Boundary Interpolation on Triangles via Neural Network Operators
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arXiv
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| Format: | Preprint |
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2024
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| _version_ | 1866917793872478208 |
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| author | Bhat, Aaqib Ayoub Khan, Asif |
| author_facet | Bhat, Aaqib Ayoub Khan, Asif |
| contents | The primary objective of this study is to develop novel interpolation operators that interpolate the boundary values of a function defined on a triangle. This is accomplished by constructing New Generalized Boolean sum neural network operator $\mathcal{B}_{n_1, n_2, ξ}$ using a class of activation functions. Its interpolation properties are established and the estimates for the error of approximation corresponding to operator $\mathcal{B}_{n_1, n_2, ξ}$ is computed in terms of mixed modulus of continuity. The advantage of our method is that it does not require training the network. Instead, the number of hidden neurons adjusts the weights and bias. Numerical examples are illustrated to show the efficacy of these newly constructed operators. Further, with the help of MATLAB, comparative and graphical analysis is given to show the validity and efficiency of the results obtained for these operators. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2410_02793 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Boundary Interpolation on Triangles via Neural Network Operators Bhat, Aaqib Ayoub Khan, Asif Numerical Analysis Functional Analysis 2020: 41A05, 41A35, 41A80 The primary objective of this study is to develop novel interpolation operators that interpolate the boundary values of a function defined on a triangle. This is accomplished by constructing New Generalized Boolean sum neural network operator $\mathcal{B}_{n_1, n_2, ξ}$ using a class of activation functions. Its interpolation properties are established and the estimates for the error of approximation corresponding to operator $\mathcal{B}_{n_1, n_2, ξ}$ is computed in terms of mixed modulus of continuity. The advantage of our method is that it does not require training the network. Instead, the number of hidden neurons adjusts the weights and bias. Numerical examples are illustrated to show the efficacy of these newly constructed operators. Further, with the help of MATLAB, comparative and graphical analysis is given to show the validity and efficiency of the results obtained for these operators. |
| title | Boundary Interpolation on Triangles via Neural Network Operators |
| topic | Numerical Analysis Functional Analysis 2020: 41A05, 41A35, 41A80 |
| url | https://arxiv.org/abs/2410.02793 |