Evaluation of nonlinear optimization methods for the learning algorithm of artificial neural networks

1992 ◽  
Vol 23 (1) ◽  
pp. 101-111 ◽  
Author(s):  
Hideyuki Takagi ◽  
Shigeo Sakaue ◽  
Hayato Togawa
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Nonlinear Optimization ◽  
Learning Algorithm ◽  
Optimization Methods ◽  
Artificial Neural ◽  
Nonlinear Optimization Methods

Web tension control of multispan roll-to-roll system by artificial neural networks for printed electronics

2013 ◽  
Vol 227 (10) ◽  
pp. 2361-2376 ◽  
Author(s):  
KyungHyun Choi ◽  
Muhammad Zubair ◽  
Ganeshthangaraj Ponniah
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Printed Electronics ◽  
Learning Algorithm ◽  
Network Control ◽  
Learning Rate ◽  
Roll To Roll ◽  
Roll System ◽  
Artificial Neural ◽  
Web Tension

The mass production of printed electronic devices can be achieved by roll-to-roll system that requires highly regulated web tension. This highly regulated tension is required to minimize printing register error and maintain proper roughness and thickness of the printed patterns. The roll-to-roll system has a continuous changing roll diameter and a strong coupling exists between the spans. The roll-to-roll system is a multi-input-multi-output, time variant, and nonlinear system. The conventional proportional–integral–derivative control, used in industry, is not able to cope with roll-to-roll system for printed electronics. In this study, multi-input-single-output decentralized control scheme is used for control of a multispan roll-to-roll system by applying regularized variable learning rate backpropagating artificial neural networks. Additional inputs from coupled spans are given to regularized variable learning rate backpropagating artificial neural network control to decouple the two spans. Experimental results show that the self-learning algorithm offers a solution to decouple speed and tension in a multispan roll-to-roll system.

Volume Learning Algorithm Artificial Neural Networks for 3D QSAR Studies

2001 ◽  
Vol 44 (15) ◽  
pp. 2411-2420 ◽  
Author(s):  
Igor V. Tetko ◽  
Vasyl V. Kovalishyn ◽  
David J. Livingstone
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Learning Algorithm ◽  
3D Qsar ◽  
Qsar Studies ◽  
Artificial Neural

scholarly journals Diagnosing Diabetes Using Artificial Neural Networks

2020 ◽  
Vol 5 (2) ◽  
pp. 221-224
Author(s):  
Joy Oyinye Orukwo ◽  
Ledisi Giok Kabari
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Success Rate ◽  
Medical Diagnosis ◽  
Learning Algorithm ◽  
Diabetes Diagnosis ◽  
Feed Forward Neural Network ◽  
The Neural Network ◽  
Artificial Neural

Diabetes has always been a silent killer and the number of people suffering from it has increased tremendously in the last few decades. More often than not, people continue with their normal lifestyle, unaware that their health is at severe risk and with each passing day diabetes goes undetected. Artificial Neural Networks have become extensively useful in medical diagnosis as it provides a powerful tool to help analyze, model and make sense of complex clinical data. This study developed a diabetes diagnosis system using feed-forward neural network with supervised learning algorithm. The neural network is systematically trained and tested and a success rate of 90% was achieved.

On determining parameters of the returned altimetry signal

2020 ◽  
Vol 954 (12) ◽  
pp. 10-19
Author(s):  
Yu.M. Neiman ◽  
L.S. Sugaipova
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Optimization Methods ◽  
Careful Analysis ◽  
Sea Surface ◽  
Modern Theory ◽  
Satellite Altimeter ◽  
Reflected Signal ◽  
Artificial Neural ◽  
Effective Approximation

The authors summarize the principle underlying the modern satellite altimetry. Careful analysis of the shape of the reflected signal enables estimating the flight altitude of the satellite altimeter above sea level, and other important parameters of the sea surface in the area under study quite reliably. Important in doing so is the reflected signal power model used. The Brown-Hayne model seems to be the most common one. The values of these parameters are determined from measurements using certain optimization methods. It is especially noted that the problem in question can be successfully solved by methods based on modern theory of artificial neural networks. Numerical experiments using real altimetric data were carried out in MATLAB environment. In this regard, the basic concepts of this theory are described and the possibilities of its use as an effective approximation of any dependence are emphasized. The Levenberg-Marquardt method and the genetic algorithm of artificial neural networks show the same results, but the latter does not require setting initial values of parameters, only limits of their possible change.

scholarly journals Enhancing the reusability and interoperability of artificial neural networks with DEVS modeling and simulation

2016 ◽  
Vol 07 (02) ◽  
pp. 1650005 ◽  
Author(s):  
David Ifeoluwa Adelani ◽  
Mamadou Kaba Traoré
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Modeling And Simulation ◽  
Computational Models ◽  
Learning Algorithm ◽  
Discrete Event ◽  
Neural Network Modeling ◽  
System Specification ◽  
Ann Models ◽  
Artificial Neural

Artificial neural networks (ANNs), a branch of artificial intelligence, has become a very interesting domain since the eighties when back-propagation (BP) learning algorithm for multilayer feed-forward architecture was introduced to solve nonlinear problems. It is used extensively to solve complex nonalgorithmic problems such as prediction, pattern recognition and clustering. However, in the context of a holistic study, there may be a need to integrate ANN with other models developed in various paradigms to solve a problem. In this paper, we suggest discrete event system specification (DEVS) be used as a model of computation (MoC) to make ANN models interoperable with other models (since all discrete event models can be expressed in DEVS, and continuous models can be approximated by DEVS). By combining ANN and DEVS, we can model the complex configuration of ANNs and express its internal workings. Therefore, we are extending the DEVS-based ANN proposed by Toma et al. [A new DEVS-based generic artficial neural network modeling approach, The 23rd European Modeling and Simulation Symp. (Simulation in Industry), Rome, Italy, 2011] for comparing multiple configuration parameters and learning algorithms and also to do prediction. The DEVS models are described using the high level language for system specification (HiLLS), [Maïga et al., A new approach to modeling dynamic structure systems, The 29th European Modeling and Simulation Symp. (Simulation in Industry), Leicester, United Kingdom, 2015] a graphical modeling language for clarity. The developed platform is a tool to transform ANN models into DEVS computational models, making them more reusable and more interoperable in the context of larger multi-perspective modeling and simulation (MAS).

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