Hodykin-Huxley 方程, 感知器模型与自适应滤波器, 再到六十年代的自组织映射网络,
神经认知机, 自适应共振网络, 许多神经计算模型都发展成为信号处理, 计算机视觉 …

Highlights•Transfer learning provided very promising performance for weed/crop
classification.•The highest classification accuracy of 98.7% was obtained with VGG-19.•All …

This study proposes a modified convolutional neural network (CNN) algorithm that is based
on dropout and the stochastic gradient descent (SGD) optimizer (MCNN-DS), after analyzing …

Feature extraction from a time sequence signal without manual information is an important
part for bearing intelligent diagnosis. With the merits of signal information and feature …

A new DNN model, called multi-channels weighted convolutional neural network (MCW-
CNN) is proposed in order to solve the feature extraction problem of CNNs that use single …

Cerebral microbleeds are important biomarkers of many cerebrovascular diseases and
cognitive dysfunctions. Their distribution patterns can indicate some underlying aetiologies …

为了解决单通道振动信号输入不能全面表达故障特征信息及齿轮箱故障早期诊断问题,
提出了一种新的深度神经网络(Deep neural network, DNN) 模型—多通道加权卷积神经网络 …

This paper investigates the stabilization issue of TS fuzzy Markov jump generalized neural
networks (GNNs) with reaction-diffusion terms. A nonfragile memorybased control strategy …

In this paper, a comparative analysis of recent techniques for character recognition is done.
Our purpose is to identify the impact of machine learning in the domain of character …

The Convolutional Neural Network (CNN) methodologies have been a fundamental deep
learning solution to smart grid applications. It is essential to investigate and evaluate the …