Title :
Systolic architectures for high order correlation artificial neural nets
Author :
Bao, Wanqun ; Bayoumi, Magdy A.
Author_Institution :
Center for Adv. Comput. Studies, Univ. of Southwestern Louisiana, Lafayette, LA, USA
Abstract :
Systolic architectures for high-order correlation neural nets are proposed. They are based on using a multiply associated high-order correlation tensor as a mathematical model. The case of triple-order correlation nets is analyzed. A design procedure based on developing a triple-order correlation dependence graph is presented. The developed method is flexible. Several implementation issues are discussed
Keywords :
cellular arrays; correlation theory; neural nets; parallel architectures; tensors; artificial neural nets; design procedure; high order correlation; implementation issues; multiply associated high-order correlation tensor; triple-order correlation nets; Adaptive control; Artificial neural networks; Computer architecture; Computer networks; Immune system; Neurons; Parallel architectures; Retina; Robots; Very large scale integration;
Conference_Titel :
Circuits and Systems, 1989., IEEE International Symposium on
Conference_Location :
Portland, OR
DOI :
10.1109/ISCAS.1989.100568
