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CLC number: O235; N93

On-line Access: 2020-06-12

Received: 2019-10-31

Revision Accepted: 2020-01-13

Crosschecked: 2020-03-31

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Yi-fei Pu


Jian Wang


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Frontiers of Information Technology & Electronic Engineering  2020 Vol.21 No.6 P.809-833


Fractional-order global optimal backpropagation machine trained by an improved fractional-order steepest descent method

Author(s):  Yi-fei Pu, Jian Wang

Affiliation(s):  College of Computer Science, Sichuan University, Chengdu 610065, China; more

Corresponding email(s):   puyifei@scu.edu.cn, wangjiannl@upc.edu.cn

Key Words:  Fractional calculus, Fractional-order backpropagation algorithm, Fractional-order steepest descent method, Mean square error, Fractional-order multi-scale global optimization

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Yi-fei Pu, Jian Wang. Fractional-order global optimal backpropagation machine trained by an improved fractional-order steepest descent method[J]. Frontiers of Information Technology & Electronic Engineering, 2020, 21(6): 809-833.

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T1 - Fractional-order global optimal backpropagation machine trained by an improved fractional-order steepest descent method
A1 - Yi-fei Pu
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J0 - Frontiers of Information Technology & Electronic Engineering
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PB - Zhejiang University Press & Springer
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DOI - 10.1631/FITEE.1900593

We introduce the fractional-order global optimal backpropagation machine, which is trained by an improved fractional-order steepest descent method (FSDM). This is a fractional-order backpropagation neural network (FBPNN), a state-of-the-art fractional-order branch of the family of backpropagation neural networks (BPNNs), different from the majority of the previous classic first-order BPNNs which are trained by the traditional first-order steepest descent method. The reverse incremental search of the proposed FBPNN is in the negative directions of the approximate fractional-order partial derivatives of the square error. First, the theoretical concept of an FBPNN trained by an improved FSDM is described mathematically. Then, the mathematical proof of fractional-order global optimal convergence, an assumption of the structure, and fractional-order multi-scale global optimization of the FBPNN are analyzed in detail. Finally, we perform three (types of) experiments to compare the performances of an FBPNN and a classic first-order BPNN, i.e., example function approximation, fractional-order multi-scale global optimization, and comparison of global search and error fitting abilities with real data. The higher optimal search ability of an FBPNN to determine the global optimal solution is the major advantage that makes the FBPNN superior to a classic first-order BPNN.





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