Active micro-vibration control based on improved variable step size LMS algorithm

The contradiction of variable step size least mean square(LMS)algorithm between fast convergence speed and small steady-state error has always existed.So,a new algorithm based on the combination of logarithmic and symbolic function and step size factor is proposed.It establishes a new updating method of step factor that is related to step factor and error signal.This work makes an analysis from 3 aspects:theoretical analysis,theoretical verification and specific experiments.The experimental results show that the proposed algorithm is superior to other variable step size algorithms in convergence speed and steady-state error.

Convergence analysis of filtered-X LMS algorithm with secondary path modeling error

A more relaxed sufficient condition for the convergence of filtered-X LMS (FXLMS) algorithm is presented. It is pointed out that if some positive real condition for secondary path transfer function and its estimates is satisfied within all the frequency bands, FXLMS algorithm converges whatever the reference signal is like. But if the above positive real condition is satisfied only within some frequency bands, the convergence of FXLMS algorithm is dependent on the distribution of power spectral density of the reference signal, and the convergence step size is determined by the distribution of some specific correlation matrix eigenvalues.Applying the conclusion above to the Delayed LMS (DLMS) algorithm, it is shown that DLMS algorithm with some error of time delay estimation converges in certain discrete frequency bands, and the width of which are determined only by the "time-delay estimation error frequency" which is equal to one fourth of the inverse of estimated error of the time delay.

A Control Algorithm for the Optimization of Batch Reactor-Based Processes

Levenberg-Marquardt(LM)algorithm is applied for the optimization of the heat transfer of a batch reactor.The validity of the approach is verified through comparison with experimental results.It is found that the mathematical model can properly describe the heat transfer relationships characterizing the considered system,with the error being kept within±2℃.Indeed,the difference between the actual measured values and the model calculated value curve is within±1.5℃,which is in agreement with the model assumptions and demonstrates the reliability and effectiveness of the algorithm applied to the batch reactor heat transfer model.Therefore,the present work provides a theoretical reference for the conversion of practical problems in the field of chemical production into mathematical models.

Aeromagnetic Compensation Algorithm Based on Levenberg-Marquard Neural Network

The magnetic compensation of aeromagnetic survey is an important calibration work,which has a great impact on the accuracy of measurement.In an aeromagnetic survey flight,measurement data consists of diurnal variation,aircraft maneuver interference field,and geomagnetic field.In this paper,appropriate physical features and the modular feedforward neural network(MFNN)with Levenberg-Marquard(LM)back propagation algorithm are adopted to supervised learn fluctuation of measuring signals and separate the interference magnetic field from the measurement data.LM algorithm is a kind of least square estimation algorithm of nonlinear parameters.It iteratively calculates the jacobian matrix of error performance and the adjustment value of gradient with the regularization method.LM algorithm’s computing efficiency is high and fitting error is very low.The fitting performance and the compensation accuracy of LM-MFNN algorithm are proved to be much better than those of TOLLES-LAWSON(T-L)model with the linear least square(LS)solution by fitting experiments with five different aeromagnetic surveys’data.

Assessing the Forecasting of Comprehensive Loss Incurred by Typhoons:A Combined PCA and BP Neural Network Model

This paper develops a joint model utilizing the principal component analysis(PCA)and the back propagation(BP)neural network model optimized by the Levenberg Marquardt(LM)algorithm,and as an application of the joint model to investigate the damages caused by typhoons for a coastal province,Fujian Province,China in 2005-2015(latest).First,the PCA is applied to analyze comprehensively the relationship between hazard factors,hazard bearing factors and disaster factors.Then five integrated indices,overall disaster level,typhoon intensity,damaged condition of houses,medical rescue and self-rescue capability,are extracted through the PCA;Finally,the BP neural network model,which takes the principal component scores as input and is optimized by the LM algorithm,is implemented to forecast the comprehensive loss of typhoons.It is estimated that an average annual loss of 138.514 billion RMB occurred for 2005-2015,with a maximum loss of 215.582 in 2006 and a decreasing trend since 2010 though the typhoon intensity increases.The model was validated using three typhoon events and it is found that the error is less than 1%.These results provide information for the government to increase medical institutions and medical workers and for the communities to promote residents’self-rescue capability.

Synthesis and Design of 5G Duplexer Based on Optimization Method

A new optimization method is proposed to realize the synthesis of duplexers.The traditional optimization method takes all the variables of the duplexer into account,resulting in too many variables to be optimized when the order of the duplexer is too high,so it is not easy to fall into the local solution.In order to solve this problem,a new optimization strategy is proposed in this paper,that is,two-channel filters are optimized separately,which can reduce the number of optimization variables and greatly reduce the probability of results falling into local solutions.The optimization method combines the self-adaptive differential evolution algorithm(SADE)with the Levenberg-Marquardt(LM)algorithm to get a global solution more easily and accelerate the optimization speed.To verify its practical value,we design a 5 G duplexer based on the proposed method.The duplexer has a large external coupling,and how to achieve a feed structure with a large coupling bandwidth at the source is also discussed.The experimental results show that the proposed optimization method can realize the synthesis of higher-order duplexers compared with the traditional methods.

Nonlinear Correction of Pressure Sensor Based on Depth Neural Network

With the global climate change,the high-altitude detection is more and more important in the climate prediction,and the input-output characteristic curve of the air pressure sensor is offset due to the interference of the tested object and the environment under test,and the nonlinear error is generated.Aiming at the difficulty of nonlinear correction of pressure sensor and the low accuracy of correction results,depth neural network model was established based on wavelet function,and Levenberg-Marquardt algorithm is used to update network parameters to realize the nonlinear correction of pressure sensor.The experimental results show that compared with the traditional neural network model,the improved depth neural network not only accelerates the convergence rate,but also improves the correction accuracy,meets the error requirements of upper-air detection,and has a good generalization ability,which can be extended to the nonlinear correction of similar sensors.

An Evolutionary Approach to Improve the Beam Steering Performance over Conventional Approach in CHAA

In this work,evolutionary algorithms are applied for the first time to achieve better radiation characteristics over the conventional beamforming algorithm in the concentric hexagonal antenna array(CHAA),which improves the performance of wireless communication.Multiple signal classification(MUSIC)algorithm is employed for direction of arrival(DoA)estimation.The conventional adaptive beam steering algorithm,least mean-square(LMS)algorithm,is used to steer the beam.Further,the proposed approach is employed by novel particle swarm optimization(NPSO)to reduce sidelobe level(SLL)even further.A six-ring CHAA with 126 elements for DoA estimation and beam steering is simulated.The simulation results of the MUSIC,LMS,NPSO,and particle swarm optimization(PSO)algorithms are provided for various DoAs.

Improved method for prediction of milled rice moisture content based on Weibull distribution

The loss on drying method,which is regarded as the standard method of rice moisture content analysis,provides the most reliable results but is both labor intensive and time consuming.In order to improve the detection efficiency of the loss on drying method,this study investigated the drying characteristics of milled rice and developed an information fusion algorithm with which to predict milled rice moisture content based on the Weibull distribution and Levenberg-Marquardt(LM)algorithm.Application of the Weibull distribution model was investigated regarding its description of the drying kinetics of milled rice during infrared drying.An adaptive mechanism was applied to algorithm design,with the starting point of the estimation algorithm determined by calculating the drying rate at each measuring point,and the end-point distinguished using a two-level threshold algorithm.The calculated results were then compared with the measured data regarding the infrared drying of milled rice.For milled rice samples varying in moisture content from 14.44%-17.67%(dry basis),the relative error between predicted and observed values ranged 0.0037-0.0589,with a reduction in test time of 50.71%-67.87%.

Properties of a general quaternion-valued gradient operator and its applications to signal processing

The gradients of a quaternion-valued function are often required for quaternionic signal processing algorithms.The HR gradient operator provides a viable framework and has found a number of applications.However,the applications so far have been limited to mainly real-valued quaternion functions and linear quaternionvalued functions.To generalize the operator to nonlinear quaternion functions,we define a restricted version of the HR operator,which comes in two versions,the left and the right ones.We then present a detailed analysis of the properties of the operators,including several different product rules and chain rules.Using the new rules,we derive explicit expressions for the derivatives of a class of regular nonlinear quaternion-valued functions,and prove that the restricted HR gradients are consistent with the gradients in the real domain.As an application,the derivation of the least mean square algorithm and a nonlinear adaptive algorithm is provided.Simulation results based on vector sensor arrays are presented as an example to demonstrate the effectiveness of the quaternion-valued signal model and the derived signal processing algorithm.