Design of Sparse Span-lateral Inhibition Neural Network Based on Connection Self-organization Development
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摘要: 针对跨越——侧抑制神经网络（Span-lateral inhibition neural network，S-LINN）的结构调整及参数学习问题，结合生物神经系统中神经元的稀疏连接特性，依据儿童及青少年智力发展水平与大脑皮层发育之间的相互关系，提出以小世界网络连接模式进行初始稀疏化的连接自组织发育稀疏跨越——侧抑制神经网络设计方法.定义网络连接稀疏度及神经元输出贡献率，设计网络连接增长——修剪规则，根据智力超常组皮层发育与智力水平的对应关系调整和控制网络连接权值，动态调整网络连接实现网络智力的自组织发育.通过非线性动力学系统辨识及函数逼近基准问题的求解，证明在同等连接复杂度的情况下，稀疏连接的跨越——侧抑制神经网络具有更好的泛化能力.
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关键词:
- 跨越-侧抑制神经网络 /
- 稀疏 /
- 小世界网络 /
- 智力发展
Abstract: Inspired by the sparse connection of neurons in biological nervous system and the relationship between children and adolescents' intellectual ability and cortical development, a connection self-organization development-based sparse span-lateral inhibition neural network (sS-LINN) is developed to solve the structure adjustment and parameter learning problem, which adopts the small-world network connection mode as the initial sparse network architecture. A growing-pruning rule of network connection is designed to adjust and control the sparseness of network connections based on the definitions of connection sparseness and neuron output contribution rate. Performance of the proposed sparse S-LINN is evaluated successfully through simulation using nonlinear dynamic system identification and function approximation benchmark problems. It is shown that the proposed sS-LINN can produce a very compact structure with good generalization ability in comparison with other methods.1) 本文责任编委 王占山 -
图 1 全连接网络及稀疏连接网络
Fig. 1 Structural diagram of fully connected network and sparse connected network
图 5 CaseA:网络测试输出及学习误差曲线
Fig. 5 CaseA: Network output for test samples and the learning error curve
图 6 CaseB:网络测试输出及学习误差曲线
Fig. 6 CaseB: Network output for test samples and the learning error curve
图 7 CaseC:网络测试输出及学习误差曲线
Fig. 7 CaseC: Network output for test samples and the learning error curve
图 8 三次独立实验中前200次迭代中网络性能对比
Fig. 8 The comparison of network performance for the 3 independent runs (the first 200 iterations)
表 1 三次独立实验中网络性能及其权值连接变化情况
Table 1 Network performance and the dynamic adjustment process of connected weight
Method
(# Total connections)Training
MSETraining
RMSETesting
MSETesting
RMSECaseA: 43-50-39 2.06 $\times 10^{-5}$ 0.0045 1.96 $\times 10^{-5}$ 0.0044 CaseB: 43-51-39 1.05 $\times 10^{-5}$ 0.0032 4.01 $\times 10^{-5}$ 0.0063 CaseC: 43-51-38 4.88 $\times 10^{-6}$ 0.0022 3.67 $\times 10^{-6}$ 0.0019 
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表 2 sS-LINN与其他神经网络方法的性能对比
Table 2 Network performance and the dynamic adjustment process of connected weight
Method # Hidden neurons /connections Testing MSE Standard RBF[34] / 0.695 Standard SVR[33] / 0.445 SVR with prior knowledge[33] / 0.354 LCRBF[34] / 0.273 CP-NN[35] $2\to9$ 1.25 $\times 10^{-4}$ $20\to 10$ 1.04 $\times 10^{-4}$ AGPNNC[35] $2\to 10$ 1.71 $\times 10^{-4}$ $20\to10$ 1.23 $\times 10^{-4}$ S-LINN[25] 8 3.82 $\times 10^{-5}$ sS-LINN 39个连接权值 2.01 $\times 10^{-5}$
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