Collaborative adaptive exponential linear-in-the-parameters nonlinear filters

by Vinal Patel, Somanath Pradhan and Nithin V. Georg

A randomized neural network for data streams

© 2017 IEEE. Randomized neural network (RNN) is a highly feasible solution in the era of big data because it offers a simple and fast working principle in processing dynamic and evolving data streams. This paper proposes a novel RNN, namely recurrent type-2 random vector functional link network (RT2McRVFLN), which provides a highly scalable solution for data streams in a strictly online and integrated framework. It is built upon the psychologically inspired concept of metacognitive learning, which covers three basic components of human learning: what-to-learn, how-to-learn, and when-to-learn. The what-to-learn selects important samples on the fly with the use of online active learning scenario, which renders our algorithm an online semi-supervised algorithm. The how-to-learn process combines an open structure of evolving concept and a randomized learning algorithm of random vector functional link network (RVFLN). The efficacy of the RT2McRVFLN has been numerically validated through two real-world case studies and comparisons with its counterparts, which arrive at a conclusive finding that our algorithm delivers a tradeoff between accuracy and simplicity

Functional Link Expansions for Nonlinear Modeling of Audio and Speech Signals

Nonlinear distortions pose a serious problem for the quality preservation of audio and speech signals. In order to address this problem, such signals are processed by nonlinear models. Functional link adaptive filter (FLAF) is a linear-in-the-parameter nonlinear model, whose nonlinear transformation of the input is characterized by a basis function expansion, thus satisfying the universal approximation property. Since the expansion type affects the nonlinear modeling according to the nature of the input signal, in this paper we investigate the FLAF modeling performance involving the most popular functional expansions when audio and speech signals are processed. A comprehensive analysis is conducted in order to provide the best suitable solution for the processing of nonlinear audio signals. Experimental results are assessed also in terms of signal quality and intelligibility