本文针对神经网络算法应用于网络消费预测中时收敛速度较慢、收敛精度不高等

本文针对神经网络算法应用于网络消费预测中时收敛速度较慢、收敛精度不高等问题，设计了一种基于混沌扰动优化粒子群的网络消费分析神经网络模型，采用混沌扰动优化策略对粒子群算法的初始种群、粒子择优和惯性权值进行改进，并将其与神经网络结合，应用于网络消费预测模型中。

結果 (英文) 1:

Paper for neural network algorithm should for network consumption forecast in the Shi convergence speed more slow, and convergence precision not high problem, design has a based on chaos disturbance optimization particles group of network consumption analysis neural network model, used chaos disturbance optimization strategy on particles group algorithm of initial population, and particles preferred and inertia right value for improved, and will its and neural network combines, should for network consumption forecast model in the.

結果 (英文) 2:

In this paper, neural network algorithm for forecasting consumption when used in network convergence slower convergence accuracy is not high, designed a neural network model analysis based on chaos particle swarm optimization network disturbance consumption, chaotic disturbance on particle swarm optimization strategy The initial population, particle merit and improved inertia weight, and combined with neural networks, used in network consumption forecasting model.

結果 (英文) 3:

According to the neural network algorithm is applied to the network consumption forecast in the slow convergence speed, convergence precision, designed a kind of chaotic optimization neural network model analysis of Internet consumption based on PSO with chaotic disturbance, optimization strategy of particle swarm optimization algorithm, particle initial population preferred and inertia weight was improved, and the combined with the neural network, is applied to the network consumption forecast model.