Yamazaki Akio, Ludermir Teresa B
Center of Informatics, Federal University of Pernambuco, Cidade Universitária, P.O. Box 7851, Recife, Pernambuco, 50.732-970, Brazil.
Int J Neural Syst. 2003 Apr;13(2):77-86. doi: 10.1142/S0129065703001467.
This paper presents an approach of using Simulated Annealing and Tabu Search for the simultaneous optimization of neural network architectures and weights. The problem considered is the odor recognition in an artificial nose. Both methods have produced networks with high classification performance and low complexity. Generalization has been improved by using the backpropagation algorithm for fine tuning. The combination of simple and traditional search methods has shown to be very suitable for generating compact and efficient networks.
本文提出了一种使用模拟退火和禁忌搜索来同时优化神经网络架构和权重的方法。所考虑的问题是人工鼻中气味的识别。两种方法都生成了具有高分类性能和低复杂度的网络。通过使用反向传播算法进行微调提高了泛化能力。简单和传统搜索方法的结合已证明非常适合生成紧凑且高效的网络。
