scholarly journals On Modelling and Comparative Study of LMS and RLS Algorithms for Synthesis of MSA

2016 ◽  
Vol 2016 ◽  
pp. 1-8 ◽  
Author(s):  
Ahmad Kamal Hassan ◽  
Adnan Affandi
Keyword(s):  
Worked Examples ◽  
Analytical Framework ◽  
Least Square ◽  
Least Mean Square ◽  
Recursive Least Square ◽  
Rls Algorithm ◽  
Contour Plots ◽  
Comprehensive Comparison ◽  
Artificial Neural Network Ann ◽  
Spread Factor

This paper deals with analytical modelling of microstrip patch antenna (MSA) by means of artificial neural network (ANN) using least mean square (LMS) and recursive least square (RLS) algorithms. Our contribution in this work is twofold. We initially provide a tutorial-like exposition for the design aspects of MSA and for the analytical framework of the two algorithms while our second aim is to take advantage of high nonlinearity of MSA to compare the effectiveness of LMS and that of RLS algorithms. We investigate the two algorithms by using gradient decent optimization in the context of radial basis function (RBF) of ANN. The proposed analysis is based on both static and adaptive spread factor. We model the forward side or synthesis of MSA by means of worked examples and simulations. Contour plots, 3D depictions, and Tableau presentations provide a comprehensive comparison of the two algorithms. Our findings point to higher accuracies in approximation for synthesis of MSA using RLS algorithm as compared with that of LMS approach; however the computational complexity increases in the former case.

Investigation of Phase Noise on the Performance of LMS-RLS Adaptive Equalizer

2013 ◽  
Vol 6 (1) ◽  
pp. 27-35
Author(s):  
Radhi Shabib Kaned
Keyword(s):  
Phase Noise ◽  
Bit Error Rate ◽  
Communication Channels ◽  
Adaptive Algorithms ◽  
Least Square ◽  
Mean Square ◽  
Least Mean Square ◽  
Recursive Least Square ◽  
Adaptive Equalizer ◽  
Rls Algorithm

This paper investigates the effect of phase noise on equalization of  communication channels using least mean square (LMS) and recursive least square (RLS) adaptive algorithms. The aim of the investigation is to mitigate inter-symbol interference (ISI) caused by the channel and to impose the bit error rate (BER) in the received signals. The equalizerusestwobasicadaptivealgorithms: LMS algorithmand RLS algorithm. Without LMS-RLS equalizer,theBER ismorethan when the system modelincludesLMS-RLS equalizer as indicated in table (1) and table (2). Equalizer algorithm is analyzed using MATLAB v.9 Communication Block Set.

scholarly journals RSS Based Wi-Fi Positioning Method Using Recursive Least Square (RLS) Algorithm

2018 ◽  
Vol 7 (2.24) ◽  
pp. 492
Author(s):  
Sreevardhan Cheerla ◽  
D Venkata Ratnam
Keyword(s):  
Least Square ◽  
Recursive Least Square ◽  
Rls Algorithm ◽  
Positioning Systems ◽  
Indoor Location ◽  
Positioning Method ◽  
Accurate Position ◽  
Software And Hardware ◽  
Newton Raphson ◽  
Raphson Method

Due to rapid increase in demand for services which depends upon exact location of devices leads to the development of numerous Wi-Fi positioning systems. It is very difficult to find the accurate position of a device in indoor environment due to substantial development of structures. There are many algorithms to determine the indoor location but they require expensive software and hardware. Hence receiving signals strength (RSS) based algorithms are implemented to find the self-positioning. In this paper Newton-Raphson, Gauss-Newton and Steepest descent algorithms are implemented to find the accurate location of Wi-Fi receiver in Koneru Lakshmaiah (K L) University, Guntur, Andhra Pradesh, India. From the results it is evident that Newton -Raphson method is better in providing accurate position estimations. 

Adaptive IIR Filtering Techniques for Dynamic Modeling of a Twin Rotor System

Volume 1 ◽  
2004 ◽  
Author(s):  
M. O. Tokhi ◽  
M. S. Alam ◽  
F. M. Aldebrez
Keyword(s):  
Input Signal ◽  
Learning Algorithms ◽  
Dynamic Modelling ◽  
Least Square ◽  
Infinite Impulse Response ◽  
Mean Square ◽  
Least Mean Square ◽  
Recursive Least Square ◽  
Twin Rotor ◽  
Filtering Techniques

This paper investigates the development of a parametric model to characterise pitch movement in a twin rotor multi-input multi-output system (TRMS) using adaptive infinite impulse response (IIR) models. The TRMS is a laboratory platform designed for control experiments. In certain aspects, its behaviour resembles that of a helicopter. It typifies a high-order nonlinear system with significant cross coupling between its two channels. It also simulates similar problems and challenges encountered in real systems. These include complex dynamics that lead to both parametric and dynamic uncertainty, unmeasurable states and sensor and actuator noise. In this work, adaptive IIR filtering techniques using least mean square (LMS) and recursive least square (RLS) algorithms are investigated for dynamic modelling of the system. The system is initially excited with random gaussian sequence input signal of sufficient bandwidth (0–10Hz) to ensure that all resonance modes of interest are captured. The magnitude of the input signal is selected so that it does not drive the system out of its linear operating range. Good excitation is achieved from 0–2.5 Hz, which includes all the important rigid body and flexible modes. Then, adaptive IIR filters based on equation error formulation are used for modelling the system. Three standard algorithms; namely, LMS, normalized LMS and RLS are utilized as learning algorithms, to update the parameters of the filter during the modelling process. A comparative assessment of the three learning algorithms, in characterising the system, is conducted. The performance of each model is assessed in terms of output tracking, minimization of the mean-square error, stability and algorithm convergence.

Noise Removal from EMG Signal Using Adaptive Enhanced Squirrel Search Algorithm

2020 ◽  
Vol 19 (04) ◽  
pp. 2050039
Author(s):  
B. Nagasirisha ◽  
V. V. K. D. V. Prasad
Keyword(s):  
Adaptive Filters ◽  
Search Algorithm ◽  
Cuckoo Search ◽  
Maximum Error ◽  
Noise Removal ◽  
Least Square ◽  
Mean Square ◽  
Least Mean Square ◽  
Recursive Least Square ◽  
Emg Signal

Electromyogram (EMG) signals are mostly affected by a large number of artifacts. Most commonly affecting artifacts are power line interference (PLW), baseline noise and ECG noise. This work focuses on a novel attenuation noise removal strategy which is concentrated on adaptive filtering concepts. In this paper, an enhanced squirrel search (ESS) algorithm is applied to remove noise using adaptive filters. The noise eliminating filters namely adaptive least mean square (LMS) filter and adaptive recursive least square (RLS) filters are designed, which is correlated with an ESS. This novel algorithm yields better performance than other existing algorithms. Here the performances are measured in terms of signal-to-noise ratio (SNR) in decibel, maximum error (ME), mean square error (MSE), standard deviation, simulation time and mean value difference. The proposed work has been implemented at the MATLAB simulation platform. Testing of their noise attenuation capability is also validated with different evolutionary algorithms namely squirrel search, particle swarm optimization (PSO), artificial bee colony (ABC), firefly, ant colony optimization (ACO) and cuckoo search (CS). The proposed work eliminates the noises and provides noise-free EMG signal at the output which is highly efficient when compared with existing methodologies. Our proposed work achieves 4%, 40%, 4%, 7%, 9% and 70% better performance than the literature mentioned in the results.

Adaptive Order Tracking Technique Using Recursive Least-Square Algorithm

2002 ◽  
Vol 124 (4) ◽  
pp. 502-511 ◽  
Author(s):  
Mingsian R. Bai ◽  
Jihjau Jeng ◽  
Chingyu Chen
Keyword(s):  
Least Square ◽  
Recursive Least Squares ◽  
Frequency Resolution ◽  
Shaft Speed ◽  
Recursive Least Square ◽  
Rls Algorithm ◽  
Tracking Technique ◽  
Order Tracking ◽  
Conventional Methods ◽  
Bandpass Signals

Order tracking technique is one of the important tools for diagnosis of rotating machinery. Conventional methods of order tracking are primarily based on Fourier analysis with reference to shaft speed. Resampling is generally required in the fast Fourier transform (FFT)-based methods to compromise between time and frequency resolution for varying shaft speeds. Conventional methods suffer from a number of shortcomings. In particular, smearing problems arise when closely spaced orders or crossing orders are present. Conventional methods also are ineffective for the applications involving multiple independent shaft speeds. This paper presents an adaptive order tracking technique based on the Recursive Least-Squares (RLS) algorithm to overcome the problems encountered in conventional methods. In the proposed method, the problem is treated as the tracking of frequency-varying bandpass signals. Order amplitudes can be calculated with high resolution by using the proposed method in real-time fashion. The RLS order tracking technique is applicable whether it is a single-axle or multi-axle system.

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