DocumentCode :
1563161
Title :
A Robust Morphological Associative Memory Endowed with Dendrites
Author :
Hu, Jinbin ; Deng, Wei
Author_Institution :
Sch. of Comput. Sci. & Technol., Soochow Univ., Suzhou
Volume :
1
fYear :
2005
Firstpage :
147
Lastpage :
149
Abstract :
Morphological neural networks are based on a new paradigm for neural computing. The basic neural computation in a morphological neuron takes the maximum or minimum of the sums of neural values and their corresponding synaptic weights. As a consequence, the properties of morphological neural networks are drastically different than those of traditional neural network models. By making the morphological neuron incorporate dendritic processes, a more realistic model is established. In this paper, we restrict our attention to morphological associative memory endowed with dendrites (MAMED). After a brief review of MAMED and a short discussion about the disadvantages of MAMED in coping with random noises, we present an efficient way of choosing parameter for MAMED taking into account position characteristics of patterns. Our experimental results demonstrate that our way not only makes MAMED be robust in the presence of random noises, but avoids a series of problems brought by choosing arbitrarily
Keywords :
content-addressable storage; dendrites; neural nets; random noise; dendrites; morphological associative memory; morphological neural networks; neural computation; random noises; Artificial neural networks; Associative memory; Biological system modeling; Computer networks; Computer science; Convergence; Kernel; Neural networks; Neurons; Noise robustness;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Neural Networks and Brain, 2005. ICNN&B '05. International Conference on
Conference_Location :
Beijing
Print_ISBN :
0-7803-9422-4
Type :
conf
DOI :
10.1109/ICNNB.2005.1614586
Filename :
1614586
Link To Document :
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