scholarly journals A Review of an Expert System Design for Crude Oil Distillation Column Using the Neural Networks Model and Process Optimization and Control Using Genetic Algorithm Framework

2013 ◽  
Vol 03 (02) ◽  
pp. 164-170 ◽  
Author(s):  
Lekan Taofeek Popoola ◽  
Gutti Babagana ◽  
Alfred Akpoveta Susu
Keyword(s):  
Genetic Algorithm ◽  
Neural Networks ◽  
Expert System ◽  
System Design ◽  
Distillation Column ◽  
Optimization And Control ◽  
The Neural Networks ◽  
Algorithm Framework ◽  
Crude Oil Distillation ◽  
And Control

scholarly journals Genetic Algorithm Optimization and Control System Design of Flexible Structures

2008 ◽  
Vol 17 (Supplement) ◽  
Author(s):  
M.O. Tokhi ◽  
M.Z. Md Zain ◽  
M.S. Alam ◽  
F.M. Aldebrez ◽  
S.Z. Mohd Hashim ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Control System ◽  
System Design ◽  
Flexible Structures ◽  
Control System Design ◽  
Algorithm Optimization ◽  
Genetic Algorithm Optimization ◽  
Optimization And Control ◽  
And Control

scholarly journals ELMAN-RECURRENT NEURAL NETWORK FOR LOAD SHEDDING OPTIMIZATION

SINERGI ◽  
2020 ◽  
Vol 24 (1) ◽  
pp. 29
Author(s):  
Widi Aribowo
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Neural Networks ◽  
Power System ◽  
Recurrent Neural Networks ◽  
Load Shedding ◽  
Feed Forward Neural Network ◽  
Linear Problems ◽  
Elman Recurrent Neural Network ◽  
And Control

Load shedding plays a key part in the avoidance of the power system outage. The frequency and voltage fluidity leads to the spread of a power system into sub-systems and leads to the outage as well as the severe breakdown of the system utility.  In recent years, Neural networks have been very victorious in several signal processing and control applications.  Recurrent Neural networks are capable of handling complex and non-linear problems. This paper provides an algorithm for load shedding using ELMAN Recurrent Neural Networks (RNN). Elman has proposed a partially RNN, where the feedforward connections are modifiable and the recurrent connections are fixed. The research is implemented in MATLAB and the performance is tested with a 6 bus system. The results are compared with the Genetic Algorithm (GA), Combining Genetic Algorithm with Feed Forward Neural Network (hybrid) and RNN. The proposed method is capable of assigning load releases needed and more efficient than other methods. 

scholarly journals Morphological Analysis for Three-Dimensional Chaotic Delay Neural Networks

2020 ◽  
Vol 2020 ◽  
pp. 1-6
Author(s):  
Yusong Lu ◽  
Ricai Luo ◽  
Yongfu Zou
Keyword(s):  
Neural Networks ◽  
Chaotic System ◽  
Morphological Analysis ◽  
Chaotic Behavior ◽  
Three Dimensional ◽  
Hopfield Neural Network ◽  
Coefficient Matrix ◽  
Initial Value ◽  
The Neural Networks ◽  
And Control

The study focuses on the chaotic behavior of a three-dimensional Hopfield neural network with time delay. We find the aspecific coefficient matrix and the initial value condition of the system and use MATLAB software to draw its graph. The result shows that their shape is very similar to the figure of Roslerʼs chaotic system. Furthermore, we analyzed the divergence, the eigenvalue of the Jacobian matrix for the equilibrium point, and the Lyapunov exponent of the system. These properties prove that the system does have chaotic behavior. This result not only confirms that there is chaos in the neural networks but also that the chaotic characteristics of the system are very similar to those of Roslerʼs chaotic system under certain conditions. This discovery provides useful information that can be applied to other aspects of chaotic Hopfield neural networks, such as chaotic synchronization and control.

Development of a System for Monitoring Tool Wear Using Artificial Intelligence Techniques

2001 ◽  
Author(s):  
Rui. G. Silva ◽  
Steven J. Wilcox ◽  
Robert L. Reuben
Keyword(s):  
Neural Networks ◽  
Expert System ◽  
Tool Wear ◽  
Knowledge Base ◽  
Tool Life ◽  
Intelligent System ◽  
Process Data ◽  
Line System ◽  
Life Model ◽  
The Neural Networks

Abstract The main objective of the work reported here was to develop an intelligent condition monitoring system able to detect when a cutting tool was worn out. To accomplish this objective the use of a hybrid intelligent system, based on an expert system and two neural networks was investigated. The neural networks were employed to process data from sensors and the classifications made by the neural networks were combined with information from the knowledge base to obtain an estimate of the wear state of the tool by the expert system. The novelty of this work is mainly associated with the configuration of the developed system. The combination of sensor-based information and inference rules, results in an on-line system that can learn from experience and update the knowledge base pertaining to information associated with different cutting conditions. The neural networks resolved the problem of interpreting the complex sensor inputs while the expert system, by keeping track of previous success, estimated which of the two neural networks was more reliable. Mis-classifications were filtered out through the use of a rough but approximate estimator, Taylor’s tool life model. The system’s modular structure would make it easy to update as required for different machines and/or processes. The use of Taylor’s tool life model, although weak as a tool life estimator, proved to be crucial in achieving higher performance levels. The application of the Self Organizing Map to tool wear monitoring proved to be slightly more reliable then the Adaptive Resonance Theory neural network although overall the system made reliable, accurate estimates of the tool wear.

Predicting foF2 in the China region using the neural networks improved by the genetic algorithm

2013 ◽  
Vol 92 ◽  
pp. 7-17 ◽  
Author(s):  
Ruopeng Wang ◽  
Chen Zhou ◽  
Zhongxin Deng ◽  
Binbin Ni ◽  
Zhengyu Zhao
Keyword(s):  
Genetic Algorithm ◽  
Neural Networks ◽  
The Neural Networks ◽  
China Region
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