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Artykuły w czasopismach na temat „Artificial neural network”

Autor: Grafiati

Data publikacji: 4 czerwca 2021

Data aktualizacji: 25 lipca 2025

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1

CVS, Rajesh, and Nadikoppula Pardhasaradhi. "Analysis of Artificial Neural-Network." International Journal of Trend in Scientific Research and Development Volume-2, Issue-6 (2018): 418–28. http://dx.doi.org/10.31142/ijtsrd18482.

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O., Sheeba, Jithin George, Rajin P. K., Nisha Thomas, and Thomas George. "Glaucoma Detection Using Artificial Neural Network." International Journal of Engineering and Technology 6, no. 2 (2014): 158–61. http://dx.doi.org/10.7763/ijet.2014.v6.687.

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Nahar, Kapil. "Artificial Neural Network." COMPUSOFT: An International Journal of Advanced Computer Technology 01, no. 02 (2012): 25–27. https://doi.org/10.5281/zenodo.14591511.

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An artificial neural network is an information-processing paradigm that is inspired by the way biological nervous systems, such as the brain, process information. The key element of this paradigm is the novel structure of the information processing system. It is composed of a large number of highly interconnected processing elements (neurons) working in unison to solve specific problems. Ann’s, like people, learn by example. An ANN is configured for a specific application, such as pattern recognition or data classification, through a learning processing. Learning in biological

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Al-Abaid, Shaimaa Abbas. "Artificial Neural Network Based Image Encryption Technique." Journal of Advanced Research in Dynamical and Control Systems 12, SP3 (2020): 1184–89. http://dx.doi.org/10.5373/jardcs/v12sp3/20201365.

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Gupta, Sakshi. "Concrete Mix Design Using Artificial Neural Network." Journal on Today's Ideas-Tomorrow's Technologies 1, no. 1 (2013): 29–43. http://dx.doi.org/10.15415/jotitt.2013.11003.

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Al-Rawi, Kamal R., and Consuelo Gonzalo. "Adaptive Pointing Theory (APT) Artificial Neural Network." International Journal of Computer and Communication Engineering 3, no. 3 (2014): 212–15. http://dx.doi.org/10.7763/ijcce.2014.v3.322.

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Mahat, Norpah, Nor Idayunie Nording, Jasmani Bidin, Suzanawati Abu Hasan, and Teoh Yeong Kin. "Artificial Neural Network (ANN) to Predict Mathematics Students’ Performance." Journal of Computing Research and Innovation 7, no. 1 (2022): 29–38. http://dx.doi.org/10.24191/jcrinn.v7i1.264.

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Predicting students’ academic performance is very essential to produce high-quality students. The main goal is to continuously help students to increase their ability in the learning process and to help educators as well in improving their teaching skills. Therefore, this study was conducted to predict mathematics students’ performance using Artificial Neural Network (ANN). The secondary data from 382 mathematics students from UCI Machine Learning Repository Data Sets used to train the neural networks. The neural network model built using nntool. Two inputs are used which are the first and the

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Jung, Jisoo, and Ji Won Yoon. "Author Identification Using Artificial Neural Network." Journal of the Korea Institute of Information Security and Cryptology 26, no. 5 (2016): 1191–99. http://dx.doi.org/10.13089/jkiisc.2016.26.5.1191.

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Yashchenko, V. O. "Artificial brain. Biological and artificial neural networks, advantages, disadvantages, and prospects for development." Mathematical machines and systems 2 (2023): 3–17. http://dx.doi.org/10.34121/1028-9763-2023-2-3-17.

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The article analyzes the problem of developing artificial neural networks within the framework of creating an artificial brain. The structure and functions of the biological brain are considered. The brain performs many functions such as controlling the organism, coordinating movements, processing information, memory, thinking, attention, and regulating emotional states, and consists of billions of neurons interconnected by a multitude of connections in a biological neural network. The structure and functions of biological neural networks are discussed, and their advantages and disadvantages a

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Begum, Afsana, Md Masiur Rahman, and Sohana Jahan. "Medical diagnosis using artificial neural networks." Mathematics in Applied Sciences and Engineering 5, no. 2 (2024): 149–64. http://dx.doi.org/10.5206/mase/17138.

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Medical diagnosis using Artificial Neural Networks (ANN) and computer-aided diagnosis with deep learning is currently a very active research area in medical science. In recent years, for medical diagnosis, neural network models are broadly considered since they are ideal for recognizing different kinds of diseases including autism, cancer, tumor lung infection, etc. It is evident that early diagnosis of any disease is vital for successful treatment and improved survival rates. In this research, five neural networks, Multilayer neural network (MLNN), Probabilistic neural network (PNN), Learning

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JORGENSEN, THOMAS D., BARRY P. HAYNES, and CHARLOTTE C. F. NORLUND. "PRUNING ARTIFICIAL NEURAL NETWORKS USING NEURAL COMPLEXITY MEASURES." International Journal of Neural Systems 18, no. 05 (2008): 389–403. http://dx.doi.org/10.1142/s012906570800166x.

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This paper describes a new method for pruning artificial neural networks, using a measure of the neural complexity of the neural network. This measure is used to determine the connections that should be pruned. The measure computes the information-theoretic complexity of a neural network, which is similar to, yet different from previous research on pruning. The method proposed here shows how overly large and complex networks can be reduced in size, whilst retaining learnt behaviour and fitness. The technique proposed here helps to discover a network topology that matches the complexity of the

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12

Rajesh, CVS. "Basics and Features of Artificial Neural Networks." International Journal of Trend in Scientific Research and Development 2, no. 2 (2018): 1065–69. https://doi.org/10.31142/ijtsrd9578.

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The models of the computing for the perform the pattern recognition methods by the performance and the structure of the biological neural network. A network consists of computing units which can display the features of the biological network. In this paper, the features of the neural network that motivate the study of the neural computing are discussed and the differences in processing by the brain and a computer presented, historical development of neural network principle, artificial neural network ANN terminology, neuron models and topology are discussed. Rajesh CVS | M. Padmanabham "B

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13

Volodymyr, Dudnyk, Sinenko Yuriy, Matsyk Mykhailo, et al. "DEVELOPMENT OF A METHOD FOR TRAINING ARTIFICIAL NEURAL NETWORKS FOR INTELLIGENT DECISION SUPPORT SYSTEMS." Eastern-European Journal of Enterprise Technologies 3, no. 2 (105) (2020): 37–47. https://doi.org/10.15587/1729-4061.2020.203301.

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A method for training artificial neural networks for intelligent decision support systems has been developed. The method provides training not only of the synaptic weights of the artificial neural network, but also the type and parameters of the membership function, architecture and parameters of an individual network node. The architecture of artificial neural networks is trained if it is not possible to ensure the specified quality of functioning of artificial neural networks due to the training of parameters of an artificial neural network. The choice of architecture, type and parameters of

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Shevchenko, Alexey V., and Alexey N. Averkin. "PROTECTING ARTIFICIAL INTELLIGENCE AND EXPLAINABLE ARTIFICIAL INTELLIGENCE FROM ADVERSARIAL ATTACKS." SOFT MEASUREMENTS AND COMPUTING 12, no. 85 (2024): 103–13. https://doi.org/10.36871/2618-9976.2024.12.009.

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The paper examines attacks on the input of a neural network (AI and XAI) that lead to loss of functionality or the state of security of the neural network. Modern approaches and methods for protecting neural networks from competitive attacks, as private attacks on the input, are presented.

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Fahd, Syed Muhammed. "Artificial Neural Network Model for Friction Stir Processing." International Journal of Engineering Research 3, no. 6 (2014): 396–97. http://dx.doi.org/10.17950/ijer/v3s6/606.

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Zainal, Azavitra. "pH Neutralization Plant Optimization Using Artificial Neural Network." Journal of Advanced Research in Dynamical and Control Systems 12, SP4 (2020): 1466–72. http://dx.doi.org/10.5373/jardcs/v12sp4/20201625.

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Singh, Vikash, Samreen Bano, and Anand Kumar Yadav Dr Sabih Ahmad. "Feasibility of Artificial Neural Network in Civil Engineering." International Journal of Trend in Scientific Research and Development Volume-3, Issue-3 (2019): 724–28. http://dx.doi.org/10.31142/ijtsrd22985.

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Walczak, Steven. "Artificial Neural Network Research in Online Social Networks." International Journal of Virtual Communities and Social Networking 10, no. 4 (2018): 1–15. http://dx.doi.org/10.4018/ijvcsn.2018100101.

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Artificial neural networks are a machine learning method ideal for solving classification and prediction problems using Big Data. Online social networks and virtual communities provide a plethora of data. Artificial neural networks have been used to determine the emotional meaning of virtual community posts, determine age and sex of users, classify types of messages, and make recommendations for additional content. This article reviews and examines the utilization of artificial neural networks in online social network and virtual community research. An artificial neural network to predict the

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19

Rajesh, CVS, and Pardhasaradhi Nadikoppula. "Analysis of Artificial Neural Network." International Journal of Trend in Scientific Research and Development 2, no. 6 (2018): 418–28. https://doi.org/10.31142/ijtsrd18482.

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An Artificial Neural Network ANN is a computational model that is inspired by the way biological neural networks in the human brain process information. Artificial Neural Networks have generated a lot of excitement in Machine Learning research and industry, thanks to many breakthrough results in speech recognition, computer vision and text processing. In this blog post we will try to develop an understanding of a particular type of Artificial Neural Network called the Multi Layer Perceptron. An Artificial Neural Network ANN is an information processing paradigm that is inspired by the way biol

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20

Talib Bon, Abdul, and Hew See Hui. "Artificial Neural Network Forecasting." International Journal of Engineering & Technology 7, no. 4.38 (2018): 1436–39. http://dx.doi.org/10.14419/ijet.v7i4.38.27894.

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Zero defect as a goal for the manufacturing sector especially when the factory engage in global market which the market is required a highest grade quality product. A defect will occur when it is fail to meet the intended design. Hence, defect prediction methods play an important role to forecast the number of product defect. For this study, Artificial Neural Network (ANN) used to forecast the product defect in furniture manufacturing in in order to develop a well suit ANN model for the product defect prediction and obtain an accurate prediction defect number for decision making. Colour defect

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Yoon, B. L. "Artificial neural network technology." ACM SIGSMALL/PC Notes 15, no. 3 (1989): 3–16. http://dx.doi.org/10.1145/74657.74658.

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Nedjah, Nadia, Ajith Abraham, and Luiza M. Mourelle. "Hybrid artificial neural network." Neural Computing and Applications 16, no. 3 (2007): 207–8. http://dx.doi.org/10.1007/s00521-007-0083-0.

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OOE, Ryosuke, Ikuo SUZUKI, Masahito YAMAMOTO, and Masashi FURUKAWA. "Composite Artificial Neural Network." Journal of the Japan Society for Precision Engineering 79, no. 6 (2013): 552–58. http://dx.doi.org/10.2493/jjspe.79.552.

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Borankulova, Gauhar Sarsenbaevna, and Aigul Turyszhanovna Tungatarova. "ARTIFICIAL NEURAL NETWORK FEATURES." Theoretical & Applied Science 72, no. 04 (2019): 71–74. http://dx.doi.org/10.15863/tas.2019.04.72.12.

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Sultana, Zakia, Md Ashikur Rahman Khan, and Nusrat Jahan. "Early Breast Cancer Detection Utilizing Artificial Neural Network." WSEAS TRANSACTIONS ON BIOLOGY AND BIOMEDICINE 18 (March 18, 2021): 32–42. http://dx.doi.org/10.37394/23208.2021.18.4.

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Breast cancer is one of the most dangerous cancer diseases for women in worldwide. A Computeraided diagnosis system is very helpful for radiologist for diagnosing micro calcification patterns earlier and faster than typical screening techniques. Maximum breast cancer cells are eventually form a lump or mass called a tumor. Moreover, some tumors are cancerous and some are not cancerous. The cancerous tumors are called malignant and non-cancerous tumors are called benign. The benign tumors are not dangerous to health. But the unchecked malignant tumors have the ability to spread in other organs

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Labinsky, Alexander. "NEURAL NETWORK APPROACH TO COGNITIVE MODELING." MONITORING AND EXPERTISE IN SAFETY SYSTEM 2024, no. 3 (2024): 38–44. http://dx.doi.org/10.61260/2304-0130-2024-3-38-44.

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Some features of cognitive modeling are presented, including the prerequisites for a cognitive approach to solving complex problems. Cognitive modeling involves the use of various artificial neural networks, including convolutional neural networks. The classification of artificial neural networks according to various characteristics is given. The features of self-organizing neural networks and networks using deep learning methods are considered. The artificial neural network, which is a three-layer unidirectional direct propagation network, the interface of a computer program used to approxima

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Schaub, Nicholas J., and Nathan Hotaling. "Assessing Efficiency in Artificial Neural Networks." Applied Sciences 13, no. 18 (2023): 10286. http://dx.doi.org/10.3390/app131810286.

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The purpose of this work was to develop an assessment technique and subsequent metrics that help in developing an understanding of the balance between network size and task performance in simple model networks. Here, exhaustive tests on simple model neural networks and datasets are used to validate both the assessment approach and the metrics derived from it. The concept of neural layer state space is introduced as a simple mechanism for understanding layer utilization, where a state is the on/off activation state of all neurons in a layer for an input. Neural efficiency is computed from state

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Kumar, Vikash, Shivam Kumar Gupta, Harsh Sharma, Uchit Bhadauriya, and Chandra Prakash Varma. "Voice Isolation Using Artificial Neural Network." International Journal for Research in Applied Science and Engineering Technology 10, no. 5 (2022): 1249–53. http://dx.doi.org/10.22214/ijraset.2022.42237.

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Abstract: The paper reflects the use of Artificial Neural Networks with the help of various machine learning algorithms for voice isolation. In particular, we consider the case of a voice sample recognition by analyzing the speech signals with the help of machine learning algorithms such as artificial neural networks, independent component analysis, activation function. The strategies by which our central nervous network decodes the network stimuli same as artificial neural network will analyze the given speech sample. After first step, a set of machine learning algorithms will be used like in

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Mitra, Manu. "Neural processor in artificial intelligence advancement." Journal of Autonomous Intelligence 1, no. 1 (2018): 2. http://dx.doi.org/10.32629/jai.v1i1.13.

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A neuron network is a computational model based on structure and functions of biological neural networks. Information that flows through the network affects the structure of the neuron network because neural network changes-or learns, in a sense-based on that input and output. Although neural network being highly complex (for example change of weights for every new data within the time frame) an experimental model of high level architecture of neural processor is proposed. Neural Processor performs all the functions that an ordinary neural network does like adaptive learning, self-organization

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Mitra, Manu. "Neural Processor in Artificial Intelligence advancement." Journal of Autonomous Intelligence 1, no. 1 (2018): 1–13. https://doi.org/10.63019/jai.v1i1.10.

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A neuron network is a computational model based on structure and functions of biological neural networks. Information that flows through the network affects the structure of the neuron network because neural network changes or learns, in a sense-based on that input and output. Although neural network being highly complex (for example change of weights for every new data within the time frame) an experimental model of high level architecture of neural processor is proposed. Neural Processor performs all the functions that an ordinary neural network does like adaptive learning, self-organization

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Deng, Limei, and Ying Chang. "Risk Management of Investment Projects Based on Artificial Neural Network." Wireless Communications and Mobile Computing 2022 (May 9, 2022): 1–13. http://dx.doi.org/10.1155/2022/5606316.

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The benefit evaluation of investment projects is the key to the whole investment activities. This paper mainly describes the risk management of investment projects using an artificial neural network. It generally adopts the index system of project risk through modern scientific measurement methods, to evaluate whether the investment project of artificial neural network is feasible or not. It establishes a benefit evaluation model based on an artificial neural network, from the analysis and consideration of 4 groups of experiments, comparing four sets of data: BP network convergence rate, artif

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Zhang, Yongqiang, Haijie Pang, Jinlong Ma, Guilei Ma, Xiaoming Zhang, and Menghua Man. "Research on Anti-Interference Performance of Spiking Neural Network Under Network Connection Damage." Brain Sciences 15, no. 3 (2025): 217. https://doi.org/10.3390/brainsci15030217.

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Background: With the development of artificial intelligence, memristors have become an ideal choice to optimize new neural network architectures and improve computing efficiency and energy efficiency due to their combination of storage and computing power. In this context, spiking neural networks show the ability to resist Gaussian noise, spike interference, and AC electric field interference by adjusting synaptic plasticity. The anti-interference ability to spike neural networks has become an important direction of electromagnetic protection bionics research. Methods: Therefore, this research

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Næs, Tormod, Knut Kvaal, Tomas Isaksson, and Charles Miller. "Artificial Neural Networks in Multivariate Calibration." Journal of Near Infrared Spectroscopy 1, no. 1 (1993): 1–11. http://dx.doi.org/10.1255/jnirs.1.

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This paper is about the use of artificial neural networks for multivariate calibration. We discuss network architecture and estimation as well as the relationship between neural networks and related linear and non-linear techniques. A feed-forward network is tested on two applications of near infrared spectroscopy, both of which have been treated previously and which have indicated non-linear features. In both cases, the network gives more precise prediction results than the linear calibration method of PCR.

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Parks, Allen D. "Characterizing Computation in Artificial Neural Networks by their Diclique Covers and Forman-Ricci Curvatures." European Journal of Engineering Research and Science 5, no. 2 (2020): 171–77. http://dx.doi.org/10.24018/ejers.2020.5.2.1689.

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The relationships between the structural topology of artificial neural networks, their computational flow, and their performance is not well understood. Consequently, a unifying mathematical framework that describes computational performance in terms of their underlying structure does not exist. This paper makes a modest contribution to understanding the structure-computational flow relationship in artificial neural networks from the perspective of the dicliques that cover the structure of an artificial neural network and the Forman-Ricci curvature of an artificial neural network’s connections

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Parks, Allen D. "Characterizing Computation in Artificial Neural Networks by their Diclique Covers and Forman-Ricci Curvatures." European Journal of Engineering and Technology Research 5, no. 2 (2020): 171–77. http://dx.doi.org/10.24018/ejeng.2020.5.2.1689.

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The relationships between the structural topology of artificial neural networks, their computational flow, and their performance is not well understood. Consequently, a unifying mathematical framework that describes computational performance in terms of their underlying structure does not exist. This paper makes a modest contribution to understanding the structure-computational flow relationship in artificial neural networks from the perspective of the dicliques that cover the structure of an artificial neural network and the Forman-Ricci curvature of an artificial neural network’s connections

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Akdeniz, Esra, Erol Egrioglu, Eren Bas, and Ufuk Yolcu. "An ARMA Type Pi-Sigma Artificial Neural Network for Nonlinear Time Series Forecasting." Journal of Artificial Intelligence and Soft Computing Research 8, no. 2 (2018): 121–32. http://dx.doi.org/10.1515/jaiscr-2018-0009.

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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 artific

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Hamdan, Baida Abdulredha. "Neural Network Principles and its Application." Webology 19, no. 1 (2022): 3955–70. http://dx.doi.org/10.14704/web/v19i1/web19261.

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Neural networks which also known as artificial neural networks is generally a computing dependent technique that formed and designed to create a simulation to the real brain of a human to be used as a problem solving method. Artificial neural networks gain their abilities by the method of training or learning, each method have a certain input and output which called results too, this method of learning works to create forming probability-weighted associations among both of input and the result which stored and saved across the net specifically among its data structure, any training process is

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Tun, Myat Thida. "Myanmar Alphabet Recognition System Based on Artificial Neural Network." International Journal of Trend in Scientific Research and Development Volume-2, Issue-5 (2018): 1343–48. http://dx.doi.org/10.31142/ijtsrd17054.

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Mahdi, Qasim Abbood, Andrii Shyshatskyi, Oleksandr Symonenko, et al. "Development of a method for training artificial neural networks for intelligent decision support systems." Eastern-European Journal of Enterprise Technologies 1, no. 9(115) (2022): 35–44. http://dx.doi.org/10.15587/1729-4061.2022.251637.

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We developed a method of training artificial neural networks for intelligent decision support systems. A distinctive feature of the proposed method consists in training not only the synaptic weights of an artificial neural network, but also the type and parameters of the membership function. In case of impossibility to ensure a given quality of functioning of artificial neural networks by training the parameters of an artificial neural network, the architecture of artificial neural networks is trained. The choice of architecture, type and parameters of the membership function is based on the c

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Qasim, Abbood Mahdi, Shyshatskyi Andrii, Symonenko Oleksandr, et al. "Development of a method for training artificial neural networks for intelligent decision support systems." Eastern-European Journal of Enterprise Technologies 1, no. 9 (115) (2022): 35–44. https://doi.org/10.15587/1729-4061.2022.251637.

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We developed a method of training artificial neural networks for intelligent decision support systems. A distinctive feature of the proposed method consists in training not only the synaptic weights of an artificial neural network, but also the type and parameters of the membership function. In case of impossibility to ensure a given quality of functioning of artificial neural networks by training the parameters of an artificial neural network, the architecture of artificial neural networks is trained. The choice of architecture, type and parameters of the membership function is based on the c

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41

Oleg, Sova, Shyshatskyi Andrii, Zhuravskyi Yurii, et al. "DEVELOPMENT OF A METHODOLOGY FOR TRAINING ARTIFICIAL NEURAL NETWORKS FOR INTELLIGENT DECISION SUPPORT SYSTEMS." Eastern-European Journal of Enterprise Technologies 2, no. 4 (104) (2020): 6–14. https://doi.org/10.15587/1729-4061.2020.199469.

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The method of training artificial neural networks for intelligent decision support systems is developed. A distinctive feature of the proposed method is that it provides training not only of the synaptic weights of the artificial neural network, but also the type and parameters of the membership function. If it is impossible to provide the specified quality of functioning of artificial neural networks due to the learning of the parameters of the artificial neural network, the architecture of artificial neural networks is trained. The choice of architecture, type and parameters of the membershi

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42

Xu, Qinyi. "How neural networks can improve the performance of electrical power systems?" Highlights in Science, Engineering and Technology 29 (January 31, 2023): 214–18. http://dx.doi.org/10.54097/hset.v29i.4571.

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As a new technology, artificial neural network is applied in more and more fields. It is not only popular in the computer field, but also in the traditional energy system. Artificial neural network can solve the problem that which traditional methods used in power system are having difficulty about speed, accuracy and efficiency. This paper will introduce the types of artificial neural networks and its application in power system to analyze how artificial neural networks improve the efficiency of power system. Artificial neural networks have been studied since the 1980s with the rise of artifi

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Jeong, Yeongsang, and Sungshin Kim. "A Study of Arrow Performance using Artificial Neural Network." Journal of Korean Institute of Intelligent Systems 24, no. 5 (2014): 548–53. http://dx.doi.org/10.5391/jkiis.2014.24.5.548.

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Wongsathan, Rati, and Pasit Pothong. "Heart Disease Classification Using Artificial Neural Networks." Applied Mechanics and Materials 781 (August 2015): 624–27. http://dx.doi.org/10.4028/www.scientific.net/amm.781.624.

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Neural Networks (NNs) has emerged as an importance tool for classification in the field of decision making. The main objective of this work is to design the structure and select the optimized parameter in the neural networks to implement the heart disease classifier. Three types of neural networks, i.e. Multi-layered Perceptron Neural Network (MLP-NN), Radial Basis Function Neural Networks (RBF-NN), and Generalized Regression Neural Network (GR-NN) have been used to test the performance of heart disease classification. The classification accuracy obtained by RBFNN gave a very high performance

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Soylak, Mustafa, Tuğrul Oktay, and İlke Turkmen. "A simulation-based method using artificial neural networks for solving the inverse kinematic problem of articulated robots." Proceedings of the Institution of Mechanical Engineers, Part E: Journal of Process Mechanical Engineering 231, no. 3 (2015): 470–79. http://dx.doi.org/10.1177/0954408915608755.

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In our article, inverse kinematic problem of a plasma cutting robot with three degree of freedom is solved using artificial neural networks. Artificial neural network was trained using joint angle values according to cartesian coordinates ( x, y, z) of end point of a robotic arm. The Levenberg–Marquardt training algorithm was applied to educate artificial neural network. To validate the designed neural network, it was tested using a new test data set which is not applied in training. A simulation was performed on a three-dimensional model of MSC.ADAMS software using angle values obtained from

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Teslyuk, Vasyl, Artem Kazarian, Natalia Kryvinska, and Ivan Tsmots. "Optimal Artificial Neural Network Type Selection Method for Usage in Smart House Systems." Sensors 21, no. 1 (2020): 47. http://dx.doi.org/10.3390/s21010047.

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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 arti

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Chen, Heng, Fengmei Lu, and Bifang He. "Topographic property of backpropagation artificial neural network: From human functional connectivity network to artificial neural network." Neurocomputing 418 (December 2020): 200–210. http://dx.doi.org/10.1016/j.neucom.2020.07.103.

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KONOVALOV, S. "FEATURES OF DIAGNOSTIC ARTIFICIAL NEURAL NETWORKS FOR HYBRID EXPERT SYSTEMS." Digital Technologies 26 (2019): 36–46. http://dx.doi.org/10.33243/2313-7010-26-36-46.

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In the proposed article, various methods of constructing an artificial neural network as one of the components of a hybrid expert system for diagnosis were investigated. A review of foreign literature in recent years was conducted, where hybrid expert systems were considered as an integral part of complex technical systems in the field of security. The advantages and disadvantages of artificial neural networks are listed, and the main problems in creating hybrid expert systems for diagnostics are indicated, proving the relevance of further development of artificial neural networks for hybrid e

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Oleg, Sova, Turinskyi Oleksandr, Shyshatskyi Andrii, et al. "DEVELOPMENT OF AN ALGORITHM TO TRAIN ARTIFICIAL NEURAL NETWORKS FOR INTELLIGENT DECISION SUPPORT SYSTEMS." Eastern-European Journal of Enterprise Technologies 1, no. 9 (103) (2020): 46–55. https://doi.org/10.15587/1729-4061.2020.192711.

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The algorithm to train artificial neural networks for intelligent decision support systems has been constructed. A distinctive feature of the proposed algorithm is that it conducts training not only for synaptic weights of an artificial neural network, but also for the type and parameters of membership function. In case of inability to ensure the assigned quality of functioning of artificial neural networks due to training of parameters of artificial neural network, the architecture of artificial neural networks is trained. The choice of the architecture, type and parameters of membership func

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Rodríguez-Alcántara, Josué U., Adrián Pozos-Estrada, and Roberto Gómez-Martinez. "Use of Artificial Neural Networks to Predict Wind-Induced External Pressure Coefficients on a Low-Rise Building: A Comparative Study." Advances in Civil Engineering 2022 (September 5, 2022): 1–14. http://dx.doi.org/10.1155/2022/8796384.

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Wind flow on a bluff body is a complex and nonlinear phenomenon that has been mainly studied experimentally or analytically. Several mathematical methods have been developed to predict the wind-induced pressure distribution on bluff bodies; however, most of them result unpractical due to the mathematical complexity required. Long-short term memory artificial neural networks with deep learning have proven to be efficient tools in the solution of nonlinear phenomena, although the choice of a more efficient network model remains a topic of open discussion for researchers. The main objective of th

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