A New Extreme Learning Machine Optimized by PSO
Haoyang Bi · Journal of Zhengzhou University · 2013
Extreme learning machine(ELM) was a new type of feedforward neural network.Compared with traditional single hidden layer feedforward neural networks,ELM possessed higher training speed and smaller error.Due to random input weights and hidden biases,ELM might need numerous hidden neurons to achieve a reasonable accuracy.A new ELM learning algorithm,which was optimized by the particle swarm optimization(PSO),was proposed.PSO algorithm was used to select the input weights and bias of hidden layer,then the output weights could be calculated.To test the validity of proposed method,two simulation experiments were drawn on the approximation curves of the Sinc function.Experimental results showed that the proposed algorithm achieved better performance with less hidden neurons than other similar methods.