文章基本信息

标题：SELECTING NEURAL NETWORK ARCHITECTURE FOR INVESTMENT PROFITABILITY PREDICTIONS
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作者：Kišasondi, Tonimir ; Lovrenčić, Alen
期刊名称：Journal of Information and Organizational Sciences
印刷版ISSN：1846-3312
电子版ISSN：1846-9418
出版年度：2006
卷号：30
期号：1
页码：93-103
出版社：Faculty of Organization and Informatics University of Zagreb
摘要：In this paper we present a modified neural network architecture and an algorithm that enables neural networks to learn vectors in accordance to user designed sequences or graph structures. This enables us to use the modified network algorithm to identify, generate or complete specified patterns that are learned in the training phase. The algorithm is based on the idea that neural networks in the human neurocortex represent a distributed memory of sequences that are stored in invariant hierarchical form with associative access. The algorithm was tested on our custom built simulator that supports the usage of our ADT neural network with standard backpropagation and our custom built training algorithms, and it proved to be useful and successful in modelling graphs.
关键词：neural networks; MLP training algorithms; neural network ADT; neural network graph modelling