This paper presents an optimized learning strategy for a bidirectional associative memory.
本文提出了一种异联想记忆模型的优化学习算法。
Associative learning theory has been used in studies of memory, learning, and verbal learning.
联想学习理论已经被应用于研究记忆、学习和言语学习的工作中。
A design method of ahead masking associative memory model with expecting fault-tolerant field is proposed by use of the general feed-forward network and sequential learning algorithm given by authors.
文中用作者提出的通用前馈网络和排序学习算法，提出了一种设计具有期望容错域的前向掩蔽联想记忆模型的方法。
This paper proposes an improved version of the associative memory learning control system (AMLCS) for industrial processes with almost completely unknown but slowly time-varying dynamics.
对相联存储自学习控制系统(AMLCS)提出了一种改进方案，可用于动态特性几乎完全未知且慢时变的工业过程。
The models used in this work were from linear associative memory method and fast compensated by adaptively learning from the given facial data, which were obtained in same condition as testing.
该方法在真实识别前，通过用与真实识别相同的环境条件下所获得的人脸图像数据对原始模型进行更新补偿，实现了模型自适应。
The models used in this work were from linear associative memory method and fast compensated by adaptively learning from the given facial data, which were obtained in same condition as testing.
该方法在真实识别前，通过用与真实识别相同的环境条件下所获得的人脸图像数据对原始模型进行更新补偿，实现了模型自适应。
应用推荐