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A self-organizing map-based initialization for hybrid training of feedforward neural networks

机译：基于自组织图的初始化用于前馈神经网络的混合训练

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摘要

This paper presents a novel hybrid algorithm for feedforward neural networks, called a self organizing map-based initialization for hybrid training based on a two stage learning approach. Firststage, a structure learning scheme which includes adding hidden neurons is used to determine the network size. Second stage, a FN (fuzzy neighborhood)-based hybrid learning scheme which we have recently proposed is used to adjust the network parameters. In this approach the weights between input and hidden layers are firstly adjusted by Kohonen algorithm with fuzzy neighborhood, whereas the weights connecting hidden and output layers are adjusted using gradient descent method. Four simulation examples are provided to demonstrate the efficiency of the approach compared with other well-known and recently proposed learning methods.
机译：本文提出了一种新颖的前馈神经网络混合算法，称为基于自组织图的初始化，用于基于两阶段学习方法的混合训练。第一步，使用结构学习方案（包括添加隐藏的神经元）来确定网络大小。第二阶段，我们最近提出的基于FN（模糊邻域）的混合学习方案用于调整网络参数。在这种方法中，首先通过带有模糊邻域的Kohonen算法来调整输入层和隐藏层之间的权重，而使用梯度下降法来调整连接隐藏层和输出层的权重。与其他众所周知的和最近提出的学习方法相比，提供了四个仿真示例来演示该方法的效率。

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