Extreme Learning Machine as a Function Approximator: Initialization of Input Weights and Biases

Extreme learning machine is a new scheme for learning the feedforward neural network, where the input weights and biases determining the nonlinear feature mapping are initiated randomly and are not learned. In this work we analyse approximation ability of the extreme learning machine depending on the activation function type and ranges from which input weights and biases are randomly generated. The studies are performed on the example of approximation of one variable function with varying complexity. The ranges of input weights and biases are determined for ensuring the sufficient flexibility of the set of activation functions to approximate the target function in the input interval.