A Transient Chaotic Associative Memory Model with Temporary Stay Function
نویسندگان
چکیده
منابع مشابه
A Chaotic Bidirectional Associative Memory
A new BAM model is presented that uses a chaotic output function operating in chaotic mode during recall. Our results show that the model develops well-defined attractor basins, with the result that our chaotic BAM is more tolerant to noise than a regular fixed point BAM. This is concluded from simulations that showed the superior performance of the new model when compared to the original BAM a...
متن کاملChaotic Complex-Valued Associative Memory
Abstract—In this paper, we propose a chaotic complexvalued associative memory which can realize a dynamic association of multi-valued patterns. The proposed model is based on a complex-valued associative memory and a chaotic associative memory. The complex-valued associative memory can treat multi-valued patterns, and the chaotic associative memory can recall stored patterns dynamically. The pr...
متن کاملImproved Chaotic Associative Memory for Successive Learning
Recently, neural networks are drawing much attention as a method to realize flexible information processing. Neural networks consider neuron groups of the brain in the creature, and imitate these neurons technologically. Neural networks have some features, especially one of the important features is that the networks can learn to acquire the ability of information processing. In the filed of ne...
متن کاملHetero Chaotic Associative Memory for Successive Learning with Give Up Function -One-to-Many Associations
In this paper, we propose a Hetero Chaotic Associative Memory for Successive Learning (HCAMSL) with give up function. The proposed model is based on a Chaotic Associative Memory for Successive Memory (CAMSL). In the proposed HCAMSL, the learning process and the recall process are not divided. When an unstored pattern is given to the network, the HCAMSL can learn the pattern successively.
متن کاملA modified chaotic associative memory system for gray-scale images
Globally coupled map (GCM) model can evolve through chaotic searching into several stable periodic orbits under properly controlled parameters. This can be exploited in information processing such as associative memory and optimization. In this paper, we propose a novel covariance learning rule for multivalue patterns and apply it in memorization of gray-scale images based on modified GCM model...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: IEEJ Transactions on Electronics, Information and Systems
سال: 2008
ISSN: 0385-4221,1348-8155
DOI: 10.1541/ieejeiss.128.1852