Knowledge Processing System Using Chaotic Associative Memory

In this paper, we propose a knowledge processing system using chaotic associative memory (KPCAM). KPCAM is based on a chaotic neural network (CAM) composed of chaotic neurons. In conventional chaotic neural network, when a stored pattern is given continuously to the network as an external input, the input pattern vicinity is searched. The CAM makes use of this property to separate superimposed patterns and to deal with many-tomany associations. In this research, the CAM is applied to knowledge processing in which knowledge is represented in a form of semantic network. The proposed KPCAM has the following features: (1) it can deal with knowledge represented in a form of semantic network; (2) it can deal with characteristic inheritance; (3) it is robust for noisy input. A series of computer simulations shows the effectiveness of the proposed system.

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A Novel Chaotic Neural Network Using Memristive Synapse with Applications in Associative Memory

Abstract and Applied Analysis ◽

10.1155/2012/405739 ◽

2012 ◽

Vol 2012 ◽

pp. 1-19 ◽

Cited By ~ 10

Author(s):

Xiaofang Hu ◽

Shukai Duan ◽

Lidan Wang

Keyword(s):

Neural Network ◽

Associative Memory ◽

Large Scale ◽

Cmos Technology ◽

Chaotic Neural Network ◽

Circuit Element ◽

Nanoscale Cmos ◽

Dynamical Behaviors ◽

Implementation Scheme ◽

Further Development

Chaotic Neural Network, also denoted by the acronym CNN, has rich dynamical behaviors that can be harnessed in promising engineering applications. However, due to its complex synapse learning rules and network structure, it is difficult to update its synaptic weights quickly and implement its large scale physical circuit. This paper addresses an implementation scheme of a novel CNN with memristive neural synapses that may provide a feasible solution for further development of CNN. Memristor, widely known as the fourth fundamental circuit element, was theoretically predicted by Chua in 1971 and has been developed in 2008 by the researchers in Hewlett-Packard Laboratory. Memristor based hybrid nanoscale CMOS technology is expected to revolutionize the digital and neuromorphic computation. The proposed memristive CNN has four significant features: (1) nanoscale memristors can simplify the synaptic circuit greatly and enable the synaptic weights update easily; (2) it can separate stored patterns from superimposed input; (3) it can deal with one-to-many associative memory; (4) it can deal with many-to-many associative memory. Simulation results are provided to illustrate the effectiveness of the proposed scheme.

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Bioinspired Associative Memories

Encyclopedia of Artificial Intelligence ◽

10.4018/978-1-59904-849-9.ch039 ◽

2011 ◽

pp. 248-255

Author(s):

Roberto A. Vazquez ◽

Humberto Sossa

Keyword(s):

Neural Network ◽

Neural Networks ◽

Associative Memory ◽

Vision System ◽

Special Kind ◽

Input Pattern ◽

3D Object Recognition ◽

Infant Vision ◽

Output Pattern ◽

Full Power

An associative memory AM is a special kind of neural network that allows recalling one output pattern given an input pattern as a key that might be altered by some kind of noise (additive, subtractive or mixed). Most of these models have several constraints that limit their applicability in complex problems such as face recognition (FR) and 3D object recognition (3DOR). Despite of the power of these approaches, they cannot reach their full power without applying new mechanisms based on current and future study of biological neural networks. In this direction, we would like to present a brief summary concerning a new associative model based on some neurobiological aspects of human brain. In addition, we would like to describe how this dynamic associative memory (DAM), combined with some aspects of infant vision system, could be applied to solve some of the most important problems of pattern recognition: FR and 3DOR.

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Knowledge processing system using improved chaotic associative memory

Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks. IJCNN 2000. Neural Computing: New Challenges and Perspectives for the New Millennium ◽

10.1109/ijcnn.2000.861531 ◽

2000 ◽

Cited By ~ 1

Author(s):

Y. Osana ◽

M. Hagiwara

Keyword(s):

Associative Memory ◽

Processing System ◽

Knowledge Processing

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Neuronless Knowledge Processing in Forests

Applied Sciences ◽

10.3390/app10072509 ◽

2020 ◽

Vol 10 (7) ◽

pp. 2509

Author(s):

Aviv Segev ◽

Dorothy Curtis ◽

Christine Balili ◽

Sukhwan Jung

Keyword(s):

Neural Network ◽

Processing System ◽

Basic Knowledge ◽

Resource Usage ◽

Knowledge Processing ◽

Single Tree ◽

Canopy Tree ◽

The Us ◽

Forest Use ◽

Tree Coverage

Neurons are viewed as the basic cells that process and transmit information. Trees and neurons share a similar structure and neurotransmitter-like substances. No evidence for structures such as neurons, synapses, or a brain has been found inside plants. Consequently, the ability of a network of trees to process information in a method similar to that of a neural network and to make decisions regarding the usage of resources is unperceived. We show that the network between trees is used for knowledge processing to implement decisions that prioritize the forest over a single tree regarding forest use and optimization of resources, similar to the processes of a biological neural network. We found that when there is resection of a network of trees in a forest, namely a trail, each network part will try optimizing its overall access to light resources, represented by canopy tree coverage, independently. This was analyzed in 323 forests in different locations across the US where forest resection is performed by trails. Our results demonstrate that neuron-like relations can occur in a forest knowledge processing system. We anticipate that other systems exist in nature where the basic knowledge processing for resource usage is performed by components other than neurons.

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