Relevant bibliographies by topics / Neural artificial system

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Neural artificial system'

Author: Grafiati
Published: 5 June 2025
Last updated: 16 July 2025

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Journal articles on the topic "Neural artificial system"

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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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WU, CATHY H., HSI-LIEN CHEN, and SHENG-CHIH CHEN. "GENE CLASSIFICATION ARTIFICIAL NEURAL SYSTEM." International Journal on Artificial Intelligence Tools 04, no. 04 (1995): 501–10. http://dx.doi.org/10.1142/s0218213095000255.

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A gene classification artificial neural system has been developed for rapid annotation of the molecular sequencing data being generated by the Human Genome Project. Three neural networks have been implemented, one full-scale system to classify protein sequences according to PIR (Protein Identification Resources) superfamilies, one system to classify ribosomal RNA sequences into RDP (Ribosomal Database Project) phylogenetic classes, and one pilot system to classify proteins according to Blocks motifs. The sequence encoding schema involved an n-gram hashing method to convert molecular sequences into neural input vectors, a SVD (singular value decomposition) method to compress vectors, and a term weighting method to extract motif information. The neural networks used were three-layered, feed-forward networks that employed back-propagation or counter-propagation learning paradigms. The system runs faster by one to two orders of magnitude than existing method and has a sensitivity of 85 to 100%. The gene classification artificial neural system is available on the Internet, and may be extended into a gene identification system for classifying indiscriminately sequenced DNA fragments.
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Wu, Cathy, George Whitson, Jerry Mclarty, Adisorn Ermongkonchai, and Tzu-Chung Chang. "Protein classification artificial neural system." Protein Science 1, no. 5 (1992): 667–77. http://dx.doi.org/10.1002/pro.5560010512.

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Chun, Sungwoo, Jong-Seok Kim, Yongsang Yoo, et al. "An artificial neural tactile sensing system." Nature Electronics 4, no. 6 (2021): 429–38. http://dx.doi.org/10.1038/s41928-021-00585-x.

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Buhari, M. I., M. H. Habaebi, and B. M. Ali. "Artificial Neural System for Packet Filtering." Journal of Computer Science 1, no. 2 (2005): 259–69. http://dx.doi.org/10.3844/jcssp.2005.259.269.

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TISDALE, E. ROBERT, and WALTER J. KARPLUS. "SYSTEM IDENTIFICATION WITH ARTIFICIAL NEURAL NETWORKS." International Journal of Pattern Recognition and Artificial Intelligence 06, no. 01 (1992): 93–111. http://dx.doi.org/10.1142/s0218001492000059.

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System identification is the term scientists and engineers use to refer to the process of building mathematical models of dynamical systems based on observed data. This paper approaches system identification as a pattern recognition problem. We use computers to simulate the system response for a variety of different mathematical models. For each distinct system model, simulated system responses tend to remain segregated in one or more amorphous regions of system response space despite (1) large variations in system parameters, (2) experimental errors, and (3) noise. The actual system response is classified with the model corresponding to the region of system response space where it is found. The classifier is an Artificial Neural Network (ANN) which implements a Generalized Vector Quantizer (GVQ). A small number of simple but powerful discriminant functions facilitate the correct classification of most of the responses in any given region. The required distribution of discriminants among the regions evolves automatically as they learn their respective functions.
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Rana, Arti, Arvind Singh Rawat, Himanshu Bahuguna, and Anchit Bijalwan. "Artificial Neural Network based Diagnosis System." International Journal of Computer Trends and Technology 48, no. 4 (2017): 189–91. http://dx.doi.org/10.14445/22312803/ijctt-v48p134.

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Devi, Kharibam Jilenkumari, and Khelchandra Thongam. "A Survey of Automatic Speaker Recognition System Using Artificial Neural Networks." Journal of Advanced Research in Dynamical and Control Systems 11, no. 10-SPECIAL ISSUE (2019): 453–56. http://dx.doi.org/10.5373/jardcs/v11sp10/20192832.

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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 expert systems. The approaches to the analysis of natural language sentences, which are used for the work of hybrid expert systems with artificial neural networks, are considered. A bulletin board is shown, its structure and principle of operation are described. The structure of the bulletin board is divided into levels and sublevels. At sublevels, a confidence factor is applied. The dependence of the values of the confidence factor on the fulfillment of a particular condition is shown. The links between the levels and sublevels of the bulletin board are also described. As an artificial neural network architecture, the «key-threshold» model is used, the rule of neuron operation is shown. In addition, an artificial neural network has the property of training, based on the application of the penalty property, which is able to calculate depending on the accident situation. The behavior of a complex technical system, as well as its faulty states, are modeled using a model that describes the structure and behavior of a given system. To optimize the data of a complex technical system, an evolutionary algorithm is used to minimize the objective function. Solutions to the optimization problem consist of Pareto solution vectors. Optimization and training tasks are solved by using the Hopfield network. In general, a hybrid expert system is described using semantic networks, which consist of vertices and edges. The reference model of a complex technical system is stored in the knowledge base and updated during the acquisition of new knowledge. In an emergency, or about its premise, with the help of neural networks, a search is made for the cause and the control action necessary to eliminate the accident. The considered approaches, interacting with each other, can improve the operation of diagnostic artificial neural networks in the case of emergency management, showing more accurate data in a short time. In addition, the use of such a network for analyzing the state of health, as well as forecasting based on diagnostic data using the example of a complex technical system, is presented.
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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 systems involves adjustment to the synaptic connections that exists between the neurons. This is true of Ann’s as well. The first artificial neuron was produced in1943 by the neurophysiologist Warren McCulloch and the logician Walter Pits.
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Dissertations / Theses on the topic "Neural artificial system"

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Ariza, Zambrano William Camilo 1989. "Controle ativo de vibrações usando redes neurais artificiais : Active vibration control using artificial neural networks." [s.n.], 2013. http://repositorio.unicamp.br/jspui/handle/REPOSIP/264927.

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Orientador: Alberto Luiz Serpa<br>Dissertação (mestrado) - Universidade Estadual de Campinas, Faculdade de Engenharia Mecânica<br>Made available in DSpace on 2018-08-23T23:04:02Z (GMT). No. of bitstreams: 1 ArizaZambrano_WilliamCamilo_M.pdf: 5789145 bytes, checksum: 151f5e3ef1780d5448a7073b85b4715f (MD5) Previous issue date: 2013<br>Resumo: Este trabalho tem como objetivo principal o estudo de um método de controle baseado no uso de redes neurais artificiais aplicado ao problema de controle de vibrações em estruturas flexíveis. Este trabalho centra-se no estudo do esquema de controle inverso-direto, que consiste em identificar a dinâmica inversa da planta através de uma rede neural artificial para ser usada como controlador. Três exemplos de aplicação foram resolvidos utilizando-se controladores projetados com o método inverso-direto. A primeira aplicação é o controle de vibrações em uma estrutura mecânica de parâmetros concentrados. O segundo exemplo de aplicação é o controle de vibrações de uma placa engastada em uma de suas extremidades. Neste caso, a placa engastada foi modelada utilizando-se o método de elementos finitos. No seguinte exemplo, o modelo da placa usado no exemplo anterior foi reduzido, deixando apenas os primeiros modos de vibração. No último exemplo tratou-se o problema de controle não colocado das vibrações em uma placa engastada. Os resultados foram analisados a partir da resposta temporal e da resposta em frequência do sistema em malha fechada. Para comparar os resultados obtidos utilizando-se o método de controle baseado em redes neurais artificiais, os exemplos citados anteriormente foram também resolvidos utilizando-se o método de controle ??. Os resultados obtidos demonstram que o método de controle baseado em modelo inverso usando redes neurais foi eficaz na resolução deste tipo de problema<br>Abstract: The goal of this work is to study a control method based on artificial neural networks applied to the vibration control of flexible structures problem. This work focuses in the direct-inverse control scheme which consists of identifing the inverse dynamics of the plant through an artificial neural network to be used as the controller. Three application examples using the direct-inverse method were solved. The first application is the vibration control in a mechanical structure of concentrated parameters. The second application example is the vibration control of a cantilever plate. The cantilever plate was modeled using the finite elements method. In the third example, a reduction of the cantilever plate model was made. In the last example a non-collocated control problem of vibration in a cantilever plate was treated. The results of the scheme were evaluated according to the temporal response and the frequency response of the closed-loop system. In order to compare the results obtained using the control method based on artificial neural networks, the previous examples were also solved using the ?? control method. The obtained results show that the control method based on inverse model using neural networks was effective in solving this kind of problem<br>Mestrado<br>Mecanica dos Sólidos e Projeto Mecanico<br>Mestre em Engenharia Mecânica
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Yildiz, Ali. "Resource-aware Load Balancing System With Artificial Neural Networks." Master's thesis, METU, 2006. http://etd.lib.metu.edu.tr/upload/12607613/index.pdf.

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As the distributed systems becomes popular, efficient load balancing systems taking better decisions must be designed. The most important reasons that necessitate load balancing in a distributed system are the heterogeneous hosts having different com- puting powers, external loads and the tasks running on different hosts but communi- cating with each other. In this thesis, a load balancing approach, called RALBANN, developed using graph partitioning and artificial neural networks (ANNs) is de- scribed. The aim of RALBANN is to integrate the successful load balancing deci- sions of graph partitioning algorithms with the efficient decision making mechanism of ANNs. The results showed that using ANNs to make efficient load balancing can be very beneficial. If trained enough, ANNs may load the balance as good as graph partitioning algorithms more efficiently.
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Chen, Zhihong. "Application of artificial neural networks to power system protection." Doctoral thesis, Universite Libre de Bruxelles, 1997. http://hdl.handle.net/2013/ULB-DIPOT:oai:dipot.ulb.ac.be:2013/212176.

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Wan, Chuen L. "Traffic representation by artificial neural system and computer vision." Thesis, Edinburgh Napier University, 1994. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.261024.

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Baud-Bovy, Gabriel. "A gaze-addressing communication system using artificial neural networks." PDXScholar, 1992. https://pdxscholar.library.pdx.edu/open_access_etds/4258.

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Severe motor disabilities can render a person almost completely incapable of communication. Nevertheless, in many cases, the sensory systems are intact and the eye movements are still under good control. In these cases, one can use a device such as the Brain Response Interface (BRI) to command a remote control (e.g. room temperature, bed position), a word-processor, a speech synthesizer, and so on.
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Brombin, Enrico <1993&gt. "A Trading System based on an Artificial Neural Network." Master's Degree Thesis, Università Ca' Foscari Venezia, 2018. http://hdl.handle.net/10579/12450.

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Il presente elaborato tratta lo studio di un sistema di trading discrezionale, basato sull'utilizzo dell'analisi tecnica applicata ad una rete neurale artificiale ricorrente, parzialmente confrontandone poi, le risultanti performance con quelle di due fondi comuni di investimento.
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McMinn, David. "Using evolutionary artificial neural networks to design hierarchical animat nervous systems." Thesis, Robert Gordon University, 2001. http://hdl.handle.net/10059/427.

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The research presented in this thesis examines the area of control systems for robots or animats (animal-like robots). Existing systems have problems in that they require a great deal of manual design or are limited to performing jobs of a single type. For these reasons, a better solution is desired. The system studied here is an Artificial Nervous System (ANS) which is biologically inspired; it is arranged as a hierarchy of layers containing modules operating in parallel. The ANS model has been developed to be flexible, scalable, extensible and modular. The ANS can be implemented using any suitable technology, for many different environments. The implementation focused on the two lowest layers (the reflex and action layers) of the ANS, which are concerned with control and rhythmic movement. Both layers were realised as Artificial Neural Networks (ANN) which were created using Evolutionary Algorithms (EAs). The task of the reflex layer was to control the position of an actuator (such as linear actuators or D.C. motors). The action layer performed the task of Central Pattern Generators (CPG), which produce rhythmic patterns of activity. In particular, different biped and quadruped gait patterns were created. An original neural model was specifically developed for assisting in the creation of these time-based patterns. It is shown in the thesis that Artificial Reflexes and CPGs can be configured successfully using this technique. The Artificial Reflexes were better at generalising across different actuators, without changes, than traditional controllers. Gaits such as pace, trot, gallop and pronk were successfully created using the CPGs. Experiments were conducted to determine whether modularity in the networks had an impact. It has been demonstrated that the degree of modularization in the network influences its evolvability, with more modular networks evolving more efficiently.
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Park, Dong Chul. "Identification of stationary/nonstationary systems using artificial neural networks /." Thesis, Connect to this title online; UW restricted, 1990. http://hdl.handle.net/1773/5822.

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Oheda, Hakim. "Artificial neural network control strategies for fuel cell hybrid system." Thesis, Cranfield University, 2013. http://dspace.lib.cranfield.ac.uk/handle/1826/7964.

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The greening of air transport is the driver for developing technologies to reduce the environmental impact of aviation with the aim of halving the amount of carbon dioxide (COଶ) emitted by air transport, cutting specific emissions of nitrogen oxides (NO୶) by 80% and halving perceived noise by the year 2020. Fuel Cells (FC) play an important role in the new power generation field as inherently clean, efficient and reliable source of power especially when comparing with the traditional fossil-fuel based technologies. The project investigates the feasibility of using an electric hybrid system consisting of a fuel cell and battery to power a small model aircraft (PiperCub J3). In order to meet the desired power requirements at different phases of flight efficiently, a simulation model of the complete system was first developed, consisting of a Proton Exchange Membrane hybrid fuel cell system, 6DoF aircraft model and neural network based controller. The system was then integrated in one simulation environment to run in real-time and finally was also tested in hardware-in-the-loop with real-time control. The control strategy developed is based on a neural network model identification technique; specifically Model Reference Control (MRC), since neural network is well suited to nonlinear systems. To meet the power demands at different phases of flight, the controller controls the battery current and rate of charging/discharging. Three case studies were used to validate and assess the performance of the hybrid system: battery fully charged (high SOC), worst case scenario and taking into account the external factors such as wind speeds and wind direction. In addition, the performance of the Artificial Neural Network Controller was compared to that of a Fuzzy Logic controller. In all cases the fuel cell act as the main power source for the PiperCub J3 aircraft. The tests were carried-out in both simulation and hardware-in-the-loop.
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Jun, Yong-Tae. "A feature-based reverse engineering system using artificial neural networks." Thesis, University of Warwick, 1999. http://wrap.warwick.ac.uk/3674/.

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Reverse Engineering (RE) is the process of reconstructing CAD models from scanned data of a physical part acquired using 3D scanners. RE has attracted a great deal of research interest over the last decade. However, a review of the literature reveals that most research work have focused on creation of free form surfaces from point cloud data. Representing geometry in terms of surface patches is adequate to represent positional information, but can not capture any of the higher level structure of the part. Reconstructing solid models is of importance since the resulting solid models can be directly imported into commercial solid modellers for various manufacturing activities such as process planning, integral property computation, assembly analysis, and other applications. This research discusses the novel methodology of extracting geometric features directly from a data set of 3D scanned points, which utilises the concepts of artificial neural networks (ANNs). In order to design and develop a generic feature-based RE system for prismatic parts, the following five main tasks were investigated. (1) point data processing algorithms; (2) edge detection strategies; (3) a feature recogniser using ANNs; (4) a feature extraction module; (5) a CAD model exchanger into other CAD/CAM systems via IGES. A key feature of this research is the incorporation of ANN in feature recognition. The use of ANN approach has enabled the development of a flexible feature-based RE methodology that can be trained to deal with new features. ANNs require parallel input patterns. In this research, four geometric attributes extracted from a point set are input to the ANN module for feature recognition: chain codes, convex/concave, circular/rectangular and open/closed attribute. Recognising each feature requires the determination of these attributes. New and robust algorithms are developed for determining these attributes for each of the features. This feature-based approach currently focuses on solving the feature recognition problem based on 2.5D shapes such as block pocket, step, slot, hole, and boss, which are common and crucial in mechanical engineering products. This approach is validated using a set of industrial components. The test results show that the strategy for recognising features is reliable.
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Books on the topic "Neural artificial system"

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D, Zhou David, and Greenbaum Elias S, eds. Implantable neural prostheses 1: Devices and applications. Springer, 2009.

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Cross, Nicola. An attentional what-where vision system using artificial neural networks. typescript, 1999.

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Jun, Yong-Tae. A feature-based reverse engineering system using artificial neural networks. typescript, 1999.

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Eeckman, Frank H. Analysis and Modeling of Neural Systems. Springer US, 1992.

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Müller, Berndt. Neural networks: An introduction. 2nd ed. Springer-Verlag, 1991.

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Müller, Berndt. Neural networks: An introduction. 2nd ed. Springer, 1995.

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Wang, Jin-Liang. Analysis and Control of Coupled Neural Networks with Reaction-Diffusion Terms. Springer Singapore, 2018.

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Artificial, Neural Networks in Engineering Conference (2003 St Louis Mo ). Smart engineering system design: Neural networks, fuzzy logic, evolutionary programming, complex systems and artificial life : proceedings of the Artificial Neural Networks in Engineering Conference (ANNIE 2003) : held November 2-5, 2003, in St. Louis, Missouri, U.S.A. ASME Press, 2003.

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Artificial, Neural Networks in Engineering Conference (11th 2001 St Louis Missouri). Smart engineering system design: Neural networks, fuzzy logic, evolutionary programming, data mining and complex systems : proceedings of the Artificial Neural Networks in Engineering Conference (ANNIE 2001), held November 4-7, 2001, in St. Louis, Missouri, U.S.A. ASME Press, 2001.

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Artificial Neural Networks in Engineering Conference (10th 2000 St. Louis, Missouri). Smart engineering system design: Neural networks, fuzzy logic, evolutionary programming, data mining and complex systems : proceedings of the Artificial Neural Networks in Engineering Conference (ANNIE 2000), held November 5-8, 2000, in St. Louis, Missouri, U.S.A. Edited by Dagli Cihan H. 1949-. ASME Press, 2000.

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Book chapters on the topic "Neural artificial system"

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da Silva, Ivan Nunes, Danilo Hernane Spatti, Rogerio Andrade Flauzino, Luisa Helena Bartocci Liboni, and Silas Franco dos Reis Alves. "Disease Diagnostic System Using ART Networks." In Artificial Neural Networks. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-43162-8_14.

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Yalçın, Müştak E., Tuba Ayhan, and Ramazan Yeniçeri. "Artificial Olfaction System." In Reconfigurable Cellular Neural Networks and Their Applications. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-17840-6_3.

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Cellier, François E. "Artificial Neural Networks and Genetic Algorithms." In Continuous System Modeling. Springer New York, 1991. http://dx.doi.org/10.1007/978-1-4757-3922-0_14.

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Trojanowski, Krzysztof, and Sławomir T. Wierzchoń. "Memory Management in Artificial Immune System." In Neural Networks and Soft Computing. Physica-Verlag HD, 2003. http://dx.doi.org/10.1007/978-3-7908-1902-1_100.

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Kim, Byung-Joo, and Il Kon Kim. "Improved Kernel Based Intrusion Detection System." In Artificial Neural Networks – ICANN 2006. Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11840930_90.

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Womble, Steve, and Stefan Wermter. "A Mirror Neuron System for Syntax Acquisition." In Artificial Neural Networks — ICANN 2001. Springer Berlin Heidelberg, 2001. http://dx.doi.org/10.1007/3-540-44668-0_172.

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Bora, Sebnem. "A Fault Tolerant System Using Collaborative Agents." In Artificial Intelligence and Neural Networks. Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11803089_25.

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Stavropoulos, Thanos G., Ageliki Tsioliaridou, George Koutitas, Dimitris Vrakas, and Ioannis Vlahavas. "System Architecture for a Smart University Building." In Artificial Neural Networks – ICANN 2010. Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-15825-4_64.

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Chliah, Hanane, Amal Battou, and Omar Baz. "Artificial Immune System and Artificial Neural Network in Intrusion Detection System." In Distributed Sensing and Intelligent Systems. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-64258-7_67.

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Stützle, T., and H. Hoos. "Improvements on the Ant-System: Introducing the MAX-MIN Ant System." In Artificial Neural Nets and Genetic Algorithms. Springer Vienna, 1998. http://dx.doi.org/10.1007/978-3-7091-6492-1_54.

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Conference papers on the topic "Neural artificial system"

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Zhou, Zhuoling, Libin Liang, Hongzhi Chen, Changjiu Teng, Shilong Zhao, and Wenjun Chen. "Vanadium Oxide-Based Artificial Synapses for Construction of Artificial Neural System." In 2024 IEEE 17th International Conference on Solid-State & Integrated Circuit Technology (ICSICT). IEEE, 2024. https://doi.org/10.1109/icsict62049.2024.10830940.

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Aboelmagd, Mohamed R., Ali Selim, and Mamdouh Abdel-Akher. "PV Grid Forecasting Analysis using Artificial Neural Network." In 2024 25th International Middle East Power System Conference (MEPCON). IEEE, 2024. https://doi.org/10.1109/mepcon63025.2024.10850132.

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Gao Ningning. "Artificial neural network management system." In 2010 2nd International Conference on Information Science and Engineering (ICISE). IEEE, 2010. http://dx.doi.org/10.1109/icise.2010.5690052.

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Bozovic, Dolores. "Physics of the Auditory System." In Neural Interfaces and Artificial Senses. Fundació Scito, 2021. http://dx.doi.org/10.29363/nanoge.nias.2021.005.

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Francelin, R., F. Gomide, and K. Loparo. "System optimization with artificial neural networks." In 1991 IEEE International Joint Conference on Neural Networks. IEEE, 1991. http://dx.doi.org/10.1109/ijcnn.1991.170324.

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Nidhil Wilfred, K. J., S. Sreeraj, B. Vijay, and V. Bagyaveereswaran. "System identification using artificial neural network." In 2015 International Conference on Circuits, Power and Computing Technologies (ICCPCT). IEEE, 2015. http://dx.doi.org/10.1109/iccpct.2015.7159360.

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George, A., and V. J. Pillai. "VNPR system using artificial neural network." In 2016 International Conference on Circuit, Power and Computing Technologies (ICCPCT). IEEE, 2016. http://dx.doi.org/10.1109/iccpct.2016.7530282.

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Wang, Liang, Nan Guo, Chao Jin, Changyuan Yu, Haw-Yaw Tam, and Chao Lu. "BOTDA system using artificial neural network." In 2017 Opto-Electronics and Communications Conference (OECC) and Photonics Global Conference (PGC). IEEE, 2017. http://dx.doi.org/10.1109/oecc.2017.8114764.

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Wilamowski, Bogdan M., and Vitaly J. Vodyanoy. "Neural network architectures for artificial noses." In 2008 Conference on Human System Interactions. IEEE, 2008. http://dx.doi.org/10.1109/hsi.2008.4581532.

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Bhartiya, Namrata, Namrata Jangid, Sheetal Jannu, Purvika Shukla, and Radhika Chapaneri. "Artificial Neural Network Based University Chatbot System." In 2019 IEEE Bombay Section Signature Conference (IBSSC). IEEE, 2019. http://dx.doi.org/10.1109/ibssc47189.2019.8973095.

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Reports on the topic "Neural artificial system"

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Powell, Bruce C. Artificial Neural Network Analysis System. Defense Technical Information Center, 2001. http://dx.doi.org/10.21236/ada392390.

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Baud-Bovy, Gabriel. A gaze-addressing communication system using artificial neural networks. Portland State University Library, 2000. http://dx.doi.org/10.15760/etd.6142.

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Byer, David E. Utilizing Routine Water Quality Instruments and Artificial Neural Networks for Monitoring Distribution System Security. Defense Technical Information Center, 2003. http://dx.doi.org/10.21236/ada414222.

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Alexander, A. Michael, and Richard W. Haskins. Application of Artificial Neural Networks to Ultrasonic Pulse Echo System for Detecting Microcracks in Concrete. Defense Technical Information Center, 1998. http://dx.doi.org/10.21236/ada347421.

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Lee, Sing H. Architecture Studies and System Demonstrations of Optical Parallel Processor for AI (Artificial Intelligence) and NI (Neural Intelligence). Defense Technical Information Center, 1988. http://dx.doi.org/10.21236/ada203241.

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Lee, Sing H. Architecture Studies and System Demonstrations of Optical Parallel Processor for AI(Artificial Intelligence) and NI(Neural Intelligence). Defense Technical Information Center, 1988. http://dx.doi.org/10.21236/ada195480.

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Markova, Oksana, Serhiy Semerikov та Maiia Popel. СoCalc as a Learning Tool for Neural Network Simulation in the Special Course “Foundations of Mathematic Informatics”. Sun SITE Central Europe, 2018. http://dx.doi.org/10.31812/0564/2250.

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The role of neural network modeling in the learning сontent of special course “Foundations of Mathematic Informatics” was discussed. The course was developed for the students of technical universities – future IT-specialists and directed to breaking the gap between theoretic computer science and it’s applied applications: software, system and computing engineering. CoCalc was justified as a learning tool of mathematical informatics in general and neural network modeling in particular. The elements of technique of using CoCalc at studying topic “Neural network and pattern recognition” of the special course “Foundations of Mathematic Informatics” are shown. The program code was presented in a CofeeScript language, which implements the basic components of artificial neural network: neurons, synaptic connections, functions of activations (tangential, sigmoid, stepped) and their derivatives, methods of calculating the network`s weights, etc. The features of the Kolmogorov–Arnold representation theorem application were discussed for determination the architecture of multilayer neural networks. The implementation of the disjunctive logical element and approximation of an arbitrary function using a three-layer neural network were given as an examples. According to the simulation results, a conclusion was made as for the limits of the use of constructed networks, in which they retain their adequacy. The framework topics of individual research of the artificial neural networks is proposed.
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Yaroshchuk, Svitlana O., Nonna N. Shapovalova, Andrii M. Striuk, Olena H. Rybalchenko, Iryna O. Dotsenko, and Svitlana V. Bilashenko. Credit scoring model for microfinance organizations. [б. в.], 2020. http://dx.doi.org/10.31812/123456789/3683.

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The purpose of the work is the development and application of models for scoring assessment of microfinance institution borrowers. This model allows to increase the efficiency of work in the field of credit. The object of research is lending. The subject of the study is a direct scoring model for improving the quality of lending using machine learning methods. The objective of the study: to determine the criteria for choosing a solvent borrower, to develop a model for an early assessment, to create software based on neural networks to determine the probability of a loan default risk. Used research methods such as analysis of the literature on banking scoring; artificial intelligence methods for scoring; modeling of scoring estimation algorithm using neural networks, empirical method for determining the optimal parameters of the training model; method of object-oriented design and programming. The result of the work is a neural network scoring model with high accuracy of calculations, an implemented system of automatic customer lending.
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Warrick, Arthur W., Gideon Oron, Mary M. Poulton, Rony Wallach, and Alex Furman. Multi-Dimensional Infiltration and Distribution of Water of Different Qualities and Solutes Related Through Artificial Neural Networks. United States Department of Agriculture, 2009. http://dx.doi.org/10.32747/2009.7695865.bard.

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The project exploits the use of Artificial Neural Networks (ANN) to describe infiltration, water, and solute distribution in the soil during irrigation. It provides a method of simulating water and solute movement in the subsurface which, in principle, is different and has some advantages over the more common approach of numerical modeling of flow and transport equations. The five objectives were (i) Numerically develop a database for the prediction of water and solute distribution for irrigation; (ii) Develop predictive models using ANN; (iii) Develop an experimental (laboratory) database of water distribution with time; within a transparent flow cell by high resolution CCD video camera; (iv) Conduct field studies to provide basic data for developing and testing the ANN; and (v) Investigate the inclusion of water quality [salinity and organic matter (OM)] in an ANN model used for predicting infiltration and subsurface water distribution. A major accomplishment was the successful use of Moment Analysis (MA) to characterize “plumes of water” applied by various types of irrigation (including drip and gravity sources). The general idea is to describe the subsurface water patterns statistically in terms of only a few (often 3) parameters which can then be predicted by the ANN. It was shown that ellipses (in two dimensions) or ellipsoids (in three dimensions) can be depicted about the center of the plume. Any fraction of water added can be related to a ‘‘probability’’ curve relating the size of the ellipse (or ellipsoid) that contains that amount of water. The initial test of an ANN to predict the moments (and hence the water plume) was with numerically generated data for infiltration from surface and subsurface drip line and point sources in three contrasting soils. The underlying dataset consisted of 1,684,500 vectors (5 soils×5 discharge rates×3 initial conditions×1,123 nodes×20 print times) where each vector had eleven elements consisting of initial water content, hydraulic properties of the soil, flow rate, time and space coordinates. The output is an estimate of subsurface water distribution for essentially any soil property, initial condition or flow rate from a drip source. Following the formal development of the ANN, we have prepared a “user-friendly” version in a spreadsheet environment (in “Excel”). The input data are selected from appropriate values and the output is instantaneous resulting in a picture of the resulting water plume. The MA has also proven valuable, on its own merit, in the description of the flow in soil under laboratory conditions for both wettable and repellant soils. This includes non-Darcian flow examples and redistribution and well as infiltration. Field experiments were conducted in different agricultural fields and various water qualities in Israel. The obtained results will be the basis for the further ANN models development. Regions of high repellence were identified primarily under the canopy of various orchard crops, including citrus and persimmons. Also, increasing OM in the applied water lead to greater repellency. Major scientific implications are that the ANN offers an alternative to conventional flow and transport modeling and that MA is a powerful technique for describing the subsurface water distributions for normal (wettable) and repellant soil. Implications of the field measurements point to the special role of OM in affecting wettability, both from the irrigation water and from soil accumulation below canopies. Implications for agriculture are that a modified approach for drip system design should be adopted for open area crops and orchards, and taking into account the OM components both in the soil and in the applied waters.
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Engel, Bernard, Yael Edan, James Simon, Hanoch Pasternak, and Shimon Edelman. Neural Networks for Quality Sorting of Agricultural Produce. United States Department of Agriculture, 1996. http://dx.doi.org/10.32747/1996.7613033.bard.

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The objectives of this project were to develop procedures and models, based on neural networks, for quality sorting of agricultural produce. Two research teams, one in Purdue University and the other in Israel, coordinated their research efforts on different aspects of each objective utilizing both melons and tomatoes as case studies. At Purdue: An expert system was developed to measure variances in human grading. Data were acquired from eight sensors: vision, two firmness sensors (destructive and nondestructive), chlorophyll from fluorescence, color sensor, electronic sniffer for odor detection, refractometer and a scale (mass). Data were analyzed and provided input for five classification models. Chlorophyll from fluorescence was found to give the best estimation for ripeness stage while the combination of machine vision and firmness from impact performed best for quality sorting. A new algorithm was developed to estimate and minimize training size for supervised classification. A new criteria was established to choose a training set such that a recurrent auto-associative memory neural network is stabilized. Moreover, this method provides for rapid and accurate updating of the classifier over growing seasons, production environments and cultivars. Different classification approaches (parametric and non-parametric) for grading were examined. Statistical methods were found to be as accurate as neural networks in grading. Classification models by voting did not enhance the classification significantly. A hybrid model that incorporated heuristic rules and either a numerical classifier or neural network was found to be superior in classification accuracy with half the required processing of solely the numerical classifier or neural network. In Israel: A multi-sensing approach utilizing non-destructive sensors was developed. Shape, color, stem identification, surface defects and bruises were measured using a color image processing system. Flavor parameters (sugar, acidity, volatiles) and ripeness were measured using a near-infrared system and an electronic sniffer. Mechanical properties were measured using three sensors: drop impact, resonance frequency and cyclic deformation. Classification algorithms for quality sorting of fruit based on multi-sensory data were developed and implemented. The algorithms included a dynamic artificial neural network, a back propagation neural network and multiple linear regression. Results indicated that classification based on multiple sensors may be applied in real-time sorting and can improve overall classification. Advanced image processing algorithms were developed for shape determination, bruise and stem identification and general color and color homogeneity. An unsupervised method was developed to extract necessary vision features. The primary advantage of the algorithms developed is their ability to learn to determine the visual quality of almost any fruit or vegetable with no need for specific modification and no a-priori knowledge. Moreover, since there is no assumption as to the type of blemish to be characterized, the algorithm is capable of distinguishing between stems and bruises. This enables sorting of fruit without knowing the fruits' orientation. A new algorithm for on-line clustering of data was developed. The algorithm's adaptability is designed to overcome some of the difficulties encountered when incrementally clustering sparse data and preserves information even with memory constraints. Large quantities of data (many images) of high dimensionality (due to multiple sensors) and new information arriving incrementally (a function of the temporal dynamics of any natural process) can now be processed. Furhermore, since the learning is done on-line, it can be implemented in real-time. The methodology developed was tested to determine external quality of tomatoes based on visual information. An improved model for color sorting which is stable and does not require recalibration for each season was developed for color determination. Excellent classification results were obtained for both color and firmness classification. Results indicted that maturity classification can be obtained using a drop-impact and a vision sensor in order to predict the storability and marketing of harvested fruits. In conclusion: We have been able to define quantitatively the critical parameters in the quality sorting and grading of both fresh market cantaloupes and tomatoes. We have been able to accomplish this using nondestructive measurements and in a manner consistent with expert human grading and in accordance with market acceptance. This research constructed and used large databases of both commodities, for comparative evaluation and optimization of expert system, statistical and/or neural network models. The models developed in this research were successfully tested, and should be applicable to a wide range of other fruits and vegetables. These findings are valuable for the development of on-line grading and sorting of agricultural produce through the incorporation of multiple measurement inputs that rapidly define quality in an automated manner, and in a manner consistent with the human graders and inspectors.
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