针对BP神经网络收敛速度慢、易陷入局部极小的缺点，将具有全局搜索能力的模拟退火（SA）算法引入到神经网络的权值优化中。并且在SA算法中引入状态接受过程和退火过程的自适应措施，增加了对当前状态最优解的"记忆能力"，避免了当前最优解的遗失，提高了算法的搜索效率。通过对XOR问题求解的比较，显示出SABP算法具有全局收敛且精度高的优越特性。最后基于实际工程的边坡数据建立了一个SABP算法模型，成功解决了具有高度非线性特点的边坡稳定性评价问题。

Since BP neural network possesses disadvantages of slow convergence and local minimum, a global optimum algorithm simulated annealing (SA) for modifying the weight values is proposed instead of local gradient descend. The adaptive steps of state accepting and temperature - lowering processes are applied to avoiding the losing of current optimization solution and to enhancing search efficiency. By the comparison in solving XOR problem, the predominant capability of SABP algorithm is presented on global optimization and convergence precision. Finally, a SABP model of slope stability evaluation is built based on practical engineering data, and it has success fully solved the slope stability evaluation problem that has the highly nonlinear character. 