Title: Non-destructive diagnosis of food products using neural-genetic algorithm

Authors: Tanya Titova; Veselin Nachev; Chavdar Damyanov

Addresses: Department of Automation, Information and Control Systems, University of Food Technologies, Plovdiv, Bulgaria ' Department of Automation, Information and Control Systems, University of Food Technologies, Plovdiv, Bulgaria ' Department of Automation, Information and Control Systems, University of Food Technologies, Plovdiv, Bulgaria

Abstract: Hybrid neuro-genetic networks are a subclass of neural networks combining random-search methods with adaptive optimisation with the direct analogy of natural selection and genetics in biological systems. This paper tries to improve the efficiency of automated classifiers in the systems for automated quality determination and sorting via hybrid structures.

Keywords: artificial neural networks; ANNs; genetic algorithms; neuro-genetic algorithms; food quality; nondestructive evaluation; food products.

DOI: 10.1504/IJRIS.2015.070913

International Journal of Reasoning-based Intelligent Systems, 2015 Vol.7 No.1/2, pp.55 - 61

Available online: 31 Jul 2015 *

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