Name
Abstract | ||
|---|---|---|
Graphical abstractDisplay Omitted HighlightsEcho State Network (ESN) is an interesting tool for dealing with time series forecasting problems.The learning performance of ESN can be affected because of the random setting of some untrained weights.PSO is introduced to ESN as a pre-training tool to optimize the untrained weights.The networks weights become suitable to the targeted application.The accuracy of ESN is considerably improved after PSO pre-training. Echo State Networks, ESNs, are standardly composed of additive units undergoing sigmoid function activation. They consist of a randomly recurrent neuronal infra-structure called reservoir. Coming up with a good reservoir depends mainly on picking up the right parameters for the network initialization. Human expertise as well as repeatedly tests may sometimes provide acceptable parameters. Nevertheless, they are non-guaranteed. On the other hand, optimization techniques based on evolutionary learning have proven their strong effectiveness in unscrambling optimal solutions in complex spaces. Particle swarm optimization (PSO) is one of the most popular continuous evolutionary algorithms. Throughout this paper, a PSO algorithm is associated to ESN to pre-train some fixed weights values within the network. Once the network's initial parameters are set, some untrained weights are selected for optimization. The new weights, already optimized, are re-squirted to the network which launches its normal training process. The performances of the network are a subject of the error and the time processing evaluation metrics. The testing results after PSO pre-training are compared to those of ESN without optimization and other existent approaches. The conceived approach is tested for time series prediction purpose on a set of benchmarks and real-life datasets. Experimental results show obvious enhancement of ESN learning results. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1016/j.asoc.2017.01.049 | Appl. Soft Comput. |
Keywords | Field | DocType |
Echo State Network,Reservoir,Evolutionary learning,Particle swarm optimization,Pre-training,Time series prediction | Particle swarm optimization,Time series,Mathematical optimization,Evolutionary algorithm,Echo state network,Artificial intelligence,Initialization,Evolutionary learning,Machine learning,Mathematics,Sigmoid function | Journal |
Volume | Issue | ISSN |
55 | C | 1568-4946 |
Citations | PageRank | References |
13 | 0.55 | 40 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Naima Chouikhi | 1 | 25 | 2.80 |
| Boudour Ammar | 2 | 73 | 7.48 |
| Nizar Rokbani | 3 | 29 | 6.80 |
| Mohamed Adel Alimi | 4 | 1947 | 217.16 |