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The proposed structure is used for identification and noise equalization of time-varying systems.
This paper presents the development of novel type-2 neuro-fuzzy system for identification of time-varying systems and equalization of time-varying channels using clustering and gradient algorithms.
In order to achieve equalization of treatment time for all cell cultures, six time checkpoints were set along the grid pattern.
This paper presents an adaptive filter which uses periodic fuzzy neural network (PFNN) to treat the equalization of nonlinear time-varying channels.
Likewise, the cost of equalization in the time domain will be upper-bounded by O B T K K ) with B T = max m B T ( m ).
However, for systems having fading channels with multi-path, channel equalization and time of arrival (TOA) estimation needs to be performed prior to demodulation.
In fact, the multiple measurement vectors (MMV) problem is encountered in many applications of sparse signal representation such as array processing [1, 6 11], magne-toencephalography [1], nonparametric spectrum analysis of time series [17], equalization of sparse communication channels [18] and so on.
Equalization of unknown frequency- and time-selective multiple input multiple output (MIMO) channels is often carried out by means of decision feedback receivers.
Also, sequential acquisition of different time windows permits to change light attenuation for each image so to properly fill the whole dynamic range of the CCD, and reach a better equalization of the signal at different time gates.
Adaptive equalization of wireless systems operating over time-varying and frequency-selective multiple-input multiple-output (MIMO) channels is considered.
Its effectiveness of the proposed algorithms is examined and compared to ordinary LMS approaches in numerical simulation of adaptive identification and equalization of a fading channel with parameters changing with a first-order function of time.
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