NDLI: A multilayer neural network with nonlinear inputs and trainable activation functions: structure and simultaneous learning algorithm

Content Provider	IEEE Xplore Digital Library
Author	Nakayama, K. Hirano, A. Ido, I.
Copyright Year	1999
Description	Author affiliation: Dept. of Electr. & Comput. Eng., Kanazawa Univ., Japan (Nakayama, K.)
Abstract	Network size of neural networks is highly dependent on activation functions. A trainable activation function is proposed, which consists of a linear combination of some basic functions. The activation functions and the connection weights are simultaneously trained. An 8-bit parity problem can be solved by using a single output unit and no hidden unit. In this paper, we expand this model to multilayer neural networks. Furthermore, nonlinear functions are used at the unit inputs in order to realize more flexible transfer functions. The previous activation functions and the new nonlinear functions are also simultaneously trained. More complex pattern classification problems can be solved with a small number of units and fast convergence.
Starting Page	1657
Ending Page	1661
File Size	338462
Page Count	5
File Format	PDF
ISBN	0780355296
ISSN	10987576
DOI	10.1109/IJCNN.1999.832622
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	1999-07-10
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Multi-layer neural network Neural networks Transfer functions Pattern classification Convergence Learning systems Parity check codes Computer simulation Computer architecture Nonlinear equations
Content Type	Text
Resource Type	Article

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4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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