scholarly journals Channel Estimation for the ISDB-TB FBMC System Using Neural Networks : A Proposal of Application of Back Propagation Training Algorithm

2017 ◽  
Author(s):  
Jefferson Jesus Hengles Almeida ◽  
Lopes P. B ◽  
Cristiano Akamine ◽  
Nizam Omar
Keyword(s):  
Neural Networks ◽  
Channel Estimation ◽  
Back Propagation ◽  
Training Algorithm

Artificial Neural Networks (ANNs) and Solution of Civil Engineering Problems

2020 ◽  
pp. 13-38
Author(s):  
Melda Yucel ◽  
Sinan Melih Nigdeli ◽  
Gebrail Bekdaş
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Structural Properties ◽  
Structural Design ◽  
Civil Engineering ◽  
Back Propagation ◽  
Training Algorithm ◽  
Design Problems ◽  
Engineering Problems ◽  
Artificial Neural

This chapter reveals the advantages of artificial neural networks (ANNs) by means of prediction success and effects on solutions for various problems. With this aim, initially, multilayer ANNs and their structural properties are explained. Then, feed-forward ANNs and a type of training algorithm called back-propagation, which was benefited for these type networks, are presented. Different structural design problems from civil engineering are optimized, and handled intended for obtaining prediction results thanks to usage of ANNs.

An adaptive training algorithm for back-propagation neural networks

1995 ◽  
Vol 25 (3) ◽  
pp. 512-514 ◽  
Author(s):  
Hsi-Chin Hsin ◽  
Ching-Chung Li ◽  
Mingui Sun ◽  
R.J. Sclabassi
Keyword(s):  
Neural Networks ◽  
Back Propagation ◽  
Training Algorithm ◽  
Adaptive Training ◽  
Back Propagation Neural Networks

scholarly journals Stability of Back Propagation Training Algorithm for Neural Networks

2012 ◽  
Vol 9 (4) ◽  
pp. 713-719
Author(s):  
Baghdad Science Journal
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Upper Bound ◽  
Back Propagation ◽  
Learning Rate ◽  
Training Algorithm ◽  
Momentum Coefficient ◽  
Artificial Neural ◽  
Updating Rule ◽  
The Stability

In this paper, we derive and prove the stability bounds of the momentum coefficient µ and the learning rate ? of the back propagation updating rule in Artificial Neural Networks .The theoretical upper bound of learning rate ? is derived and its practical approximation is obtained

Finding the inverse kinematics of manipulator arm using artificial neural network with lookup table

Robotica ◽  
1997 ◽  
Vol 15 (6) ◽  
pp. 617-625 ◽  
Author(s):  
A.S. Morris ◽  
A. Mansor
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Network ◽  
Inverse Kinematics ◽  
Back Propagation ◽  
Lookup Table ◽  
Training Algorithm ◽  
Manipulator Arm ◽  
The Neural Networks ◽  
Artificial Neural

Neural networks were used to find the inverse kinematics of a two-link planar and three-link manipulator arms. The neural networks utilised were multi-layered perceptions with a back-propagation training algorithm. Because of the redundancy in the manipulators studied, this work used lookup tables for the different configurations of the manipulator arm.

Fuzzy ART Neural Network Model for Automated Detection of Freeway Incidents

1998 ◽  
Vol 1634 (1) ◽  
pp. 56-63 ◽  
Author(s):  
Sherif S. Ishak ◽  
Haitham M. Al-Deek
Keyword(s):  
Neural Networks ◽  
False Alarm ◽  
False Alarm Rate ◽  
Back Propagation ◽  
Incident Detection ◽  
Traffic Patterns ◽  
Traffic Pattern ◽  
Detection Algorithms ◽  
Fuzzy Art ◽  
Freeway Incidents

Pattern recognition techniques such as artificial neural networks continue to offer potential solutions to many of the existing problems associated with freeway incident-detection algorithms. This study focuses on the application of Fuzzy ART neural networks to incident detection on freeways. Unlike back-propagation models, Fuzzy ART is capable of fast, stable learning of recognition categories. It is an incremental approach that has the potential for on-line implementation. Fuzzy ART is trained with traffic patterns that are represented by 30-s loop-detector data of occupancy, speed, or a combination of both. Traffic patterns observed at the incident time and location are mapped to a group of categories. Each incident category maps incidents with similar traffic pattern characteristics, which are affected by the type and severity of the incident and the prevailing traffic conditions. Detection rate and false alarm rate are used to measure the performance of the Fuzzy ART algorithm. To reduce the false alarm rate that results from occasional misclassification of traffic patterns, a persistence time period of 3 min was arbitrarily selected. The algorithm performance improves when the temporal size of traffic patterns increases from one to two 30-s periods for all traffic parameters. An interesting finding is that the speed patterns produced better results than did the occupancy patterns. However, when combined, occupancy–speed patterns produced the best results. When compared with California algorithms 7 and 8, the Fuzzy ART model produced better performance.

An Improved Training Algorithm for Quantum Neural Networks

2014 ◽  
Vol 35 (7) ◽  
pp. 1630-1635
Author(s):  
Yi-peng Zhang ◽  
Liang Chen ◽  
Huan Hao
Keyword(s):  
Neural Networks ◽  
Training Algorithm

scholarly journals Deep neural networks using a single neuron: folded-in-time architecture using feedback-modulated delay loops

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Florian Stelzer ◽  
André Röhm ◽  
Raul Vicente ◽  
Ingo Fischer ◽  
Serhiy Yanchuk
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Deep Neural Network ◽  
Single Neuron ◽  
Deep Neural Networks ◽  
Back Propagation ◽  
Local Network ◽  
Multiple Time ◽  
Learning Tools ◽  
Back Propagation Algorithm

AbstractDeep neural networks are among the most widely applied machine learning tools showing outstanding performance in a broad range of tasks. We present a method for folding a deep neural network of arbitrary size into a single neuron with multiple time-delayed feedback loops. This single-neuron deep neural network comprises only a single nonlinearity and appropriately adjusted modulations of the feedback signals. The network states emerge in time as a temporal unfolding of the neuron’s dynamics. By adjusting the feedback-modulation within the loops, we adapt the network’s connection weights. These connection weights are determined via a back-propagation algorithm, where both the delay-induced and local network connections must be taken into account. Our approach can fully represent standard Deep Neural Networks (DNN), encompasses sparse DNNs, and extends the DNN concept toward dynamical systems implementations. The new method, which we call Folded-in-time DNN (Fit-DNN), exhibits promising performance in a set of benchmark tasks.

scholarly journals A Dynamic Factor and Neural Networks Analysis of the Co-movement of Public Revenues in the EMU

2021 ◽  
Author(s):  
Cosimo Magazzino ◽  
Marco Mele
Keyword(s):  
Neural Networks ◽  
Factor Model ◽  
Common Factor ◽  
Back Propagation ◽  
Dynamic Factor ◽  
Common Component ◽  
Economic Cycles ◽  
Composite Nature ◽  
Empirical Demonstration ◽  
Public Revenues

AbstractThis paper shows that the co-movement of public revenues in the European Monetary Union (EMU) is driven by an unobserved common factor. Our empirical analysis uses yearly data covering the period 1970–2014 for 12 selected EMU member countries. We have found that this common component has a significant impact on public revenues in the majority of the countries. We highlight this common pattern in a dynamic factor model (DFM). Since this factor is unobservable, it is difficult to agree on what it represents. We argue that the latent factor that emerges from the two different empirical approaches used might have a composite nature, being the result of both the more general convergence of the economic cycles of the countries in the area and the increasingly better tuned tax structure. However, the original aspect of our paper is the use of a back-propagation neural networks (BPNN)-DF model to test the results of the time-series. At the level of computer programming, the results obtained represent the first empirical demonstration of the latent factor’s presence.

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