10.1016/j.neucom.2022.03.047] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
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Salimi-Badr A. IT2CFNN: An interval type-2 correlation-aware fuzzy neural network to construct non-separable fuzzy rules with uncertain and adaptive shapes for nonlinear function approximation. Appl Soft Comput 2022. [DOI: 10.1016/j.asoc.2021.108258] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/02/2022]
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Gu X, Shen Q, Angelov PP. Particle Swarm Optimized Autonomous Learning Fuzzy System. IEEE TRANSACTIONS ON CYBERNETICS 2021; 51:5352-5363. [PMID: 32092025 DOI: 10.1109/tcyb.2020.2967462] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
Abstract
The antecedent and consequent parts of a first-order evolving intelligent system (EIS) determine the validity of the learning results and overall system performance. Nonetheless, the state-of-the-art techniques mostly stress on the novelty from the system identification point of view but pay less attention to the optimality of the learned parameters. Using the recently introduced autonomous learning multiple model (ALMMo) system as the implementation basis, this article introduces a particle swarm-based approach for the EIS optimization. The proposed approach is able to simultaneously optimize the antecedent and consequent parameters of ALMMo and effectively enhance the system performance by iteratively searching for optimal solutions in the problem spaces. In addition, the proposed optimization approach does not adversely influence the "one pass" learning ability of ALMMo. Once the optimization process is complete, ALMMo can continue to learn from new data to incorporate unseen data patterns recursively without full retraining. The experimental studies with a number of real-world benchmark problems validate the proposed concept and general principles. It is also verified that the proposed optimization approach can be applied to other types of EISs with similar operating mechanisms.
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Samanta S, Pratama M, Sundaram S, Srikanth N. Learning elastic memory online for fast time series forecasting. Neurocomputing 2020. [DOI: 10.1016/j.neucom.2019.07.105] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/01/2022]
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Luo C, Wang H. Fuzzy forecasting for long-term time series based on time-variant fuzzy information granules. Appl Soft Comput 2020. [DOI: 10.1016/j.asoc.2019.106046] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
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Samanta S, Suresh S, Senthilnath J, Sundararajan N. A new Neuro-Fuzzy Inference System with Dynamic Neurons (NFIS-DN) for system identification and time series forecasting. Appl Soft Comput 2019. [DOI: 10.1016/j.asoc.2019.105567] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/26/2022]
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12
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Luo C, Tan C, Wang X, Zheng Y. An evolving recurrent interval type-2 intuitionistic fuzzy neural network for online learning and time series prediction. Appl Soft Comput 2019. [DOI: 10.1016/j.asoc.2019.02.032] [Citation(s) in RCA: 71] [Impact Index Per Article: 14.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
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Anh N, Suresh S, Pratama M, Srikanth N. Interval prediction of wave energy characteristics using meta-cognitive interval type-2 fuzzy inference system. Knowl Based Syst 2019. [DOI: 10.1016/j.knosys.2019.01.025] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/27/2022]
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Learning evolving T–S fuzzy systems with both local and global accuracy – A local online optimization approach. Appl Soft Comput 2018. [DOI: 10.1016/j.asoc.2017.05.046] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
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A novel meta-cognitive fuzzy-neural model with backstepping strategy for adaptive control of uncertain nonlinear systems. Neurocomputing 2017. [DOI: 10.1016/j.neucom.2016.12.030] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
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Das AK, Anh N, Suresh S, Srikanth N. An interval type-2 fuzzy inference system and its meta-cognitive learning algorithm. EVOLVING SYSTEMS 2016. [DOI: 10.1007/s12530-016-9148-6] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
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Venkatesh Babu R, Rangarajan B, Sundaram S, Tom M. Human action recognition in H.264/AVC compressed domain using meta-cognitive radial basis function network. Appl Soft Comput 2015. [DOI: 10.1016/j.asoc.2015.06.054] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
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Sivachitra M, Vijayachitra S. A Metacognitive Fully Complex Valued Functional Link Network for solving real valued classification problems. Appl Soft Comput 2015. [DOI: 10.1016/j.asoc.2015.04.022] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
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Keramati A, Jafari-Marandi R, Aliannejadi M, Ahmadian I, Mozaffari M, Abbasi U. Improved churn prediction in telecommunication industry using data mining techniques. Appl Soft Comput 2014. [DOI: 10.1016/j.asoc.2014.08.041] [Citation(s) in RCA: 99] [Impact Index Per Article: 9.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/01/2022]
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Imbalanced Learning for Air Pollution by Meta-Cognitive Online Sequential Extreme Learning Machine. Cognit Comput 2014. [DOI: 10.1007/s12559-014-9301-0] [Citation(s) in RCA: 19] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
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Subramanian K, Savitha R, Suresh S. A complex-valued neuro-fuzzy inference system and its learning mechanism. Neurocomputing 2014. [DOI: 10.1016/j.neucom.2013.06.009] [Citation(s) in RCA: 31] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/26/2022]
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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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Yu WS, Weng CC. An observer-based adaptive neural network tracking control of robotic systems. Appl Soft Comput 2013. [DOI: 10.1016/j.asoc.2013.06.009] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/26/2022]
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Babu GS, Suresh S. Sequential projection-based metacognitive learning in a radial basis function network for classification problems. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2013; 24:194-206. [PMID: 24808275 DOI: 10.1109/tnnls.2012.2226748] [Citation(s) in RCA: 22] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/03/2023]
Abstract
In this paper, we present a sequential projection-based metacognitive learning algorithm in a radial basis function network (PBL-McRBFN) for classification problems. The algorithm is inspired by human metacognitive learning principles and has two components: a cognitive component and a metacognitive component. The cognitive component is a single-hidden-layer radial basis function network with evolving architecture. The metacognitive component controls the learning process in the cognitive component by choosing the best learning strategy for the current sample and adapts the learning strategies by implementing self-regulation. In addition, sample overlapping conditions and past knowledge of the samples in the form of pseudosamples are used for proper initialization of new hidden neurons to minimize the misclassification. The parameter update strategy uses projection-based direct minimization of hinge loss error. The interaction of the cognitive component and the metacognitive component addresses the what-to-learn, when-to-learn, and how-to-learn human learning principles efficiently. The performance of the PBL-McRBFN is evaluated using a set of benchmark classification problems from the University of California Irvine machine learning repository. The statistical performance evaluation on these problems proves the superior performance of the PBL-McRBFN classifier over results reported in the literature. Also, we evaluate the performance of the proposed algorithm on a practical Alzheimer's disease detection problem. The performance results on open access series of imaging studies and Alzheimer's disease neuroimaging initiative datasets, which are obtained from different demographic regions, clearly show that PBL-McRBFN can handle a problem with change in distribution.
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Meta-cognitive RBF Network and its Projection Based Learning algorithm for classification problems. Appl Soft Comput 2013. [DOI: 10.1016/j.asoc.2012.08.047] [Citation(s) in RCA: 86] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022]
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SUBRAMANIAN K, SURESH S. HUMAN ACTION RECOGNITION USING META-COGNITIVE NEURO-FUZZY INFERENCE SYSTEM. Int J Neural Syst 2012. [DOI: 10.1142/s0129065712500281] [Citation(s) in RCA: 39] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
We propose a sequential Meta-Cognitive learning algorithm for Neuro-Fuzzy Inference System (McFIS) to efficiently recognize human actions from video sequence. Optical flow information between two consecutive image planes can represent actions hierarchically from local pixel level to global object level, and hence are used to describe the human action in McFIS classifier. McFIS classifier and its sequential learning algorithm is developed based on the principles of self-regulation observed in human meta-cognition. McFIS decides on what-to-learn, when-to-learn and how-to-learn based on the knowledge stored in the classifier and the information contained in the new training samples. The sequential learning algorithm of McFIS is controlled and monitored by the meta-cognitive components which uses class-specific, knowledge based criteria along with self-regulatory thresholds to decide on one of the following strategies: (i) Sample deletion (ii) Sample learning and (iii) Sample reserve. Performance of proposed McFIS based human action recognition system is evaluated using benchmark Weizmann and KTH video sequences. The simulation results are compared with well known SVM classifier and also with state-of-the-art action recognition results reported in the literature. The results clearly indicates McFIS action recognition system achieves better performances with minimal computational effort.
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Affiliation(s)
- K. SUBRAMANIAN
- School of Computer Engineering, Nanyang Technological University, Nanyang Avenue, Singapore 639798, Singapore
| | - S. SURESH
- School of Computer Engineering, Nanyang Technological University, Nanyang Avenue, Singapore 639798, Singapore
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