基于改进的BP神经网络和禁忌表的图像压缩与重构

Based on the improved BP neural network and image compression and reconstruction of tabu table

针对BP神经网络容易陷入局部极小值的问题,借鉴模拟退火算法中metropolis接受准则的思想并加以改进,并引入禁忌(taboo)搜索算法中的禁忌表,在系统跳出局部极小值时对极小点进行记录比较,使得在处理多局部极小值系统时更加高效与精确。将改进后的算法应用于BP网络,从而构造出一种更易跳出局部极小值的改进的神经网络。最后,运用改进后的神经网络算法进行图像压缩与重构,实验结果表明改进后的神经网络收敛速度更快,具有更高的效率与精度。

For the problem that BP neural network is easy to fall into the local minimum, the metropolis acceptance criteria in the mechanism of simulated annealing algorithm and the taboo list in the taboo searching algorithm are introduced in the system to escape from the local minimum, and the values of minima were recorded and compared in the taboo list, which is more efficient in dealing with multiple local minima and accurate system. The modified algorithm is applied to the BP network to construct a neural network which is easier to jump off from local minima. At last, the modified neural network algorithm is used for image compression and reconstruction. The experimental results show the higher efficiency and accuracy of the modified neural network.