An Immune and a Gradient-Based Method to Train Multi-Layer Perceptron Neural Networks
An Immune and a Gradient-Based Method to Train Multi-Layer Perceptron Neural Networks
复制标题
DOI：
10.1109/ijcnn.2006.246977
复制
发表时间：
2006-10
期刊：
影响因子：
--
通讯作者：
Rodrigo Pasti;L. Castro
中科院分区：
文献类型：
--
作者：
Rodrigo Pasti;L. Castro
多层感知器（MLP）神经网络训练可以看作是函数逼近的一种特殊情况，其中没有假设数据的显式模型。在其最简单的形式中，它对应于找到一组适当的权重，使网络训练和泛化误差最小化。可以使用各种方法来确定这些权重，从标准优化方法（例如，基于梯度的算法）到生物启发的算法（例如，进化算法）。针对如何为MLP网络找到合适的权向量的问题，本文提出了一种免疫算法和基于二阶梯度的技术来训练MLP。分类和函数逼近任务的结果，并比较不同的方法，它们更适合的问题类型。
Multi-layer perceptron (MLP) neural network training can be seen as a special case of function approximation, where no explicit model of the data is assumed. In its simplest form, it corresponds to finding an appropriate set of weights that minimize the network training and generalization errors. Various methods can be used to determine these weights, from standard optimization methods (e.g., gradient-based algorithms) to bio-inspired heuristics (e.g., evolutionary algorithms). Focusing on the problem of finding appropriate weight vectors for MLP networks, this paper proposes the use of an immune algorithm and a second-order gradient-based technique to train MLPs. Results are obtained for classification and function approximation tasks and the different approaches are compared in relation to the types of problems they are more suitable for.