Extreme Learning Machine and its Variants for Time-Varying Neural Networks
نویسنده
چکیده
System identification in nonstationary environment represents a challenging problem and an advaned neural architecture namely Time-Varying Neural Networks (TV-NN) has shown remarkable identification properties in nonlinear and nonstationary conditions. Timevarying weights, each being a linear combination of a certain set of basis functions, are used in such kind of networks instead of stable ones, which inevitalbly increases the number of free parameters. Therefore, an Extreme Learning Machine (ELM) approach is developed to accelerate the training procedure for TV-NN. What is more, in order to obtain a more compact structure, or determine several important parameters, or update the network more efficiently in online case, several variants of ELM-TV are proposed and discussed in the works. Related computer simulations have been carried out and show the effectiveness of the algorithms.
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