The optimisation for local coupled extreme learning machine using differential evolution

Many strategies have been exploited for the task of reinforcing the effectiveness and efficiency of extreme learning machine (ELM), from both methodology and structure perspectives. By activating all the hidden nodes with different degrees, local coupled extreme learning machine (LC-ELM) is capable of decoupling the link architecture between the input layer and the hidden layer in ELM. Such activated degrees are jointly determined by the associated addresses and fuzzy membership functions assigned to the hidden nodes. In order to further refine the weight searching space of LC-ELM, this paper implements an optimisation, entitled evolutionary local coupled extreme learning machine (ELC-ELM). This method makes use of the differential evolutionary (DE) algorithm to optimise the hidden node addresses and the radiuses of the fuzzy membership functions, until the qualified fitness or the maximum iteration step is reached. The efficacy of the presented work is verified through systematic simulated experimentations in both regression and classification applications. Experimental results demonstrate that the proposed technique outperforms three ELM alternatives, namely, the classical ELM, LC-ELM, and OSFuzzyELM, according to a series of reliable performances.