基于混合结构的粒子群优化ESN

Zohaib Y Ahmad, Kaizhe Nie, J. Qiao, Cuili Yang
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引用次数: 1

摘要

近年来,回声状态网络(ESNs)得到了广泛的研究。ESN初始化后,储层的输入权值和内部权值都是固定的,只需要优化输出权值矩阵。为了计算回声状态网络的输出权值,提出了混合拓扑的粒子群优化算法(PSO)。所提出的粒子群结构由探索能力强的规则网络和探索能力强的无标度网络混合而成。仿真结果表明,所提出的回声状态网络比传统的回声状态网络具有更好的预测性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Hybrid Structure Based PSO for ESN Optimization
Recently, the echo state networks (ESNs) have been widely studied. In an ESN, the input weights and internal weights of reservoir are fixed after initialization, only the output weight matrix needs to be optimized. To calculate the output weights of ESN, the particle swarm optimization algorithm (PSO) with hybrid topology is proposed. The structure of the proposed PSO is mixed with regular network with strong exploration ability and scale-free network with good exploration ability. Simulation results show that the proposed ESN has good prediction performance than the traditional ESN.
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