An Adaptive Neuro-Fuzzy System With Integrated Feature Selection and Rule Extraction for High-Dimensional Classification Problems

Xue, Guangdong; Chang, Qin; Wang, Jian; Zhang, Kai; Pal, Nikhil R.

doi:10.1109/tfuzz.2022.3220950

Cited by 69 publications

(14 citation statements)

References 42 publications

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“…Using fuzzy control principles and combining the advantages of both FLC and ANN, the neuro-fuzzy controller has many advantages, such as the learning capabilities of neural networks, parallel knowledge/data processing capabilities, and human fuzzy logic reasoning capabilities [87][88][89][90]. The ANFIS (adaptive neural network fuzzy inference system) is a fuzzy inference system based on the Takagi-Sugeno model [91].…”

Section: Adaptive Neuro-fuzzy Algorithm Optimizationmentioning

confidence: 99%

Control and Intelligent Optimization of a Photovoltaic (PV) Inverter System: A Review

Zhang,

Zhai,

Mao

et al. 2024

Energies

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PV power generation is developing fast in both centralized and distributed forms under the background of constructing a new power system with high penetration of renewable sources. However, the control performance and stability of the PV system is seriously affected by the interaction between PV internal control loops and the external power grid. The impact of the PV system on the reliability, stability, and power quality of power systems has restricted them to further participate in power supplies with a large capacity. Traditional control methods have become ineffective at dealing with these problems as the PV system becomes increasingly complex and nonlinear. Intelligent control as a more advanced technology has been integrated into the PV system to improve system control performance and stability. However, intelligent control for the PV system is still in the early stages due to the extensive calculation and intricate implementation of intelligent algorithms. Further investigations should be carried out to effectively combine intelligent control with the PV system to constitute an intelligent PV power system with multiple functions, high stability, and high-performance. This paper provides a systematic classification and detailed introduction of various intelligent optimization methods in a PV inverter system based on the traditional structure and typical control. The future trends and research topics are given to provide a reference for the intelligent optimization control in the PV system.

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Section: Adaptive Neuro-fuzzy Algorithm Optimizationmentioning

confidence: 99%

Control and Intelligent Optimization of a Photovoltaic (PV) Inverter System: A Review

Zhang,

Zhai,

Mao

et al. 2024

Energies

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“…To solve problem (1), dimensionality reduction strategies [8,9] (feature selection [10] and feature extraction [11]) are applied to hyperspectral image classification tasks. To solve problem (2), morphological contours [12] and Gabor features [13] are used to ex-tract spatial information, and the morphological kernel [14] and composite kernel [15] methods are used to extract spectral-spatial information.…”

Section: Introductionmentioning

confidence: 99%

Hyperspectral Image Classification Based on Dense Pyramidal Convolution and Multi-Feature Fusion

Zhang,

Zhao,

Jiang

et al. 2023

Remote Sensing

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In recent years, hyperspectral image classification techniques have attracted a lot of attention from many scholars because they can be used to model the development of different cities and provide a reference for urban planning and construction. However, due to the difficulty in obtaining hyperspectral images, only a limited number of pixels can be used as training samples. Therefore, how to adequately extract and utilize the spatial and spectral information of hyperspectral images with limited training samples has become a difficult problem. To address this issue, we propose a hyperspectral image classification method based on dense pyramidal convolution and multi-feature fusion (DPCMF). In this approach, two branches are designed to extract spatial and spectral features, respectively. In the spatial branch, dense pyramid convolutions and non-local blocks are used to extract multi-scale local and global spatial features in image samples, which are then fused to obtain spatial features. In the spectral branch, dense pyramidal convolution layers are used to extract spectral features in image samples. Finally, the spatial and spectral features are fused and fed into fully connected layers to obtain classification results. The experimental results show that the overall accuracy (OA) of the method proposed in this paper is 96.74%, 98.10%, 98.92% and 96.67% on the four hyperspectral datasets, respectively. Significant improvements are achieved compared to the five methods of SVM, SSRN, FDSSC, DBMA and DBDA for hyperspectral classification. Therefore, the proposed method can better extract and exploit the spatial and spectral information in image samples when the number of training samples is limited. Provide more realistic and intuitive terrain and environmental conditions for urban planning, design, construction and management.

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“…As a flexible, interpretable machine learning model, fuzzy neural networks (FNNs) have been widely used in various fields, such as image processing [1], fuzzy control [2,3], ranking challenges, risks and threats [4], actual classification and prediction [5][6][7][8], and so on. One of the most commonly used FNN structures is the Takagi-Sugeno-Kang (TSK) [9] fuzzy system, also called TSK neuro-fuzzy system because it can be represented as a neural network [10][11][12].…”

Section: Introductionmentioning

confidence: 99%

“…The training of FNNs is a necessary work. There are many existing training algorithms, such as backpropagation [5], particle swarm algorithm [13], hybrid algorithm [14] and so on [15]. Although evolutionary algorithms and hybrid-type algorithms work well, they require considerable running time.…”

Section: Introductionmentioning

confidence: 99%

“…Since too many rules will weaken the interpretability of FNNs, a reduction in the number of rules is necessary and meaningful. In order to reduce the number of FRs, various methods have been proposed, such as direct extraction techniques from numerical data [21,22], genetic algorithm-based approaches [23,24], and embedded neuro-fuzzy approaches [5,[25][26][27]. Among these methods, the embedded neuro-fuzzy approaches simultaneously perform the rule extraction and evaluation of the model, enhancing the effectiveness and reducing the computational burden.…”

Section: Introductionmentioning

confidence: 99%

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First-Order Sparse TSK Nonstationary Fuzzy Neural Network Based on the Mean Shift Algorithm and the Group Lasso Regularization

Zhang,

Wang,

Gong

et al. 2023

Mathematics

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Nonstationary fuzzy inference systems (NFIS) are able to tackle uncertainties and avoid the difficulty of type-reduction operation. Combining NFIS and neural network, a first-order sparse TSK nonstationary fuzzy neural network (SNFNN-1) is proposed in this paper to improve the interpretability/translatability of neural networks and the self-learning ability of fuzzy rules/sets. The whole architecture of SNFNN-1 can be considered as an integrated model of multiple sub-networks with a variation in center, variation in width or variation in noise. Thus, it is able to model both “intraexpert” and “interexpert” variability. There are two techniques adopted in this network: the Mean Shift-based fuzzy partition and the Group Lasso-based rule selection, which can adaptively generate a suitable number of clusters and select important fuzzy rules, respectively. Quantitative experiments on six UCI datasets demonstrate the effectiveness and robustness of the proposed model.

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An Adaptive Neuro-Fuzzy System With Integrated Feature Selection and Rule Extraction for High-Dimensional Classification Problems

Cited by 69 publications

References 42 publications

Control and Intelligent Optimization of a Photovoltaic (PV) Inverter System: A Review

Control and Intelligent Optimization of a Photovoltaic (PV) Inverter System: A Review

Hyperspectral Image Classification Based on Dense Pyramidal Convolution and Multi-Feature Fusion

First-Order Sparse TSK Nonstationary Fuzzy Neural Network Based on the Mean Shift Algorithm and the Group Lasso Regularization

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