On a generalized Gaussian radial basis function: Analysis and applications

Karimi, N.; Kazem, Saeed; Ahmadian, Davood; Adibi, Hojatollah; Ballestra, Luca Vincenzo

doi:10.1016/j.enganabound.2019.11.011

Cited by 36 publications

(15 citation statements)

References 36 publications

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“…This paper uses RBFNN classifier for recognizing negative emotions of undergraduates. RBFNN is a single hidden layer forward neural networks model ( 29 ).…”

Section: Negative Emotions Recognition Methodsmentioning

confidence: 99%

Intelligent Recognition and Analysis of Negative Emotions of Undergraduates Under COVID-19

Zhang

2022

Front. Public Health

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BackgroundThe outbreak and spread of COVID-19 has brought a tremendous impact on undergraduates' study and life, and also caused anxiety, depression, fear and loneliness among undergraduates. If these individual negative emotions are not timely guided and treated, it is easy to cause the amplification of social negative emotions, resulting in individual and collective irrational behavior, and ultimately destroy social stability and trust foundation. Therefore, how to strengthen the analysis and guidance of negative emotions of undergraduates has become an important issue to be urgently solved in the training of undergraduates.MethodThis paper presents a weight and structure double-determination method. Based on this method, a Radial Basis Function Neural Networks (RBFNN) classifier is constructed for recognizing negative emotions of undergraduates. After classifying the input psychological crisis intervention scale samples by the RBFNN classifier, recognition of negative emotions for undergraduates are divided into normal, mild depression, moderate depression and severe depression.ExperimentsAfterwards, we analyze negative emotions of undergraduates and give some psychological adjustment strategies. In addition, the experiment results demonstrate that the proposed method has a good performance in terms of classification accuracy, classification time and recognition rate of negative emotions among undergraduates.

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“…This paper uses RBFNN classifier for recognizing negative emotions of undergraduates. RBFNN is a single hidden layer forward neural networks model ( 29 ).…”

Section: Negative Emotions Recognition Methodsmentioning

confidence: 99%

Intelligent Recognition and Analysis of Negative Emotions of Undergraduates Under COVID-19

Zhang

2022

Front. Public Health

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“…We refer to the study by Simonenko et al [32] which uses the Dirichlet boundary conditions given by (22) in all the boundaries of the beam. The relevant parameters are L = 12; H = 2; E = 1000; µ = 0.3; p = −10.…”

Section: Cantilever Beammentioning

confidence: 99%

“…Some scholars bypassed the process of seeking optimal shape parameters and proposed a nontraditional RBF. For instance, Karimi [22] introduces a new Gaussian RBF (GRBF), which is both stable and accurate and is free of instability issues for small values of the shape parameter. Similarly, Zhang [17] proposed a new global RBF based on the coupling of infinitely smooth RBF with the conical spines, known as the coupled radial basis function (CRBF).…”

Section: Introductionmentioning

confidence: 99%

A Hybrid RBF Collocation Method and Its Application in the Elastostatic Symmetric Problems

Chen

Yao

et al. 2022

Symmetry

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In this paper, a new hybrid radial basis function collocation method (HRBF-CM) is proposed to help resolve two-dimensional elastostatic symmetric problems. In the new approach, the hybrid radial basis function (HRBF) combines the infinitely smooth RBF and piecewise smooth RBF, containing two parameters (the shape parameter and the weight parameter). Discretization schemes are presented in detail. We use MATLAB to implement the HRBF-CM and produce numerical results which demonstrate the potential of this method. The new method’s accuracy is higher than that of the traditional methods, especially in the case of a more significant number of nodes. We discuss the new method’s effectiveness compared to the widely used traditional RBF and also investigate the effect of parameters on the method’s performance under the new method.

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“…Other functions adopted in the RBF architecture include linear kernels, thin-plate splines, logistic functions, and multiquadratic functions [8]- [11]. Hardy's multiquadratic functions motivate an activation function for RBFs used by Karimi et al and Zhao et al [12], [13]. Du et al proposed a kernel for digital signal processing (DSP) units 9.…”

Section: Related Workmentioning

confidence: 99%

Radial Basis Function Networks for Convolutional Neural Networks to Learn Similarity Distance Metric and Improve Interpretability

Amirian

Schwenker

2020

IEEE Access

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Radial basis function neural networks (RBFs) are prime candidates for pattern classification and regression and have been used extensively in classical machine learning applications. However, RBFs have not been integrated into contemporary deep learning research and computer vision using conventional convolutional neural networks (CNNs) due to their lack of adaptability with modern architectures. In this paper, we adapt RBF networks as a classifier on top of CNNs by modifying the training process and introducing a new activation function to train modern vision architectures end-to-end for image classification. The specific architecture of RBFs enables the learning of a similarity distance metric to compare and find similar and dissimilar images. Furthermore, we demonstrate that using an RBF classifier on top of any CNN architecture provides new human-interpretable insights about the decision-making process of the models. Finally, we successfully apply RBFs to a range of CNN architectures and evaluate the results on benchmark computer vision datasets.

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On a generalized Gaussian radial basis function: Analysis and applications

Cited by 36 publications

References 36 publications

Intelligent Recognition and Analysis of Negative Emotions of Undergraduates Under COVID-19

Intelligent Recognition and Analysis of Negative Emotions of Undergraduates Under COVID-19

A Hybrid RBF Collocation Method and Its Application in the Elastostatic Symmetric Problems

Radial Basis Function Networks for Convolutional Neural Networks to Learn Similarity Distance Metric and Improve Interpretability

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