2 research outputs found
Connected Hidden Neurons (CHNNet): An Artificial Neural Network for Rapid Convergence
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