CHNNet: An Artificial Neural Network With Connected Hidden Neurons
Fuente:
arXiv
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| Autori principali: | , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2023
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| _version_ | 1866915616834715648 |
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| author | Shahir, Rafiad Sadat Humayun, Zayed Tamim, Mashrufa Akter Saha, Shouri Alam, Md. Golam Rabiul Khan, Abu Mohammad |
| author_facet | Shahir, Rafiad Sadat Humayun, Zayed Tamim, Mashrufa Akter Saha, Shouri Alam, Md. Golam Rabiul Khan, Abu Mohammad |
| contents | In contrast to biological neural circuits, conventional artificial neural networks are commonly organized as strictly hierarchical architectures that exclude direct connections among neurons within the same layer. Consequently, information flow is primarily confined to feedforward and feedback pathways across layers, which limits lateral interactions and constrains the potential for intra-layer information integration. We introduce an artificial neural network featuring intra-layer connections among hidden neurons to overcome this limitation. Owing to the proposed method for facilitating intra-layer connections, the model is theoretically anticipated to achieve faster convergence compared to conventional feedforward neural networks. The experimental findings provide further validation of the theoretical analysis. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2305_10468 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | CHNNet: An Artificial Neural Network With Connected Hidden Neurons Shahir, Rafiad Sadat Humayun, Zayed Tamim, Mashrufa Akter Saha, Shouri Alam, Md. Golam Rabiul Khan, Abu Mohammad Neural and Evolutionary Computing Machine Learning In contrast to biological neural circuits, conventional artificial neural networks are commonly organized as strictly hierarchical architectures that exclude direct connections among neurons within the same layer. Consequently, information flow is primarily confined to feedforward and feedback pathways across layers, which limits lateral interactions and constrains the potential for intra-layer information integration. We introduce an artificial neural network featuring intra-layer connections among hidden neurons to overcome this limitation. Owing to the proposed method for facilitating intra-layer connections, the model is theoretically anticipated to achieve faster convergence compared to conventional feedforward neural networks. The experimental findings provide further validation of the theoretical analysis. |
| title | CHNNet: An Artificial Neural Network With Connected Hidden Neurons |
| topic | Neural and Evolutionary Computing Machine Learning |
| url | https://arxiv.org/abs/2305.10468 |