FAST NON-NEGATIVE LEAST-SQUARES LEARNING IN THE RANDOM NEURAL NETWORK

作     者：Timotheou, Stelios 

作者机构：Univ Cyprus KIOS Res Ctr Intelligent Syst & Networks CY-1678 Nicosia Cyprus 

出 版 物：《PROBABILITY IN THE ENGINEERING AND INFORMATIONAL SCIENCES》 (Probab. Eng. Inf. Sci.)

年 卷 期：2016年第30卷第3期

页      面：379-402页

核心收录：

学科分类：1201[管理学-管理科学与工程(可授管理学、工学学位)] 08[工学] 0714[理学-统计学（可授理学、经济学学位）] 

主　　题：NEURAL networks (Computer science) LEAST squares QUASI-Newton methods RANDOM effects model COMPUTER algorithms PROBABILITY theory 

摘      要：The random neural network is a biologically inspired neural model where neurons interact by probabilistically exchanging positive and negative unit-amplitude signals that has superior learning capabilities compared to other artificial neural networks. This paper considers non-negative least squares supervised learning in this context, and develops an approach that achieves fast execution and excellent learning capacity. This speedup is a result of significant enhancements in the solution of the non-negative least-squares problem which regard (a) the development of analytical expressions for the evaluation of the gradient and objective functions and (b) a novel limited-memory quasi-Newton solution algorithm. Simulation results in the context of optimizing the performance of a disaster management problem using supervised learning verify the efficiency of the approach, achieving two orders of magnitude execution speedup and improved solution quality compared to state-of-the-art algorithms.