A novel modular RBF neural network based on a brain-like partition method

In this study, a modular design methodology inherited from cognitive neuroscience and neurophysiology is proposed to develop artificial neural networks, aiming to realize the powerful capability of brain—divide and conquer—when tackling complex problems. First, a density-based brain-like partition m...

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Published in:	Neural computing & applications Vol. 32; no. 3; pp. 899 - 911
Main Authors:	Qiao, Jun-Fei, Meng, Xi, Li, Wen-Jing, Wilamowski, Bogdan M.
Format:	Journal Article
Language:	English
Published:	London Springer London 01.02.2020 Springer Nature B.V
Subjects:	Architecture Artificial Intelligence Artificial neural networks Brain Computational Biology/Bioinformatics Computational Science and Engineering Computer Science Data Mining and Knowledge Discovery Image Processing and Computer Vision Learning theory Modular construction Modular design Modular structures Neural networks Neurophysiology Original Article Partitions Probability and Statistics in Computer Science Radial basis function Radial basis function (RBF) network Brain-like partition Second-order algorithm Modular neural network
ISSN:	0941-0643, 1433-3058
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Abstract	In this study, a modular design methodology inherited from cognitive neuroscience and neurophysiology is proposed to develop artificial neural networks, aiming to realize the powerful capability of brain—divide and conquer—when tackling complex problems. First, a density-based brain-like partition method is developed to construct the modular architecture, with a highly connected center in each sub-network as the human brain. The whole task is also divided into different sub-tasks at this stage. Then, a compact radial basis function (RBF) network with fast learning speed and desirable generalization performance is applied as the sub-network to solve the corresponding task. On the one hand, the modular structure helps to improve the ability of neural networks on complex problems by implementing divide and conquer. On the other hand, sub-networks with considerable ability could guarantee the parsimonious and generalization of the entire neural network. Finally, the novel modular RBF (NM-RBF) network is evaluated through multiple benchmark numerical experiments, and results demonstrate that the NM-RBF network is capable of constructing a relative compact architecture during a short learning process with achievable satisfactory generalization performance, showing its effectiveness and outperformance.
AbstractList	In this study, a modular design methodology inherited from cognitive neuroscience and neurophysiology is proposed to develop artificial neural networks, aiming to realize the powerful capability of brain—divide and conquer—when tackling complex problems. First, a density-based brain-like partition method is developed to construct the modular architecture, with a highly connected center in each sub-network as the human brain. The whole task is also divided into different sub-tasks at this stage. Then, a compact radial basis function (RBF) network with fast learning speed and desirable generalization performance is applied as the sub-network to solve the corresponding task. On the one hand, the modular structure helps to improve the ability of neural networks on complex problems by implementing divide and conquer. On the other hand, sub-networks with considerable ability could guarantee the parsimonious and generalization of the entire neural network. Finally, the novel modular RBF (NM-RBF) network is evaluated through multiple benchmark numerical experiments, and results demonstrate that the NM-RBF network is capable of constructing a relative compact architecture during a short learning process with achievable satisfactory generalization performance, showing its effectiveness and outperformance.
Author	Wilamowski, Bogdan M. Meng, Xi Li, Wen-Jing Qiao, Jun-Fei
Author_xml	– sequence: 1 givenname: Jun-Fei surname: Qiao fullname: Qiao, Jun-Fei organization: Faculty of Information Technology, Beijing University of Technology, Beijing Key Laboratory of Computational Intelligence and Intelligence System – sequence: 2 givenname: Xi surname: Meng fullname: Meng, Xi email: mengxi@bjut.edu.cn organization: Faculty of Information Technology, Beijing University of Technology, Beijing Key Laboratory of Computational Intelligence and Intelligence System – sequence: 3 givenname: Wen-Jing surname: Li fullname: Li, Wen-Jing organization: Faculty of Information Technology, Beijing University of Technology, Beijing Key Laboratory of Computational Intelligence and Intelligence System – sequence: 4 givenname: Bogdan M. surname: Wilamowski fullname: Wilamowski, Bogdan M. organization: Department of Electrical and Computer Engineering, Auburn University
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SubjectTerms	Architecture Artificial Intelligence Artificial neural networks Brain Computational Biology/Bioinformatics Computational Science and Engineering Computer Science Data Mining and Knowledge Discovery Image Processing and Computer Vision Learning theory Modular construction Modular design Modular structures Neural networks Neurophysiology Original Article Partitions Probability and Statistics in Computer Science Radial basis function
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Title	A novel modular RBF neural network based on a brain-like partition method
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