Combination forecast for urban rail transit passenger flow based on fuzzy information granulation and CPSO-LS-SVM

In order to obtain the trend of urban rail transit traffic flow and grasp the fluctuation range of passenger flow better,this paper proposes a combined forecasting model of passenger flow fluctuation range based on fuzzy information granulation and least squares support vector machine(LS-SVM)optimized by chaos particle swarm optimization(CPSO).Due to the nonlinearity and fluctuation of the passenger flow,firstly,fuzzy information granulation is used to extract the valid data from the window according to the requirement.Secondly,CPSO that has strong global search ability is applied to optimize the parameters of the LS-SVM forecasting model.Finally,the combined model is used to forecast the fluctuation range of early peak passenger flow at Tiyu Xilu Station of Guangzhou Metro Line 3 in 2014,and the results are compared and analyzed with other models.Simulation results demonstrate that the combined forecasting model can effectively track the fluctuation of passenger flow,which provides an effective method for predicting the fluctuation range of short-term passenger flow in the future.

Forecasting Model Based on Information-Granulated GA-SVR and ARIMA for Producer Price Index

The accuracy of predicting the Producer Price Index(PPI)plays an indispensable role in government economic work.However,it is difficult to forecast the PPI.In our research,we first propose an unprecedented hybrid model based on fuzzy information granulation that integrates the GA-SVR and ARIMA(Autoregressive Integrated Moving Average Model)models.The fuzzy-information-granulation-based GA-SVR-ARIMA hybrid model is intended to deal with the problem of imprecision in PPI estimation.The proposed model adopts the fuzzy information-granulation algorithm to pre-classification-process monthly training samples of the PPI,and produced three different sequences of fuzzy information granules,whose Support Vector Regression(SVR)machine forecast models were separately established for their Genetic Algorithm(GA)optimization parameters.Finally,the residual errors of the GA-SVR model were rectified through ARIMA modeling,and the PPI estimate was reached.Research shows that the PPI value predicted by this hybrid model is more accurate than that predicted by other models,including ARIMA,GRNN,and GA-SVR,following several comparative experiments.Research also indicates the precision and validation of the PPI prediction of the hybrid model and demonstrates that the model has consistent ability to leverage the forecasting advantage of GA-SVR in non-linear space and of ARIMA in linear space.

Pattern classification using fuzzy relation and genetic algorithm

Purpose–This paper aims to consider a soft computing approach to pattern classification using the basic tools of fuzzy relational calculus(FRC)and genetic algorithm(GA).Design/methodology/approach–The paper introduces a new interpretation of multidimensional fuzzy implication(MFI)to represent the author’s knowledge about the training data set.It also considers the notion of a fuzzy pattern vector(FPV)to handle the fuzzy information granules of the quantized pattern space and to represent a population of training patterns in the quantized pattern space.The construction of the pattern classifier is essentially based on the estimate of a fuzzy relation Ri between the antecedent clause and consequent clause of each one-dimensional fuzzy implication.For the estimation of Ri floating point representation of GA is used.Thus,a set of fuzzy relations is formed from the new interpretation of MFI.This set of fuzzy relations is termed as the core of the pattern classifier.Once the classifier is constructed the non-fuzzy features of a test pattern can be classified.Findings–The performance of the proposed scheme is tested on synthetic data.Subsequently,the paper uses the proposed scheme for the vowel classification problem of an Indian language.In all these case studies the recognition score of the proposed method is very good.Finally,a benchmark of performance is established by considering Multilayer Perceptron(MLP),Support Vector Machine(SVM)and the proposed method.The Abalone,Hosse colic and Pima Indians data sets,obtained from UCL database repository are used for the said benchmark study.The benchmark study also establishes the superiority of the proposed method.Originality/value–This new soft computing approach to pattern classification is based on a new interpretation of MFI and a novel notion of FPV.A set of fuzzy relations which is the core of the pattern classifier,is estimated using floating point GA and very effective classification of patterns under vague and imprecise environment is performed.This new approach to pattern classification avoids the curse of high dimensionality of feature vector.It can provide multiple classifications under overlapped classes.