An enhanced extreme learning machine with a double parallel structure and its application to modeling complex chemical processes

Extreme learning machine (ELM) with a hidden layer has been successfully applied in many fields. In order to enhance the generalization performance of ELM, an enhanced ELM with a double parallel structure (DP-ELM) was proposed. In the proposed DP-ELM, a special connection between the input nodes and the output nodes was built. With the connection between the hidden nodes and output nodes, the output nodes of DP-ELM can receive the information from hidden nodes. With the special connection, the self-information from inputs can be directly passed to the output nodes. To verify the performance of the proposed DP-ELM, simulations are carried out using 5 regression datasets, including two process modeling applications. Simulation results show that, compared with ELM, the proposed DP-ELM could achieve higher accuracy better stable ability.