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Novel fast training algorithm for multilayer feedforward neural network

Novel fast training algorithm for multilayer feedforward neural network

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© The Institution of Electrical Engineers
Published

A new fast learning algorithm for a multilayer feedforward neural network is presented. The new algorithm, based on an innovative variable step size and gradient averaging, has excellent properties to overcome major drawbacks of the backpropagation algorithm and shows high convergence speed. The improved performance of the new algorithm in comparison with other algorithms is verified via computer simulations.

Inspec keywords: convergence; learning systems; neural nets

Other keywords: backpropagation algorithm; multilayer feedforward neural network; convergence speed; gradient averaging; fast learning algorithm; variable step size; computer simulations

Subjects: Adaptive system theory; Neural nets (theory)

References

    1. 1)
      • R.P. Lippman . An introduction to computing with neural nets. IEEE ASSP Mag.
    2. 2)
      • 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.
    3. 3)
      • Parker, D.B.: `Optimal algorithms for adaptive networks: second order back propagation, second order direct propagation, and second order Hebbian learning', Proc. 1st ICNN, 1987, San Diego, 2, p. 593–600.
    4. 4)
      • R.A. Jacobs . Increased rates of convergence through learning rate adaptation. Neural Netw. , 195 - 207
    5. 5)
      • M.L. Minsky , S. Papert . (1969) , Perceptrons.
    6. 6)
      • J.J. Shynk . Performance surfaces of a single-layer perceptron. IEEE Trans. Neural Netw. , 268 - 274
    7. 7)
      • Chen, J.R., Mars, P.: `Stepsize variation methods for accelerating the back propagation algorithm', Proc. IJCNN, 1990, Washington, DC, 1, p. 601–604.
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