A Novel Cooperative Divide-and-Conquer Neural Networks Algorithm

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Author(s): Pan Wang (Wuhan University of Technology, China), Yandi Zuo (Wuhan University of Technology, China), Jiasen Wang (Hithink RoyalFlush Information Network Co., Ltd., China)and Jian Zhang (Wuhan University of Technology, China)
Copyright: 2020
Pages: 32
Source title: Novel Theories and Applications of Global Information Resource Management
Source Author(s)/Editor(s): Zuopeng (Justin) Zhang (University of North Florida, USA)
DOI: 10.4018/978-1-7998-1786-4.ch011

Keywords: Information Resources Management / Information Science Reference / Library & Information Science

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Abstract

Dynamic modularity is one of the fundamental characteristics of the human brain. Cooperative divide and conquer strategy is a basic problem solving approach. This chapter proposes a new subnet training method for modular neural networks with the inspiration of the principle of “an expert with other capabilities.” The key point of this method is that a subnet learns the neighbor data sets while fulfilling its main task: learning the objective data set. Additionally, a relative distance measure is proposed to replace the absolute distance measure used in the classical method and its advantage is theoretically discussed. Both methodology and empirical study are presented. Two types of experiments respectively related with the approximation problem and the prediction problem in nonlinear dynamic systems are designed to verify the effectiveness of the proposed method. Compared with the classical learning method, the average testing error is dramatically decreased and more stable. The superiority of the relative distance measure is also corroborated. Finally, a mind-gut frame is proposed.

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A Novel Cooperative Divide-and-Conquer Neural Networks Algorithm

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