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Su Lee Goh

Publications and source records attributed to Su Lee Goh.

2 recordsLinked to original sources

A complex-valued RTRL algorithm for recurrent neural networks.

A complex-valued real-time recurrent learning (CRTRL) algorithm for the class of nonlinear adaptive filters realized as fully connected recurrent neural networks is introduced. The proposed CRTRL is derived for a general complex activation function of a neuron, which makes it suitable for nonlinear adaptive filtering of complex-valued nonlinear and nonstationary signals and complex signals with strong component correlations. In addition, this algorithm is generic and represents a natural extension of the real-valued RTRL. Simulations on benchmark and real-world complex-valued signals support the approach.

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Recurrent neural networks with trainable amplitude of activation functions.

An adaptive amplitude real time recurrent learning (AARTRL) algorithm for fully connected recurrent neural networks (RNNs) employed as nonlinear adaptive filters is proposed. Such an algorithm is beneficial when dealing with signals that have rich and unknown dynamical characteristics. Following the approach from, three different cases for the algorithm are considered; a common adaptive amplitude shared among all the neurons; each layer has its own adaptive amplitude; different adaptive amplitude for each neuron. Experimental results show the AARTRL outperforms the standard RTRL algorithm.

Algorithms↗