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Fuzzy neural network with backpropagation for fuzzy quadratic programming problems and portfolio optimization problems / Khan, Izaz Ullah (CC BY)

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Titel:
Nonlinear engineering
Erschienen:
Berlin Boston, Mass.: ˜deœ Gruyter
Fußnote:
Gesehen am 31.05.13
Open Access
Namensnennung 4.0 International
Umfang:
Online-Ressource
ISSN:
2192-8029
ZDB-ID:
2718224-1 ZDB
VÖBB-Katalog:
34256841
Schlagworte:
Zeitschrift
ZLB-Systematik:
Technik
Sammlung:
Technik
Copyright:
Rechte vorbehalten
Zugriffsberechtigung:
Freier Zugang
Titel:
Nonlinear engineering
Erschienen:
Berlin Boston, Mass.: ˜deœ Gruyter
Fußnote:
Gesehen am 31.05.13
Open Access
Namensnennung 4.0 International
Umfang:
Online-Ressource
ISSN:
2192-8029
ZDB-ID:
2718224-1 ZDB
VÖBB-Katalog:
34256841
Schlagworte:
Zeitschrift
ZLB-Systematik:
Technik
Sammlung:
Technik
Copyright:
Rechte vorbehalten
Zugriffsberechtigung:
Freier Zugang

Aufsatz

Verfasser:
Khan, Izaz Ullah
Khan, Raja Muhammad Usama Ali
Ullah, Mehran
Shah, Muhammad Shahbaz
Titel:
Fuzzy neural network with backpropagation for fuzzy quadratic programming problems and portfolio optimization problems
Erschienen:
Berlin Boston, Mass.: ˜deœ Gruyter, 2024
Sprache:
Englisch
Zusammenfassung:
Abstract: The study aspires to adopt back propagation fuzzy neural networks to solve fuzzy quadratic programming problems. The main motivation behind proposing a back propagation neural network is that it can easily adjust and fine-tune the weights of the network from the error rate obtained at the previous layer. The error rate customarily called the loss function is the dissimilarity between the desired and predicted outputs. The chain and power rules of the derivative allow back propagation and successively update the weights of the network to perform efficiently. Thus, the gradient of the loss function is calculated by iterating backward layer by layer but one at a time to reduce the difference between the desired and the predicted outputs. The research flow is such that first of all the quadratic programming problem is formulated in a fuzzy environment. The problem with fuzzy quadratic programming is formulated as a lower, central, and upper model. The formulated models are then solved with backpropagation fuzzy neural networks. The proposed method is then implemented in the capital market to identify the optimal portfolio for potential investors in the Pakistan Stock Exchange. Six leading stocks traded on the stock exchange from Jan 2016 to Oct 2020 were taken into consideration. At all three levels (lower, central, and upper), the results of identifying the best investment portfolio for investors are consistent. The proposed three models identify the investors to invest in ATHL, MCB and ARPL, whereas, the remaining three IGIHL, INIL and POL are not desirable for investment. In all three cases, the convergence is obtained at 475 iterations which is faster than the previously conducted studies. Moreover, another advantage of the proposed technique is that it brings an improvement of 28.77% in the objective function of mean variance optimization MVO model.
Umfang:
Online-Ressource
Fußnote:
Open Access
Archivierung/Langzeitarchivierung gewährleistet
Schlagworte:
fuzzy quadratic programming ; back propagation fuzzy neural networks ; portfolio optimization ; mean variance portfolio model
ZLB-Systematik:
Technik
Sonstiges
URN:
urn:nbn:de:101:1-2412211640194.512230161661
Sammlung:
Technik
Sonstiges
Copyright:
CC BY
Zugriffsberechtigung:
Freier Zugang
Verfasser:
Khan, Izaz Ullah
Khan, Raja Muhammad Usama Ali
Ullah, Mehran
Shah, Muhammad Shahbaz
Titel:
Fuzzy neural network with backpropagation for fuzzy quadratic programming problems and portfolio optimization problems
Erschienen:
Berlin Boston, Mass.: ˜deœ Gruyter, 2024
Sprache:
Englisch
Zusammenfassung:
Abstract: The study aspires to adopt back propagation fuzzy neural networks to solve fuzzy quadratic programming problems. The main motivation behind proposing a back propagation neural network is that it can easily adjust and fine-tune the weights of the network from the error rate obtained at the previous layer. The error rate customarily called the loss function is the dissimilarity between the desired and predicted outputs. The chain and power rules of the derivative allow back propagation and successively update the weights of the network to perform efficiently. Thus, the gradient of the loss function is calculated by iterating backward layer by layer but one at a time to reduce the difference between the desired and the predicted outputs. The research flow is such that first of all the quadratic programming problem is formulated in a fuzzy environment. The problem with fuzzy quadratic programming is formulated as a lower, central, and upper model. The formulated models are then solved with backpropagation fuzzy neural networks. The proposed method is then implemented in the capital market to identify the optimal portfolio for potential investors in the Pakistan Stock Exchange. Six leading stocks traded on the stock exchange from Jan 2016 to Oct 2020 were taken into consideration. At all three levels (lower, central, and upper), the results of identifying the best investment portfolio for investors are consistent. The proposed three models identify the investors to invest in ATHL, MCB and ARPL, whereas, the remaining three IGIHL, INIL and POL are not desirable for investment. In all three cases, the convergence is obtained at 475 iterations which is faster than the previously conducted studies. Moreover, another advantage of the proposed technique is that it brings an improvement of 28.77% in the objective function of mean variance optimization MVO model.
Umfang:
Online-Ressource
Fußnote:
Open Access
Archivierung/Langzeitarchivierung gewährleistet
Schlagworte:
fuzzy quadratic programming ; back propagation fuzzy neural networks ; portfolio optimization ; mean variance portfolio model
ZLB-Systematik:
Technik
Sonstiges
URN:
urn:nbn:de:101:1-2412211640194.512230161661
Sammlung:
Technik
Sonstiges
Copyright:
CC BY
Zugriffsberechtigung:
Freier Zugang

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