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Error prediction and structure determination for CMAC neural network based on the uniform design method
Expert Systems ( IF 3.3 ) Pub Date : 2020-08-18 , DOI: 10.1111/exsy.12614
Zhiwei Kong 1 , Yong Zhang 1 , Xudong Wang 1 , Shuanzhu Sun 2 , Chunlei Zhou 2 , Dou Li 2 , Baosheng Jin 1
Affiliation  

Insufficient study on error bound of cerebellar model articulation controller (CMAC) severely limits its application. To investigate the error prediction and structure determination of CMAC for multi‐dimensional and data‐generation objects, this paper builds a 10‐input 2‐output model for a desulfurization system to test 44,640 sets of operation data. Four test groups and one prediction group are designed and performed based on uniform design method. Regression analysis and curve fitting methods are applied for error analyses. The focus of regression analysis method is the influence of uniform table's level on its prediction formulas' accuracies, whereas curve fitting's is the impact of theoretical memory space (location number) and actual storage space (address number) of CMAC on output error. Based on the results, the following conclusions are obtained. (a) The prediction accuracy of the linear regression equation is not monotonous with the level of the uniform design table, but there is a distinct region with local high precision. (b) Compared with regression analysis and address number fitting methods, location number analysis method has distinct advantages of prediction range, accuracy and flexibility. (c) In terms of location number analysis, different intervals may correspond to different optimal fitting functions, but only power function maintains high prediction accuracy in the whole range where the method works. Besides, the scope of location number analysis is also studied, of which the lower borders are 109 and 1010 approximately for model's two outputs, respectively.

中文翻译:

基于均匀设计方法的CMAC神经网络误差预测与结构确定

小脑模型关节控制器(CMAC)的误差界限研究不足,严重限制了其应用。为了研究多维和数据生成对象的CMAC的错误预测和结构确定,本文为脱硫系统建立了一个10输入2输出模型,以测试44,640套运行数据。基于统一设计方法设计并执行了四个测试组和一个预测组。回归分析和曲线拟合方法用于误差分析。回归分析方法的重点是统一表级别对其预测公式的准确性的影响,而曲线拟合的方法是CMAC的理论存储空间(位置编号)和实际存储空间(地址编号)对输出误差的影响。根据结果​​,得到以下结论。(a)线性回归方程的预测精度与统一设计表的水平并不单调,而是存在一个局部精度较高的区域。(b)与回归分析和地址编号拟合方法相比,位置编号分析方法具有预测范围,准确性和灵活性的明显优势。(c)就位置编号分析而言,不同的时间间隔可能对应于不同的最佳拟合函数,但是只有幂函数才能在该方法起作用的整个范围内保持较高的预测精度。此外,还研究了位置编号分析的范围,其中下边界为10 但存在一个局部精度较高的独特区域。(b)与回归分析和地址编号拟合方法相比,位置编号分析方法具有预测范围,准确性和灵活性的明显优势。(c)就位置编号分析而言,不同的时间间隔可能对应于不同的最佳拟合函数,但是只有幂函数才能在该方法起作用的整个范围内保持较高的预测精度。此外,还研究了位置编号分析的范围,其中下边界为10 但存在一个局部精度较高的独特区域。(b)与回归分析和地址编号拟合方法相比,位置编号分析方法具有预测范围,准确性和灵活性的明显优势。(c)就位置编号分析而言,不同的时间间隔可能对应于不同的最佳拟合函数,但是只有幂函数才能在该方法起作用的整个范围内保持较高的预测精度。此外,还研究了位置编号分析的范围,其中下边界为10 不同的时间间隔可能对应于不同的最佳拟合函数,但是只有幂函数才能在该方法起作用的整个范围内保持较高的预测精度。此外,还研究了位置编号分析的范围,其中下边界为10 不同的时间间隔可能对应于不同的最佳拟合函数,但是只有幂函数才能在该方法起作用的整个范围内保持较高的预测精度。此外,还研究了位置编号分析的范围,其中下边界为10模型的两个输出分别约为9和10 10
更新日期:2020-08-18
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