Deep Reservoir Computing Based Random Vector Functional Link for Non-sequential Classification

Abstract

Reservoir Computing (RC) is well-suited for simpler sequential tasks which require inexpensive, rapid training, and the Echo State Network (ESN) plays a significant role in RC. In this article, we proposed variations of the Random Vector Functional Link (RVFL) network based on reservoir computing for non-sequential tasks. To commence, we present a plain echo state-based RVFL (esRVFL) that is distinguished from randomly generated input weights by the fact that esRVFL generates sparse matrices randomly to complete the initialization of the neuron weights. Following that, we extended it to a deep structure and introduced several network topologies. We also follow esRVFL and replace the single layer of echo state with a multi-layer stacked echo state network, where the entire network only needs to compute a set of output weights, which is called deep esRVFL (desRVFL). We evaluated our method on several public datasets and compared it with related methods. Experiments have shown that the proposed method can handle the classification tasks for tabular data and outperform some state-of-the-art randomized neural networks.