Volume 5, Issue 1, June 2019, Page: 7-11
An Overview of Neural Network
Mohaiminul Islam, School of Intelligent Technology and Engineering, Chongqing University of Science and Technology, Chongqing, China
Guorong Chen, School of Intelligent Technology and Engineering, Chongqing University of Science and Technology, Chongqing, China
Shangzhu Jin, School of Intelligent Technology and Engineering, Chongqing University of Science and Technology, Chongqing, China
Received: May 8, 2019;       Accepted: Jun. 17, 2019;       Published: Jun. 29, 2019
DOI: 10.11648/j.ajnna.20190501.12      View  27      Downloads  31
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.
Artificial Intelligence, Neural Network, Sigmoid Function, Neurons, Nodes
To cite this article
Mohaiminul Islam, Guorong Chen, Shangzhu Jin, An Overview of Neural Network, American Journal of Neural Networks and Applications. Vol. 5, No. 1, 2019, pp. 7-11. doi: 10.11648/j.ajnna.20190501.12
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This article is an open access article distributed under the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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