Parameter optimization in Eidos brain-state-in-a-box artificial neural network model
无监督Eidos表相盒中脑状态人工神经元网络模型参数优化选取

The parameter optimization for the Eidos brain-state-in-a-box(Eidos BSB) artificial neural network model is considered. By an in-depth analysis to the eigenvalues of the model-connected matrix, it can be found that the network's classification ability relies on the stability and distinction of the valid eigenvalues. Thereby, a novel parameter optimization technique is proposed, which is based on the ratio of the valid eigenvalues' mean to the others. Then, the details of this parameter optimization method are presented. According to the simulation results, this optimized Eidos BSB model is immune to noise and provides better classification results. More than 94% correct classification rate can be attained for the samples with 100% noise contamination rate by employing this optimized neural network.