Universal approximation theorem for a radial basis function fuzzy neural network

Shanthi, S. Anita; Sathiyapriya, G.

doi:10.1016/j.matpr.2021.11.576

Cited by 3 publications

(3 citation statements)

References 6 publications

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“…Probabilistic neural network is usually composed of three layers, namely, input layer, hidden layer, and output layer [ 20 ]. Its structural model is shown in Figure 3 .…”

Section: Radial Basis Function (Rbf) Neural Networkmentioning

confidence: 99%

Clustering Analysis Algorithm of Volleyball Simulation Based on Radial Fuzzy Neural Network

Yan

Wang

2022

Computational Intelligence and Neuroscience

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Aiming at a series of problems existing in volleyball, based on radial fuzzy neural network theory, the optimized simulation clustering analysis algorithm is used to monitor and analyze volleyball. By analyzing the feature weights of nodes at different stages, the optimal radial fuzzy neural network was constructed, which was combined with the simulation clustering algorithm to obtain the relevant optimization model describing volleyball. The accuracy of the optimization model is verified by comparing with the original model. The results show that with the increase of response, the response curves of different algorithms show fluctuation. Among them, the fluctuation range of MPDR (minimum power distortionless response) algorithm is larger, and the value of the curve obtained by MVDR (minimum variance distortionless response) algorithm differs greatly from that of the optimization algorithm at some key nodes, while the beam changing chart obtained by the optimization algorithm can better reflect the changing trend of the beam. Model indexes under different algorithms are different. When the number of iterative steps is less than 30, indexes under different algorithms are all greater than the standard value. When the number of iterations is more than 30, the indexes under different algorithms are all less than the standard value. Through verification, it can be seen that the original model can only describe the first stage of volleyball, while the optimization model can describe the whole process of volleyball. It shows that the optimization model can be used to describe and analyze volleyball-related data. And the algorithm can be used to better predict and analyze volleyball, and the analysis results can provide relevant guidance for volleyball. The optimization model provides basis and theoretical support for the application of volleyball simulation clustering algorithm, so as to better promote volleyball and better guide the movement.

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“…Probabilistic neural network is usually composed of three layers, namely, input layer, hidden layer, and output layer [ 20 ]. Its structural model is shown in Figure 3 .…”

Section: Radial Basis Function (Rbf) Neural Networkmentioning

confidence: 99%

Clustering Analysis Algorithm of Volleyball Simulation Based on Radial Fuzzy Neural Network

Yan

Wang

2022

Computational Intelligence and Neuroscience

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“…We then pass the output from the DNN as input to the RBFNN. The RBFNN is a type of neural network that employs radial basis functions as its activation functions [1]. These functions assess the distance between the input data and a set of predefined center points, and they combine their outputs to generate the final predictions or outputs of the network.…”

Section: Introductionmentioning

confidence: 99%

A Hybrid DNN-RBFNN Model for Intrusion Detection System

Oboya,

Gichuhi,

Wanjoya

2023

JDAIP

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Intrusion Detection Systems (IDS) are pivotal in safeguarding computer networks from malicious activities. This study presents a novel approach by proposing a Hybrid Dense Neural Network-Radial Basis Function Neural Network (DNN-RBFNN) architecture to enhance the accuracy and efficiency of IDS. The hybrid model synergizes the strengths of both dense learning and radial basis function networks, aiming to address the limitations of traditional IDS techniques in classifying packets that could result in Remote-to-local (R2L), Denial of Service (Dos), and User-to-root (U2R) intrusions.

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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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Universal approximation theorem for a radial basis function fuzzy neural network

Cited by 3 publications

References 6 publications

Clustering Analysis Algorithm of Volleyball Simulation Based on Radial Fuzzy Neural Network

Clustering Analysis Algorithm of Volleyball Simulation Based on Radial Fuzzy Neural Network

A Hybrid DNN-RBFNN Model for Intrusion Detection System

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

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