Static and Dynamic Neural Networks: From Fundamentals to Advanced Theory

By Madan M. Gupta, N. Homma, Liang Jin. 2003

699 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

A new approach to signal classification using spectral correlation and neural networks (2005, methodology): “Each MLPN was trained with a back propagation algorithm [23] with an initial learning rate η = 0.”
Review on forecasting of photovoltaic power generation based on machine learning and metaheuristic techniques (2019, background): “There are two stages of learning process based on synaptic weight [83].”
Stochastic Synchronization of Markovian Jump Neural Networks With Time-Varying Delay Using Sampled Data (2013, background): “INTRODUCTION IN THE past decade, dynamical properties of neural networks have been extensively investigated due to the fact that neural networks have successful applications in a variety of areas, including signal processing, pattern recognition, static image processing, associative memory, and comb”