自适应联想记忆细胞神经网络的优化设计

Optimal design for adaptive associative memory cellular neural networks

针对训练自适应联想记忆细胞神经网络(AM-CNN)过程收敛慢,设计出的网络抗噪性能不高的特点,通过融合蚁群优化算法和粒子群算法的思想,提出以目标网络对噪声模式的输出误差为目标函数,在目标函数的一个阈值分成的两个区间内,分别采取局部搜索和全局搜索策略,训练出AM-CNN的克隆模板的设计方法。数字模拟表明,与以往的设计方法相比,该算法能在细胞神经网络4～6次的迭代过程中稳定输出期望模式,收敛速度更快,设计出的AM-CNN性能比较稳定,并对噪声鲁棒,对高斯噪声N(0,0.8)准确率达到90%左右。

In order to speed up the convergence of self-training AM-CNN （Associative Memories Cellular Neural Network） and enhance the performance of achieved AM-CNN, an algorithm for obtaining the space-invariant cloning templates of AM-CNN was proposed, which took the output error of objective CNN as objective function and took local searching and global searching respectively in two internals separated by a given objective function threshold, coupled with the idea of ant optimization algorithm and Particle Swarm Optimization （ PSO）. Concluded from the numerical simulation results, the proposed algorithm outputs the objective AM-CNN and converges quickly. Meanwhile, the performance of the achieved AM-CNN is better and more stable compared with previous methods. The achieved AM-CNN is also robust to Gauss noise of N（ 0, 0.8） with recall rate of about 90%.