Radial basis function bipolar fuzzy neural network

Preethi, R.; Sathiyapriya, G.; Shanthi, S. Anita

doi:10.1016/j.matpr.2022.10.186

Cited by 6 publications

(5 citation statements)

References 9 publications

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“…The use of interpolation and fitting techniques for simulating atmospheric pollutant concentrations has been a consistent focus of research. The accuracy and precision of interpolation have always been key considerations in these studies [22][23][24][25][26][27][28][29][30][31][32][33][34][35][36]. In the study by Li et al [31], the results indicated that the optimal polynomial fitting (OPF) method accurately reconstructed PM 2.5 fields.…”

Section: Discussionmentioning

confidence: 99%

“…The primary advantage of the RBF method lies in its ability to effectively extract pertinent information from discrete point data, resulting in a smoother and more natural magnified image. The radial basis function (RBF) interpolation method is known for its computational efficiency, making it a favorable choice when compared to other interpolation methods [32][33][34]. One of its valuable features is the ability to address the limitation of uniform grids by distributing discrete nodes in irregular regions for constructing the grid model.…”

Section: Introductionmentioning

confidence: 99%

“…Orthogonal polynomial fitting is a highly precise and efficient technique for modeling intricate data, which has been extensively applied in various fields including artificial intelligence, image processing, and marine science [31][32][33][34][35][36]. Li et al [31] utilized the Chebyshev polynomial fitting technique to derive the temporal-spatial distribution of PM 2.5 in central and southern regions of China, followed by analysis thereof.…”

Section: Introductionmentioning

confidence: 99%

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Application of Trigonometric Polynomial Fitting Method in Simulating the Spatial Distribution of PM2.5 Concentration in South-Central China

Chen,

Li,

et al. 2023

Atmosphere

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Near-surface PM2.5 estimates remain a global scientific research challenge due to their effect on human fitness and atmospheric environmental quality. However, practical near-surface PM2.5 estimates are impeded by the incomplete monitoring data. In this study, we propose the trigonometric polynomial fitting (TPF) method to estimate near-surface PM2.5 concentrations in south-central China during 2015. We employ 10-fold cross-validation (CV) to assess the reliability of TPF in estimating practical PM2.5 values. When compared to alternative methods such as the orthogonal polynomial fitting (OBF) method based on Chebyshev basis functions, Kriging interpolation, and radial basis function (RBF) interpolation, our results show that utilizing TPF31, with a maximum order of 3 in the x direction and a maximum order of 1 in the y direction, leads to superior efficiency through error minimization. TPF31 reduces MAE and RMSE by 1.93%, 24%, 6.96% and 3.6%, 23.07%, 10.43%, respectively, compared to the other three methods. In addition, the TPF31 method effectively reconstructs the spatial distribution of PM2.5 concentrations in the unevenly distributed observation stations of Inner Mongolia and the marginal regions of the study area. The reconstructed spatial distribution is remarkably smooth. Despite the non-uniform distribution of observation stations and the presence of missing data, the TPF31 method demonstrates exceptional effectiveness in accurately capturing the inherent physical attributes of spatial distribution. The theoretical and experimental results emphasize that the TPF method holds significant potential for accurately reconstructing the spatial distribution of PM2.5 in China.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Application of Trigonometric Polynomial Fitting Method in Simulating the Spatial Distribution of PM2.5 Concentration in South-Central China

Chen,

Li,

et al. 2023

Atmosphere

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“…Each unit of this intermediate layer is intentionally defined and characterized by an activation function. The GRNN is a kind of neural network whose function is to construct the internal representation of the input model through the proper configuration of the middle layer and can personalize the “features” of each input belonging to the training set [ 36 ]. The GRNN is a four-layer forward network, as shown in Figure 1 [ 37 ]: the first layer is the input layer, and the number of neurons is equal to the dimension of the input vector; the second layer is the pattern layer, and the number of neurons is equal to the number of learning samples; the third layer is the summation layer, sum over all the neurons in the hidden layer; the fourth layer is the output layer, the number of nodes is equal to the dimension of the output vector [ 30 ].…”

Section: Equilibrium Optimizer-generalized Regression Neural Networkmentioning

confidence: 99%

Prediction of the Sound Absorption Coefficient of Three-Layer Aluminum Foam by Hybrid Neural Network Optimization Algorithm

Guo

Lisi

et al. 2022

Materials

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The combination of multilayer aluminum foam can have high sound absorption coefficients (SAC) at low and medium frequencies, and predicting its absorption coefficient can help the optimal structural design. In this study, a hybrid EO-GRNN model was proposed for predicting the sound absorption coefficient of the three-layer composite structure of the aluminum foam. The generalized regression neural network (GRNN) model was used to predict the sound absorption coefficient of three-layer composite structural aluminum foam due to its outstanding nonlinear problem-handling capability. An equilibrium optimization (EO) algorithm was used to determine the parameters in the neuronal network. The prediction results show that this method has good accuracy and high precision. The calculation result shows that this proposed hybrid model outperforms the single GRNN model, the GRNN model optimized by PSO (PSO-GRNN), and the GRNN model optimized by FOA(FOA-GRNN). The prediction results are expressed in terms of root mean square error (RMSE), absolute error, and relative error, and this method performs well with an average RMSE of only 0.011.

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“…Muhammed et al [16] analyzed Gaussian and multiquadratic functions to select the appropriate activation function based on RBNN structure and data. Anita et al [17] applied trapezoidal fuzzy number in RBFNN and introduced RBFNN on bipolar fuzzy sets in [18]. Based on these concepts bipolar picture fuzzy soft RBFNN is developed.…”

Section: Introductionmentioning

confidence: 99%

Evaluating types of soil suitable for construction using radial basis function fuzzy neural network

Shanthi¹,

Keerthana²

2023

E3S Web of Conf.

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This paper deals with radial basis fuzzy neural network using bipolar picture fuzzy soft sets. Firstly a bipolar picture fuzzy decision matrix is constructed. A bipolar picture fuzzy Gaussian membership function is defined. Next degree values are calculated and used for finding the entropy. These entropy values help in determining the weights of bipolar picture fuzzy sets. Output is obtained by multiplication of weight values and BPFS Gaussian membership values. By using this method, the soil suitable for construction of buildings is found.

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Radial basis function bipolar fuzzy neural network

Cited by 6 publications

References 9 publications

Application of Trigonometric Polynomial Fitting Method in Simulating the Spatial Distribution of PM2.5 Concentration in South-Central China

Application of Trigonometric Polynomial Fitting Method in Simulating the Spatial Distribution of PM2.5 Concentration in South-Central China

Prediction of the Sound Absorption Coefficient of Three-Layer Aluminum Foam by Hybrid Neural Network Optimization Algorithm

Evaluating types of soil suitable for construction using radial basis function fuzzy neural network

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