Connected Hidden Neurons (CHNNet): An Artificial Neural Network for Rapid Convergence

Rafiad Sadat Shahir, Zayed Humayun, Mashrufa Akter Tamim, Shouri Saha, Md. Golam Rabiul Alam

Primary Area: general machine learning (i.e., none of the above)

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Keywords: Artificial Neural Network, Connected Hidden Neurons, Rapid Convergence.

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TL;DR: We propose an artificial neural network, namely CHNNet, where the hidden neurons, residing in the same hidden layer, are interconnected.

Abstract: Despite artificial neural networks being inspired by the functionalities of biological neural networks, unlike biological neural networks, conventional artificial neural networks are often structured hierarchically, which can impede the flow of information between neurons as the neurons in the same layer have no connections between them. Hence, we propose a more robust model of artificial neural networks where the hidden neurons, residing in the same hidden layer, are interconnected that leads to rapid convergence. With the experimental study of our proposed model in deep networks, we demonstrate that the model results in a noticeable increase in convergence rate compared to the conventional feed-forward neural network.

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Submission Number: 5952