scholarly journals A Novel Adaptive LMS Algorithm with Genetic Search Capabilities for System Identification of Adaptive FIR and IIR Filters

Information ◽  
2019 ◽  
Vol 10 (5) ◽  
pp. 176 ◽  
Author(s):  
Amjad J. Humaidi ◽  
Ibraheem Kasim Ibraheem ◽  
Ahmed R. Ajel
Keyword(s):  
System Identification ◽  
Input Signal ◽  
Digital Filters ◽  
Learning Algorithm ◽  
Search Space ◽  
Lms Algorithm ◽  
Adaptation Algorithm ◽  
Genetic Search ◽  
Mutation Operators ◽  
Input Signals

In this paper we introduce a novel adaptation algorithm for adaptive filtering of FIR and IIR digital filters within the context of system identification. The standard LMS algorithm is hybridized with GA (Genetic Algorithm) to obtain a new integrated learning algorithm, namely, LMS-GA. The main aim of the proposed learning tool is to evade local minima, a common problem in standard LMS algorithm and its variants and approaching the global minimum by calculating the optimum parameters of the weights vector when just estimated data are accessible. In the proposed LMS-GA technique, first, it works as the standard LMS algorithm and calculates the optimum filter coefficients that minimize the mean square error, once the standard LMS algorithm gets stuck in local minimum, the LMS-GA switches to GA to update the filter coefficients and explore new region in the search space by applying the cross-over and mutation operators. The proposed LMS-GA is tested under different conditions of the input signal like input signals with colored characteristics, i.e., correlated input signals and investigated on FIR adaptive filter using the power spectral density of the input signal and the Fourier-transform of the input’s correlation matrix. Demonstrations via simulations on system identification of IIR and FIR adaptive digital filters revealed the effectiveness of the proposed LMS-GA under input signals with different characteristics.

scholarly journals The Precompression Processing of LMS Algorithm in Noise Elimination

2019 ◽  
Vol 2019 ◽  
pp. 1-8 ◽  
Author(s):  
Pengfei Lin ◽  
Chunsheng Lin ◽  
Ning Zhang ◽  
Xingya wu
Keyword(s):  
Input Signal ◽  
Lms Algorithm ◽  
Noise Elimination ◽  
Least Mean Squares ◽  
Filter Performance ◽  
Nlms Algorithm ◽  
Input Signals ◽  
Current Value ◽  
Signal Changes ◽  
Normal Input

In this study, the authors propose a novel precompression processing (PCP) of the least mean squares (LMS) algorithm based on a regulator factor. The novelty of the PCP algorithm is that the compressed input signals vary from each other on different components at each iteration. The input signal of the improved LMS algorithm is precompressed based on the regulator factor. The precompressed input signal is not only related to the regulator factor α and the current value of the input signal at each iteration but also related to the amplitude of the input signal before this iteration. The improved algorithm can eliminate the influence of input signal mutation on the filter performance. In the numerical simulations, we compare the improved LMS algorithm and NLMS algorithm in the cases of normal input signal and input signal with mutation and the influence of different regulator factors on the noise elimination. Results show that the PCP algorithm has good noise elimination effect when the input signal changes abruptly and the regulator factor α = 0.01 can meet the requirements.

Dementia key gene identification with multi-layered SNP-gene-disease network

2020 ◽  
Vol 36 (Supplement_2) ◽  
pp. i831-i839
Author(s):  
Dong-gi Lee ◽  
Myungjun Kim ◽  
Sang Joon Son ◽  
Chang Hyung Hong ◽  
Hyunjung Shin
Keyword(s):  
Candidate Genes ◽  
Learning Algorithm ◽  
Search Space ◽  
Supplementary Information ◽  
Gene Identification ◽  
Nucleotide Polymorphisms ◽  
Disease Network ◽  
Single Nucleotide ◽  
Key Genes ◽  
Significant Attention

Abstract Motivation Recently, various approaches for diagnosing and treating dementia have received significant attention, especially in identifying key genes that are crucial for dementia. If the mutations of such key genes could be tracked, it would be possible to predict the time of onset of dementia and significantly aid in developing drugs to treat dementia. However, gene finding involves tremendous cost, time and effort. To alleviate these problems, research on utilizing computational biology to decrease the search space of candidate genes is actively conducted. In this study, we propose a framework in which diseases, genes and single-nucleotide polymorphisms are represented by a layered network, and key genes are predicted by a machine learning algorithm. The algorithm utilizes a network-based semi-supervised learning model that can be applied to layered data structures. Results The proposed method was applied to a dataset extracted from public databases related to diseases and genes with data collected from 186 patients. A portion of key genes obtained using the proposed method was verified in silico through PubMed literature, and the remaining genes were left as possible candidate genes. Availability and implementation The code for the framework will be available at http://www.alphaminers.net/. Supplementary information Supplementary data are available at Bioinformatics online.

Sparse Incremental Delta-Bar-Delta for System Identification

2014 ◽  
Vol 665 ◽  
pp. 643-646
Author(s):  
Ying Liu ◽  
Yan Ye ◽  
Chun Guang Li
Keyword(s):  
System Identification ◽  
Cost Function ◽  
Learning Algorithm ◽  
Learning System ◽  
The Other ◽  
Sparse System ◽  
Speed Up ◽  
Sparse System Identification ◽  
The Cost ◽  
Zero Attractor

Metalearning algorithm learns the base learning algorithm, targeted for improving the performance of the learning system. The incremental delta-bar-delta (IDBD) algorithm is such a metalearning algorithm. On the other hand, sparse algorithms are gaining popularity due to their good performance and wide applications. In this paper, we propose a sparse IDBD algorithm by taking the sparsity of the systems into account. Thenorm penalty is contained in the cost function of the standard IDBD, which is equivalent to adding a zero attractor in the iterations, thus can speed up convergence if the system of interest is indeed sparse. Simulations demonstrate that the proposed algorithm is superior to the competing algorithms in sparse system identification.

Ternary input signal design for system identification

2007 ◽  
Vol 1 (5) ◽  
pp. 1224-1233 ◽  
Author(s):  
H.A. Barker ◽  
K.R. Godfrey ◽  
A.H. Tan
Keyword(s):  
System Identification ◽  
Input Signal ◽  
Signal Design

scholarly journals Reducing the Impact of the Frequency Change on the Formation of Orthogonal Components of the Relay Protection Input Signals

2020 ◽  
Vol 63 (1) ◽  
pp. 42-54 ◽  
Author(s):  
E. A. Romaniuk ◽  
V. Yu. Rumiantsev ◽  
Yu. V. Rumiantsev ◽  
A. A. Dziaruhina
Keyword(s):  
Fourier Transform ◽  
Discrete Fourier Transform ◽  
Digital Filter ◽  
Input Signal ◽  
Digital Filters ◽  
Signal Frequency ◽  
Filter Model ◽  
Input Signal Frequency ◽  
Orthogonal Components ◽  
The Impact

Digital filters made with the use of discrete Fourier Transform are applied in most microprocessor protections produced both in the home country and abroad. When the input signal frequency deviates from the value to which these filters are configured, a signal is generated at their output with oscillation amplitude that is proportional to the deviation of the signal frequency from the specified one. The article proposes an algorithm for compensating the oscillations of orthogonal components of the output signals of digital filters implemented on the basis of a discrete Fourier transform, when the input signal frequency deviates from the nominal one. A mathematical model of the proposed digital filter with an algorithm for compensating the oscillations of its orthogonal components, as well as a signal model for reproducing input effects, is implemented in the MatLab-Simulink dynamic modeling environment. The digital filter model is provided with two channels, viz. a current channel and a voltage channel, which makes it possible to simulate their operation in relation to protections that use one or two input values, for example, for current and remote protection. Verification of the functioning of the digital filter model with compensation for fluctuations in its output signal was carried out with the use of two types of test effects, viz. a sinusoidal signal with a frequency of 48–51 Hz (idealized effect), and the effects that are close to the real secondary signals of measuring current transformers and voltage transformers in case of short circuits accompanied by a decrease in frequency. The conducted computational experiments with deviation of frequency from the nominal one, revealed the presence of undamped oscillations at the output of standard digital Fourier filters and their almost complete absence in the proposed digital filters. This makes us possible to recommend digital filters based on a discrete Fourier transform supplemented by an algorithm for compensation of fluctuations in the amplitudes of the output signals for the use in microprocessor protection.

scholarly journals Adaptive method to predict and track unknown system behaviors using RLS and LMS algorithms

2021 ◽  
Vol 34 (1) ◽  
pp. 133-140
Author(s):  
Teimour Tajdari
Keyword(s):  
Input Signal ◽  
Output Signal ◽  
Adaptive Filters ◽  
Adaptive Method ◽  
Lms Algorithm ◽  
Recursive Least Squares ◽  
Least Mean Square ◽  
Filter Output ◽  
System Output ◽  
Unknown System

This study investigates the ability of recursive least squares (RLS) and least mean square (LMS) adaptive filtering algorithms to predict and quickly track unknown systems. Tracking unknown system behavior is important if there are other parallel systems that must follow exactly the same behavior at the same time. The adaptive algorithm can correct the filter coefficients according to changes in unknown system parameters to minimize errors between the filter output and the system output for the same input signal. The RLS and LMS algorithms were designed and then examined separately, giving them a similar input signal that was given to the unknown system. The difference between the system output signal and the adaptive filter output signal showed the performance of each filter when identifying an unknown system. The two adaptive filters were able to track the behavior of the system, but each showed certain advantages over the other. The RLS algorithm had the advantage of faster convergence and fewer steady-state errors than the LMS algorithm, but the LMS algorithm had the advantage of less computational complexity.

scholarly journals Generalization and search in risky environments

2017 ◽  
Author(s):  
Eric Schulz ◽  
Charley M. Wu ◽  
Quentin J. M. Huys ◽  
Andreas Krause ◽  
Maarten Speekenbrink
Keyword(s):  
Cross Validation ◽  
Learning Algorithm ◽  
Search Space ◽  
Risk And Uncertainty ◽  
Exploration And Exploitation ◽  
Function Learning ◽  
Spatially Correlated ◽  
Sampling Behavior ◽  
The Face ◽  
Optimization Routine

AbstractHow do people pursue rewards in risky environments, where some outcomes should be avoided at all costs? We investigate how participant search for spatially correlated rewards in scenarios where one must avoid sampling rewards below a given threshold. This requires not only the balancing of exploration and exploitation, but also reasoning about how to avoid potentially risky areas of the search space. Within risky versions of the spatially correlated multi-armed bandit task, we show that participants’ behavior is aligned well with a Gaussian process function learning algorithm, which chooses points based on a safe optimization routine. Moreover, using leave-one-block-out cross-validation, we find that participants adapt their sampling behavior to the riskiness of the task, although the underlying function learning mechanism remains relatively unchanged. These results show that participants can adapt their search behavior to the adversity of the environment and enrich our understanding of adaptive behavior in the face of risk and uncertainty.

The Application of Grey Prediction on the Fast Control Power Supply in EAST

2013 ◽  
Vol 427-429 ◽  
pp. 1739-1742
Author(s):  
Hai Hong Huang ◽  
Jia Miao ◽  
Hai Xin Wang ◽  
Feng Feng Wang
Keyword(s):  
Input Signal ◽  
Power Supply ◽  
Grey Theory ◽  
Grey Prediction ◽  
Grey System ◽  
Control Power ◽  
Input Signals ◽  
Simulation And Experiment ◽  
One Step ◽  
Fast Control

Based on the grey theory, a novel model is built to predict the input signal of fast control power supply used in Experimental Advanced Superconducting Tokamak (EAST). The model can be used as online metabolic grey filtering and one-step prediction of different input signals. Results of simulation and experiment show that the predicting algorithm based on the grey system model can predict the input signal primarily.

On a Certain Class of Expanding Systems

2010 ◽  
Vol 17 (2) ◽  
pp. 299-306
Author(s):  
Adam Żuchowski
Keyword(s):  
Transfer Function ◽  
Input Signal ◽  
Initial Conditions ◽  
System Optimization ◽  
Gain Coefficient ◽  
Time Intervals ◽  
System Parameters ◽  
Linear Dynamic ◽  
Subsequent Time ◽  
Input Signals

On a Certain Class of Expanding Systems The interesting properties of a class of expanding systems are discussed. The operation of the considered systems can be described as follows: the input signal is processed by a linear dynamic converter in subsequent time intervals, each of them is equal to Ti. Processing starts at the moments n · Ti, always after zeroing of converter initial conditions. For smooth input signals and a given transfer function of the converter one can suitably choose Ti and the gain coefficient in order to realize the postulated linear operations on input signals, which is quite different comparing it to the operation realized by the converter. The errors of postulated operations are mainly caused by non-smooth components of the input signal. The principles for choice of system parameters and rules for system optimization are presented in the paper. The referring examples are attached too.

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