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An Overview of Neural Network

Received: 8 May 2019     Accepted: 17 June 2019     Published: 29 June 2019
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Abstract

Neural networks represent a brain metaphor for information processing. These models are biologically inspired rather than an exact replica of how the brain actually functions. Neural networks have been shown to be very promising systems in many forecasting applications and business classification applications due to their ability to learn from the data. This article aims to provide a brief overview of artificial neural network. The artificial neural network learns by updating the network architecture and connection weights so that the network can efficiently perform a task. It can learn either from available training patterns or automatically learn from examples or input-output relations. Neural network-based models continue to achieve impressive results on longstanding machine learning problems, but establishing their capacity to reason about abstract concepts has proven difficult. Building on previous efforts to solve this important feature of general-purpose learning systems, our latest paper sets out an approach for measuring abstract reasoning in learning machines, and reveals some important insights about the nature of generalization itself. Artificial neural networks can learn by example like the way humans do. An artificial neural net is configured for a specific application like pattern recognition through a learning process. Learning in biological systems consists of adjustments to the synaptic connections that exist between neurons. This is true of artificial neural networks as well. Artificial neural networks can be applied to an increasing number of real-world problems of considerable complexity. They are used for solving problems that are too complex for conventional technologies or those types of problems that do not have an algorithmic solution.

Published in American Journal of Neural Networks and Applications (Volume 5, Issue 1)
DOI 10.11648/j.ajnna.20190501.12
Page(s) 7-11
Creative Commons

This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.

Copyright

Copyright © The Author(s), 2019. Published by Science Publishing Group

Keywords

Artificial Intelligence, Neural Network, Sigmoid Function, Neurons, Nodes

References
[1] Neural Networks at Pacific Northwest National Laboratory http://www.emsl.pnl.gov:2080/docs/cie/neural/neural.homepage.html
[2] Klimasauskas, CC. (1989). The 1989 Neuro Computing Bibliography. Hammerstrom, D. (1986). A Connectionist/Neural Network Bibliography.
[3] N. Murata, S. Yoshizawa, and S. Amari, ―Learning curves, model selection and complexity of neural networks,‖ in Advances in Neural Information Processing Systems 5, S. Jose Hanson, J. D. Cowan, and C. Lee Giles, ed. San Mateo, CA: Morgan Kaufmann, 1993, pp. 607-614.
[4] Bradshaw, J. A., Carden, K. J., Riordan, D., 1991. Ecological ―Applications Using a Novel Expert System Shell‖. Comp. Appl. Biosci. 7, 79–83.
[5] Lippmann, R. P., 1987. An introduction to computing with neural nets. IEEE Accost. Speech Signal Process. Mag., April: 4-22.
[6] Murphy, K. P. Machine Learning: A Probabilistic Perspective. Cambridge, Massachusetts: The MIT Press, 2012.
[7] Hubel, H. D. and Wiesel, T. N. '' Receptive Fields of Single neurones in the Cat’s Striate Cortex.'' Journal of Physiology. Vol 148, pp. 574-591, 1959.
[8] Jeannette Lawrence, Data Preparation for a Neural Network, Neural NetworkSpecial Report: A Miller Freeman Publication, 1992.
[9] Gene Bylinsky, Computers That Learn By Doing, Fortune, September 6, 1993.
[10] Dennis Collins, Brain Maker: Strange, Captivating, Easy to Use, California Computer News, July, 1990.
[11] 1993 Readers' Choice Awards Winner: Brain Maker Professional, Software: Artificial Intelligence, Technical Analysis of Stocks & Commodities, Bonus Issue 1994.
[12] Carlton F. Vogt Jr., Brain Maker: This Will Teach Your Computer to Think, Design News, December 3, 1990.
[13] John Vester, Artificial Intelligence and Real Life, Computor Edge, October 22, 1993.
[14] George W. Dombi and Jeannette Lawrence, Analysis of protein transmembrane helicalregions by a neural network, Protein Science (1994), 3:557-566.
[15] Henrik Lundstedt, Neural Networks and Predictions of Solar-terrestrial Effects, Lund Observatory, Lund, Sweden, 1990.
Cite This Article
  • APA Style

    Mohaiminul Islam, Guorong Chen, Shangzhu Jin. (2019). An Overview of Neural Network. American Journal of Neural Networks and Applications, 5(1), 7-11. https://doi.org/10.11648/j.ajnna.20190501.12

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    ACS Style

    Mohaiminul Islam; Guorong Chen; Shangzhu Jin. An Overview of Neural Network. Am. J. Neural Netw. Appl. 2019, 5(1), 7-11. doi: 10.11648/j.ajnna.20190501.12

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    AMA Style

    Mohaiminul Islam, Guorong Chen, Shangzhu Jin. An Overview of Neural Network. Am J Neural Netw Appl. 2019;5(1):7-11. doi: 10.11648/j.ajnna.20190501.12

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  • @article{10.11648/j.ajnna.20190501.12,
      author = {Mohaiminul Islam and Guorong Chen and Shangzhu Jin},
      title = {An Overview of Neural Network},
      journal = {American Journal of Neural Networks and Applications},
      volume = {5},
      number = {1},
      pages = {7-11},
      doi = {10.11648/j.ajnna.20190501.12},
      url = {https://doi.org/10.11648/j.ajnna.20190501.12},
      eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajnna.20190501.12},
      abstract = {Neural networks represent a brain metaphor for information processing. These models are biologically inspired rather than an exact replica of how the brain actually functions. Neural networks have been shown to be very promising systems in many forecasting applications and business classification applications due to their ability to learn from the data. This article aims to provide a brief overview of artificial neural network. The artificial neural network learns by updating the network architecture and connection weights so that the network can efficiently perform a task. It can learn either from available training patterns or automatically learn from examples or input-output relations. Neural network-based models continue to achieve impressive results on longstanding machine learning problems, but establishing their capacity to reason about abstract concepts has proven difficult. Building on previous efforts to solve this important feature of general-purpose learning systems, our latest paper sets out an approach for measuring abstract reasoning in learning machines, and reveals some important insights about the nature of generalization itself. Artificial neural networks can learn by example like the way humans do. An artificial neural net is configured for a specific application like pattern recognition through a learning process. Learning in biological systems consists of adjustments to the synaptic connections that exist between neurons. This is true of artificial neural networks as well. Artificial neural networks can be applied to an increasing number of real-world problems of considerable complexity. They are used for solving problems that are too complex for conventional technologies or those types of problems that do not have an algorithmic solution.},
     year = {2019}
    }
    

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    JF  - American Journal of Neural Networks and Applications
    JO  - American Journal of Neural Networks and Applications
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    AB  - Neural networks represent a brain metaphor for information processing. These models are biologically inspired rather than an exact replica of how the brain actually functions. Neural networks have been shown to be very promising systems in many forecasting applications and business classification applications due to their ability to learn from the data. This article aims to provide a brief overview of artificial neural network. The artificial neural network learns by updating the network architecture and connection weights so that the network can efficiently perform a task. It can learn either from available training patterns or automatically learn from examples or input-output relations. Neural network-based models continue to achieve impressive results on longstanding machine learning problems, but establishing their capacity to reason about abstract concepts has proven difficult. Building on previous efforts to solve this important feature of general-purpose learning systems, our latest paper sets out an approach for measuring abstract reasoning in learning machines, and reveals some important insights about the nature of generalization itself. Artificial neural networks can learn by example like the way humans do. An artificial neural net is configured for a specific application like pattern recognition through a learning process. Learning in biological systems consists of adjustments to the synaptic connections that exist between neurons. This is true of artificial neural networks as well. Artificial neural networks can be applied to an increasing number of real-world problems of considerable complexity. They are used for solving problems that are too complex for conventional technologies or those types of problems that do not have an algorithmic solution.
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Author Information
  • School of Intelligent Technology and Engineering, Chongqing University of Science and Technology, Chongqing, China

  • School of Intelligent Technology and Engineering, Chongqing University of Science and Technology, Chongqing, China

  • School of Intelligent Technology and Engineering, Chongqing University of Science and Technology, Chongqing, China

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