ART2神经网络学习算法的改进

自适应共振(ART)神经网络具有无监督学习功能,能对时序信号进行实时学习、实时处理,能对已学习过的样本作出快速响应,自动识别等优点,尤其以ART2网络更具有实用性。但是传统的ART2网络存在幅度信息丢失和模式漂移等现象,针对这一情况,本文把模式漂移的方向作为一个因素进行考虑,通过设置漂移上限系数,引入栈结构对模式漂移的相反方向相互抵消,同一方向累加的方法有效限制了模式的飘移,对各改进算法进行比较体现本文算法的优越性。

Improved algorithm for ART2 neural network

Adaptive Resonance Theory (ART) neural network not only has the function of the unsupervised learning ,but also can learn and process the time sequence signal timing in real time. ART neural network can not only make the response quickly to the learned samples,but also can recognize automatically. In practical,ART2 has the good practicability. But the traditional ART2 network has the disadvantage of the losing of the amplitude information and pattern drifting,so an improved ATR2 algorithm is proposed. This ...