Neural Network-Based Speed Control of A Two-Mass-Model System

1999 ◽  
Vol 3 (5) ◽  
pp. 427-430 ◽  
Author(s):  
Rached Dhaouadi ◽  
◽  
Khaled Nouri
Keyword(s):  
Neural Network ◽  
Inverse Dynamics ◽  
Mass Model ◽  
Neural Network Learning ◽  
Inverse Control ◽  
Network Learning ◽  
The Neural Network ◽  
Control Scheme ◽  
On Line ◽  
Neural Network Structure

We present an application of artificial neural networks to the problem of controlling the speed of an elastic drive system. We derive a neural network structure to simulate the inverse dynamics of the system, then implement the direct inverse control scheme in a closed loop. The neural network learning is done on-line to adaptively control the speed to follow a stepwise changing reference. The experimental results with a two-mass-model analog board confirm the effectiveness of the proposed neurocontrol scheme.

A New Adaptive Control Scheme Using Dynamic Neural Networks

2008 ◽  
Vol 20 (1) ◽  
pp. 171-177 ◽  
Author(s):  
Khaled Nouri ◽  
◽  
Rached Dhaouadi ◽  
Naceur Benhadj Braiek ◽  
Keyword(s):  
Neural Network ◽  
Adaptive Control ◽  
Inverse Dynamics ◽  
Drive System ◽  
Motor Drive ◽  
Dynamic Neural Networks ◽  
The Neural Network ◽  
Control Scheme ◽  
On Line ◽  
Motor Drive System

A new adaptive neuro-control structure is proposed for the speed control of a nonlinear motor drive system and the compensation of the nonlinearities. A dynamic artificial neural network is used for the on-line adaptive control of the nonlinear motor drive system with high static and Coulomb friction. The neural network is first trained off-line to learn the inverse dynamics of the motor drive system using a layer decoupled extended Kalman filter algorithm. The proposed control scheme is validated experimentally on a dc motor drive system using a standard personal computer. The results obtained confirm the excellent tracking performance and disturbance rejection properties of the system.

scholarly journals Kernel-blending connection approximated by a neural network for image classification

2020 ◽  
Vol 6 (4) ◽  
pp. 467-476
Author(s):  
Xinxin Liu ◽  
Yunfeng Zhang ◽  
Fangxun Bao ◽  
Kai Shao ◽  
Ziyi Sun ◽  
...  
Keyword(s):  
Neural Network ◽  
Image Classification ◽  
Function Approximation ◽  
Feature Space ◽  
Neural Network Learning ◽  
Network Learning ◽  
Hinge Loss ◽  
The Neural Network ◽  
Good Classification ◽  
Kernel Mapping

AbstractThis paper proposes a kernel-blending connection approximated by a neural network (KBNN) for image classification. A kernel mapping connection structure, guaranteed by the function approximation theorem, is devised to blend feature extraction and feature classification through neural network learning. First, a feature extractor learns features from the raw images. Next, an automatically constructed kernel mapping connection maps the feature vectors into a feature space. Finally, a linear classifier is used as an output layer of the neural network to provide classification results. Furthermore, a novel loss function involving a cross-entropy loss and a hinge loss is proposed to improve the generalizability of the neural network. Experimental results on three well-known image datasets illustrate that the proposed method has good classification accuracy and generalizability.

scholarly journals ANALYSIS OF NEURAL NETWORK CLASSIFICATION ALGORITHM TO KNOW THE SUCCESS LEVEL OF IMMUNOTHERAPY

2020 ◽  
Vol 17 (1) ◽  
pp. 57-62
Author(s):  
Agung Fazriansyah ◽  
Mochammad Abdul Azis ◽  
Yudhistira Yudhistira
Keyword(s):  
Neural Network ◽  
High Accuracy ◽  
Large Datasets ◽  
Neural Network Learning ◽  
Network Learning ◽  
Neural Network Classification ◽  
The Neural Network ◽  
Neural Network Algorithm ◽  
Auc Value ◽  
Learning Machine

Cancer is a disease that is feared by humans at this stage, the genetic term of most diseases that have the characteristics of abnormal cell growth and beyond the normal cell limits so that they can attack cells that cover and are able to spread to other organs. For cancer recovery therapy is immunization therapy. Of course in this alternative treatment still needs to be done research to determine the level of success with existing conditions and parameters. Increasingly sophisticated, developing technology that helps human work. The neural network algorithm is used to analyze large datasets, the purpose of this study is to find the accuracy and immunotherapy methods of the dataset using a neural network learning machine with 200 data training cycles, 0.9 momentum and 0.01 learning levels that produce quite high accuracy 80 % and AUC value of 0.738

scholarly journals Tutorial on Using Machine Learning for Activity Recognition Via a Smartphone

Telecom IT ◽  
2020 ◽  
Vol 8 (2) ◽  
pp. 92-108
Author(s):  
I. Zelichenok ◽  
R. Pirmagomedov
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Data Collection ◽  
Activity Recognition ◽  
Learning Process ◽  
Daily Activity ◽  
Application Development ◽  
Neural Network Learning ◽  
Network Learning ◽  
The Neural Network

This article provides a tutorial for developing a simple machine learning application in Python. More spe-cifically, the paper considers daily activity recognition using sensors of a smartphone. For development, we used TensorFlow, Skikit learn, NumPy, Pandas, and Matplotlib. The paper explains in detail the main steps of the application development, including data collection and pre-processing, design of the neural network, learning process, and use of a trained model. The overall accuracy of the developed application when recognizing the activity is about 95 %. This paper can be useful for students and specialists who want to start work on machine learning.

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