A Chebyshev polynomial feedforward neural network trained by differential evolution and its application in environmental case studies

2020 ◽  
Vol 126 ◽  
pp. 104663
Author(s):  
Ioannis A. Troumbis ◽  
George E. Tsekouras ◽  
John Tsimikas ◽  
Christos Kalloniatis ◽  
Dias Haralambopoulos
Keyword(s):  
Neural Network ◽  
Differential Evolution ◽  
Case Studies ◽  
Chebyshev Polynomial ◽  
Feedforward Neural Network

Modified single-output Chebyshev-polynomial feedforward neural network aided with subset method for classification of breast cancer

2019 ◽  
Vol 350 ◽  
pp. 128-135 ◽  
Author(s):  
Long Jin ◽  
Zhiguan Huang ◽  
Liangming Chen ◽  
Mei Liu ◽  
Yuhe Li ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Neural Network ◽  
Chebyshev Polynomial ◽  
Feedforward Neural Network ◽  
Single Output

Operational Determination of the Activated Sludge Process Using Neural Networks

1992 ◽  
Vol 26 (9-11) ◽  
pp. 2461-2464 ◽  
Author(s):  
R. D. Tyagi ◽  
Y. G. Du
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Steady State ◽  
Activated Sludge ◽  
Feedforward Neural Network ◽  
Training Data ◽  
Activated Sludge Process

A steady-statemathematical model of an activated sludgeprocess with a secondary settler was developed. With a limited number of training data samples obtained from the simulation at steady state, a feedforward neural network was established which exhibits an excellent capability for the operational prediction and determination.

scholarly journals A Neural Network Technique for the Derivation of Runge-Kutta Pairs Adjusted for Scalar Autonomous Problems

Mathematics ◽  
2021 ◽  
Vol 9 (16) ◽  
pp. 1842
Author(s):  
Vladislav N. Kovalnogov ◽  
Ruslan V. Fedorov ◽  
Yuri A. Khakhalev ◽  
Theodore E. Simos ◽  
Charalampos Tsitouras
Keyword(s):  
Neural Network ◽  
Differential Evolution ◽  
Initial Value Problem ◽  
Fitness Function ◽  
Initial Value ◽  
Runge Kutta

We consider the scalar autonomous initial value problem as solved by an explicit Runge-Kutta pair of orders 6 and 5. We focus on an efficient family of such pairs, which were studied extensively in previous decades. This family comes with 5 coefficients that one is able to select arbitrarily. We set, as a fitness function, a certain measure, which is evaluated after running the pair in a couple of relevant problems. Thus, we may adjust the coefficients of the pair, minimizing this fitness function using the differential evolution technique. We conclude with a method (i.e. a Runge-Kutta pair) which outperforms other pairs of the same two orders in a variety of scalar autonomous problems.

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