云过程神经网络模型及算法研究

该文针对输入输出具有不确定性特征并与时间或过程有关的复杂非线性系统建模和求解问题，利用过程神经网络对时变信号的动态处理能力，结合云模型对定性定量概念的转化能力，构建了一种具有不确定性信息处理能力的云过程神经网络模型，并采用猫群优化算法同时对网络结构和参数进行并行优化设计，提高了网络逼近及泛化能力，实现了神经网络在时间域和不确定信息处理领域上的有效扩展。仿真实验结果验证了模型和算法的可行性和有效性。

Research on Cloud Process Neural Network Model and Algorithm

For modeling and solving problems of complex nonlinear systems whose input/output have uncertainty and are associated with time or process, a cloud process neural network model is built in the paper. It has uncertainty information processing ability by combining process neural networks processing ability for time-varying signal with cloud model transformation ability between qualitative and quantitative concepts. In addition, the cat swarm optimization algorithm is used to optimize the network structure and parameters simultaneously, and it helps to improve network approximation?and generalization performance. The effective extension of neural networks in time domain and uncertain information processing field is realized. Experimental results verify the effectiveness and feasibility of the model and algorithm.