V. Concluding Remarks

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These results are better than those reported in [10] using LVQ, radial basis function and multilayerperceptron based classifiers for the same set of inputs and outputs. Moreover, in all the cases of misclassification, the correct class corresponds to the second largest component in the output vector, and has a value close to the largest component (selected class). This suggests that a knowledge-based system can be used in conjunction with the pi-sigma network and additional information to make a final decision for the "outlying" signals. misclassified classes original classes 1 2 3 4 1 2 3 4 2 4 1 3 Figure 4. Confusion matrix for the 10 misclassified signals from the transient data set. The simulation results indicate that fairly complex approximation and classification problems can be tackled by the pi-sigma network using only three or four summing units. This, coupled with the property of stable learning even with values of η over 1, and the modification of only a subset of weights in each epoch, results in a network with significantly reduced training times. We observe that summing units can be added incrementally till an appropriate order of the network is attained without overfitting of the function. Although we only report results using a constant η , various monotonically decreasing functions of η such as η(t) = a e-αt and η(t) = c/t , where t is the epoch step and a, α, c are constants, can be used for more accurate learning. This also allows a larger initial value for the learning rate. Similarly addition of a momentum term and a more selective choice of summing units to be updated can result in further improvements.

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تاریخ انتشار 1991