This paper proposes the generalized regression neural network(GRNN)model and multi-GRNN model with a gating network by selecting the data of Shanghai index,the stocks of Shanghai Pudong Development Bank(SPDB),Dongfeng Automobile and Baotou Steel.We analyze the two models using Matlab software to predict the opening price respectively.Through building a softmax excitation function,the multi-GRNN model with a gating network can obtain the best weights.Using the data of the four groups,the average of forecasting errors of 4 groups by GRNN neural model is 0.012 208,while the average of the multi-GRNN models's with a gating network is 0.002 659.Compared with the real data,it is found that the both results predicted by the two models have small mean square prediction errors.So the two models are suitable to be adopted to process a large quantity of data,furthermore the multi-GRNN model with a gating network is better than the GRNN model.
The transfer system,an important subsystem in urban citizen passenger transport system,is a guarantee of public transport priority and is crucial in the whole urban passenger transport traffic.What the majority of bus passengers consider is the convenience and comfort of the bus ride,which reduces the transfer time of bus passengers."Transfer time" is considered to be the first factor by the majority of bus passengers who select the routes.In this paper,according to the needs of passengers,optimization algorithm,with the minimal distance being the first goal,namely,the improved Dijkstra algorithm based on the minimal distance,is put forward on the basis of the optimization algorithm with the minimal transfer time being the first goal.
This paper has concluded six features that belong to passenger vehicle types based on genetic algorithm(GA)of feature selection.We have obtained an optimal feature subset,including length,ratio of width and length,and ratio of height and length.And then we apply this optimal feature subset as well as another feature set,containing length,width and height,to the network input.Back-propagation(BP)neural network and support vector machine(SVM)are applied to classify the passenger vehicle type.There are four passenger vehicle types.This paper selects 400 samples of passenger vehicles,among which 320 samples are used as training set(each class has 80 samples)and the other 80 samples as testing set,taking the feature of the samples as network input and taking four passenger vehicle types as output.For the test,we have applied BP neural network to choose the optimal feature subset as network input,and the results show that the total classification accuracy rate can reach 96%,and the classification accuracy rate of first type can reach 100%.In this condition,we obtain a conclusion that this algorithm is better than the traditional ones[9].
