Discussion of: “Artificial neural network and mathematical modeling comparative analysis of nonisothermal diffusion of moisture in wood” by Stavros Avramidis and Hongwei Wu

2007 ◽  
Vol 66 (1) ◽  
pp. 71-73 ◽  
Author(s):  
Margareta Schmid
Keyword(s):  
Neural Network ◽  
Mathematical Modeling ◽  
Artificial Neural Network ◽  
Comparative Analysis ◽  
Artificial Neural ◽  
Nonisothermal Diffusion

Comparative analysis of logistic regression and artificial neural network for computer-aided diagnosis of breast masses

2005 ◽  
Vol 12 (4) ◽  
pp. 487-495 ◽  
Author(s):  
Jae H. Song ◽  
Santosh S. Venkatesh ◽  
Emily A. Conant ◽  
Peter H. Arger ◽  
Chandra M. Sehgal
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Logistic Regression ◽  
Comparative Analysis ◽  
Computer Aided Diagnosis ◽  
Breast Masses ◽  
Computer Aided ◽  
Artificial Neural ◽  
Aided Diagnosis

scholarly journals SOFTWARE IMPLEMENTATION OF THE ARTIFICIAL NEURAL NETWORK FOR VIRTUAL OBJECTS СONTROL

2018 ◽  
pp. 72-78 ◽  
Author(s):  
Yu. B. Popova ◽  
S. V. Yatsynovich
Keyword(s):  
Neural Network ◽  
Mathematical Modeling ◽  
Artificial Neural Network ◽  
High Speed ◽  
Back Propagation ◽  
Computer Game ◽  
Training Sample ◽  
Software Implementation ◽  
Virtual Objects ◽  
Artificial Neural

Artificial neural networks (ANN) are now widely used in control and forecasting problems. The purpose of this work is the implementation of an artificial neural network for virtual objects control in a computer game of football. To achieve this goal, it is necessary to solve a number of problems related to mathematical modeling of ANN, algorithmization and software implementation. The paper deals with the mathematical modeling of an artificial neural network by the method of back propagation of an error, the algorithms for calculating neurons and for teaching ANN are presented. The software implementation of the artificial neural network was performed in the JavaScript language using the Node. js library, which assumed the role of a server for managing the game process. Some functions of the Underscore. js library were used to work with data arrays. The training sample consisted of more than 1000 sets of inputs and outputs, reflecting all possible situations. The results of software implementation of an artificial neural network are described on the example of virtual players control for a computer game. The results of the work show that ANN with a sufficiently high speed in real time gives the necessary direction for the player’s movement. The use of an artificial neural network has reduced the use of CPU time, which is extremely important in problems where rapid decision making is required, because complex calculations and prediction algorithms can not always be invested in 20 ms, which is fraught with skipping moves and losses. The simulated artificial neural network and the implemented algorithm of its learning can be used to solve other problems, for which only new data of the surrounding world are needed.

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