A self-tuning system for dam behavior modeling based on evolving artificial neural networks

2016 ◽  
Vol 97 ◽  
pp. 85-95 ◽  
Author(s):  
B. Stojanovic ◽  
M. Milivojevic ◽  
N. Milivojevic ◽  
D. Antonijevic
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Behavior Modeling ◽  
Self Tuning ◽  
Artificial Neural

Design and implementation of a self-tuning control using artificial neural networks of second order

2006 ◽  
Author(s):  
Mario Ibarra-manzano ◽  
Dora Almanza-ojeda ◽  
Andres Hernandez-Gutierrez ◽  
Juan Amezquita-sanchez ◽  
Luis Lopez-martinez
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Second Order ◽  
Design And Implementation ◽  
Self Tuning ◽  
Artificial Neural

scholarly journals An Artificial Neural Network Framework for Pedestrian Walking Behavior Modeling and Simulation

2020 ◽  
Vol 5 ◽  
Author(s):  
Peter Kielar ◽  
André Borrmann
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Influence Factors ◽  
Behavior Modeling ◽  
Movement Behavior ◽  
Walking Behavior ◽  
Crowd Simulations ◽  
Training Simulation ◽  
Artificial Neural ◽  
Behavior Models

Movement behavior models of pedestrian agents form the basis of computational crowd simulations. In contemporary research, a large number of models exist. However, there is still no walking behavior model that can address the various influence factors of movement behavior holistically. Thus, we endorse the use of artificial neural networks to develop walking behavior models because machine learning methods can integrate behavioral factors efficiently, automatically, and data-driven. In this paper, we support this approach by providing a framework that describes how to include artificial neural networks into a pedestrian research context. The framework comprises 5 phases: data, replay, training, simulation, and validation. Furthermore, we describe and discuss a prototype of the framework.

scholarly journals A Self-Tuning PID Controller Fused Artificial Neural Networks

1996 ◽  
Vol 29 (1) ◽  
pp. 5066-5071
Author(s):  
Toru Yamamoto ◽  
Masahiro Kaneda ◽  
Toshitaka Oki
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Pid Controller ◽  
Self Tuning ◽  
Artificial Neural

FUTURE OF ARTIFICIAL NEURAL NETWORKS IN SPACE EXPLORATION AND SPACE ENGINEERING

2018 ◽  
pp. 6-11
Author(s):  
Kobiljon Kh. Zoidov ◽  
◽  
Svetlana V. Ponomareva ◽  
Daniel I. Serebryansky ◽  
◽  
...  
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Space Exploration ◽  
Artificial Neural
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