一种用于序列学习的自组织模糊神经网络
A self-organizing fuzzy neural network for sequence learning
摘要：
本文提出了一种新的自组织模糊神经网络模型，它能够准确地学习和再现不同的序列。序列学习对于完成技巧性任务很重要，比如写作和弹钢琴。该网络的结构由两部分组成：1-序列识别器，它根据序列的初始样本计算一个新的序列身份值，并根据适当的模糊规则检测序列身份; 2-序列定位器，它定位序列中的输入样本。因此，通过在模糊规则中整合这两个部分的输出，网络能够基于序列的当前状态产生适当的输出。为了学习所提出的结构，提出了一个渐进的学习过程。首先，学习是通过添加新的模糊规则，覆盖措施的基础上，使用可用的正确数据。然后，基于反馈的近似网络输出作为下一个输入，通过梯度下降算法对初始化参数进行微调。该方法具有动态结构，能够在线学习新的序列。所提出的方法是用来学习和再现不同的序列同时这是该方法的新颖之处。
英文摘要：
In this paper, a new self-organizing fuzzy neural network model is presented which is able to learn and reproduce different sequences accurately. Sequence learning is important in performing skillful tasks, such as writing and playing piano. The structure of the proposed network is composed of two parts: 1-sequence identifier which computes a novel sequence identity value based on initial samples of a sequence, and detects the sequence identity based on proper fuzzy rules, and 2-sequence locator, which locates the input sample in the sequence. Therefore, by integrating outputs of these two parts in fuzzy rules, the network is able to produce the proper output based on current state of the sequence. To learn the proposed structure, a gradual learning procedure is proposed. First, learning is performed by adding new fuzzy rules, based on coverage measure, using available correct data. Next, the initialized parameters are fine-tuned, by gradient descent algorithm, based on fed back approximated network output as the next input. The proposed method has a dynamic structure which is able to learn new sequences online. The proposed method is used to learn and reproduce different sequences simultaneously which is the novelty of this method.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
