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 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.