Journal ArticleDOI
Model of MT and MST areas using an autoencoder
Reads0
Chats0
TLDR
The response properties of the MST neurons are similar to those obtained from neurophysiological experiments, and a cost function of the autoencoder is defined from which a learning rule is derived by a gradient descent method within a mean-field approximation.Abstract:
We propose a model for a system with middle temporal neurons and medial superior temporal (MST) neurons by using a three-layered autoencoder. Noise effect is taken into account by using the framework of statistical physics. We define a cost function of the autoencoder, from which a learning rule is derived by a gradient descent method, within a mean-field approximation. We find a pair of values of two noise levels at which a minimum value of the cost function is attained. We investigate response properties of the MST neurons to optical flows for various types of motion at the pair of optimal values of two noise levels. We obtain that the response properties of the MST neurons are similar to those obtained from neurophysiological experiments.read more
Citations
More filters
Journal ArticleDOI
Gearbox fault diagnosis based on deep random forest fusion of acoustic and vibratory signals
Chuan Li,Chuan Li,René-Vinicio Sánchez,Grover Zurita,Mariela Cerrada,Diego Cabrera,Rafael E. Vásquez +6 more
TL;DR: In this article, the authors proposed a deep random forest fusion (DRFF) technique to improve fault diagnosis performance for gearboxes by using measurements of an acoustic emission (AE) sensor and an accelerometer that are used for monitoring the gearbox condition simultaneously.
Journal ArticleDOI
Gearbox Fault Identification and Classification with Convolutional Neural Networks
TL;DR: An implementation of deep learning algorithm convolutional neural network used for fault identification and classification in gearboxes using different combinations of condition patterns based on some basic fault conditions indicates that the proposed approach is highly reliable and applicable in fault diagnosis of industrial reciprocating machinery.
Journal ArticleDOI
Multimodal deep support vector classification with homologous features and its application to gearbox fault diagnosis
TL;DR: This paper addresses a multimodal deep support vector classification (MDSVC) approach, which employs separation-fusion based deep learning in order to perform fault diagnosis tasks for gearboxes, and shows that the proposed model achieves the best fault classification rate in experiments when compared to representative deep and shallow learning methods.
Journal ArticleDOI
An Ensemble Deep Convolutional Neural Network Model with Improved D-S Evidence Fusion for Bearing Fault Diagnosis.
TL;DR: This paper proposed IDSCNN, a novel bearing fault diagnosis algorithm based on ensemble deep convolutional neural networks and an improved Dempster–Shafer theory based evidence fusion, which can achieve better fault diagnosis performance than existing machine learning methods.
Proceedings ArticleDOI
Bearing fault identification and classification with convolutional neural network
TL;DR: Conceptal Neural Network is used as back-end classifier for bearing fault detection and exhibits good performance compared to peer algorithms.
References
More filters
Book
Self-Organizing Maps
TL;DR: The Self-Organising Map (SOM) algorithm was introduced by the author in 1981 as mentioned in this paper, and many applications form one of the major approaches to the contemporary artificial neural networks field, and new technologies have already been based on it.
Journal ArticleDOI
Distributed Hierarchical Processing in the Primate Cerebral Cortex
TL;DR: A summary of the layout of cortical areas associated with vision and with other modalities, a computerized database for storing and representing large amounts of information on connectivity patterns, and the application of these data to the analysis of hierarchical organization of the cerebral cortex are reported on.
Journal ArticleDOI
Preface: Cerebral Cortex Has Come of Age
Journal ArticleDOI
The "Wake-Sleep" Algorithm for Unsupervised Neural Networks
TL;DR: An unsupervised learning algorithm for a multilayer network of stochastic neurons is described, where bottom-up "recognition" connections convert the input into representations in successive hidden layers, and top-down "generative" connections reconstruct the representation in one layer from the representations in the layer above.
Journal ArticleDOI
Direction and orientation selectivity of neurons in visual area MT of the macaque
TL;DR: The notion that area MT represents a further specialization over area V1 for stimulus motion processing is supported and the marked similarities between direction and orientation tuning in area MT in macaque and owl monkey support the suggestion that these areas are homologues.