分布參數(shù)神經(jīng)網(wǎng)絡(luò)與偏微分方程求解
DISTRIBUTED PARAMETER NEURAL NETWORKS FOR SOLVING PARTIAL DIFFERENTIAL EQUATIONS
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摘要: 本文提出一類用于求解偏微分方程的分布參數(shù)神經(jīng)網(wǎng)絡(luò),并且在連續(xù)時空上研究了它的動態(tài)特性。最后還給出了兩個模擬試驗(yàn),用于檢驗(yàn)這類神經(jīng)網(wǎng)絡(luò)的有效性。Abstract: Novel distributed parameter neural networks are proposed for solving partial differential equations, and their dynamic performances are studied in Hilbert space. The locally connected neural networks are obtained by separating distributed parameter neural networks. Two simulations are also given. Both theoretical and practical results illustrate that the distributed parameter neural networks are effective and efficient for solving partial differential equation problems.
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