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This paper presents the architecture and learning procedure underlying ANFIS (Adaptive-Network-based Fuzzy Inference System), a fuzzy inference system implemented in the framework of adaptive networks. By using a hybrid learning procedure, the proposed ANFIS can construct an input-output mapping based on both human knowledge (in the form of fuzzy if-then(More)
| F undamental and advanced developments in neuro-fuzzy synergisms for modeling and control are reviewed. The essential part of neuro-fuzzy synergisms comes from a common framework called adaptive networks, which uniies both neural networks and fuzzy models. The fuzzy models under the framework of adaptive n e t works is called ANFIS (Adaptive-Network-based(More)
A generalized control strategy that enhances fuzzy controllers with self-learning capability for achieving prescribed control objectives in a near-optimal manner is presented. This methodology, termed temporal backpropagation, is model-sensitive in the sense that it can deal with plants that can be represented in a piecewise-differentiable format, such as(More)
It is shown that, under some minor restrictions, the functional behavior of radial basis function networks (RBFNs) and that of fuzzy inference systems are actually equivalent. This functional equivalence makes it possible to apply what has been discovered (learning rule, representational power, etc.) for one of the models to the other, and vice versa. It is(More)
Compressive sampling (CS) is a new research topic in signal processing that has piqued the interest of a wide range of researchers in different fields recently. In this paper , we present a CS-based classifier for music genre classification , with two sets of features, including short-time and long-time features of audio music. The proposed clas-sifier(More)
This paper describes a framework for modeling the machine transliteration problem. The parameters of the proposed model are automatically acquired through statistical learning from a bilingual proper name list. Unlike previous approaches, the model does not involve the use of either a pronunciation dictionary for converting source words into phonetic(More)
This paper proposes a novel and effective approach to extract the pitches of the singing voice from monaural polyphonic songs. The sinusoidal partials of the musical audio signals are first extracted. The Fourier transform is then applied to extract the vibrato/tremolo information of each partial. Some criteria based on this vibrato/tremolo information are(More)
In this paper, we propose a hybrid method for singing pitch extraction from polyphonic audio music. We have observed several kinds of pitch errors made by a previously proposed algorithm based on trend estimation. We also noticed that other pitch tracking methods tend to have other types of pitch error. Then it becomes intuitive to combine the results of(More)
We present a quick and straightfoward way of input selection for neuro-fuzzy modeling using ANFIS. The method is tested on two real-world problems: the non-linear regression problem of automobile MPG (miles per gallon) prediction, and the nonlinear system identi-cation using the Box and Jenkins gas furnace data 1].