Abstract:As a supervised learning model, traditional extreme learning machine assigns input weights and bias of nodes of hidden layer arbitrarily, and completes learning process by calculating output weights of nodes of hidden layer. Aiming at the problem that traditional extreme learning machine does not work well in prediction research, an improved extreme learning machine model based on simulated annealing algorithm was proposed. Firstly, traditional extreme learning machine method was used to learn the training set, and output weight of hidden layer is obtained. The evaluation standard of prediction result was selected to assess prediction result. Then, using the simulated annealing algorithm, input weights and bias of hidden layer of traditional extreme learning machine were regarded as the initial solution, and the evaluation standard was regarded as the objective function. The optimal solution was found in cooling process that was input weights and bias of hidden layer of extreme learning machine with the smallest prediction error. Iris classification data and Boston house price forecast data were used to conduct experiments. The experiment finds that compared with traditional extreme learning machine, extreme learning machine based on simulated annealing is better on classification and regression.