An improved hybrid intelligent algorithm based on differential evolution(DE) and particle swarm optimization (PSO) is proposed. The performance of DEPSO algorithm is verified by simulations on 10 benchmark functions. Then, an improved learning algorithm named DEPSO extreme learning machine(DEPSO-ELM) algorithm for single hidden layer feedforward networks(SLFNs) is proposed. In DEPSO-ELM, DEPSO is used to optimize the network hidden node parameters, and ELM is used to analytically determine the output weights. Simulation results of 6 real world datasets regression problems show that the DEPSO-ELM algorithm performs better than DE-ELM and SaE-ELM. Finally, the effectiveness of the DEPSO-ELM algorithm is verified in the prediction of NC machine tool thermal errors.