scholarly journals A Robust Adaptive Filter for a Complex Hammerstein System

Entropy ◽  
2019 ◽  
Vol 21 (2) ◽  
pp. 162 ◽  
Author(s):  
Guobing Qian ◽  
Dan Luo ◽  
Shiyuan Wang
Keyword(s):  
Adaptive Filter ◽  
Adaptive Filters ◽  
Impulsive Noise ◽  
Complex Domain ◽  
Mean Square ◽  
Least Mean Square ◽  
Hammerstein System ◽  
Robust Adaptive ◽  
The Stability ◽  
Maximum Correntropy Criterion

The Hammerstein adaptive filter using maximum correntropy criterion (MCC) has been shown to be more robust to outliers than the ones using the traditional mean square error (MSE) criterion. As there is no report on the robust Hammerstein adaptive filters in the complex domain, in this paper, we develop the robust Hammerstein adaptive filter under MCC to the complex domain, and propose the Hammerstein maximum complex correntropy criterion (HMCCC) algorithm. Thus, the new Hammerstein adaptive filter can be used to directly handle the complex-valued data. Additionally, we analyze the stability and steady-state mean square performance of HMCCC. Simulations illustrate that the proposed HMCCC algorithm is convergent in the impulsive noise environment, and achieves a higher accuracy and faster convergence speed than the Hammerstein complex least mean square (HCLMS) algorithm.

scholarly journals A Dual Adaptive Filter Spike-Based Hardware Architecture for Implementation of a New Active Noise Control Structure

Electronics ◽  
2021 ◽  
Vol 10 (16) ◽  
pp. 1945
Author(s):  
Eduardo Pichardo ◽  
Ángel Vázquez ◽  
Esteban R. Anides ◽  
Juan C. Sánchez ◽  
Hector Perez ◽  
...  
Keyword(s):  
Adaptive Filter ◽  
Adaptive Filters ◽  
Noise Control ◽  
Active Noise Control ◽  
Computational Cost ◽  
Development Trend ◽  
Mean Square ◽  
Least Mean Square ◽  
Filter Performance ◽  
Active Noise

Presently, the technology development trend of active noise control (ANC) systems is focused on implementing advanced adaptive filters in resource-constrained electronic appliances. Recently, several authors have proved that the use of two adaptive filter algorithms significantly improves the overall adaptive filter performance. However, the computational cost of these approaches is significantly increased since they use two filters simultaneously. Consequently, these filters cannot be implemented in these devices. To solve this problem, we propose a new ANC structure with switching selection based on filtered-x normalized least mean square (FxNLMS) and filtered-x sign least mean square (FxSLMS) algorithms to reduce the computational cost of the ANC system. The improvement of this factor has allowed us to introduce for the first time an advanced spike-based architecture, which can perform dual filter operations using dynamic routing, to be used in real ANC applications. The results have demonstrated that the computational cost of the proposed dual D-FxNLMS/SLMS algorithm is lower compared with previously reported solutions.

scholarly journals System Identification using Adaptive Filters

2020 ◽  
Vol 9 (9) ◽  
pp. 409-414
Keyword(s):  
System Identification ◽  
Adaptive Filter ◽  
Adaptive Filters ◽  
Lms Algorithm ◽  
Mean Square ◽  
Least Mean Square ◽  
Iir Filter ◽  
Unknown System

Identification of system is one of the major applications of an adaptive filters, mainly Least Mean Square (LMS) algorithm, because of its ease in calculations, the ability to withstand or overcome any conditions. The unknown System can be a FIR or an IIR filter. Same input is fed into both undefined system (which is unknown to us) and the adaptive filter, their outputs will be subtracted and the error subtracted signal will be given to adaptive filter. The adaptive filter is modified until the system which is unknown and the adaptive filter becomes relatively equal. System identification defines the type and functionality of the system. By utilizing the weights, the output of the system for any input can be predicted.

Improved least mean square adaptive filter algorithm

2013 ◽  
Vol 32 (7) ◽  
pp. 2078-2081
Author(s):  
Cheng-xi WANG ◽  
Yi-an LIU ◽  
Qiang ZHANG
Keyword(s):  
Adaptive Filter ◽  
Mean Square ◽  
Least Mean Square

scholarly journals FPGA based least mean square algorithm for noise cancellation in communication system

2018 ◽  
Vol 7 (3.3) ◽  
pp. 165
Author(s):  
Praveen Reddy ◽  
Dr Baswaraj Gadgay
Keyword(s):  
Adaptive Filter ◽  
Communication System ◽  
Noise Cancellation ◽  
Mean Square ◽  
Least Mean Square ◽  
Distributed Arithmetic ◽  
Negative Effect ◽  
Least Mean Square Algorithm ◽  
Design Comparison

We present modified Distributed Arithmetic (DA) based architecture for LMS Adaptive filter which has improved the throughput of the filter also area and power has been comparatively been reduced. As we know, the adaptive filter uses continuous recalculation and generation of new coefficients will generate the negative effect on the use of algorithm. We have used a special temporary LUT addressing technique has overcome the issues resulting in better performance and good results. In this paper, we have discussed about the adaptive filter and implementation of DA adaptive filter and also discussed the results obtained from the design. Comparison with traditional de-sign has also been done to show the effectiveness of the algorithm.   

scholarly journals A Proportionate Normalized Maximum Correntropy Criterion Algorithm with Correntropy Induced Metric Constraint for Identifying Sparse Systems

Symmetry ◽  
2018 ◽  
Vol 10 (12) ◽  
pp. 683 ◽  
Author(s):  
Yingsong Li ◽  
Yanyan Wang ◽  
Laijun Sun
Keyword(s):  
Impulse Response ◽  
Adaptive Filter ◽  
Finite Impulse Response ◽  
Least Mean Square ◽  
Sparse Systems ◽  
Simulation Results ◽  
Maximum Correntropy Criterion ◽  
Non Gaussian ◽  
Fir Systems ◽  
Filter Algorithms

A proportionate-type normalized maximum correntropy criterion (PNMCC) with a correntropy induced metric (CIM) zero attraction terms is presented, whose performance is also discussed for identifying sparse systems. The proposed sparse algorithms utilize the advantage of proportionate schemed adaptive filter, maximum correntropy criterion (MCC) algorithm, and zero attraction theory. The CIM scheme is incorporated into the basic MCC to further utilize the sparsity of inherent sparse systems, resulting in the name of the CIM-PNMCC algorithm. The derivation of the CIM-PNMCC is given. The proposed algorithms are used for evaluating the sparse systems in a non-Gaussian environment and the simulation results show that the expanded normalized maximum correntropy criterion (NMCC) adaptive filter algorithms achieve better performance than those of the squared proportionate algorithms such as proportionate normalized least mean square (PNLMS) algorithm. The proposed algorithm can be used for estimating finite impulse response (FIR) systems with symmetric impulse response to prevent the phase distortion in communication system.

scholarly journals Identical Instruments Detection using Least Mean Square Based Adaptive Filter

2019 ◽  
Vol 1201 ◽  
pp. 012013
Author(s):  
M Sa’adah ◽  
D P Wulandari ◽  
Y K Suprapto
Keyword(s):  
Adaptive Filter ◽  
Mean Square ◽  
Least Mean Square
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