IEEE

Transient Modeling of Nonlinear Components using Global Gated Deep Recurrent Neural Network Masked With Dropout

Abstract: This article introduces a novel approach for nonlinear electronic circuit modeling called Global Gated Deep Recurrent Neural Network (GGDRNN). GGDRNNs leverage the stacking of multiple ...
Hosted on MSN

Neural network Python from scratch with softmax

Implement Neural Network in Python from Scratch ! In this video, we will implement MultClass Classification with Softmax by making a Neural Network in Python from Scratch. We will not use any build in ...
IEEE

Adaptive Output Feedback Control Using Lyapunov-Based Deep Recurrent Neural Networks (Lb-DRNNs)

Abstract: Unlike traditional feedforward neural networks, recurrent neural networks (RNNs) possess a recurrent connection that allows them to retain past information. This internal memory enables RNNs ...
GitHub

time-series-prediction-rnn

This project implements a robust time series forecasting pipeline to predict the closing prices of the IBOVESPA index, Brazil’s main stock market benchmark. It combines advanced data preprocessing ...
eLife

Neural dynamics of reversal learning in the prefrontal cortex and recurrent neural networks

The findings of this study are valuable, offering insights into the neural representation of reversal probability in decision-making tasks, with potential implications for understanding flexible ...
Scientific Research Publishing

Medsker, L.R. and Jain, L. (2001) Recurrent Neural Networks: Design and Applications. CRC Press.

ABSTRACT: The stochastic configuration network (SCN) is an incremental neural network with fast convergence, efficient learning and strong generalization ability, and is widely used in fields such as ...