Tên bài báo:

A Hybrid Gravitational Search Algorithm and Back-Propagation for Training Feedforward Neural Networks
Tác giả:
Đỗ Quang Hưng
Tham gia cùng:
Tạp chí:
Knowledge and Systems Engineering, Advances in Intelligent Systems and Computing
Năm xuất bản:
Từ trang 381 đến trang 392
Lĩnh vực:
Công nghệ thông tin
Phạm vi:
Quốc tế

Tóm tắt:

Presenting a satisfactory and efficient training algorithm for artificial neural networks (ANN) has been a challenging task. The Gravitational Search Algorithm (GSA) is a novel heuristic algorithm based on the law of gravity and mass interactions. Like most other heuristic algorithms, this algorithm has a good ability to search for the global optimum, but suffers from slow searching speed. On the contrary, the Back-Propagation (BP) algorithmcan achieve a faster convergent speed around the global optimum. In this study, a hybrid of GSA and BP is proposed to make use of the advantage of both the GSA and BP algorithms. The proposed hybrid algorithm is employed as a new training method for feedforward neural networks (FNNs). To investigate the performance of the proposed approach, two benchmark problems are used and the results are compared with those obtained from FNNs trained by original GSA and BP algorithms. The experimental results show that the proposed hybrid algorithm outperforms both GSA and BP in training FNNs.

Từ khóa:

Gravitational search algorithm Back-Propagation algorithm Feedforward neural networks
Thông tin tác giả
Đỗ Quang Hưng

Đỗ Quang Hưng

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