Comparison of simulation algorithms for the Hopfield neural network: An application of economic dispatch
Küçük Resim Yok
Tarih
2000
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Cilt Başlığı
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Erişim Hakkı
info:eu-repo/semantics/openAccess
Özet
This paper is mainly concerned with an investigation of the suitability of Hopfield neural network structures in solving the power economic dispatch problem. For Hopfield neural network applications to this problem three important questions have been answered: what the size of the power system is; how efficient the computational method; and how to handle constraints. A new mapping process is formulated and a computational method for obtaining the weights and biases is described. A few simulation algorithms used to solve the dynamic equation of the Hopfield neural network are discussed. The results are compared with those of a classical technique, Hopfield neural network approaches and an improved Hopfield neural network approach [1].
This paper is mainly concerned with an investigation of the suitability of Hopfield neural network structures in solving the power economic dispatch problem. For Hopfield neural network applications to this problem three important questions have been answered: what the size of the power system is; how efficient the computational method; and how to handle constraints. A new mapping process is formulated and a computational method for obtaining the weights and biases is described. A few simulation algorithms used to solve the dynamic equation of the Hopfield neural network are discussed. The results are compared with those of a classical technique, Hopfield neural network approaches and an improved Hopfield neural network approach [1].
This paper is mainly concerned with an investigation of the suitability of Hopfield neural network structures in solving the power economic dispatch problem. For Hopfield neural network applications to this problem three important questions have been answered: what the size of the power system is; how efficient the computational method; and how to handle constraints. A new mapping process is formulated and a computational method for obtaining the weights and biases is described. A few simulation algorithms used to solve the dynamic equation of the Hopfield neural network are discussed. The results are compared with those of a classical technique, Hopfield neural network approaches and an improved Hopfield neural network approach [1].
Açıklama
Anahtar Kelimeler
Mühendislik, Elektrik ve Elektronik
Kaynak
Turkish Journal of Electrical Engineering and Computer Sciences
WoS Q Değeri
Scopus Q Değeri
Q3
Cilt
8
Sayı
1
