57 results about "Chaotic particle swarm optimization" patented technology

The invention provides a method for forecasting a hybrid neural network and recognizing scenic spot meteorological elements. The method includes the steps of firstly, collecting and conducting normalization processing on data banks of meteorological stations; secondly, determining the number of RBF network hidden nodes established by the main meteorological elements of the meteorological stations through a subtractive clustering algorithm according to the data banks of the n meteorological stations; thirdly, obtaining RBF network model parameters of the m meteorological elements established by the n meteorological stations respectively through chaotic particle swarm optimization algorithm; fourthly, forecasting future meteorological element values of an assigned number of days of the n meteorological stations through optimum RBF network prediction models of the elements obtained by the n meteorological stations; fifthly, conducting autoregression adjustment on soft factor information of a certain scenic spot according to the n meteorological elements and forecasting the meteorological element values of the scenic spot; sixthly, establishing an ART2 network to recognize and record weather phenomena of the scenic spot. The method has the advantages that the hybrid neural network prediction models have good generalization performance, are high in accuracy for forecasting the weather in the scenic spot and have application value.