Monfroglio A
Int J Neural Syst. 1999 Feb;9(1):11-25. doi: 10.1142/s0129065799000034.
First a Linear Programming formulation is considered for the satisfiability problem, in particular for the satisfaction of a Conjunctive Normal Form in the Propositional Calculus and the Simplex algorithm for solving the optimization problem. The use of Recurrent Neural Networks is then described for choosing the best pivot positions and greatly improving the algorithm performance. The result of hard cases testing is reported and shows that the technique can be useful even if it requires a huge amount of size for the constraint array and Neural Network Data Input.
首先，针对可满足性问题，特别是命题演算中合取范式的可满足性问题，考虑一种线性规划公式，并采用单纯形算法来求解优化问题。接着描述了如何使用递归神经网络来选择最佳的主元位置，从而显著提高算法性能。报告了对困难实例的测试结果，结果表明该技术即使在约束数组和神经网络数据输入需要大量空间的情况下仍可能有用。
