混沌神经网络预测算法评价准则与性能分析

为比较各种混沌神经网络预测算法的误差性能 ,该文提出了预测误差评价准则 ,即根均方误差 ,偏差 ,预测精度 ,决定度系数 ,绝对误差 ,以及一些归一化根均方误差等 ,并分析了它们在描述误差特征上的具体含义。针对两种混沌神经网络预测算法 (即全局神经网络算法和局部神经网络算法 ) ,利用该准则进行了性能分析 ,给出了合理的评价。结果表明 ,与混沌神经网络预测的局部模型算法相比 ,全局模型算法有更好的预测效果 ,且训练时间短 ,占用资源少 ,推广能力好

Analysis of algorithms and error evaluation criteria in chaotic neural network predictions

This article proposes several criteria for evaluating the performance of prediction algorithms, such as the root mean square error, bias, prediction accuracy, coefficient of determination, absolute error, and other normalized root mean square errors. The meanings of these criteria are then describe in detail. The two chaotic neural network prediction methods are then analyzed using the criteria. The results show that the global model algorithm has better performance than local model algorithm in chaotic neural network prediction and has a shorter training period, less source demand, and better generalization ability.