Top

Soft Computing

Published in:

03-11-2017 | Methodologies and Application

Bike sharing demand prediction using artificial immune system and artificial neural network

Authors: Pei-Chann Chang, Jheng-Long Wu, Yahui Xu, Min Zhang, Xiao-Yong Lu

Published in: Soft Computing | Issue 2/2019

Activate our intelligent search to find suitable subject content or patents.

search-config

AI-assisted search

Off

Abstract

From the viewpoint of bike sharing service, the rental number is a critical performance indicator for managers and controllers to assess the demand. Bike demand prediction in bike sharing systems is hence a key indicator in economic systems. In this study, a novel prediction framework integrating AIS and the artificial neural network forecasting technique is developed for numerical predication; it is named AIS-ANN. In this proposed AIS-ANN prediction framework, there are three major mechanisms applied to build the predication system which includes cell creation by ANN, antibody generation by clonal selection, and antibody’s center adaption by similarity measuring. The experimental results show that our proposed AIS-ANN has better performance when compared with other 6 forecasting models.

previous article An improved algorithm for sentiment analysis based on maximum entropy

next article A survey on data mining techniques in recommender systems

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

über 102.000 Bücher
über 537 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Finance + Banking
Management + Führung
Marketing + Vertrieb
Maschinenbau + Werkstoffe
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 340 Zeitschriften

aus folgenden Fachgebieten:

Bauwesen + Immobilien
Business IT + Informatik
Finance + Banking
Management + Führung
Marketing + Vertrieb
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 390 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Maschinenbau + Werkstoffe

Jetzt Wissensvorsprung sichern!

inform now

Aickelin T, Cayzer S (2002) The danger theory and its application to artificial immune systems. University of Kent at Canterbury, KentCrossRefMATH

Aish AM, Zaqoot HA, Abdeljawad SM (2015) Artificial neural network approach for predicting reverse osmosis desalination plants performance in the Gaza Strip. Desalination 367(1):240–247CrossRef

Al-Masri AN, Ab Kadir MZA, Hizam H, Mariun N (2015) Simulation of an adaptive artificial neural network for power system security enhancement including control action. Appl Soft Comput 29:1–11CrossRef

Askarzadeh A (2013) An innovative artificial immune optimization algorithm for solving complex optimization problems. Int J Bio-Inspired Comput 6(6):409–415CrossRef

Aydin I, Karakose M, Akin E (2011) A multi-objective artificial immune algorithm for parameter optimization in support vector machine. Appl Soft Comput 11:120–129CrossRef

Bateni M, Baraani A, Ghorbani A (2012) Alert correlation using artificial immune recognition system. Int J Bio-Inspired Comput 4(3):181–195CrossRef

Breiman L (1996) Bagging predictors. Mach Learn 24(2):123–140MATH

Chardon CMD, Caruso G (2015) Estimating bike-share trips using station level data. Transp Res Part B 78:260–279CrossRef

Clark P, Niblett T (1989) The CN2 rule induction algorithm. Mach Learn 3:261–284

Cui M, Liu H, Li Z, Tang Y, Guan X (2015) Identification of Hammerstein model using functional link artificial neural network. Neurocomputing 161(5):220–228

Fanaee-T H, Gama J (2013) Event labeling combining ensemble detectors and background knowledge. Prog Artif Intell 2(2–3):113–127

Farmer JD, Packard NH, Perelson AS (1986) The immune system, adaptation and machine learning. Physica D 22(D):187–204MathSciNetCrossRef

Fernández JC, Hervás C, Martínez-Estudillo FJ, Gutiérrez PA (2011) Memetic Pareto evolutionary artificial neural networks to determine growth/no-growth in predictive microbiology. Appl Soft Comput 11:534–550CrossRef

Frank E, Bouckaert RR (2009) Conditional density estimation with class probability estimators. In: Zhou E-H, Washio T (eds) Proceedings of first Asian conference on machine learning, ACML 2009, Nanjing, pp 65–81

Frank R, Hall M, Pfahringer B (2003) Locally weighted naive bayes. In: Proceedings of the 19th conference in uncertainty in artificial intelligence, Acapulco, Mexico pp 249–256

Frank E, Mayo M, Kramer S (2015) Alternating model tree. In: Proceedings of the 30th annual ACM symposium on applied computing, pp 871–878

Friedman JH (2002) Stochastic gradient boosting. Comput Stat Data Anal 38(4):367–378MathSciNetCrossRefMATH

García-Palomares JC, Gutiérrez J, Latorre M (2012) Optimizing the location of stations in bike-sharing programs: a GIS approach. Appl Geogr 35:235–246CrossRef

Ghiassi M, Olschimke M, Moon B, Arnaudo P (2012) Automated text classification using a dynamic artificial neural network model. Expert Syst Appl 39:10967–10976CrossRef

Halvaiee NH, Akbari MK (2014) A novel model for credit card fraud detection using artificial immune systems. Appl Soft Comput 24:40–49CrossRef

Hastie T, Tibshirani R, Friedman J (2009) Elements of statistical learning, 2nd edn. Springer, New YorkCrossRefMATH

Kohavi R (1996) The power of decision tables. In: Proceedings of 8th european conference on machine learning, Heraclion, Crete, pp 174–189

Kuo RJ, Chen CM, Warren T (2013) Liao and F.C. Tien, Hybrid of artificial immune system and particle swarm optimization-based support vector machine for Radio Frequency Identification-based positioning system. Comput Ind Eng 64:333–341CrossRef

Labadi K, Benarbia T, Barbot JP, Hamaci S, Omari A (2015) Stochastic petri net modeling, simulation and analysis of public bicycle sharing systems. IEEE Trans Autom Sci Eng 12(4):1380–1395CrossRef

Louppe G, Geurts P (2012) Ensembles on random patches. In: Machine learning and knowledge discovery in databases, Springer, Berlin, pp 346–361

Montechiesi L, Cocconcelli M, Rubini R (2016) Artificial immune system via Euclidean Distance Minimization for anomaly detection in bearings. Mech Syst Signal Process 76–77:380–393

Özbakir L, Baykasoğlu A, Kulluk S (2010) A soft computing-based approach for integrated training and rule extraction from artificial neural networks: DIFACONN-miner. Appl Soft Comput 10:304–317CrossRef

Özsen S, Yucelbas C (2015) On the evolution of ellipsoidal recognition regions in artificial immune systems. Appl Soft Comput 31:210–222CrossRef

Rojas R (2005) Neural networks: a systematic introduction. Springer, BerlinMATH

Shevade SK, Keerthi SS, Bhattacharyya C, Murthy KRK (2000) Improvements to the SMO algorithm for SVM regression. IEEE Trans Neural Netw 11(5):1188–1193CrossRef

Szymczyk P, Szymczyk M (2015) Classification of geological structure using ground penetrating radar and Laplace transform artificial neural networks. Neurocomputing 148(19):354–362

Tan KC, Goh CK, Mamun AA, Ei EZ (2008) An evolutionary artificial immune system for multi-objective optimization. Eur J Oper Res 187:371–392MathSciNetCrossRefMATH

Wang DD, Chang PC, Zhang L, Wu JL, Zhou CL (2013) The stability analysis for a novel feedback neural network with partial connection. Neurocomputing 116(20):22–29CrossRef

Xiao R, Chen T (2013) Relationships of swarm intelligence and artificial immune system. Int J Bio-Inspired Comput 5(1):35–51CrossRef

Yardimci A (2009) Soft computing in medicine. Appl Soft Comput 9:1029–1043CrossRef

Zhang C, Yi Z (2010) A danger theory inspired artificial immune algorithm for on-line supervised two-class classification problem. Neurocomputing 73(7–9):1244–1255CrossRef

Title: Bike sharing demand prediction using artificial immune system and artificial neural network
Authors: Pei-Chann Chang
Jheng-Long Wu
Yahui Xu
Min Zhang
Xiao-Yong Lu
Publication date: 03-11-2017
Publisher: Springer Berlin Heidelberg
Published in: Soft Computing / Issue 2/2019
Print ISSN: 1432-7643
Electronic ISSN: 1433-7479
DOI: https://doi.org/10.1007/s00500-017-2909-8

Springer Professional

Abstract

Please log in to get access to your license.

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Springer Professional "Wirtschaft"

Springer Professional "Technik"

Other articles of this Issue 2/2019

Interval type-2 fuzzy logic and its application to occupational safety risk performance in industries

D-AHP method with different credibility of information

Event-based k-nearest neighbors query processing over distributed sensory data using fuzzy sets

0–1 linear integer programming method for granule knowledge reduction and attribute reduction in concept lattices

An approach to neutrosophic graph theory with applications

Homomorphism between ordered decision systems

Premium Partner