粒子群优化算法在神经网络识别γ能谱中的应用

在神经网络识别γ能谱的应用中,针对BP算法极易陷入局部极小、收敛速度慢的缺点,根据粒子群优化算法具有全局寻优的特点,本文将PSO与BP算法结合起来形成一种训练神经网络的新算法--混合PSO-BP算法.将该算法应用到γ能谱识别中,克服了BP算法极易陷入局部极小的缺点,并且训练好的网络具有很好的泛化能力,识别正确率为100%.实例表明,混合PSO-BP算法用于γ能谱识别是非常理想的、有效的.

Application of particle swarm optimization to identify gamma spectrum with neural network

In applying neural network to identification of gamma spectra back propagation(BP) algorithm is usually trapped to a local optimum and has a low speed of convergence.,whereas particle swarm optimization(PSO) is ad-vantageous in terms of globe optimal searching.In,this paper,we propose a new algorithm for neural network train-ing,i.e.combined BP and PSO optimization,or PSO-BP algorithm.Practical example shows that the new algorithm can overcome shortcomings of BP algorithm and the neural network trained by it has a high ability of generalization with identification result of 100% correctness.It can be used effectively and reliably to identify gamma spectra.