Optimize TSK Fuzzy Systems for Regression Problems: Minibatch Gradient Descent With Regularization, DropRule, and AdaBound (MBGD-RDA)

Wu, Dongrui; Yuan, Ye; Huang, Jian; Tan, Yihua

doi:10.1109/tfuzz.2019.2958559

Cited by 83 publications

(80 citation statements)

References 29 publications

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“…Due to complex structure of RCNN, the estimation network may drop into local optimum when trained by traditional adaptive gradient optimization method. The newly developed AdaBound method can address this difficulty by adaptively tighten the clip interval and keep the optimizer steady [40].…”

Section: Optimization Methodsmentioning

confidence: 99%

A Novel Recurrent Convolutional Neural Network-Based Estimation Method for Switching Guidance Law

et al. 2020

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This paper presents a recurrent convolutional neural network-based estimation method of guidance parameters of the pursuer under augmented proportional navigation (APN) guidance law with a time-varying switching navigation ratio. For the highly maneuvering pursuit-evasion process, realistic factors in the guidance law estimation are considered, such as the pursuer's estimation error and delay of the evader's acceleration in the APN guidance law. In view of the enormous measurement data, time dependency, transient change and unknown factors' disturbance in switching guidance law estimation, a novel neural network structure is built. 1-D CNN layer is used to extract features from enormous data obtained by the multiple previous measurements. The features extracted are processed by the recurrent cell to exploit the time dependency and eliminate the error caused by unknown factors. The result of ablation test shows the proposed RCNN's improved performance over single CNN or RNN. Compared to the multiple model guidance law estimation method, the proposed method can simplify the design of guidance law estimation system and reduce calculation load. The estimation result for switching guidance law shows the proposed method has higher accuracy and faster convergence rate than traditional interactive multiple model methods. INDEX TERMS Guidance law estimation, pursuit-evasion process, recurrent convolutional neural network, switching navigation ratio, time dependency.

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Section: Optimization Methodsmentioning

confidence: 99%

A Novel Recurrent Convolutional Neural Network-Based Estimation Method for Switching Guidance Law

et al. 2020

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“…Phan et al solved the problem of predicting the fracture pressure of defective pipelines more effectively through the combination of PCA and the ANFIS [25]. Meanwhile, it is worth referring to the efficient training algorithm named MBGD-RDA proposed by Wu et al for TSK FSs in 2020 [26]. PCA was applied to constrain the maximum input dimensionality to five, and several novel techniques were proposed in MBGD-RDA.…”

Section: Introductionmentioning

confidence: 99%

Deep Neural Fuzzy System Oriented toward High-Dimensional Data and Interpretable Artificial Intelligence

et al. 2021

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Fuzzy systems (FSs) are popular and interpretable machine learning methods, represented by the adaptive neuro-fuzzy inference system (ANFIS). However, they have difficulty dealing with high-dimensional data due to the curse of dimensionality. To effectively handle high-dimensional data and ensure optimal performance, this paper presents a deep neural fuzzy system (DNFS) based on the subtractive clustering-based ANFIS (SC-ANFIS). Inspired by deep learning, the SC-ANFIS is proposed and adopted as a submodule to construct the DNFS in a bottom-up way. Through the ensemble learning and hierarchical learning of submodules, DNFS can not only achieve faster convergence, but also complete the computation in a reasonable time with high accuracy and interpretability. By adjusting the deep structure and the parameters of the DNFS, the performance can be improved further. This paper also performed a profound study of the structure and the combination of the submodule inputs for the DNFS. Experimental results on five regression datasets with various dimensionality demonstrated that the proposed DNFS can not only solve the curse of dimensionality, but also achieve higher accuracy, less complexity, and better interpretability than previous FSs. The superiority of the DNFS is also validated over other recent algorithms especially when the dimensionality of the data is higher. Furthermore, the DNFS built with five inputs for each submodule and two inputs shared between adjacent submodules had the best performance. The performance of the DNFS can be improved by distributing the features with high correlation with the output to each submodule. Given the results of the current study, it is expected that the DNFS will be used to solve general high-dimensional regression problems efficiently with high accuracy and better interpretability.

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“…Currently, classifiers mainly include two types based on machine learning and deep learning. In the field of machine learning, the commonly used classification models mainly include the Gaussian model [23][24], SVM [25][26], AdaBoost [27][28], and fuzzy system [29][30]. The use of machine learning algorithms often needs to be equipped with suitable feature extraction methods to get the desired results.…”

Section: Introductionmentioning

confidence: 99%

A Violation Information Recognition Method of Live-Broadcasting Platform Based on Machine Learning Technology

Shen

Yuan

2021

Wireless Communications and Mobile Computing

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With the development of the live broadcast industry, security issues in the live broadcast process have become increasingly apparent. At present, the supervision of various live broadcast platforms is basically in a state of human supervision. Manpower supervision is mainly through user reporting and platform supervision measures. However, there are a large number of live broadcast rooms at the same time, and only relying on human supervision can no longer meet the monitoring needs of live broadcasts. Based on this situation, this study proposes a violation information recognition method of a live-broadcasting platform based on machine learning technology. By analyzing the similarities and differences between normal live broadcasts and violation live broadcasts, combined with the characteristics of violation image data, this study mainly detects human skin color and sensitive parts. A prominent feature of violation images is that they contain a large area of naked skin, and the ratio of the area of naked skin to the overall image area of the violation image will exceed the threshold. Skin color recognition plays a role in initial target positioning. The accuracy of skin color recognition is directly related to the recognition accuracy of the entire system, so skin color recognition is the most important part of violation information recognition. Although there are many effective skin color recognition technologies, the accuracy and stability of skin color recognition still need to be improved due to the influence of various external factors, such as light intensity, light source color, and physical equipment. When it is detected that the area of the skin color in the live screen exceeds the threshold, it is preliminarily determined to be a suspected violation video. In order to improve the recognition accuracy, it is necessary to detect sensitive parts of the suspected video. Naked female breasts are a very obvious feature in violation images. This study uses a chest feature extraction method to detect the chest in the image. When the recognition result is a violation image, it is determined that the live broadcast involves violation content. The machine learning algorithm is simple to implement, and the parameters are easy to adjust. The classifier training requires a short time and is suitable for live violation information recognition scenarios. The experimental results on the adopted data set show that the method used in this article can effectively detect videos with violation content. The recognition rate is as high as 85.98%, which is suitable for a real-life environment and has good practical significance.

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Optimize TSK Fuzzy Systems for Regression Problems: Minibatch Gradient Descent With Regularization, DropRule, and AdaBound (MBGD-RDA)

Cited by 83 publications

References 29 publications

A Novel Recurrent Convolutional Neural Network-Based Estimation Method for Switching Guidance Law

A Novel Recurrent Convolutional Neural Network-Based Estimation Method for Switching Guidance Law

Deep Neural Fuzzy System Oriented toward High-Dimensional Data and Interpretable Artificial Intelligence

A Violation Information Recognition Method of Live-Broadcasting Platform Based on Machine Learning Technology

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