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Accelerated training of backpropagation networks by using adaptive momentum step

Accelerated training of backpropagation networks by using adaptive momentum step

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© The Institution of Electrical Engineers
Published

Considerable research into the training of neural networks by the backpropagation technique has been undertaken in recent years. Introduction of a momentum term into the training equation can accelerate the training process. In the Letter a new momentum step and a scheme for dynamically selecting the momentum rate are described. It is shown that these give improved acceleration of training and strong global convergence characteristics. Results are presented for four benchmark training tasks.

Inspec keywords: neural nets; adaptive systems; learning systems

Other keywords: backpropagation networks; training equation; benchmark training tasks; adaptive momentum step; global convergence characteristics; training; momentum term; acceleration

Subjects: Adaptive system theory; Neural nets (theory)

References

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      • Fahlman, S.: `Faster-learning variations on backpropagation: an empirical study', Proc. 1988 Connectionist Models Summer School, 1988, Carnegie-Mellon University, p. 38–51.
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      • A.D. Pierre . (1969) , Optimization theory with applications.
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      • D.E. Rumelhart , G.E. Hinton , R.J. Williams , D.E. Rumelhart , J.L. McClelland . (1986) Learning internal representations by error propagation, Parallel distributed processing: explorations in Microstructure of cognition.
    4. 4)
      • L.W. Chan , F. Fallside . An adaptive training algorithm for backpropagation networks. Computer Speech and Language , 205 - 218
    5. 5)
      • R. Jacobs . Increased rates of convergence through earning rate adaptation. Neural Netw. , 4 , 295 - 307
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