動態類神經網路在非線性系統鑑別與控制器設計之應用

Random Forest: Theory and Practice Neural Network Motivation.. Neural Network Hypothesis Neural Network Training Deep

Pascanu et al., “On the difficulty of training recurrent neural networks,” in ICML, 2013..

To tackle these problems, this study develops a novel approach integrated with some graph-based heuristic working rules, robust back-propagation neural network (BPNN) engines

Moreover, this chapter also presents the basic of the Taguchi method, artificial neural network, genetic algorithm, particle swarm optimization, soft computing and

This study proposed the Minimum Risk Neural Network (MRNN), which is based on back-propagation network (BPN) and combined with the concept of maximization of classification margin

To solve this problem, this study proposed a novel neural network model, Ecological Succession Neural Network (ESNN), which is inspired by the concept of ecological succession

In this study, variable weights are added into the probabilistic density function of Elliptical Probabilistic Neural Network (EPNN), so that the kernel function can be adjusted

This study proposed the ellipse-space probabilistic neural network (EPNN), which includes three kinds of network parameters that can be adjusted through training: the variable