A Back-Propagation Neural Network for Recognizing Objects

Wen-Yen Wu
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Wen-Yen Wu: I-Shou University, Taiwan.

European Journal of Engineering and Technology Research, 2022, vol. 7, issue 5, 102-109

Abstract: This paper proposes a neural network approach to recognize two-dimensional objects. Firstly, image preprocessing is applied to the object to obtain its main features, and feature extraction is used to represent the features of the object. The proposed method uses fuzzy K-nearest neighbor clustering and a back-propagation neural network in the following. The extracted features are classified by fuzzy Knearest neighbors to improve their efficiency. And use the following back-propagation neural network to identify the classified features. The experimental results show that the recognition effect can be improved by using pre-classified features, and show that the method is effective in object recognition.

Keywords: Back-Propagation Neural Network; Clustering; Feature; Fuzzy K-Nearest-Neighbor. (search for similar items in EconPapers)
Date: 2022
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Persistent link: https://EconPapers.repec.org/RePEc:epw:ejeng0:v:7:y:2022:i:5:id:62895

DOI: 10.24018/ejeng.2022.7.5.2895

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