Learning in a non-stationary environment is an inevitable problem when
applying machine learning algorithm to real world environment. Learning new
tasks without forgetting the previous knowledge is a challenge issue in machine
learning. We propose a Kalman Filter based modifier to maintain the performance
of Neural Network models under non-stationary environments. The result shows
that our proposed model can preserve the key information and adapts better to
the changes. The accuracy of proposed model decreases by 0.4% in our
experiments, while the accuracy of conventional model decreases by 90% in the
drifts environment.