Recognizing partially occluded objects by a bidirectional associative memory neural network

1993 ◽  
Vol 32 (7) ◽  
pp. 1539 ◽  
Author(s):  
Nirwan Ansari
Keyword(s):  
Neural Network ◽  
Associative Memory ◽  
Bidirectional Associative Memory ◽  
Occluded Objects ◽  
Partially Occluded

A spatiotemporal neural network for recognizing partially occluded objects

1998 ◽  
Vol 46 (7) ◽  
pp. 1991-2000 ◽  
Author(s):  
Pau-Choo Chung ◽  
E-Liang Chen ◽  
Jia-Bin Wu
Keyword(s):  
Neural Network ◽  
Occluded Objects ◽  
Partially Occluded

Recognition of partially occluded objects using neural network based indexing

1999 ◽  
Vol 32 (10) ◽  
pp. 1737-1749 ◽  
Author(s):  
Navin Rajpal ◽  
Santanu Chaudhury ◽  
Subhashis Banerjee
Keyword(s):  
Neural Network ◽  
Occluded Objects ◽  
Partially Occluded

scholarly journals A Study of Associative Memories with Hopfield neural network Model for handwritten character recognition

2018 ◽  
Vol 3 (01) ◽  
Author(s):  
Sandeep Kumar ◽  
Manu Pratap Singh
Keyword(s):  
Neural Network ◽  
Associative Memory ◽  
Network Model ◽  
Neural Network Model ◽  
Character Recognition ◽  
Hopfield Neural Network ◽  
Recurrent Network ◽  
Associative Memories ◽  
Bidirectional Associative Memory ◽  
Content Addressable Memory

Neural network is the most important model which has been studied in past decades by several researchers. Hopfield model is one of the network model proposed by J.J. Hopfield that describes the organization of neurons in such a way that they function as associative memory or also called content addressable memory. This is a recurrent network similar to recurrent layer of the hamming network but which can effectively perform the operation of both layer hamming network. The design of recurrent network has always been interesting problems to research and a lot of work is going on present application. In present paper we will discuss about the design of Hopfield Neural Network (HNNs), bidirectional associative memory (BAMs) and multidirectional associative memory (MAMs) for handwritten characters recognition. Recognized characters are Hindi alphabets.

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.

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