Deep neural networks are at the heart of artificial intelligence, ranging from pattern recognition to large language and ...

This paper presents a novel adaptive learning-rate backpropagation neural network (ALR-BPNN) algorithm based on the minimization of mean-square deviation (MSD) to implement a fast convergence rate and ...

Natural neural systems have inspired innovations in machine learning and neuromorphic circuits designed for energy-efficient data processing. However, implementing the backpropagation algorithm, a ...

A new technical paper titled “Hardware implementation of backpropagation using progressive gradient descent for in situ training of multilayer neural networks” was published by researchers at ...

Backpropagation neural networks are commonly utilized to solve complicated issues in various disciplines. However, optimizing their settings remains a significant task. Traditional gradient-based ...

In this regard, Hinton proposes the FF algorithm as an alternative to backpropagation for neural network learning. The FF algorithm is inspired by Boltzmann machines (Hinton and Sejnowski, 1986) and ...

Backpropagation in neural network emerged when researchers realized they could use it to adjust input weights in neural network, which was not possible in feedforward neural net.