Evolving Connection Weights for Pattern Storage and Recall in Hopfield Model of Feedback Neural Networks Using a Genetic Algorithm

Singh, Thipendra P.

doi:10.5121/ijsc.2012.3205

Cited by 6 publications

(3 citation statements)

References 15 publications

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“…In (Heckerling, Gerber, Tape, & Wigton, 2004) ANN is used for predicting community-acquired pneumonia among patients with respiratory complaints where GA has been used to search for optimal hidden-layer architectures, connectivity, and training parameters (learning rate and momentum parameters) of ANN. In (Shanthi, Sahoo, & Saravanan, 2009) ANN is applied in a medical problem for predicting stroke disease where GA has been used to initialize and optimize the connection weight of ANN to improve the performance of ANN. In (Murali, Puri, & Prabhakaran, 2010) ANN has been used for worker assignment into Virtual Manufacturing Cells(VMC) application where GA has been used to optimize the ANN parameters namely: learning rate, momentum coefficient, Activation function, Number of hidden layers and number of nodes of ANN.…”

Section: The Hybrid Model Of Ga and Bp Annmentioning

confidence: 99%

Application of Genetic Algorithm and Back Propagation Neural Network for Effective Personalize Web Search-Based on Clustered Query Sessions

Chawla

2016

International Journal of Applied Evolutionary Computation

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In this paper novel method is proposed using hybrid of Genetic Algorithm (GA) and Back Propagation (BP) Artificial Neural Network (ANN) for learning of classification of user queries to cluster for effective Personalized Web Search. The GA- BP ANN has been trained offline for classification of input queries and user query session profiles to a specific cluster based on clustered web query sessions. Thus during online web search, trained GA –BP ANN is used for classification of new user queries to a cluster and the selected cluster is used for web page recommendations. This process of classification and recommendations continues till search is effectively personalized to the information need of the user. Experiment was conducted on the data set of web user query sessions to evaluate the effectiveness of Personalized Web Search using GA optimized BP ANN and the results confirm the improvement in the precision of search results.

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Section: The Hybrid Model Of Ga and Bp Annmentioning

confidence: 99%

Application of Genetic Algorithm and Back Propagation Neural Network for Effective Personalize Web Search-Based on Clustered Query Sessions

Chawla

2016

International Journal of Applied Evolutionary Computation

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“…In order to automate this process new techniques involving the evolution of ANN and search for the optimal weight and topology has been developed. One such technique, which is the Neuroevolution of Augmented Topology (NEAT), makes use of genetic algorithm (GA) as a means to evolve the ANN and performs well on benchmark problems such as the XOR, pole balancing and the double pole balancing [5] [6]. Even though these benchmark problems provide a challenging task to neuroevolution, they are fairly simple compared to real life applications.…”

Section: Introductionmentioning

confidence: 99%

Application of predictive coding in the evolution of artificial neural network

Mohabeer

Soyjaudah

2012

2012 IEEE 3rd International Conference on Cognitive Infocommunications (CogInfoCom)

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In this paper we propose a new encoding scheme utilises predictive coding technique in order to increase the efficiency of evolving artificial neural network. The predictor encodes the sample data fed to the system and the artificial neural network acts as the decoder. The latter is trained using a data model created via predictive coding, which is generated from the initial sample. Only the residual data output from the encoder is fed to the artificial neural network for authentication. Distributed and local processing has been simultaneously used in parallel and in synchrony. Comparison of the simulation results with those obtained using traditional methods such as selective biometric features shows an improvement in efficiency of up to 80% while utilising a lower complexity neural network.

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“…The potential of artificial neural network relies on massively parallel architecture composed of large but finite number of artificial neurons which act as simple computational elements connected by edges with variable weights [82]. There are various models of neural networks which have been reported in literature with trend setting models of neural networkssuch Perception [16], [177], Adaptive Neural Network [80], [130], Linear Associator Model [32], Little and Shaw Model [95], Hopfield Model [194], Grossberg Models [74], Self-Organizing Network [140], [150] and Back Propagation Network [81]. As a result of the ever-increasing existence of all these networks, the application of ANN is increasing tremendously in different areas of interest and research.…”

Section: Neural Network (Nn)mentioning

confidence: 99%

A Holistic Review of Soft Computing Techniques

Philip¹,

Mom²,

Igwue³

2017

ACM

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Due to notable technological convergence that brought about exponential growth in computer world, SoftComputing (SC) has played a vital role with automation capability features to new levels of complex applications. In this research paper, the authors reviewed journals related to the subject matter with the aim of striking a convincing balance between a system that is capable of tolerance to uncertainty, imprecision, approximate reasoning and partial truth to achieve tractability, robustness, economy of communication, high machine intelligence quotient (MIQ), low cost solution and better rapport with reality to conventional techniques. This paper gives an insight on four major consortiums of SC that sprang from the concept of cybernetics, explores and reviews the different techniques, methodologies; application areas and algorithms are formulated to give an idea on how these computing techniques are applied to create intelligent agents to solve a variety of problems. The mechanisms highlighted can serve as an inspiration platform and awareness to new and old researchers that are not or fully grounded in this unique area of research and to create avenue in order to fully embrace the techniques in research communities.

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Evolving Connection Weights for Pattern Storage and Recall in Hopfield Model of Feedback Neural Networks Using a Genetic Algorithm

Abstract: In this paper, implementation of a genetic algorithm has been described to store and later, recall of some prototype patterns in Hopfield neural network associative memory. Various operators of genetic algorithm (mutation, cross-over, elitism etc)

Cited by 6 publications

References 15 publications

Application of Genetic Algorithm and Back Propagation Neural Network for Effective Personalize Web Search-Based on Clustered Query Sessions

Application of Genetic Algorithm and Back Propagation Neural Network for Effective Personalize Web Search-Based on Clustered Query Sessions

Application of predictive coding in the evolution of artificial neural network

A Holistic Review of Soft Computing Techniques

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