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首页> 外文期刊>Arabian Journal for Science and Engineering. Section A, Sciences >Estimation of the Smoothing Parameter in Probabilistic Neural Network Using Evolutionary Algorithms
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Estimation of the Smoothing Parameter in Probabilistic Neural Network Using Evolutionary Algorithms

机译：进化算法估计概率神经网络中的平滑参数

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摘要

The probabilistic neural network (PNN) is an efficient approach that can compute nonlinear decision boundaries, widelyused for classification. In this paper, the often used Gaussian distribution function is replaced by a new probability densityfunction which provides a new variant of the PNN method. Most of the higher-dimensional data are statistically found to benot from the normal distribution, and hence, we have replaced it by the symmetric Laplace distribution. Further, the estimationof the smoothing parameter in the proposed PNN model is carried out with three different evolutionary algorithms, namelybat algorithm (BA), grey wolf optimizer (GWO), and whale optimization algorithm (WOA) with a novel fitness function.These different proposed PNN models with variable smoothing parameter estimation methods are tested on five differentbenchmark data sets. The performance of proposed three Laplace distribution-based variants of PNN incorporated with BA,GWO, andWOA are reported and compared with Gaussian-based variants of PNN and also other commonly used classifiers:the conventional PNN, extreme learning machine, and K-nearest neighbor in terms of measurement accuracy. The resultsdemonstrate that the proposed approaches using evolutionary algorithms can provide as much as a ten percent increase inaccuracy over the conventional PNN method.
机译：概率神经网络（PNN）是一种有效的方法，可以计算非线性决策边界，广泛用于分类。在本文中，常用的高斯分布函数被新的概率密度函数所取代，后者提供了PNN方法的新变体。统计上发现大多数高维数据并非来自正态分布，因此，我们已将其替换为对称的拉普拉斯分布。此外，使用三种具有新的适应度函数的进化算法（即蝙蝠算法（BA），灰狼优化器（GWO）和鲸鱼优化算法（WOA））对提出的PNN模型中的平滑参数进行估计。在五个不同的基准数据集上测试了具有可变平滑参数估计方法的PNN模型。报告了建议的三种结合BA，GWO和WOA的基于Laplace分布的PNN的性能，并将其与基于高斯的PNN变体以及其他常用分类器进行了比较：常规PNN，极限学习机和K近邻在测量精度方面。结果表明，与传统的PNN方法相比，所提出的使用进化算法的方法可提供多达10％的不准确性增加。

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