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Study on Machine Vision Fuzzy Recognition Based on Matching Degree of Multi-characteristics

机译:基于多特征匹配度的机器视觉模糊识别研究

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摘要

This paper presents a new method used for fruit category recognition based on machine vision and total matching degree of fruit's multi-characteristics. The ladder membership function was used to express each characteristic. The matching degree of each characteristic was calculated by its membership function, and then the total matching degree was calculated, fruit category recognition can be determined by the total matching degree. In this paper, a 5-input 1-output zero-order Takagi-Sugeno fuzzy neural network was constructed to achieve non-linear mapping between fruit characteristics and fruit type, then the parameters of membership function for each characteristic was designed as learning parameters of the network. Training the fuzzy neural network through a large amount of sample data, the corresponding parameters of the membership functions of recognized fruit can be determined. Taking apple recognition as an example, the experimental results show that the method is simple, effective, highly precise, easy to implement.
机译:本文提出了一种基于机器视觉和水果多特征总匹配度的水果类别识别新方法。阶梯隶属度函数用于表达每个特征。通过其隶属度函数计算出每个特征的匹配度,然后计算出总的匹配度,就可以通过总的匹配度来确定水果类别的识别度。本文建立了一个5输入1输出零阶Takagi-Sugeno模糊神经网络,实现了水果特性与水果类型之间的非线性映射,然后将每个特性的隶属度函数的参数设计为A的学习参数。网络。通过大量样本数据训练模糊神经网络,可以确定已识别水果的隶属度函数的相应参数。实验结果表明,该方法简单,有效,精度高,易于实现。

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