液态机概念的研究
Research on the Concept of Liquid State Machine
摘要：
液态机（Liquid State Machine，LSM）是一种具有实时的计算功能的神经网络模型，它将时变输入流转换到高维空间。LSM的概念是生物启发计算的一个新的研究领域，大多数研究工作都集中在训练模型以及寻找最佳学习方法上。在这篇综述中，LSM模型的性能进行了研究，使用两种学习方法，在线学习和离线（批）学习方法。该综述表明，LSM的最佳性能是通过在线方法记录的，因为计算空间和与批学习相关的其他复杂性被消除。
英文摘要：
Liquid State Machine (LSM) is a neural model with real time computations which transforms the time varying inputs stream to a higher dimensional space. The concept of LSM is a novel field of research in biological inspired computation with most research effort on training the model as well as finding the optimum learning method. In this review, the performance of LSM model was investigated using two learning method, online learning and offline (batch) learning methods. The review revealed that optimal performance of LSM was recorded through online method as computational space and other complexities associated with batch learning is eliminated.
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。
