Anti-Hebbian learning in a non-linear neural network

1990 ◽  
Vol 64 (2) ◽  
Author(s):  
A. Carlson
Keyword(s):  
Neural Network ◽  
Hebbian Learning ◽  
Non Linear ◽  
Linear Neural Network

Anti-Hebbian learning in a non-linear neural network

1990 ◽  
Vol 64 (2) ◽  
pp. 171-176 ◽  
Author(s):  
A. Carlson
Keyword(s):  
Neural Network ◽  
Hebbian Learning ◽  
Non Linear ◽  
Linear Neural Network

scholarly journals A Semi-Parametric Non-linear Neural Network Filter: Theory and Empirical Evidence

2016 ◽  
Vol 51 (3) ◽  
pp. 637-675 ◽  
Author(s):  
Panayotis G. Michaelides ◽  
Efthymios G. Tsionas ◽  
Angelos T. Vouldis ◽  
Konstantinos N. Konstantakis ◽  
Panagiotis Patrinos
Keyword(s):  
Neural Network ◽  
Empirical Evidence ◽  
Filter Theory ◽  
Non Linear ◽  
Linear Neural Network

Cluster analysis from molecular similarity matrices using a non-linear neural network

1996 ◽  
Vol 20 (2) ◽  
pp. 385-394 ◽  
Author(s):  
R. Cruz ◽  
G. Rojas ◽  
M. Quintero ◽  
N. L�pez
Keyword(s):  
Neural Network ◽  
Cluster Analysis ◽  
Molecular Similarity ◽  
Non Linear ◽  
Linear Neural Network ◽  
Similarity Matrices

scholarly journals A 0.61-μJ/frame Pipelined Wired-logic DNN Processor in 16-nm FPGA Using Convolutional Non-Linear Neural Network

2021 ◽  
pp. 1-1
Author(s):  
Atsutake Kosuge ◽  
Yao-Chung Hsu ◽  
Mototsugu Hamada ◽  
Tadahiro Kuroda
Keyword(s):  
Neural Network ◽  
Non Linear ◽  
Linear Neural Network

scholarly journals Relative entropy minimizing noisy non-linear neural network to approximate stochastic processes

2014 ◽  
Vol 56 ◽  
pp. 10-21 ◽  
Author(s):  
Mathieu N. Galtier ◽  
Camille Marini ◽  
Gilles Wainrib ◽  
Herbert Jaeger
Keyword(s):  
Neural Network ◽  
Stochastic Processes ◽  
Relative Entropy ◽  
Non Linear ◽  
Linear Neural Network

Mapping soil-water profile utilizing non-linear neural network based model

2010 ◽  
pp. 887-893
Author(s):  
Muhammad Mukhlisin ◽  
Ahmed El-Shafie ◽  
Mohd Taha
Keyword(s):  
Neural Network ◽  
Soil Water ◽  
Non Linear ◽  
Linear Neural Network ◽  
Water Profile

scholarly journals A Hybrid Hidden Markov Model for Pipeline Leakage Detection

2021 ◽  
Vol 11 (7) ◽  
pp. 3138
Author(s):  
Mingchi Zhang ◽  
Xuemin Chen ◽  
Wei Li
Keyword(s):  
Neural Network ◽  
Markov Model ◽  
Hidden Markov Model ◽  
Gaussian Mixture Model ◽  
Mixture Model ◽  
Deep Neural Network ◽  
Hidden Markov ◽  
Gaussian Mixture ◽  
State Sequence ◽  
Non Linear

In this paper, a deep neural network hidden Markov model (DNN-HMM) is proposed to detect pipeline leakage location. A long pipeline is divided into several sections and the leakage occurs in different section that is defined as different state of hidden Markov model (HMM). The hybrid HMM, i.e., DNN-HMM, consists of a deep neural network (DNN) with multiple layers to exploit the non-linear data. The DNN is initialized by using a deep belief network (DBN). The DBN is a pre-trained model built by stacking top-down restricted Boltzmann machines (RBM) that compute the emission probabilities for the HMM instead of Gaussian mixture model (GMM). Two comparative studies based on different numbers of states using Gaussian mixture model-hidden Markov model (GMM-HMM) and DNN-HMM are performed. The accuracy of the testing performance between detected state sequence and actual state sequence is measured by micro F1 score. The micro F1 score approaches 0.94 for GMM-HMM method and it is close to 0.95 for DNN-HMM method when the pipeline is divided into three sections. In the experiment that divides the pipeline as five sections, the micro F1 score for GMM-HMM is 0.69, while it approaches 0.96 with DNN-HMM method. The results demonstrate that the DNN-HMM can learn a better model of non-linear data and achieve better performance compared to GMM-HMM method.

scholarly journals Excitation Forces Estimation for Non-linear Wave Energy Converters: A Neural Network Approach

2020 ◽  
Vol 53 (2) ◽  
pp. 12334-12339
Author(s):  
M. Bonfanti ◽  
F. Carapellese ◽  
S.A. Sirigu ◽  
G. Bracco ◽  
G. Mattiazzo
Keyword(s):  
Neural Network ◽  
Wave Energy ◽  
Linear Wave ◽  
Network Approach ◽  
Neural Network Approach ◽  
Wave Energy Converters ◽  
Non Linear ◽  
Energy Converters

scholarly journals Recurrent Multiplicative Neuron Model Artificial Neural Network for Non-linear Time Series Forecasting

2014 ◽  
Vol 41 (2) ◽  
pp. 249-258 ◽  
Author(s):  
Erol Egrioglu ◽  
Ufuk Yolcu ◽  
Cagdas Hakan Aladag ◽  
Eren Bas
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Artificial Neural Network ◽  
Neuron Model ◽  
Linear Time ◽  
Time Series Forecasting ◽  
Non Linear ◽  
Artificial Neural
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