Title :
Frequency-based model of memory in neural networks
Author :
Moussa, M. Bacem ; McKeeman, John C.
Author_Institution :
Bradley Dept. of Electr. Eng., Virginia Polytech. Inst. & State Univ., Blacksburg, VA, USA
Abstract :
A neural-network model is presented that uses the frequency content of a signal to store and process information. The model is described theoretically and compared to amplitude-based neural networks. A version of the network is also simulated and its storage performance evaluated. A theoretical motivation for such a model is derived from M.L. Minsky´s (1986) knowledge-line (k-line) theory of memory. The concept of a k-line is interpreted and associated with a frequency supportive network and a practical model for the implementation of a k-line-based memory is proposed. The implementation of a frequency-based network as a pattern recognizer is considered, along with possible specific applications
Keywords :
neural nets; pattern recognition; frequency content; frequency supportive network; k-line; knowledge-line theory; memory; neural networks; neural-network model; pattern recognizer; storage performance; Biological neural networks; Biological system modeling; Biology computing; Brain modeling; Frequency; Hopfield neural networks; Intelligent networks; Neural networks; Neurons; Signal processing;
Conference_Titel :
Aerospace and Electronics Conference, 1988. NAECON 1988., Proceedings of the IEEE 1988 National
Conference_Location :
Dayton, OH
DOI :
10.1109/NAECON.1988.195171
