基于变形统计自由能模型的无监督源分离
Deformed Statistics Free Energy Model for Source Separation using Unsupervised Learning
Systems Research Corporation（系统研究公司）；IFLP, National University La Plata（拉普拉塔国立大学IFLP）；National Research Council (CONICET)（国家研究委员会（CONICET））
摘要：
借助于Tsallis熵的可加性对偶性，并利用正态平均描述的约束条件，建立了源分离的广义统计变分原理。将变分原理与Hopfield类学习规则相结合，得到一个无监督学习模型。更新规则的制定与援助的q-变形演算。数值算例验证了该模型的有效性。
英文摘要：
A generalized-statistics variational principle for source separation is formulated by recourse to Tsallis' entropy subjected to the additive duality and employing constraints described by normal averages. The variational principle is amalgamated with Hopfield-like learning rules resulting in an unsupervised learning model. The update rules are formulated with the aid of q-deformed calculus. Numerical examples exemplify the efficacy of this model.
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。
