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Computing Method and Circuit Realization of Neural Network on Finite Element Analysis

机译:关于有限元分析神经网络的计算方法和电路实现

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The finite element analysis in theory of elasticity is corresponded to the quadratic programming with equality constraint, which can be further transformed into the unconstrained optimization. In the paper, the question is solved by modified Hopfield neural network based on the energy function of the neural network equals to the objective function of the finite element method and the minimum point, which is the stable equilibrium point of the network system, is the solution. In addition the authors present the computer simulation and analogue circuit experiment to verify this method. The results are revealed that: 1) The results of improved Hopfield neural network are reliable and accuracy; 2) The improved Hopfield neural network model has an advantage on circuit realization and the computing time, which is unrelated with complexity of the structure, is constant. It is practical significance for the research and calculation.
机译:弹性理论上的有限元分析对应于具有平等约束的二次编程,这可以进一步转化为无约束优化。在本文中,基于神经网络的能量函数等于有限元方法的客观函数和网络系统稳定平衡点的最小点,该问题通过修改的Hopfield神经网络解决了问题。解决方案。此外,作者介绍了计算机仿真和模拟电路实验,以验证这种方法。结果表明:1)改进的Hopfield神经网络的结果可靠,准确性; 2)改进的Hopfield神经网络模型在电路实现中具有优势,并且计算时间与结构复杂性无关,是恒定的。这对研究和计算具有实际意义。

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