A dynamic adjustment of parameters for the particle swarm optimization (PSO) utilizing an interval type-2 fuzzy inference system is proposed in this work. A fuzzy neural network with interval type-2 fuzzy number weights using S-norm and T-norm is optimized with the proposed method. A dynamic adjustment of the PSO allows the algorithm to behave better in the search for optimal results because the dynamic adjustment provides good synchrony between the exploration and exploitation of the algorithm. Results of experiments and a comparison between traditional neural networks and the fuzzy neural networks with interval type-2 fuzzy numbers weights using T-norms and S-norms are given to prove the performance of the proposed approach. For testing the performance of the proposed approach, some cases of time series prediction are applied, including the stock exchanges of Germany, Mexican, Dow-Jones, London, Nasdaq, Shanghai, and Taiwan.
In this paper we propose a new technique to characterize audio-signals. We use Shannon's Entropy to estimate the level of information content per chroma and we show that involving entropy contributes for a more robust audio characterization. A new audio fingerprint (AFP) based on this feature is proposed in this paper which we have called Entropy-Chroma Fingerprint (ECFP). Two approaches were considered to estimate entropy; the first assumes the spectral coe f ficients distribute normally, while the second, estimates its probability density function (PDF) with the Parzen Windows Estimation method. We compared the robustness of the ECFP against the Chromagram-Based Audio-Fingerprint (CBFP) which is determined using the Constant Q Transform (CQT). Three thousand and five hundred AFPs were determined from songs of several genres. A subset of 350 songs were severely degraded and searched for using excerpts of 5 seconds for that matter. The ECFP determined assuming gaussianity on the PDF turned out to be much more robust than the CBFP. The ECFP determined assuming gaussianity is much faster to process than both, the CBFP and the ECFP determined with Parzen Windows and still more robust.
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