神经元网络在安全评价中的应用

利用人工神经元网络进行安全评价可以克服传统的安全评价方法的缺点,提高安全评价方法的精确度和可靠性。笔者在BP神经网络基本原理的基础上,利用机会约束的思想建立了综合安全评价模型;运用反向传播算法和遗传算法对神经元网络进行训练;进而就系统综合安全评价模型进行求解,并对运用神经元网络进行综合安全评价的优点进行了分析。最后,通过对实例进行综合安全评价,得出计算结果,同时也证明将人工神经元网络应用于安全评价的可行性

Application of Neural Network in Safety Assessment

Safety assessment method using BP neural network can overcome the defects of traditional safety assessment methods and enhance its precision and reliability. The basic principle of BP neural network is introduced and the comprehensive safety assessment model using the chance constrained programming is constructed. The study of BP neural network is exercised by the counter propagation algorithm and genetic algorithm, based on this analysis the comprehensive safety assessment of the system is solved, and the merit of which is analyzed. The method is exemplified in a real case to obtain calculated results proving that the method is feasible.