An improved blind detection algorithm of chaos Hopfield neural network - Dawei YU; Shaowei CHEN; Shujuan YU (Dianxin kexue, 2018)
In order to improve the flexibility of the activation function of the blind detection algorithm in Hopfield neural network,an activation function with better nonlinear approximation ability near the origin was proposed.For the case where the algorithm trapped in local optima,utilizing the good ergodicity and randomness of chaos mapping,chaos was used to generate the initial sequence at the starting point of the algorithm,and small-amplitude chaotic perturbation was performed when the current global optimum value was constant,so as to reduce the error performance of the algorithm.The simulation results show that the proposed algorithm reduces the sensitivity of neurons to input values,has strong anti-interference ability and fast convergence speed,and improves the blind detection performance.