基于互信息和Just-in-Time优化的回声状态网络

Optimized Echo State Network on the Basis of Mutual Information and Just-in-time

为了提高回声状态网络(ESN)的适应性,提出基于互信息(MI)和Just-in-Time(JIT)的优化方法,对ESN的输入伸缩参数以及输出层进行优化,所得网络称为MI-JIT-ESN.ESN的优化方法分为两部分:一是基于网络输入与输出之间的互信息,对网络的多个输入伸缩参数进行调整;二是基于JIT优化的局部输出层,对ESN的隐层输出数据进行局部重新建模,从而提升ESN输出层的回归拟合精度.将MI-JIT-ESN应用于青霉素补料分批发酵过程建模.结果显示,MI-JIT优化方法能提高模型的适应性,并优于其他比较方法.

To improve the adaptability of echo state network（ ESN）,an optimization method based on mutual information（ MI） and Just-In-Time（ JIT） learning was proposed in this paper to optimize the input scaling and the output layer of ESN. The method was named as MI-JIT optimization method and the obtained new network was MI-JIT-ESN. The optimization method mainly consists of two parts. Firstly,the scaling parameters of multiple inputs were adjusted on the basis of MI between the network inputs and outputs. Secondly,based on JIT learning,a partial model of output layer was established. The new partial model could make the regression results more accurate. Further,a multi-input multi-output MI-JIT-ESN model was developed for the fed-batch penicillin fermentation process. The experimental results showed that the obtained MI-JIT-ESN model performed well,and that it had better adaptability than ESN model without optimization and other neural network models.