In crane systems, lifting, carrying and lowering the load from one place have different dynamic effects on the system. One of these dynamic effects is the moving load problem caused by the movement of the load on the crane system. With the increasing technology in recent years, production speeds have increased. For this reason, it has made the requirements for fast-running cranes mandatory for the transportation and loading of products. Therefore, it is important to know the dynamic effects of the moving load in fast working conditions. In this experimental study, the dynamic effects occurring on the crane beams with different loads and different working speeds during the transportation of the load on the crane are analysed. Here, there are multiple cars on the crane, and these cars are designed in different numbers on the crane and can be operated at different speeds. Under these conditions, the dynamic effects that have arisen have been tested. Also, vibration measurements were carried out at different points on the bridges. And then, these parameters obtained were used in two different proposed neural network types to predict the vibrations that occur on the crane system. Simulation results show that two approaches suggested that a radial basis neural network type can be used as an adaptive predictor for such systems in the experimental applications.

在起重机系统中，在一处举升，搬运和降低负载对系统的动力影响不同。这些动态影响之一是由起重机系统上的载荷运动引起的运动载荷问题。近年来，随着技术的进步，生产速度提高了。因此，它对产品运输和装载提出了对快速运行起重机的要求。因此，重要的是要了解快速工作条件下移动负载的动态影响。在本实验研究中，分析了在起重机上负载运输过程中，不同载荷和不同工作速度的起重机梁上产生的动力效应。在这里，起重机上有多辆汽车，这些汽车在起重机上设计的数量不同，可以以不同的速度运行。在这些条件下，已经测试了产生的动态效果。另外，在桥梁的不同点进行了振动测量。然后，将获得的这些参数用于两种不同的拟议神经网络类型中，以预测起重机系统上发生的振动。仿真结果表明，两种方法表明径向基神经网络类型可以在实验应用中用作此类系统的自适应预测器。这些获得的参数被用于两种不同的拟议神经网络类型中，以预测起重机系统上发生的振动。仿真结果表明，两种方法表明径向基神经网络类型可以在实验应用中用作此类系统的自适应预测器。这些获得的参数被用于两种不同的拟议神经网络类型中，以预测起重机系统上发生的振动。仿真结果表明，两种方法表明径向基神经网络类型可以在实验应用中用作此类系统的自适应预测器。