A comparative study of adaptive load frequency controller designs in a power system with dynamic neural network models

2005 ◽  
Vol 46 (15-16) ◽  
pp. 2656-2668 ◽  
Author(s):  
Yusuf Oysal
Keyword(s):  
Neural Network ◽  
Power System ◽  
Comparative Study ◽  
Network Models ◽  
Neural Network Models ◽  
Dynamic Neural Network ◽  
Load Frequency ◽  
Frequency Controller

A Comparative Study of Various Hybrid Wavelet Feedforward Neural Network Models for Runoff Forecasting

2017 ◽  
Vol 32 (1) ◽  
pp. 83-103 ◽  
Author(s):  
Muhammad Shoaib ◽  
Asaad Y. Shamseldin ◽  
Sher Khan ◽  
Mudasser Muneer Khan ◽  
Zahid Mahmood Khan ◽  
...  
Keyword(s):  
Neural Network ◽  
Comparative Study ◽  
Network Models ◽  
Feedforward Neural Network ◽  
Neural Network Models ◽  
Runoff Forecasting

scholarly journals A comparative study of neural network models

1990 ◽  
Vol 14 ◽  
pp. 300-304 ◽  
Author(s):  
S. Hudon ◽  
Y. Yan ◽  
W. Kinsner
Keyword(s):  
Neural Network ◽  
Comparative Study ◽  
Network Models ◽  
Neural Network Models

STUDY ON CHEMOTAXIS BEHAVIORS OF C. ELEGANS USING DYNAMIC NEURAL NETWORK MODELS: FROM ARTIFICIAL TO BIOLOGICAL MODEL

2010 ◽  
Vol 18 (spec01) ◽  
pp. 3-33 ◽  
Author(s):  
JIAN-XIN XU ◽  
XIN DENG
Keyword(s):  
Neural Network ◽  
Sensory Neurons ◽  
Motor Neurons ◽  
Learning Algorithm ◽  
Network Models ◽  
Biological Models ◽  
Neural Network Models ◽  
Dynamic Neural Network ◽  
C Elegans ◽  
Logical Functions

With the anatomical understanding of the neural connection of the nematode Caenorhabditis elegans (C. elegans), its chemotaxis behaviors are investigated in this paper through the association with the biological nerve connections. The chemotaxis behaviors include food attraction, toxin avoidance and mixed-behaviors (finding food and avoiding toxin concurrently). Eight dynamic neural network (DNN) models, two artifical models and six biological models, are used to learn and implement the chemotaxis behaviors of C. elegans. The eight DNN models are classified into two classes with either single sensory neuron or dual sensory neurons. The DNN models are trained to learn certain switching logics according to different chemotaxis behaviors using real time recurrent learning algorithm (RTRL). First we show the good performance of the two artifical models in food attraction, toxin avoidance and the mixed-behaviors. Next, six neural wire diagrams from sensory neurons to motor neurons are extracted from the anatomical nerve connection of C. elegans. Then the extracted biological wire diagrams are trained using RTRL directly, which is the first time in this field of research by associating chemotaxis behaviors with biological neural models. An interesting discovery is the need for a memory neuron when single-sensory models are used, which is consistent with the anatomical understanding on a specific neuron that functions as a memory. In the simulations, the chemotaxis behaviors of C. elegans can be depicted by several switch logical functions which can be learned by RTRL for both artifical and biological models.

scholarly journals Data Driven Broiler Weight Forecasting using Dynamic Neural Network Models

2017 ◽  
Vol 50 (1) ◽  
pp. 5398-5403 ◽  
Author(s):  
Simon V. Johansen ◽  
Jan D. Bendtsen ◽  
Martin R.-Jensen ◽  
Jesper Mogensen
Keyword(s):  
Neural Network ◽  
Network Models ◽  
Data Driven ◽  
Neural Network Models ◽  
Dynamic Neural Network
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