Abstract
Compared with other traditional neural network algorithms, the Extreme Learning Machine (ELM) has the advantages of simple structure, fast learning speed and good generalization performance. However, there are still some shortages that restrict the further development of ELM. For example, the randomly generated input weights, biases and the ill-conditioned appearance of the hidden layer design matrix all affect the generalization performance and robustness of the ELM algorithm model. In order to overcome the adverse affects of both, an improved ensemble extreme learning machine regression algorithm (ECV-ELM) is proposed in this paper. The method first generates multiple sub CV-ELM model through AdaBoost..RT method, and the selects the best set of sub-models to integrate. The ECV-ELM algorithm makes use of ensemble learning method to complement each other among sub-models, so that the generalization performance and robustness of the algorithm are better than that of the sub-model. The results of regression experiments on multiple data sets show that the ECV_ELM algorithm can effectively reduce the influence of the ill conditioned matrix, the random input weight and bias, and has good generalization performance and robustness.
Highlights
Artificial neural network (ANN) has been widely used in various fields due to its good learning ability and high speed optimization capability [1-2]
ECV-Extreme Learning Machine (ELM) overcomes the shortcomings of poor model stability due to the random generation of input weight, bias and incomplete cancellation of CV-ELM noise. It combines the ensemble learning method with the CV-ELM regression algorithm and uses some common methods to selet the appropriate sub CV-ELM model, which can further improve the performance of the entire CV-ELM
G1,G2, ⋯,GT, set the activation function of all sub models to g(x), and the number of hidden layer neurons is L; (2) Initialization t=1; (3) Determine whether it reaches the number of iterations, that is, t
Summary
Artificial neural network (ANN) has been widely used in various fields due to its good learning ability and high speed optimization capability [1-2]. ELM has fast learning speed and good generalization performance, when some column vectors in the hidden layer design array are approximated to linear correlation, that is, the hidden layer design array has multiple collinearity or ill posed. It can result in poor generalization performance and stability by using ordinary least square method to estimate the solution of the ill conditioned matrix. The CV-ELM algorithm improves the generalization performance of the extreme learning machine to a certain extent and can ensure good algorithm robustness This algorithm still has defects in some cases, so that the model can not achieve the minimum error. Through regression experiments of multiple data sets, it is proved that this method can achieve good generalization performance and stability
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