Abstract: The stochastic expected value model belongs to a class of stochastic programming problems，which has wide application backgrounds，in order to search an algorithm which can solve this problem effectively，in the paper，random simulation is used to produce training samples for BP neural network to approximate the stochastic function.And a hybrid intelligent algorithm for stochastic expected value models combined particle swarm optimization with BP neural network for approximation of the fitness function and checking feasibility of solution is presented.Finally，the simulation results of two examples are given to show the correctness and effectiveness of the algorithm.

摘要： 随机期望值模型是一类有着广泛应用背景的随机规划问题，为了寻找更为高效的求解随机期望值模型的算法，采用随机仿真产生样本训练BP网络以逼近随机函数，然后应用微粒群算法并以逼近随机函数的神经元网络作为适应值估计和实现为了检验解的可行性，从而提出了一种求解随机期望值模型的混合智能算法。最后通过两个实例的仿真结果说明了算法的正确性和有效性。