Study on scheme optimization of urban flood disaster prevention and reduction
The decision in flood disaster prevention and reduction will affect the evaluation of flood disaster directly. How to apply the natural, economic and social information, through anticipatory analysis, to ensure the minimum damage after practicing certain optimum scheme is a core problem in the flood disaster prevention and reduction decision-making. In this paper, the factors affecting this decision-making are analyzed, the fuzzy optimum selection and neural network are applied in scheme optimization of urban flood disaster prevention and reduction, and the model of scheme optimization is established.
Particle Swarm Optimization Fuzzy Neural Network and its Application in Soft-Sensor Modeling of Acrylonitrile Yield
Firstly, particle swarm optimization fuzzy neural network (PSOFNN) is proposed and the algorithm flow of PSOFNN are given in this paper. Secondly, PSOFNN is applied in soft-sensor modeling of acrylonitrile yield. The new method assumes that fuzzy neural network (FNN) is used to construct the soft-sensor model of acrylonitrile yield and particle swarm optimization algorithm (PSO) is employed to optimize parameters of FNN. Moreover, how to choose the auxiliary variables of soft-sensor is studied carefully. Experiment results show that the model based on PSOFNN has higher precision and better performance than the model based on PSONN. The method proposed by this paper is feasible and effective in soft-sensor of acrylonitrile yield.
Large-dimensional wavelet networks and its application in (predicting) yield of acrylonitrile
To large-dimensional wavelet networks based on single scaling wavelet frames theory, a systematic design method is proposed. First, method for initialization of wavelet basis in wavelet frames is proposed; second, according to the data distribution, a modified method for rough selection of wavelet basis is given; and the last, an adaptive projection (algorithm) combined with AIC criterion is used to purify the wavelet basis, meanwhile finishing the parameters (identification.) The proposed method is applied in predicting the yield of acrylonitrile, and the results show the (effectiveness) of this method.