Parameter identification of a shell transfer arm using FDA and optimized ELM
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摘要: 為實現彈藥傳輸機械臂中不可測參數的辨識,建立了機械臂的虛擬樣機,并將其作為樣本數據的來源;考慮到樣本數據的連續性和平滑特性,使用函數型數據分析和函數型主成分分析對樣本數據進行了特征提取,并利用提取的特征參數和待辨識參數作為訓練樣本對極限學習機(ELM)進行了訓練.為提高極限學習機的辨識精度和泛化能力,利用粒子群算法對極限學習機的輸入層與隱含層的連接權值和隱含層節點的閾值進行了優化.最后,分別利用仿真數據與測試數據對此方法進行了驗證,仿真數據的辨識結果表明,優化后的極限學習機具有更高的辨識精度和泛化能力;同時,通過對比將測試數據的辨識結果代入模型中進行仿真得到的支臂角速度與測試角速度,驗證了此方法的可行性和有效性.Abstract: To identify the unmeasurable parameters of a shell transfer arm, a virtual prototype of the shell transfer arm was built, and the built virtual prototype is regard as the source of the sample data. Considering the continuity and smoothness properties of the sample data, features of the curves were extracted by functional data analysis and functional principal component analysis, and the features and unknown parameters were used to train the extreme learning machine (ELM). At the meantime, the weight connecting the input layer and hidden layer and the threshold of the hidden nodes were optimized by particle swarm optimization (PSO) to improve the identification accuracy and generalization performance of ELM. At last, the presented method was verified by simulation data and test data. The identification results of the simulation data show that the optimized ELM has higher identification accuracy and better generalization performance. Also, the presented method is proved to be feasible and effective by comparing the real angular velocity and the angular velocity from the virtual prototype with respect to the test data identification results.
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參考文獻
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