基于蚁群算法优化回声状态网络的研究

针对输出权值采用最小二乘法的回声状态网络(ESN),在随机选取输入权值和隐层神经元阈值时,存在收敛速度慢、预测精度不稳定等问题,提出了基于蚁群算法优化回声状态网络(ACO-ESN)的算法。该算法将优化回声状态网络的初始输入权值、隐层神经元阈值问题转化为蚁群算法中蚂蚁寻找最佳路径的问题,输出权值采用最小二乘法计算,通过蚁群算法的更新、变异、遗传等操作训练回声状态网络,选择出使回声状态网络预测误差最小的输入权值和阈值,从而提高其预测性能。将ACO-ESN与ELM、I-ELM、OS-ELM、B-ELM等神经网络的仿真结果进行对比,结果验证经过蚁群算法优化的回声状态网络加快了其收敛速度,改善了其预测性能,并增强了隐层神经元的敏感度。

Echo state network based on ant colony algorithm

When the echo state network (ESN) based on the least squares method randomly selects the input weights and the neuron thresholds in hidden layers, the convergence speed is slow and the prediction accuracy is not stable. We propose a modified ESN based on ant colony algorithm (ACO-ESN). The optimization of the initial input weights and neuron thresholds in hidden layers can be changed into a problem of finding the best path by the ants in the ant colony algorithm. The output weights are calculated by the least squares method. The ESN is trained through update, variation and genetic operations of the ant colony algorithm, and the input weights and thresholds which have the minimum ESN prediction error are selected to increase the ESN's prediction performance. Compared with other 4 ELM neural networks, the simulation results show that the ESN optimized by the ant colony algorithm can accelerate the convergence speed, and improve its prediction performance and the sensitivity of the neurons in hidden layers.