Accelerated training algorithm for feedforward neural networks based on least squares method
Related Research Unit(s)
|Journal / Publication||Neural Processing Letters|
|Publication status||Published - Jul 1995|
|Link to Scopus||https://www.scopus.com/record/display.uri?eid=2-s2.0-0004305684&origin=recordpage|
A least squares based training algorithm for feedforward neural networks is presented. By decomposing each neuron of the network into a linear part and a nonlinear part, the learning error can then be minimized on each neuron by applying the least squares method to solve the linear part of the neuron. In all the problems investigated, the proposed algorithm is capable of achieving the required error level in one training iteration. Comparing to the conventional backpropagation algorithm and other fast training algorithms, the proposed training algorithm provides a major breakthrough in speeding up the training process. © 1995 Kluwer Academic Publishers.
Neural Processing Letters, Vol. 2, No. 4, 07.1995, p. 20-25.
Research output: Journal Publications and Reviews (RGC: 21, 22, 62) › 21_Publication in refereed journal
Yam, YF & Chow, TWS 1995, 'Accelerated training algorithm for feedforward neural networks based on least squares method', Neural Processing Letters, vol. 2, no. 4, pp. 20-25. https://doi.org/10.1007/BF02279934