Evolving fuzzy and neuro-fuzzy approaches in clustering, regression, identification, and classification: A Survey

Škrjanc, Igor; Iglesias, José Antonio; Sanchís, Araceli; Leite, Daniel; Lughofer, Edwin; Gomide, Fernando

doi:10.1016/j.ins.2019.03.060

Cited by 250 publications

(146 citation statements)

References 158 publications

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“…Besides, their controller was evaluated by incorporating uncertain wind gust effect in their MAVs plant. A comprehensive survey of evolving fuzzy system for regression, classification and control tasks is provided in [29].…”

Section: Main Featuresmentioning

confidence: 99%

PAC: A novel self-adaptive neuro-fuzzy controller for micro aerial vehicles

Ferdaus

Pratama

Anavatti

et al. 2020

Information Sciences

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There exists an increasing demand for a flexible and computationally efficient controller for micro aerial vehicles (MAVs) due to a high degree of environmental perturbations. In this work, an evolving neuro-fuzzy controller, namely Parsimonious Controller (PAC) is proposed. It features fewer network parameters than conventional approaches due to the absence of rule premise parameters. PAC is built upon a recently developed evolving neuro-fuzzy system known as parsimonious learning machine (PALM) and adopts new rule growing and pruning modules derived from the approximation of bias and variance. These rule adaptation methods have no reliance on user-defined thresholds, thereby increasing the PAC's autonomy for real-time deployment. PAC adapts the consequent parameters with the sliding mode control (SMC) theory in the single-pass fashion. The boundedness and convergence of the closed-loop control system's tracking error and the controller's consequent parameters are confirmed by utilizing the LaSalle-Yoshizawa theorem. Lastly, the controller's efficacy is evaluated by observing various trajectory tracking performance from a bio-inspired flapping wing micro aerial vehicle (BI-FWMAV) and a rotary wing micro aerial vehicle called hexacopter. Furthermore, it is compared to three distinctive controllers. Our PAC outperforms the linear PID controller and feedforward neural network (FFNN) based nonlinear adaptive controller. Compared to its predecessor, G-controller, the tracking accuracy is comparable, but the PAC incurs significantly fewer parameters to attain similar or better performance than the G-controller.

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Section: Main Featuresmentioning

confidence: 99%

PAC: A novel self-adaptive neuro-fuzzy controller for micro aerial vehicles

Ferdaus

Pratama

Anavatti

et al. 2020

Information Sciences

Self Cite

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“…e detailed survey of NF models from 2000 to 2017 for classification is described in [37]. Das and Pratihar [38] used neuro-fuzzy with multiobjective optimization techniques to inherent fuzziness in the manufacturing process.Škrjanc et al [39] addressed a review on evolving neuro-fuzzy and fuzzy rule-based models used in real-world environments for classification, clustering, regression, and system identification. In the data analysis process, dimensionality reduction techniques such as feature selection and feature reduction are used in the preprocessing [40] stage in which the original features are transformed into either original feature or transformed features.…”

Section: Literature Surveymentioning

confidence: 99%

A Hybrid Neuro-Fuzzy and Feature Reduction Model for Classification

Das

Naik

Behera

2020

Advances in Fuzzy Systems

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The evolvement of the fuzzy system has shown influential and successful in many universal approximation capabilities and applications. This paper proposes a hybrid Neuro-Fuzzy and Feature Reduction (NF-FR) model for data analysis. This proposed NF-FR model uses a feature-based class belongingness fuzzification process for all the patterns. During the fuzzification process, all the features are expanded based on the number of classes available in the dataset. It helps to deal with the uncertainty issues and assists the Artificial Neural Network- (ANN-) based model to achieve better performance. However, the complexity of the problem increases due to this expansion of input features in the fuzzification process. These expanded features may not always contribute significantly to the model. To overcome this problem, feature reduction (FR) is used to filter out the insignificant features, resulting the network less computational cost. These reduced significant features are used in the ANN-based model to classify the data. The effectiveness of this proposed model is tested and validated with ten benchmark datasets (both balanced and unbalanced) to demonstrate the performance of the proposed NF-FR model. The performance comparison of the NF-FR model with other counterparts has been carried out based on various performance measures such as classification accuracy, root means square error, precision, recall, and f-measure for quantitative analysis of the results. The obtained simulated results have been tested using the Friedman, Holm, and ANOVA tests under the null hypothesis for statistical validity and correctness proof of the results. The result analysis and statistical analysis show that the NF-FR model has achieved a considerable improvement in accuracy and is found to be efficient in eliminating redundant and noisy information.

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“…The absence of SLASH in configuration C contributes to around 3% degradation on accuracy. The concept of self-evolving structure has been studied intensively in the context of fuzzy and RBF networks [26].…”

Section: Ablation Studymentioning

confidence: 99%

Weakly Supervised Deep Learning Approach in Streaming Environments

Pratama

Ashfahani

Hady

2019

2019 IEEE International Conference on Big Data (Big Data)

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The feasibility of existing data stream algorithms is often hindered by the weakly supervised condition of data streams. A self-evolving deep neural network, namely Parsimonious Network (ParsNet), is proposed as a solution to various weakly-supervised data stream problems. A self-labelling strategy with hedge (SLASH) is proposed in which its auto-correction mechanism copes with the accumulation of mistakes significantly affecting the model's generalization. ParsNet is developed from a closed-loop configuration of the self-evolving generative and discriminative training processes exploiting shared parameters in which its structure flexibly grows and shrinks to overcome the issue of concept drift with/without labels. The numerical evaluation has been performed under two challenging problems, namely sporadic access to ground truth and infinitely delayed access to the ground truth. Our numerical study shows the advantage of ParsNet with a substantial margin from its counterparts in the high-dimensional data streams and infinite delay simulation protocol. To support the reproducible research initiative, the source code of ParsNet along with supplementary materials are made available at https://bit.ly/2qNW7p4.

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Evolving fuzzy and neuro-fuzzy approaches in clustering, regression, identification, and classification: A Survey

Cited by 250 publications

References 158 publications

PAC: A novel self-adaptive neuro-fuzzy controller for micro aerial vehicles

PAC: A novel self-adaptive neuro-fuzzy controller for micro aerial vehicles

A Hybrid Neuro-Fuzzy and Feature Reduction Model for Classification

Weakly Supervised Deep Learning Approach in Streaming Environments

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