三角基函數(shù)神經(jīng)網(wǎng)絡算法在數(shù)值積分中的應用研究
Numerical Integration Study Based on Triangle Basis Neural Network Algorithm
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摘要: 該文提出了一種基于三角基函數(shù)神經(jīng)網(wǎng)絡算法求解數(shù)值積分的新方法,提出并證明了神經(jīng)網(wǎng)絡算法的收斂定理和數(shù)值積分的求解定理及推論。最后給出了數(shù)值積分算例,并與傳統(tǒng)計算方法作了比較分析.分析結(jié)果表明,該文提出的數(shù)值積分方法計算精度高,適應性強,而且不需要知道被積函數(shù),因此該數(shù)值積分算法在電子學等工程實際中有較大的應用價值。Abstract: A new appproach to solve numerical integration is developsed in this paper, based on the algorithm of neural networks with triangle basis functions. The convergence theorem of the neural networks algorithm and the theorem of numerical integration solution and its inferences are presented and proved. By the examples of numerical integration the comparison is carried out with tradional methods. The results show that the numerical integration approach has the characteristics such as high precision, strong adaptablity, and the intergration of unknown fountions can be solved. Therefore, the numerical integration approach values significantly in many engineering applications, such as electronics, etc.
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