大型衡器是智能交通、货运物流等质量称量的关键计量器具，是典型的非线性、强耦合性多传感器系统。现有大型衡器通过人工调节偏载误差，工作量大、补偿效果差、称量准确度低，是国内外静态称重领域亟待解决的难题。本项目研究大型衡器偏载误差机理与多传感器称量融合方法，主要包括：①基于有限元法的偏载误差机理研究，揭示偏载误差规律与多传感器关联性，为误差补偿提供理论依据；②构造基于先验知识的约束条件，建立改进型升余弦-径向基函数(RC-RBF)神经网络集成优化方法，在此基础上构建大型衡器偏载误差补偿与称重信息融合模型，实现偏载误差自动补偿与准确称重；③构建多传感器称重模拟实验平台，验证理论方法的准确性与实用性。项目研究可形成大型衡器偏载误差补偿与称量融合理论与方法，解决现有大型衡器存在的严重问题，关键技术与方法对其它多传感器测控系统的误差补偿与检测估计具有重要借鉴意义，也为神经网络集成优化研究开辟一种新思路。

Large-scale weighing instrument(LSWI) is the key measurement apparatus on the field of intelligent traffic, freight forwarding and logistics, etc, and it is a typical nonlinear and badly coupled multi-sensor system. The eccentric error compensation of the existed LSWIs is based on manually adjusting method, which has the disadvantages of difficult compensation on the eccentric error, hard labor, and low accuracy of weighing result. This is the urgent problem of static weighing filed. The aim of this project is to study eccentric error mechanism and weighing fusion method for LSWI, and supply the way of solving this problem in LSWI. The major works include: ①the study of LSWI's eccentric error mechanism by finite-element method, and the exploration of the eccentric error law and multi-sensor correlation, which is the theory basis of eccentric error compensation; ②the construction of constraint condition based the prior knowledge of LSWI and the optimum design method for improved raised-cosine radial basis fuction(RC-RBF) neural network ensemble, and then the establishment of the weighing fusion model of LSWI, which can perform the eccentric error automatically compensating and the LSWI accurately weighing; ③the construction of weighing experimental platform based on multiple load cells and the simulation and testing of weighing fusion and eccentric error compensation method, and then the optimization of the model's parameters. The project will present the theory and method of LSWI's eccentirc error compensation and weighing fusion, and solve the problem of difficult compensation and low accuracy in the existed LSWI. This proposed method is useful to detect and estimate the signal and compensate the errors.of mesurement and control system with multiple sensors, and this project will provide a new idea of nerual network ensembles optimun design.