scholarly journals Performance Evaluation of Pattern Storage Network of Associative memory with Sub-optimal GA for Hand written Hindi SWARS

2013 ◽  
Vol 3 (1) ◽  
pp. 83-93
Author(s):  
Rajesh Lavania ◽  
Manu Pratap Singh
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Associative Memory ◽  
Hopfield Neural Network ◽  
Input Pattern ◽  
Weight Matrix ◽  
Initial Population ◽  
Optimal Form ◽  
Optimal Weight ◽  
Rate Of Success

In this paper we are performing the evaluation of Hopfield neural network as Associative memory for recalling of memorized patterns from the Sub-optimal genetic algorithm for Handwritten of Hindi language. In this process the genetic algorithm is employed from sub-optimal form for recalling of memorized patterns corresponding to the presented noisy prototype input patterns. The sub-optimal form of GA is considered as the non-random initial population or solution. So, rather than random start, the GA explores from the sum of correlated weight matrices for the input patterns of training set. The objective of this study is to determine the optimal weight matrix for correct recalling corresponds to approximate prototype input pattern of Hindi ‘SW. In this study the performance of neural network is evaluated in terms of the rate of success for recalling of memorized Hindi  for presented approximate prototype input pattern with GA in two aspects. The first aspect reflects the random nature of the GA and the second one exhibit the suboptimal nature of the GA for its exploration.The simulated results demonstrate the better performance of network for recalling of the memorized Hindi SWARS using genetic algorithm to evolve the population of weights from sub-optimal weight matrix. 

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.

Effectiveness of Neural Network and Genetic Algorithm in Hull Form Design

2001 ◽  
Author(s):  
M. Reaz H. Khondoker ◽  
Chowdhury Mofizur Rahman ◽  
Mohammad Mahfuzul Islam
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Weight Matrix ◽  
Design Parameters ◽  
Close Relative ◽  
Convergence Criterion ◽  
Hull Form ◽  
Form Design ◽  
Hull Form Design ◽  
Improved Design

Abstract This paper aims at developing a fully automated hull form design technique employing an Neural Network and Genetic Algorithm methods resulting in accelerated convergence. For generating an input data that will be, by and large, a close relative of the desired hull, a linear relation has been assumed between the half breadth of different sections and principal dimensions (length, breadth, draft or (displacement)1/3) of a particular type of vessel. Compared to starting with a random value of the input, this technique resulted in faster convergence. The weight matrix for each of these parameters is produced from data obtained from the population. The half-breadth table for a new vessel can be obtained by multiplying the weight matrix with corresponding parameter. However, the half-breadth table obtained in such way may not provide the required displacement and speed of the vessel. Therefore, some readjustments of some of the principal dimensions are required. Neural Networks (Wasserman, 1989) has been used to find the required values of such improved design parameters (principal dimensions). The final design process consists of searching for the exact solution by examining several generations generated by the GA (Goldberg, 1989). The convergence criterion is the summed offset error, which is to be within the envelope defined by the tolerances. Since GA doesn’t guarantee fairness of the surface of the hull form, B-spline curve fitting method is used to obtain a fair hull. Thus, the hull form generated through this process is fully automated, accurate and having fair surface. The technique is also found to be an efficient one.

Multi-Connect Architecture (MCA) Associative Memory: A Modified Hopfield Neural Network

2012 ◽  
Vol 18 (3) ◽  
pp. 279-296 ◽  
Author(s):  
Emad I Abdul Kareem ◽  
Wafaa A.H Ali Alsalihy ◽  
Aman Jantan
Keyword(s):  
Neural Network ◽  
Associative Memory ◽  
Hopfield Neural Network

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.

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