Multi-Frequency Signal Classification by Multilayer Neural Networks and Linear Filter Methods

摘要

This paper compares signal classification performance of multilayer neural networks (MLNNs) and linear filters (LFs). The MLNNs are useful for arbitrary waveform signal classification. On the other hand, LFs are useful for the signals, which are specified with frequency components. In this paper, both methods are compared based on frequency selective performance. The signals to be classified contain several frequency components. Furthermore, effects of the number of the signal samples are investigated. In this case, the frequency information may be lost to some extent. This makes the classification problems difficult. From practical viewpoint, computational complexity is also limited to the same level in both methods. IIR and FIR filters are compared. FIR filters with a direct form can save computations, which is independent of the filter order. IIR filters, on the other hand, cannot provide good signal classification due to their phase distortion, and require a large amount of computations due to their recursive structure. When the number of the input samples is strictly limited, the signal vectors are widely distributed in the multi-dimensional signal space. In this case, signal classification by the LF method cannot provide a good performance. Because, they are designed to extract the frequency components. On the other hand, the MLNN method can form class regions in the signal vector space with high degree of freedom. When the number of the signal samples is not so limited, both the MLNN and LF methods can provide the same high classification rates. In this case, since the signal vectors are distributed in the specific region, the MLNN method has some convergence problem, that is local minimum problem. The initial weights should be carefully determined around the optimum solution. Another point is robustness for noisy signal. The LFs can suppress wide-band noise by using very high-Q filters. However, the MLNN method can be also robust. Rather, it is a little superior to the LF method when the computational load is limited.