Journal of Intelligent Computing

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Journal of Intelligent Computing

Hybrid Mode Neuro-genetic Networks to Understand Genetic Features

Tanya Titova, Veselin Nachev, Chavdar Damyanov and Nanko Bozukov
Tanya Titova, Department Automation and Control Systems University of Food Technologies, 26 Maritza Blvd, Plovdiv 4002, Bulgaria., Veselin Nachev, Department Automation and Control Systems University of Food Technologies, 26 Maritza Blvd Plovdiv, 4002, Bu

Abstract: Neural networks have large applications and have the characteristics of many classes. The hybrid mode neurogenetic networks are one such division of neural networks. They have adaptive optimization with the character of natural selection and genetical features. The automated classifiers performance can be enhanced to record efficiency in the systems for quality determination using the hybrid structure sorting. We have discussed these issues in this paper.

Keywords: Artificial Neural Network, Genetic Algorithm, Neuro-Genetic Algorithm, Food Quality Hybrid Mode Neuro-genetic Networks to Understand Genetic Features

DOI:https://doi.org/10.6025/jic/2023/14/1/1-9

Full_Text PDF 1.49 MB Download: 89 times

References:

[1] Admuthe L. S., S. D. Apte, Neuro-Genetic Cost Optimization Model: Application of Textile Spinning Process, International Journal of Computer Theory and Engineering, Vol. 1, No. 4, 441-444, October 2009
[2] Damyanov C., Non-destructive Quality Evaluation in Automatic Sorting Systems, UFT Academic Publishing House, Plovdiv, p.360, 2006
[3] Petridis V., Paterakis E., Kehagias “A hybrid neural–genetic multimodel parameter estimation algorithm” IEEE Trans. on neural networks, vol.9 no.5 1998
[4] Shanthi D., Dr.G.Sahoo, Dr.N.Saravanan. Input Feature Selection using Hybrid Neuro-Genetic Approach in the Diagnosis of Stroke Disease, IJCSNS International Journal of Computer Science and Network Security, Vol.8 No.12, 99-107, December 2008
[5] Siddique, M.N.H.; Tokhi, M.O, Training neural networks: backpropagation vs. genetic algorithms, International Joint Conference on Neural Networks, Vol. 4, p. 2673 – 2678, 2001
[6] Titova T., V. Nachev A structural genetic algorithm to optimize neural network architecture, "Food Science Engineering and Technology 2007" Scientific Works of the UFT - Plovdiv, Volume LIV, St. 3, 101-107, 19-20.10. 2007
[7] Titova T., V. Nachev, Ch. Damyanov. Application of Genetic Algorithms for Neural Networks Trainings, International Conference Automatics and Informatics’ 07, II-9–II-12, October 3 – 6, 2007, Sofia
[8] Wang W. J., Tang X. C., Li W. C., A variable structure neural networks model and its applications, IEEE TENCON, Vol. 2:5, p. 799-802, 1993.

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