粒子群优化算法在神经网络识别γ能谱中的应用

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

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。

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 advantageous in terms of globe optimal searching. In, this paper, we propose a new algorithm for neural network training, 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.