Weak-Consciousness Echo State Network for Time Series Prediction

After training, the parameters and weights of the standard echo state network are fixed, which will result in poor prediction accuracy for different types of input data. Therefore, a weak-consciousness echo state network (W-ESN) for time series prediction is proposed in this article. By introducing the concept of “label” in the network, the network can select the optimal network weights and parameters for different tasks based on the defined labels. Moreover, the network structure is improved by adding multiple reservoirs in series. Finally, the effectiveness of the W-ESN is proved by two examples.