Title :
A new k-winners-take-all neural network and its array architecture
Author :
Yen, Jui-Cheng ; Guo, Jiun-In ; Chen, Hun-Chen
Author_Institution :
Dept. of Electron. Eng., Nat. Lien-Ho Coll. of Technol. & Commerce, Taiwan, China
fDate :
9/1/1998 12:00:00 AM
Abstract :
In this paper, a new neural-network model called WINSTRON and its novel array architecture are proposed. Based on a competitive learning algorithm that is originated from the coarse-fine competition, WINSTRON can identify the k larger elements or the k smaller ones in a data set. We will then prove that WINSTRON converges to the correct state in any situation. In addition, the convergence rates of WINSTRON for three special data distributions will be derived. In order to realize WINSTRON, its array architecture with low hardware complexity and high computing speed is also detailed. Finally, simulation results are included to demonstrate its effectiveness and its advantages over three existing networks
Keywords :
neural net architecture; parallel architectures; WINSTRON; array architecture; coarse-fine competition; competitive learning algorithm; computing speed; data distributions; hardware complexity; winners-take-all neural network; Analog circuits; Arithmetic; Artificial neural networks; Computational modeling; Computer architecture; Convergence; Hardware; Neural networks; Neurons; Pattern recognition;
Journal_Title :
Neural Networks, IEEE Transactions on
