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.

scholarly journals An ARMA Type Pi-Sigma Artificial Neural Network for Nonlinear Time Series Forecasting

2018 ◽  
Vol 8 (2) ◽  
pp. 121-132 ◽  
Author(s):  
Esra Akdeniz ◽  
Erol Egrioglu ◽  
Eren Bas ◽  
Ufuk Yolcu
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Time Series ◽  
Artificial Neural Network ◽  
Artificial Neural Networks ◽  
Real Life ◽  
Life Time ◽  
High Order ◽  
Non Linear ◽  
Artificial Neural

Abstract Real-life time series have complex and non-linear structures. Artificial Neural Networks have been frequently used in the literature to analyze non-linear time series. High order artificial neural networks, in view of other artificial neural network types, are more adaptable to the data because of their expandable model order. In this paper, a new recurrent architecture for Pi-Sigma artificial neural networks is proposed. A learning algorithm based on particle swarm optimization is also used as a tool for the training of the proposed neural network. The proposed new high order artificial neural network is applied to three real life time series data and also a simulation study is performed for Istanbul Stock Exchange data set.

Artificial Neural Networks for Tool Wear Prediction Based on Sensor Fusion Monitoring of CFRP/CFRP Stack Drilling

2018 ◽  
Vol 12 (3) ◽  
pp. 275-281 ◽  
Author(s):  
Alessandra Caggiano ◽  
Luigi Nele ◽  
◽  
◽  
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Decision Making ◽  
Artificial Neural Network ◽  
Tool Wear ◽  
Sensor Fusion ◽  
Tool Wear Prediction ◽  
Multiple Sensor ◽  
Tool Replacement ◽  
Artificial Neural

An intelligent sensor monitoring procedure was implemented to monitor the drilling of carbon fiber reinforced plastic (CFRP)/CFRP stacks used in the assembly of aircraft fuselage panels; the signals from these sensors were then used to develop an artificial neural network-based cognitive paradigm to predict tool wear, which would allow on-line decision making regarding tool replacement. A multiple sensor system, capable of acquiring signals relative to thrust force, torque, and acoustic emission RMS, was employed during experimental drilling tests, under different rotational speed and feed conditions. Advanced sensor signal processing techniques, including signal conditioning and segmentation, as well as statistical feature extraction and data fusion, were implemented on the acquired signals. Selected statistical features extracted from the multiple sensor signals in the time domain were combined via sensor fusion techniques to construct sensor fusion pattern vectors. These were then fed to artificial neural networks for pattern recognition, with the goal of finding correlations which would allow the prediction of the corresponding tool wear. The tool wear prediction performed by the artificial neural network can be utilized to support decision making at the appropriate time for worn tool replacement, which is extremely useful for drilling automation, as well as for estimating the quality of the drilled holes.

scholarly journals TRAINING OF ARTIFICIAL NEURAL NETWORK

2021 ◽  
pp. 110-114
Author(s):  
I. Sh. Didmanidze ◽  
G. A. Kakhiani ◽  
D. Z. Didmanidze
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Decision Making ◽  
Artificial Neural Network ◽  
Adaptive Systems ◽  
Artificial Neural

The methodology of neural networks is even more often applied in tasks of management and decision-making, including in the sphere of trade and finance. The basis of neural networks is made by nonlinear adaptive systems which proved the efficiency at the solution of problems of forecasting.

scholarly journals Consumer Decision-Making Power Based on BP Neural Network and Fuzzy Mathematical Model

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Weijie Li
Keyword(s):  
Neural Network ◽  
Mathematical Model ◽  
Decision Making ◽  
Artificial Neural Network ◽  
Bp Neural Network ◽  
Recognition Rate ◽  
Real Life ◽  
Practical Significance ◽  
Fuzzy Mathematics ◽  
Artificial Neural

In real life, because of the uncertainty of risk, incomplete information, perceived cost, and other factors, there are irrational behaviors in the decision-making power of consumers, so it is of great practical significance to study the decision-making power of consumers in the choice of countermeasures and personalized product recommendation. The purpose of this paper is to analyze the decision-making power of consumers based on the BP neural network and fuzzy mathematical model. First, the basic theory of artificial neural network and the concepts of set theory and fuzzy reasoning of fuzzy mathematics are described. Second, the behavior prediction model with the equal emphasis on rationality and irrationality and the integration of artificial neural network and fuzzy mathematics are constructed. The comments of a certain mobile phone are selected as the experimental objects to analyze the decision-making reasoning and prediction of individual consumers in the network and the decision-making reasoning of group consumers in the network, the experimental results show that through Mamdani reasoning, behavioral   intention = 5.72 . Through the fuzzy set processing, it is finally determined that the consumer’s purchase intention is close to the VT mode, which is “very inclined.” In the first method, the user’s recognition rate of product C1 is 82%, and in the second method, the user’s recognition rate is 55%. The comparison of the two methods is in line with the expectation. The first method extracts the user’s emotion and evaluation information from the comments, fully considers the personalized needs of consumers, and is closer to the prediction results of the system.

scholarly journals Automatically Determining Lumbar Load during Physically Demanding Work: A Validation Study

Sensors ◽  
2021 ◽  
Vol 21 (7) ◽  
pp. 2476
Author(s):  
Charlotte Christina Roossien ◽  
Christian Theodoor Maria Baten ◽  
Mitchel Willem Pieter van der Waard ◽  
Michiel Felix Reneman ◽  
Gijsbertus Jacob Verkerke
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Discriminant Validity ◽  
Real Life ◽  
Working Environment ◽  
Physically Active ◽  
Perceived Workload ◽  
Artificial Neural ◽  
Lumbar Load ◽  
Physically Demanding Work

A sensor-based system using inertial magnetic measurement units and surface electromyography is suitable for objectively and automatically monitoring the lumbar load during physically demanding work. The validity and usability of this system in the uncontrolled real-life working environment of physically active workers are still unknown. The objective of this study was to test the discriminant validity of an artificial neural network-based method for load assessment during actual work. Nine physically active workers performed work-related tasks while wearing the sensor system. The main measure representing lumbar load was the net moment around the L5/S1 intervertebral body, estimated using a method that was based on artificial neural network and perceived workload. The mean differences (MDs) were tested using a paired t-test. During heavy tasks, the net moment (MD = 64.3 ± 13.5%, p = 0.028) and the perceived workload (MD = 5.1 ± 2.1, p < 0.001) observed were significantly higher than during the light tasks. The lumbar load had significantly higher variances during the dynamic tasks (MD = 33.5 ± 36.8%, p = 0.026) and the perceived workload was significantly higher (MD = 2.2 ± 1.5, p = 0.002) than during static tasks. It was concluded that the validity of this sensor-based system was supported because the differences in the lumbar load were consistent with the perceived intensity levels and character of the work tasks.

scholarly journals Optimal Artificial Neural Network Type Selection Method for Usage in Smart House Systems

Sensors ◽  
2020 ◽  
Vol 21 (1) ◽  
pp. 47
Author(s):  
Vasyl Teslyuk ◽  
Artem Kazarian ◽  
Natalia Kryvinska ◽  
Ivan Tsmots
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Network ◽  
Artificial Neural Networks ◽  
Input Data ◽  
Optimization Criterion ◽  
Fuzzy Input ◽  
Different Types ◽  
Smart House ◽  
Artificial Neural

In the process of the “smart” house systems work, there is a need to process fuzzy input data. The models based on the artificial neural networks are used to process fuzzy input data from the sensors. However, each artificial neural network has a certain advantage and, with a different accuracy, allows one to process different types of data and generate control signals. To solve this problem, a method of choosing the optimal type of artificial neural network has been proposed. It is based on solving an optimization problem, where the optimization criterion is an error of a certain type of artificial neural network determined to control the corresponding subsystem of a “smart” house. In the process of learning different types of artificial neural networks, the same historical input data are used. The research presents the dependencies between the types of neural networks, the number of inner layers of the artificial neural network, the number of neurons on each inner layer, the error of the settings parameters calculation of the relative expected results.

scholarly journals Application of artificial neural networks to predict the deflections of reinforced concrete beams

2016 ◽  
Vol 38 (2) ◽  
pp. 37-46 ◽  
Author(s):  
Mateusz Kaczmarek ◽  
Agnieszka Szymańska
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Network ◽  
Reinforced Concrete ◽  
Concrete Beams ◽  
Reinforced Concrete Beams ◽  
The Neural Network ◽  
Eurocode 2 ◽  
Artificial Neural ◽  
Concrete Elements

Abstract Nonlinear structural mechanics should be taken into account in the practical design of reinforced concrete structures. Cracking is one of the major sources of nonlinearity. Description of deflection of reinforced concrete elements is a computational problem, mainly because of the difficulties in modelling the nonlinear stress-strain relationship of concrete and steel. In design practise, in accordance with technical rules (e.g., Eurocode 2), a simplified approach for reinforced concrete is used, but the results of simplified calculations differ from the results of experimental studies. Artificial neural network is a versatile modelling tool capable of making predictions of values that are difficult to obtain in numerical analysis. This paper describes the creation and operation of a neural network for making predictions of deflections of reinforced concrete beams at different load levels. In order to obtain a database of results, that is necessary for training and testing the neural network, a research on measurement of deflections in reinforced concrete beams was conducted by the authors in the Certified Research Laboratory of the Building Engineering Institute at Wrocław University of Science and Technology. The use of artificial neural networks is an innovation and an alternative to traditional methods of solving the problem of calculating the deflections of reinforced concrete elements. The results show the effectiveness of using artificial neural network for predicting the deflection of reinforced concrete beams, compared with the results of calculations conducted in accordance with Eurocode 2. The neural network model presented in this paper can acquire new data and be used for further analysis, with availability of more research results.

STRUCTURE OF THE DECISION SUPPORT INFORMATION SYSTEM BY THE NPP OPERATOR

2022 ◽  
pp. 8-12
Author(s):  
М.Е. Ушков ◽  
В.Л. Бурковский
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Network ◽  
Support System ◽  
Control Object ◽  
Information Support ◽  
Quality Analysis ◽  
Inference System ◽  
Artificial Neural ◽  
Information Support System

Рассматривается структура системы информационной поддержки процессов принятия решений оператором АЭС в оперативных условиях. Анализируются функциональные возможности системы информационной поддержки оператора (СИПО) на примере Нововоронежской атомной электростанции (НВ АЭС). Данная система дает возможность оператору, управляющему распределенным комплексом технологических объектов АЭС, проводить качественный анализ и обработку больших объемов сложностpуктурированной информации и принимать своевременные адекватные решения в темпе реального времени. Кроме того, рассматривается объект управления и его структура, приводятся рекомендации, направленные на увеличение функциональных возможностей СИПО на базе искусственных нейронных сетей. Одной из многочисленных функций СИПО является прогнозирование состояния объекта управления на основе реализации программно-технологического комплекса модели энергоблока (ПТК МЭ). Однако существующая модель не способна учесть все факторы, влияющие на производственный процесс. Альтернативой здесь выступает искусственная нейронная сеть, которая в процессе обучения может сформировать искомые зависимости между большим числом параметров объекта управления и получить более полный и достоверный прогноз. Предложена структура искусственной нейронной сети на базе нечёткой системы вывода, которая реализует возможности нейронных сетей и нечеткой логики We considered the structure of the information support system for decision-making by the NPP operator in operational conditions. We analyzed the functional capabilities of the operator information support system (SIPO) using the example of the Novovoronezh nuclear power plant (NV NPP). This system provides the operator managing the distributed complex of NPP technological facilities to carry out high-quality analysis and processing of large volumes of complex structured information and make timely adequate decisions in real time. In addition, we considered the control object and its structure and made recommendations aimed at increasing the functionality of the SIPO based on artificial neural networks. One of the many functions of the SIPO is to predict the state of the control object based on the implementation of the software and technological complex of the power unit model. However, the existing model is not able to take into account all the factors influencing the production process. An alternative here is an artificial neural network, which in the learning process can form the required dependencies between a large number of parameters of the control object and get a more complete and reliable forecast. The proposed structure of an artificial neural network based on a fuzzy inference system, which implements the capabilities of neural networks and fuzzy logic

Analysis of neural networks efficiency when accounting for weight coefficients jitter, leading to reduction in output signal/noise ratio

2021 ◽  
Author(s):  
S.V. Zimina
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Network ◽  
Output Signal ◽  
Signal To Noise Ratio ◽  
Signal To Noise ◽  
Useful Signal ◽  
Artificial Neural ◽  
Noise Ratio ◽  
Weight Coefficients

Setting up artificial neural networks using iterative algorithms is accompanied by fluctuations in weight coefficients. When an artificial neural network solves the problem of allocating a useful signal against the background of interference, fluctuations in the weight vector lead to a deterioration of the useful signal allocated by the network and, in particular, losses in the output signal-to-noise ratio. The goal of the research is to perform a statistical analysis of an artificial neural network, that includes analysis of losses in the output signal-to-noise ratio associated with fluctuations in the weight coefficients of an artificial neural network. We considered artificial neural networks that are configured using discrete gradient, fast recurrent algorithms with restrictions, and the Hebb algorithm. It is shown that fluctuations lead to losses in the output signal/noise ratio, the level of which depends on the type of algorithm under consideration and the speed of setting up an artificial neural network. Taking into account the fluctuations of the weight vector in the analysis of the output signal-to-noise ratio allows us to correlate the permissible level of loss in the output signal-to-noise ratio and the speed of network configuration corresponding to this level when working with an artificial neural network.

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