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首页> 外文期刊>American journal of applied sciences >HALF OF THRESHOLD ALGORITHM: AN ENHANCED LINEAR ADAPTIVE SKIPPING TRAINING ALGORITHM OR MULTILAYER FEEDFORWARD NEURAL NETWORKS | Science Publications
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HALF OF THRESHOLD ALGORITHM: AN ENHANCED LINEAR ADAPTIVE SKIPPING TRAINING ALGORITHM OR MULTILAYER FEEDFORWARD NEURAL NETWORKS | Science Publications

机译：一半的阈值算法：增强的线性自适应跳闸训练算法或多层前馈神经网络|科学出版物

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摘要

> Multilayer Feed Forward Neural Network (MFNN) has been successfully administered architectures for solving a wide range of supervised pattern recognition tasks. The most problematic task of MFNN is training phase which consumes very long training time on very huge training datasets. An enhanced linear adaptive skipping training algorithm for MFNN called Half of Threshold (HOT) is proposed in this research paper. The core idea of this study is to reduce the training time through random presentation of training input samples without affecting the network?s accuracy. The random presentation is done by partitioning the training dataset into two distinct classes, classified and misclassified class, based on the comparison result of the calculated error measure with half of threshold value. Only the input samples in the misclassified class are presented to the next epoch for training, whereas the correctly classified class is skipped linearly which dynamically reducing the number of input samples exhibited at every single epoch without affecting the network?s accuracy. Thus decreasing the size of the training dataset linearly can reduce the total training time, thereby speeding up the training process. This HOT algorithm can be implemented with any training algorithm used for supervised pattern classification and its implementation is very simple and easy. Simulation study results proved that HOT training algorithm achieves faster training than the other standard training algorithm.
机译： >多层馈送前向神经网络（MFNN）已成功地管理架构，以解决广泛的监督模式识别任务。 MFNN最有问题的任务是训练阶段，在非常庞大的训练数据集上消耗很长的培训时间。该研究纸上提出了一种增强的MFNN的线性自适应跳转训练算法，称为阈值（热）的一半。本研究的核心思想是通过随机呈现训练输入样本来减少培训时间，而不会影响网络的准确性。基于计算出的错误测量的比较结果将训练数据集分为两个不同的类，分类和错误分类的类，通过将训练数据集分为两个不同的类别，分类和错误分类的类来完成。仅将错误分类类中的输入样本呈现给下一个时期进行培训，而正确分类的类是线性的，这是线性的，这动作地减少了在每个单个时期在每一个时期展出的输入样本数而不影响网络的准确性。从而降低训练数据集的大小线性可以减少总训练时间，从而加速训练过程。这种热算法可以用任何用于监督模式分类的训练算法来实现，其实现非常简单且简单。仿真研究结果证明，热训练算法比其他标准训练算法更快地实现培训。

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