# An Experimental Analysis of the Echo State Network Initialization Using the Particle Swarm Optimization

arXiv: https://arxiv.org/abs/1501.00436 · HTML: https://mlsanity.com/paper/1501.00436/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Jan 2015.

## Built on by (most cited first, 3 of 3)

- [A Survey on Reservoir Computing and its Interdisciplinary Applications Beyond Traditional Machine Learning](https://mlsanity.com/paper/2307.15092/) (2023)
- [Evolutionary Echo State Network: evolving reservoirs in the Fourier space](https://mlsanity.com/paper/2206.04951/) (2022)
- [Re-visiting Reservoir Computing architectures optimized by Evolutionary Algorithms](https://mlsanity.com/paper/2211.06254/) (2022)

## Abstract (arXiv)

This article introduces a robust hybrid method for solving supervised learning tasks, which uses the Echo State Network (ESN) model and the Particle Swarm Optimization (PSO) algorithm. An ESN is a Recurrent Neural Network with the hidden-hidden weights fixed in the learning process. The recurrent part of the network stores the input information in internal states of the network. Another structure forms a free-memory method used as supervised learning tool. The setting procedure for initializing the recurrent structure of the ESN model can impact on the model performance. On the other hand, the PSO has been shown to be a successful technique for finding optimal points in complex spaces. Here, we present an approach to use the PSO for finding some initial hidden-hidden weights of the ESN model. We present empirical results that compare the canonical ESN model with this hybrid method on a wide range of benchmark problems.