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首页> 外文会议>Workshop on Application of Stock Identification in Defining Marine Distribution and Migration of Salmon >Evaluating the Efficacy of Probabilistic Neural Networks to Determine Stock Structure in Sockeye Salmon Using Fourier Transformed Luminance Profiles of Scale Circuli
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Evaluating the Efficacy of Probabilistic Neural Networks to Determine Stock Structure in Sockeye Salmon Using Fourier Transformed Luminance Profiles of Scale Circuli

机译：评估概率神经网络的功效，使用傅立叶尺度辐射亮度谱法测定红鲑鱼中的股票结构

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摘要

Patterns of circuli groupings within scales are used to determine the stock structure of sockeye salmon (Oncorhynchus nerka), The methodology typically employed involves using trained scale readers to interpret and manually measure circuli spacing patterns. These measurements are used as input into Linear Discriminant function Analysis (LDA) to determine stock structure. This pilot study introduces a new technique, probabilistic neural networks, to evaluate scale patterns for stock composition. We compare the method directly to LDA by using the same measurement data as input. We then explore Fourier analysis of luminance profiles of the scale images as an objective means to classify scale patterns. The samples used in the pilot study are from two Canadian stocks and one Alaskan stock encountered in South-east Alaskan fisheries. Correctly identifying these stocks has been a challenging problem for fisheries management.
机译：尺度内的线轮分组模式用于确定红鲑鱼的股票结构（Oncorynchus nerka），通常采用的方法涉及使用训练的秤读取器来解释和手动测量线轮间距模式。这些测量用作线性判别函数分析（LDA）的输入以确定股票结构。该试点研究介绍了一种新的技术，概率神经网络，评估库存组成的规模模式。通过使用与输入相同的测量数据，我们将该方法直接与LDA进行比较。然后，我们探讨刻度图像的亮度简档的傅里叶分析，作为分类比例模式的目标手段。试点研究中使用的样品来自两个加拿大股票，并在阿拉斯加东南部渔业中遇到的一股阿拉斯加股票。正确识别这些股票对渔业管理有挑战性问题。

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