一种基于广义Armijo线搜索技术的快速神经网络共轭梯度算法模型

onjugate gradient method (CG) requires low memory and performs fast convergent behaviors in practical applications. A novel conjugate gradient method with generalized Armijo search is proposed for three-layer BP neural networks (BPNNs) in this paper. Theoretically, the proof of the deterministic convergent properties, which include weak and strong convergence, for this learning method is given by employing a novel technique. The gradient of error function tends to zero which results in the weak convergent behavior. For the so-called strong convergence, it represents that the sequence with respect to weights tends to a fixed point. Additionally, compared with the existing literature, the restrictive assumptions of the deterministic convergence are more relaxed. For checking the theoretical results, this paper gives some numerical experiments.