RETRIEVAL IN INTERACTING NEURAL NETWORKS

1992 ◽  
Vol 03 (04) ◽  
pp. 389-393 ◽  
Author(s):  
MÁRCIA M. OCHI ◽  
O.L.T. DE MENEZES
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Associative Memory ◽  
Global Pattern ◽  
Connected Network ◽  
Common Learning

We study the associative memory properties for a particular structure of neural network. It is assumed a low connectivity between two highly connected network regions. As process of common learning correlates the subpatterns stored in each region. The retrieval of the global pattern is studied as a function of the overlap between the associated subpatterns.

Bioinspired Associative Memories

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.

scholarly journals An Empirical Analysis of Corporate Financial Management Risk Prediction Based on Associative Memory Neural Network

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Hui Chu
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Risk Management ◽  
Human Brain ◽  
Associative Memory ◽  
Financial Management ◽  
Financial Risk ◽  
Control Measures ◽  
Time Lags ◽  
Time Varying

As a human brain-like computational model that can reflect the cognitive function of the brain, the problem of dynamic analysis of associative memory neural networks has attracted the attention of scholars. This paper combines associative memory neural networks with enterprise financial management risks, studies the synchronization control and stability analysis problems of unidirectional associative memory-like human brain amnestic neural networks with perturbation and mixed time-varying time lags, proposes a bidirectional associative memory-like brain stochastic amnestic neural network model with mixed time-varying time lags, designs a discrete-time sampling control strategy based on the model, and studies various types of recent financial risks. Based on the early warning research, based on the associative memory neural network method, we propose to reconstruct the risk categories, including improving the enterprise risk management system, enhancing the awareness of financial risk management from top to bottom, and strengthening the core competitiveness of the enterprise itself and control measures for financing, investment, operation, and cash flow risks.

Global Stability and Bifurcation in Delayed Bidirectional Associative Memory Neural Networks With an Arbitrary Number of Neurons

2017 ◽  
Vol 139 (8) ◽  
Author(s):  
Elham Javidmanesh
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Hopf Bifurcation ◽  
Global Stability ◽  
Associative Memory ◽  
Bam Neural Networks ◽  
Bidirectional Associative Memory ◽  
Numerical Examples ◽  
Bam Neural Network ◽  
General Method

In this paper, delayed bidirectional associative memory (BAM) neural networks, which consist of one neuron in the X-layer and other neurons in the Y-layer, will be studied. Hopf bifurcation analysis of these systems will be discussed by proposing a general method. In fact, a general n-neuron BAM neural network model is considered, and the associated characteristic equation is studied by classification according to n. Here, n can be chosen arbitrarily. Moreover, we find an appropriate Lyapunov function that under a hypothesis, results in global stability. Numerical examples are also presented.

scholarly journals Stability analysis of almost periodic solutions for discontinuous bidirectional associative memory (BAM) neural networks with discrete and distributed delays

2020 ◽  
Vol 0 (0) ◽  
Author(s):  
Weijun Xie ◽  
Fanchao Kong ◽  
Hongjun Qiu ◽  
Xiangying Fu
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Periodic Solutions ◽  
Associative Memory ◽  
Stability Result ◽  
Distributed Delays ◽  
Bam Neural Networks ◽  
Almost Periodic Solutions ◽  
Almost Periodic ◽  
Bidirectional Associative Memory

AbstractThis paper aims to discuss a class of discontinuous bidirectional associative memory (BAM) neural networks with discrete and distributed delays. By using the set-valued map, differential inclusions theory and fundamental solution matrix, the existence of almost-periodic solutions for the addressed neural network model is firstly discussed under some new conditions. Subsequently, based on the non-smooth analysis theory with Lyapunov-like strategy, the global exponential stability result of the almost-periodic solution for the proposed neural network system is also established without using any additional conditions. The results achieved in the paper extend some previous works on BAM neural networks to the discontinuous case and it is worth mentioning that it is the first time to investigate the almost-periodic dynamic behavior for the BAM neural networks like the form in this paper. Finally, in order to demonstrate the effectiveness of the theoretical schemes, simulation results of two topical numerical examples are delineated.

scholarly journals A Biometric AsymmetricCryptosystem Software Module Based on Convolutional Neural Networks

2021 ◽  
Vol 13 (6) ◽  
pp. 1-12
Author(s):  
Ilyenko Anna ◽  
◽  
Ilyenko Sergii ◽  
Herasymenko Marharyta
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Associative Memory ◽  
Bidirectional Associative Memory ◽  
Software Module ◽  
Convolutional Networks ◽  
Cryptographic Key ◽  
Authentication System ◽  
And Training

During the research, the analysis of the existing biometric cryptographic systems was carried out. Some methods that help to generate biometric features were considered and compared with a cryptographic key. For comparing compact vectors of biometric images and cryptographic keys, the following methods are analyzed: designing and training of bidirectional associative memory; designing and training of single-layer and multilayer neural networks. As a result of comparative analysis of algorithms for extracting primary biometric features and comparing the generated image to a private key within the proposed authentication system, it was found that deep convolutional networks and neural network bidirectional associative memory are the most effective approach to process the data. In the research, an approach based on the integration of a biometric system and a cryptographic module was proposed, which allows using of a generated secret cryptographic key based on a biometric sample as the output of a neural network. The RSA algorithm is chosen to generate a private cryptographic key by use of convolutional neural networks and Python libraries. The software authentication module is implemented based on the client-server architecture using various internal Python libraries. Such authentication system should be used in systems where the user data and his valuable information resources are stored or where the user can perform certain valuable operations for which a cryptographic key is required. Proposed software module based on convolutional neural networks will be a perfect tool for ensuring the confidentiality of information and for all information-communication systems, because protecting information system from unauthorized access is one of the most pressing problems. This approach as software module solves the problem of secure generating and storing the secret key and author propose combination of the convolutional neural network with bidirectional associative memory, which is used to recognize the biometric sample, generate the image, and match it with a cryptographic key. The use of this software approach allows today to reduce the probability of errors of the first and second kind in authentication system and absolute number of errors was minimized by an average of 1,5 times. The proportion of correctly recognized images by the comparating together convolutional networks and neural network bidirectional associative memory in the authentication software module increased to 96,97%, which is on average from 1,08 times up to 1,01 times The authors further plan a number of scientific and technical solutions to develop and implement effective methods, tools to meet the requirements, principles and approaches to cybersecurity and cryptosystems for provide integrity and onfidentiality of information in experimental computer systems and networks.

ASSOCIATIVE MEMORY IN NEURAL NETWORKS WITH BINARY SYNAPSES

1990 ◽  
Vol 04 (11) ◽  
pp. 713-716 ◽  
Author(s):  
M.V. TSODYKS
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Associative Memory ◽  
Learning Algorithm ◽  
The Neural Network ◽  
One Step

The simple learning algorithm in the neural network with binary synapses, which take one step for storing one pattern is considered. The resulting model turns out to be palimpsestic, and the number of patterns which can be effectively retrieved is L~N1/2.

RECOGNITION OF TELUGU CHARACTERS USING NEURAL NETWORKS

1995 ◽  
Vol 06 (03) ◽  
pp. 317-357 ◽  
Author(s):  
M.B. SUKHASWAMI ◽  
P. SEETHARAMULU ◽  
ARUN K. PUJARI
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Learning Strategies ◽  
Associative Memory ◽  
Hopfield Neural Network ◽  
Hopfield Network ◽  
Initial Attempt ◽  
Multiple Neural Networks ◽  
Digitized Documents ◽  
Multiple Neural Network

The aim of the present work is to recognize printed and handwritten Telugu characters using artificial neural networks (ANNs). Earlier work on recognition of Telugu characters has been done using conventional pattern recognition techniques. We make an initial attempt here of using neural networks for recognition with the aim of improving upon earlier methods which do not perform effectively in the presence of noise and distortion in the characters. The Hopfield model of neural network working as an associative memory is chosen for recognition purposes initially. Due to limitation in the capacity of the Hopfield neural network, we propose a new scheme named here as the Multiple Neural Network Associative Memory (MNNAM). The limitation in storage capacity has been overcome by combining multiple neural networks which work in parallel. It is also demonstrated that the Hopfield network is suitable for recognizing noisy printed characters as well as handwritten characters written by different “hands” in a variety of styles. Detailed experiments have been carried out using several learning strategies and results are reported. It is shown here that satisfactory recognition is possible using the proposed strategy. A detailed preprocessing scheme of the Telugu characters from digitized documents is also described.

scholarly journals AN IMPROVED ARCHITECTURE FOR COMPETITIVE AND COOPERATIVE NEURONS (CCNS) IN NEURAL NETWORKS

2014 ◽  
pp. 8-15
Author(s):  
M. Kamrul Islam
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Associative Memory ◽  
Input Pattern ◽  
Learning Phase ◽  
Input Vector ◽  
Memory Model ◽  
Output Vector ◽  
Recall Phase

In neural networks, the associative memory is one in which applying some input pattern leads to the response of a corresponding stored pattern. During the learning phase the memory is fed with a number of input vectors and in the recall phase when some known input is presented to it, the network recalls and reproduces the output vector. Here, we improve and increase the storing ability of the memory model proposed in [1]. We show that there are certain instances where their algorithm can not produce the desired performance by retrieving exactly the correct vector. That is, in their algorithm, a number of output vectors can become activated from the stimulus of an input vector while the desired output is just a single vector. Our proposed solution overcomes this and uniquely determines the output vector as some input vector is applied. Thus we provide a more general scenario of this neural network memory model consisting of Competitive Cooperative Neurons (CCNs).

ASSOCIATIVE MEMORY USING SYNCHRONIZATION IN A CHAOTIC NEURAL NETWORK

2001 ◽  
Vol 12 (01) ◽  
pp. 19-29 ◽  
Author(s):  
Z. TAN ◽  
M. K. ALI
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Associative Memory ◽  
Storage Capacity ◽  
Initial Conditions ◽  
Neural Systems ◽  
Chaotic Neural Network ◽  
Relative Time ◽  
Chaotic Neural Networks ◽  
And Storage

Synchronization is introduced into a chaotic neural network model to discuss its associative memory. The relative time of synchronization of trajectories is used as a measure of pattern recognition by chaotic neural networks. The retrievability of memory is shown to be connected to synapses, initial conditions and storage capacity. The technique is simple and easy to apply to neural systems.

scholarly journals Design of Memristive Hopfield Neural Network using Memristor Bridges

2018 ◽  
Vol 7 (3.12) ◽  
pp. 652
Author(s):  
Monurajan P ◽  
Ruhanbevi A ◽  
Manjula J
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Associative Memory ◽  
Optimization Problems ◽  
Travelling Salesman Problem ◽  
Hopfield Neural Network ◽  
Hopfield Neural Networks ◽  
Significant Feature ◽  
Mathematical Equations ◽  
Areas Of Interest

Artificial Neural Networks are interconnection of neurons inspired from the biological neural network of the brain. ANN is claimed to rule the future, spreads its wings to various areas of interest to name a few such as optimization, information technology, cryptography, image processing and even in medical diagnosis. There are devices which possess synaptic behaviour, one such device is memristor. Bridge circuit of memristors can be combined together to form neurons. Neurons can be made into a network with appropriate parameters to store data or images. Hopfield neural networks are chosen to store the data in associative memory. Hopfield neural networks are a significant feature in ANN which are recurrent in nature and in general are used as associative memory and in solving optimization problems such as the Travelling Salesman Problem. The paper deals on the construction of memristive Hopfield neural network using memristor bridging circuit and its application in the associative memory. This paper also illustrates the experiment with mathematical equations and the associative memory concept of the network using Matlab.  

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