scholarly journals Intellectualization of decision making in security systems of protected objects

2021 ◽  
Vol 2131 (4) ◽  
pp. 042004
Author(s):  
E Tsarkova
Keyword(s):  
Neural Network ◽  
Decision Making ◽  
Artificial Neural Network ◽  
Fiber Optic ◽  
Training Sample ◽  
Security Systems ◽  
Informative Signal ◽  
Filtering Algorithm ◽  
Signal Features ◽  
Speed And Accuracy

Abstract The paper discusses the prospects for building decision blocks (DB) for modern security systems built on the basis of fiber optic detectors. An algorithm has been developed that makes it possible to train an artificial neural network as part of a DB. Approaches to the formation of a training sample are outlined. To reduce the dimension of the problem of recognizing informative signal features, an extreme filtering algorithm is presented, which makes it possible to increase the speed and accuracy of training.

Other Diseases Detection

2014 ◽  
pp. 170-180
Keyword(s):  
Neural Network ◽  
Decision Making ◽  
Artificial Neural Network ◽  
Multilayer Perceptron ◽  
Medical Diagnosis ◽  
Intelligent System ◽  
Associated Symptoms ◽  
Artificial Neural ◽  
Inputs And Outputs

Considering the importance of the problem of medical diagnosis, this chapter investigates the application of an intelligent system based on artificial neural network for decision making for Hepatitis. First, datasets are provided for detecting Hepatitis, based on the requirements of artificial neural network inputs and outputs consisting of associated symptoms of each disease as fields of patients' records. Then multilayer perceptron (MLP) artificial neural network is trained to classify Hepatitis disease. In the next sections, details are described.

An Artificial Neural Network Recognition Method for Single-Phase Earth Fault of Ultra-High Voltage Power Grid

2013 ◽  
Vol 333-335 ◽  
pp. 1659-1662
Author(s):  
Hai Wei Lu ◽  
Gang Wu ◽  
Chao Xiong
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Single Phase ◽  
Training Sample ◽  
Normal Operation ◽  
Recognition Method ◽  
Time Motion ◽  
Training Samples ◽  
Artificial Neural ◽  
Network Diagnosis

Fault diagnosis is very important to make the system return to normal operation quickly after an accident. This paper diagnoses the specific component failure and failure area when the real-time motion information of inputting protection and switch transferred to a trained artificial neural network model by building an artificial neural network diagnosis model of components such as transmission line, bus bar and transformer, training the artificial neural network through taking the failure rule which is found by the historic fault data as a training sample. This method has obvious advantages in the accuracy and speed of diagnosis compared with the previous artificial neural network and overcomes the shortcomings of the incompletion of training samples and not well dealing with the heuristic knowledge.

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.

scholarly journals Fuzzy Matrix Game: A Fast Approach using Artificial Hybrid Neural-Net Logic-Gate Switching Circuit

2022 ◽  
Author(s):  
Ankan Bhaumik ◽  
Sankar Kumar Roy
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Decision Making ◽  
Artificial Neural Network ◽  
Logic Gate ◽  
Real Life ◽  
Neural Nets ◽  
Matrix Game ◽  
Switching Circuit ◽  
Artificial Neural

Abstract Introducing neuro -fuzzy concept in decision making problems, makes a new way in artificial intelligence and expert systems. Sometimes, neural networks are used to optimize certain performances. In general, knowledge acquisition becomes difficult when problem's variables, constraints, environment, decision maker's attitude and complex behavior are encountered with. A sense of fuzziness prevails in these situations; sometimes numerically and sometimes linguistically. Neural networks (or neural nets) help to overcome this problem. Neural networks are explicitly and implicitly hyped to draw out fuzzy rules from numerical information and linguistic information. Logic-gate and switching circuit mobilize the fuzzy data in crisp environment and can be used in artificial neural network, also. Game theory has a tremendous scope in decision making; and consequently decision makers' hesitant characters play an important role in it. In this paper, a game situation is clarified under artificial neural network through logic-gate switching circuit in hesitant fuzzy environment with a suitable example; and this concept can be applied in future for real-life situations.

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