Two novel methods for analyzing multipath interference in distributed fiber Raman amplifiers

Abstract

from the training sample set. For comparison, conventional threelayer MLP models are also developed with the same sets of training and testing data. Figure 3 shows the responses given by the FDTD simulation, the MLP models, and the NNKBN models. It is obvious that the NNKBN models have much better results than those given by the MLP models.The extrapolation properties of the NNKBN and MLP models are explored and compared. The input data of w beyond the range of training sample set by 50% are selected to test the two kinds of models. With the use of the FDTD simulation data as desired outputs, the average absolute extrapolating errors (AAEE) are given in Table 2. From Table 2, it is found that the NNKBN models have great improvement in extrapolation. CONCLUSIONCompared with the conventional MLP model, the NNKBN model requires fewer neurons in the hidden layer, and has better accuracy with less training samples and higher reliability of extrapolation.The NNKBN model preserves the accuracy of the full-wave FDTD simulations yet simplifies their CPU requirement. Furthermore, it maintains reliable finite extrapolation capability by incorporating the analytic prior knowledge. The potential power of the NNKBN model is useful for the interactive CAD of the stripline interconnect design in the HSDICs.