Single multiplicative neuron artificial neural networks have different importance than many other artificial neural networks because they do not have complex architecture problem, too many parameters and they need more computation time to use. In single multiplicative neuron artificial neural network, it is assumed that there is a one data generation process for time series. Many time series need an assumption that they have two data generation process or more. Based on this idea, the threshold model structure can be employed in a single multiplicative neuron model artificial neural network for taking into considering data generation processes problem. In this study, a new artificial neural network type is proposed and it is called a threshold single multiplicative neuron artificial neural network. It is assumed that time series have two data generation processes according to the architecture of single multiplicative neuron artificial neural network. Training algorithms are proposed based on harmony search algorithm and particle swarm optimization for threshold single multiplicative neuron artificial neural network. The proposed method is tested by various time series data sets and compared with well-known forecasting methods by considering different error measures. Finally, the performance of the proposed method is evaluated by a simulation study.

单个乘法神经元人工神经网络与许多其他人工神经网络的重要性不同，因为它们不具有复杂的体系结构问题，参数太多并且需要更多的计算时间才能使用。在单个乘法神经元人工神经网络中，假设存在一个用于时间序列的数据生成过程。许多时间序列需要一个假设，即他们有两个或两个以上的数据生成过程。基于此思想，阈值模型结构可用于单个乘法神经元模型人工神经网络中，以考虑数据生成过程问题。在这项研究中，提出了一种新的人工神经网络类型，称为阈值单乘法神经元人工神经网络。假设时间序列根据单个乘法神经元人工神经网络的架构具有两个数据生成过程。提出了基于和谐搜索算法和粒子群算法的阈值单乘神经元人工神经网络训练算法。通过各种时间序列数据集对提出的方法进行了测试，并通过考虑不同的误差度量与已知的预测方法进行了比较。最后，通过仿真研究评估了所提出方法的性能。通过各种时间序列数据集对提出的方法进行了测试，并通过考虑不同的误差度量与已知的预测方法进行了比较。最后，通过仿真研究评估了所提出方法的性能。通过各种时间序列数据集对提出的方法进行了测试，并通过考虑不同的误差度量与已知的预测方法进行了比较。最后，通过仿真研究评估了所提出方法的性能。