Numerical solution of nonlinear singular initial value problems of Emden–Fowler type using Chebyshev Neural Network method

2015 ◽  
Vol 149 ◽  
pp. 975-982 ◽  
Author(s):  
Susmita Mall ◽  
S. Chakraverty
Keyword(s):  
Neural Network ◽  
Numerical Solution ◽  
Initial Value Problems ◽  
Initial Value ◽  
Neural Network Method ◽  
Chebyshev Neural Network ◽  
Network Method

scholarly journals Chebyshev neural network model with linear and nonlinear active functions

2016 ◽  
Vol 5 (3) ◽  
pp. 182
Author(s):  
Sarkhosh Seddighi Chaharborj ◽  
Yaghoub Mahmoudi
Keyword(s):  
Neural Network ◽  
Neural Network Model ◽  
Initial Value Problems ◽  
High Accuracy ◽  
Numerical Results ◽  
Initial Value ◽  
Linear Ordinary Differential Equations ◽  
Chebyshev Neural Network ◽  
Active Function ◽  
Linear And Nonlinear

In this paper the second order non-linear ordinary differential equations of Lane-Emden type as singular initial value problems using Chebyshev Neural Network (ChNN) with linear and nonlinear active functions has been studied. Active functions as, \(\texttt{F(z)=z}, \texttt{sinh(x)}, \texttt{tanh(z)}\) are considered to find the numerical results with high accuracy. Numerical results from Chebyshev Neural Network shows that linear active function has more accuracy and is more convenient compare to other functions.

Methods for evaluation the manufacturability of a vehicle

2020 ◽  
pp. 152-160
Keyword(s):  
Neural Network ◽  
Initial Data ◽  
Comparative Data ◽  
Training Algorithm ◽  
Neural Network Method ◽  
The Neural Network ◽  
Network Method ◽  
Calculation Results ◽  
Production And Operation

Methods for evaluation the manufacturability of a vehicle in the field of production and operation based on an energy indicator, expert estimates and usage of a neural network are stated. By using the neural network method the manufacturability of a car in a complex and for individual units is considered. The preparation of the initial data at usage a neural network for predicting the manufacturability of a vehicle is shown; the training algorithm and the architecture for calculating the manufacturability of the main units are given. According to the calculation results, comparative data on the manufacturability vehicles of various brands are given.

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