Structurally adaptive modular networks for nonstationary environments
Structurally adaptive modular networks for nonstationary environments
复制标题
DOI：
10.1109/72.737501
复制
发表时间：
1999
影响因子：
--
通讯作者：
V. Ramamurti;Joydeep Ghosh
中科院分区：
文献类型：
--
作者：
V. Ramamurti;Joydeep Ghosh
This paper introduces a neural network capable of dynamically adapting its architecture to realize time variant nonlinear input-output maps. This network has its roots in the mixture of experts framework but uses a localized model for the gating network. Modules or experts are grown or pruned depending on the complexity of the modeling problem. The structural adaptation procedure addresses the model selection problem and typically leads to much better parameter estimation. Batch mode learning equations are extended to obtain on-line update rules enabling the network to model time varying environments. Simulation results are presented throughout the paper to support the proposed techniques.
