Optimization of stochastic networks using simulated annealing for the storage and recalling of compressed images using SOM
In this paper we are studying the optimization of Stochastic Hopfield neural network and the hybrid SOM–Hopfield neural network for the storage and recalling of fingerprint images. The feature extraction of these images has been performed using FFT, DWT and SOM. The feature vectors are stored in the...
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Published in | Engineering applications of artificial intelligence Vol. 26; no. 10; pp. 2383 - 2396 |
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Format | Journal Article |
Language | English |
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01.11.2013
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Abstract | In this paper we are studying the optimization of Stochastic Hopfield neural network and the hybrid SOM–Hopfield neural network for the storage and recalling of fingerprint images. The feature extraction of these images has been performed using FFT, DWT and SOM. The feature vectors are stored in the Hopfield network with Hebbian learning and modified Pseudoinverse learning rules. The study explores the tolerance of Hopfield neural networks for reducing the effect of spurious minima in the recalling process by employing the Simulated annealing process. It is observed from the simulations that the capabilities of the Hopfield network can be sufficiently enhanced by making modifications in the feature extraction of the input data. DWT and SOM together can be used to significantly enhance the recall efficiency. The probability of error in recall in the form of spurious minima is minimized by adopting simulated annealing process in the pattern recalling process.
•DWT and SOM together can be used to significantly enhance the recall efficiency of the HNN.•Performance with DWT Filtered Patterns as compared to FFT Filtered patterns is better.•SOM-HNN suffers from a high rate of spurious minima due to overlapping of the feature spaces.•HNN can be optimized for reducing the effect of spurious minima, by employing Simulated Annealing in the recalling process. |
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AbstractList | In this paper we are studying the optimization of Stochastic Hopfield neural network and the hybrid SOMaHopfield neural network for the storage and recalling of fingerprint images. The feature extraction of these images has been performed using FFT, DWT and SOM. The feature vectors are stored in the Hopfield network with Hebbian learning and modified Pseudoinverse learning rules. The study explores the tolerance of Hopfield neural networks for reducing the effect of spurious minima in the recalling process by employing the Simulated annealing process. It is observed from the simulations that the capabilities of the Hopfield network can be sufficiently enhanced by making modifications in the feature extraction of the input data. DWT and SOM together can be used to significantly enhance the recall efficiency. The probability of error in recall in the form of spurious minima is minimized by adopting simulated annealing process in the pattern recalling process. In this paper we are studying the optimization of Stochastic Hopfield neural network and the hybrid SOM–Hopfield neural network for the storage and recalling of fingerprint images. The feature extraction of these images has been performed using FFT, DWT and SOM. The feature vectors are stored in the Hopfield network with Hebbian learning and modified Pseudoinverse learning rules. The study explores the tolerance of Hopfield neural networks for reducing the effect of spurious minima in the recalling process by employing the Simulated annealing process. It is observed from the simulations that the capabilities of the Hopfield network can be sufficiently enhanced by making modifications in the feature extraction of the input data. DWT and SOM together can be used to significantly enhance the recall efficiency. The probability of error in recall in the form of spurious minima is minimized by adopting simulated annealing process in the pattern recalling process. •DWT and SOM together can be used to significantly enhance the recall efficiency of the HNN.•Performance with DWT Filtered Patterns as compared to FFT Filtered patterns is better.•SOM-HNN suffers from a high rate of spurious minima due to overlapping of the feature spaces.•HNN can be optimized for reducing the effect of spurious minima, by employing Simulated Annealing in the recalling process. |
Author | Dixit, Rinku Sharma Singh, Manu Pratap |
Author_xml | – sequence: 1 givenname: Manu Pratap surname: Singh fullname: Singh, Manu Pratap email: manu_p_singh@hotmail.com organization: Department of Computer Science, Institute of Engineering and Technology, Dr. B.R. Ambedkar University, Khandari, Agra, UP, India – sequence: 2 givenname: Rinku Sharma surname: Dixit fullname: Dixit, Rinku Sharma email: rinkudixit07@gmail.com, rinkudixit.mrce@mrei.ac.in organization: Manav Rachna College of Engineering, Sector-43, Aravali Hills, Surajkund, Badkal Road, Faridabad, Haryana, India |
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CitedBy_id | crossref_primary_10_1007_s12046_023_02083_6 crossref_primary_10_1016_j_neucom_2023_126286 crossref_primary_10_4018_IJFSA_296592 crossref_primary_10_1007_s42979_024_02885_3 crossref_primary_10_1007_s11042_022_13778_z |
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Snippet | In this paper we are studying the optimization of Stochastic Hopfield neural network and the hybrid SOM–Hopfield neural network for the storage and recalling... In this paper we are studying the optimization of Stochastic Hopfield neural network and the hybrid SOMaHopfield neural network for the storage and recalling... |
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SubjectTerms | Computer simulation DWT FFT Hopfield neural network Simulated annealing SOM Stochastic networks |
Title | Optimization of stochastic networks using simulated annealing for the storage and recalling of compressed images using SOM |
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