A trigonometric Functional link single layer Neural Network approach for non linear dynamic plant identification using second order Levenberg-Marquardt Algorithm

2015 ◽  
Author(s):  
S.K. Padhi ◽  
S. Behera ◽  
B.N. Sahu ◽  
M.N. Mohanty
Keyword(s):  
Neural Network ◽  
Single Layer ◽  
Network Approach ◽  
Plant Identification ◽  
Neural Network Approach ◽  
Functional Link ◽  
Linear Dynamic ◽  
Dynamic Plant ◽  
Marquardt Algorithm ◽  
Levenberg Marquardt

scholarly journals Probabilistic Fatigue Life Prediction of Dissimilar Material Weld Using Accelerated Life Method and Neural Network Approach

Computation ◽  
2019 ◽  
Vol 7 (1) ◽  
pp. 10 ◽  
Author(s):  
Hafiz Waqar Ahmad ◽  
Jeong Ho Hwang ◽  
Kamran Javed ◽  
Umer Masood Chaudry ◽  
Dong Ho Bae
Keyword(s):  
Neural Network ◽  
Fatigue Life ◽  
Life Prediction ◽  
Fatigue Life Prediction ◽  
Dissimilar Material ◽  
Bayesian Regularization ◽  
Network Approach ◽  
Neural Network Approach ◽  
Accelerated Life ◽  
Levenberg Marquardt

Welding alloy 617 with other metals and alloys has been receiving significant attention in the last few years. It is considered to be the benchmark for the development of economical hybrid structures to be used in different engineering applications. The differences in the physical and metallurgical properties of dissimilar materials to be welded usually result in weaker structures. Fatigue failure is one of the most common failure modes of dissimilar material welded structures. In this study, fatigue life prediction of dissimilar material weld was evaluated by the accelerated life method and artificial neural network approach (ANN). The accelerated life testing approach was evaluated for different distributions. Weibull distribution was the most appropriate distribution that fits the fatigue data very well. Acceleration of fatigue life test data was attained with 95% reliability for Weibull distribution. The probability plot verified that accelerating variables at each level were appropriate. Experimental test data and predicted fatigue life were in good agreement with each other. Two training algorithms, Bayesian regularization (BR) and Levenberg–Marquardt (LM), were employed for training ANN. The Bayesian regularization training algorithm exhibited a better performance than the Levenberg–Marquardt algorithm. The results confirmed that the assessment methods are effective for lifetime prediction of dissimilar material welded joints.

Performance of the Levenberg–Marquardt neural network approach in nuclear mass prediction

2017 ◽  
Vol 44 (4) ◽  
pp. 045110 ◽  
Author(s):  
Hai Fei Zhang ◽  
Li Hao Wang ◽  
Jing Peng Yin ◽  
Peng Hui Chen ◽  
Hong Fei Zhang
Keyword(s):  
Neural Network ◽  
Network Approach ◽  
Nuclear Mass ◽  
Neural Network Approach ◽  
Levenberg Marquardt

scholarly journals A deep neural network approach for sentiment analysis of medically related texts: an analysis of tweets related to concussions in sports

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Kayvan Tirdad ◽  
Alex Dela Cruz ◽  
Alireza Sadeghian ◽  
Michael Cusimano
Keyword(s):  
Neural Network ◽  
Sentiment Analysis ◽  
Deep Neural Network ◽  
Short Term Memory ◽  
Single Layer ◽  
Final Decision ◽  
Network Approach ◽  
Neural Network Approach ◽  
Neural Network System ◽  
Sporting Activities

AbstractAnnually, over three million people in North America suffer concussions. Every age group is susceptible to concussion, but youth involved in sporting activities are particularly vulnerable, with about 6% of all youth suffering a concussion annually. Youth who suffer concussion have also been shown to have higher rates of suicidal ideation, substance and alcohol use, and violent behaviors. A significant body of research over the last decade has led to changes in policies and laws intended to reduce the incidence and burden of concussions. However, it is also clear that youth engaging in high-risk activities like sport often underreport concussion, while others may embellish reports for specific purposes. For such policies and laws to work, they must operate effectively within a facilitative social context so understanding the culture around concussion becomes essential to reducing concussion and its consequences. We present an automated deep neural network approach to analyze tweets with sport-related concussion context to identify the general public’s sentiment towards concerns in sport-related concussion. A single-layer and multi-layer convolutional neural networks, Long Short-Term Memory (LSTM) networks, and Bidirectional LSTM were trained to classify the sentiments of the tweets. Afterwards, we train an ensemble model to aggregate the predictions of our networks to provide a final decision of the tweet’s sentiment. The system achieves an evaluation F1 score of 62.71% based on Precision and Recall. The trained system is then used to analyze the tweets in the FIFA World Cup 2018 to measure audience reaction to events involving concussion. The neural network system provides an understanding of the culture around concussion through sentiment analysis.

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