no code implementations • 7 Sep 2021 • Chunyuan Zhang, Qi Song, Hui Zhou, Yigui Ou, Hongyao Deng, Laurence Tianruo Yang
In this paper, to overcome these drawbacks, we propose three novel RLS optimization algorithms for training feedforward neural networks, convolutional neural networks and recurrent neural networks (including long short-term memory networks), by using the error backpropagation and our average-approximation RLS method, together with the equivalent gradients of the linear least squares loss function with respect to the linear outputs of hidden layers.
