60 results about "Least mean square algorithm" patented technology

The invention relates to the technical field of voiceprint recognition, and discloses an air-ground conversation speaker recognition method for short voice and complex noise, which dynamically suppresses background noise through a self-adaptive filter in combination with a minimum mean square algorithm, and improves the voice signal quality. A multi-scale feature fusion technology is adopted, short-time Fourier transform, filter bank energy features and multi-scale convolution are combined, a random time and frequency shielding data enhancement strategy is introduced, and the short speech feature expression ability is enhanced; bi-GRU is integrated in the ECAPA-TDNN model, and context information of multiple rounds of conversations is captured through context time sequence modeling, so that the recognition stability in a dynamic scene is improved; and an AAM-Softmax loss function is adopted to strengthen the distinguishing capability of the model, and finally identity judgment is realized through cosine similarity matching. According to the method, the voiceprint recognition precision and robustness under complex noise and short voice conditions are remarkably improved, and the safety risk caused by identity misjudgment in aviation communication is effectively reduced.

The invention discloses an underwater acoustic communication equalization method based on a leakage normalized least mean square algorithm, and belongs to the technical field of underwater acoustic communication. The method comprises the following steps: acquiring a baseband sampling signal; initializing a weight vector w = 0, an instantaneous power vector p = 0 and an error vector e = 0; and taking the baseband sampling signal x as an input signal, taking the training sequence as an expected signal d, and carrying out gradual leakage updating on the weight of a filter through iterative calculation based on a leakage factor control strategy so as to complete self-adaptive adjustment in a signal equalization process. According to the method, the leakage factor control strategy is introduced, and the adaptive step parameter design is adopted, so that the stability and the convergence speed of the weight of the filter are effectively balanced, finally, the received signal is accurately balanced, and the robustness and the signal quality of the underwater acoustic communication system are remarkably improved.

The present invention discloses a direction of arrival estimation method, direction of arrival estimation device, and system. The method comprises: using antennas to form a uniform linear array, establishing a received signal model based on array signal input signals, and determining a received signal matrix; performing low-rank matrix approximation on the received signal matrix using a low-rank matrix estimation algorithm to obtain a low-noise, low-dimensional matrix; updating the variable step size of a least mean square algorithm (LMS) using statistical parameters of the low-noise, low-dimensional matrix to update and iteratively calculate weight vector coefficients; and calculating the antenna array pattern and spatial spectrum based on the obtained weight vector coefficients to estimate the direction of arrival of the signal. The present invention uses the low-rank matrix approximation method to obtain a low-noise, low-dimensional matrix from the received signal matrix. Utilizing the instantaneous prediction error of the low-noise, low-dimensional matrix and the variable step size of the noise reduction signal power update algorithm, the method can ensure accurate DOA estimation even under unfavorable conditions such as low signal-to-noise ratio, a small number of antenna array elements, and a small amount of signal sampling data, due to prior noise reduction preprocessing.

The invention discloses a slope protection drainage reinforcement management system based on cloud computing. Multi-source slope data is preprocessed and then transmitted to a cloud computing module; receiving the processed multi-source slope data, performing filtering processing on the multi-source slope data by using an LMS (least mean square) algorithm, establishing a Transform-XL slope space-time coupling prediction model, introducing a PINN physical information neural network into the model, and embedding physical constraints of finite element analysis into the model for training; inputting the initial multi-source slope data into a slope space-time coupling prediction model for risk assessment; slope protection digital twins are established based on the slope risk assessment state, sensor data and a BIM model are synchronized in real time, the effects of different reinforcement schemes are simulated through HPC cloud high-performance calculation, and a multi-target optimization scheme is generated. The scheme can be dynamically adjusted according to real-time monitoring data, and intelligent and precise management of slope protection drainage reinforcement engineering is achieved.

The invention discloses a high-precision torque control method for a permanent magnet motor of a new energy automobile, which comprises the following steps of: constructing a neural network torque observer by taking the voltage, the current, the temperature and the electrical angular velocity of the motor as input, and carrying out online parameter identification on the motor by introducing historical data of the motor under different working conditions; and partial derivative information of the torque to the current is calculated through the identified parameters. According to the method, partial derivative information of torque to current and observation torque of a torque observer are respectively substituted into a Taylor formula of historical data to judge whether network output is correct or not, and the inside of the network is adjusted by using a minimum mean square algorithm; according to the method, the torque observation precision of the network is improved, the defect that internal parameters of a traditional network are fixed and cannot adapt to time-varying working conditions is overcome, the accuracy and robustness of the network are improved, and the method is suitable for the working conditions of parameter change caused by motor temperature change, torque control precision reduction and MTPA track offset.

The invention relates to the technical field of wireless communication anti-interference, in particular to a communication transceiving method and system for adaptive frequency hopping filtering, and the method comprises the following steps: S1, collecting and storing channel state information for channel quality evaluation and interference behavior prediction through continuously monitoring the interference level, signal-to-noise ratio and frequency spectrum time sequence characteristics of a current communication channel; according to the invention, dynamic optimization generation of the frequency hopping pattern is realized through interference trend prediction and weighted correction based on the attention mechanism-LSTM network, and channel interference is effectively suppressed by combining interference residual feature prediction and adaptive filtering parameter pre-configuration and real-time optimization of the least mean square algorithm. And high-precision blind synchronization is completed through joint detection and maximum likelihood estimation by using implicit pilot frequency and chaos frequency hopping sequence characteristics, and cooperative improvement of anti-interference performance, synchronization reliability and data recovery accuracy of a communication link in a complex electromagnetic environment is finally realized.

The invention discloses an optical fiber vibration and temperature measurement diagnosis method and system for drainage pipe leakage, and relates to the field of temperature measurement. Determining a temperature characteristic signal based on the vibration signal and a Raman scattering effect; performing wavelet packet transformation processing on the vibration signal to obtain a plurality of equal-broadband signals; eliminating noise in the plurality of equal-broadband signals, the similarity of which to a preset noise feature library is greater than a preset similarity threshold value, by adopting a minimum mean square algorithm to obtain a denoised vibration feature signal; an LSTM-GRU hybrid neural network model is constructed; splicing the output features of the LSTM layer and the GRU layer to obtain fusion features; and matching the fusion feature with a preset fault mode classification table to obtain a fault type of the drainage pipe, adding the fault type to a preset template, and outputting a diagnosis result of the drainage pipe. According to the method, the identification capability of tiny leakage in a complex environment is improved, the false alarm rate is reduced, and the accuracy of leakage detection of the drainage pipe network is improved.

The invention relates to the technical field of roll angle measurement, in particular to a dynamic roll angle measurement method based on an antenna intensity change trend, and the method comprises the steps: designing a phased array radar beam, carrying out the target tracking of a missile body, obtaining an expected signal in real time, and obtaining a receiving signal, reflected by an antenna on the missile body, of each array element after the signal is transmitted; calculating a disorder coefficient of each modal component; determining an interference coefficient of each array element at the current moment; determining a step length parameter of a minimum mean square algorithm at the current moment; based on the expected signal and the step length parameter, in combination with a minimum mean square algorithm, adjusting the beam direction of each array element transmitting signal in real time; and each array element performs signal emission according to the adjusted emission signal, and the roll angle of the projectile body is measured according to the condition that the intensity of the signal received by the antenna on the projectile body has a minimum value. According to the invention, the step length parameter in the least mean square algorithm is dynamically adjusted, and the precise measurement of the roll angle of the projectile body is improved.

This invention relates to a low-current harmonic direct torque control method for a dual three-phase permanent magnet synchronous motor (PMSM), belonging to the field of PMSM control technology. The method is as follows: S1: Establish mathematical models of the fundamental and harmonic subspaces of the dual three-phase PMSM under vector space decoupling, and clarify the voltage vector distribution characteristics of the two subspaces; S2: In the fundamental subspace, design a duty cycle modulation strategy based on the principle of minimizing the error vector, and an improved deadbeat flux linkage torque controller; S3: In the harmonic subspace, design an online learning harmonic canceller, using the least mean square algorithm to update the weight coefficients in real time; S4: Select the D1 vector set as the effective vector of the harmonic subspace and perform sector division; S5: Add the effective voltage vector action time of the fundamental and harmonic subspaces to generate the final switching sequence, which is output to the dual three-phase inverter to effectively suppress torque ripple and harmonic current of the dual three-phase PMSM.

The invention belongs to the field of aircraft noise reduction, and discloses an aircraft cabin active noise reduction control method and related equipment, and the method comprises the steps: collecting the vibration, rotating speed, cabin sound pressure and other multi-modal data of an aircraft in real time, and carrying out the flight state classification through a pre-trained multilayer neural network model; and the current accurate working condition is determined by combining state machine conversion logic, and finally, the parameters of the feed-forward filter are dynamically adjusted through a minimum mean square algorithm to realize noise reduction. According to the method, the high-frequency component suppression capability in a multi-source coupled noise scene is effectively improved, the noise reduction efficiency is kept stable in the whole flight period, the convergence speed is improved compared with a traditional scheme, and the local optimum problem is avoided through a state self-adaptive feed-forward mechanism.

The invention provides a ship low-frequency line spectrum vibration noise active control secondary channel compensation modeling method in an equipment operation state. Aiming at the problems that the existing offline identification needs to frequently start and stop equipment, the calculation burden of online identification is large, the indirect identification precision is low and the like, a least mean square (LMS) algorithm is adopted, and a mapping relation between a reference signal and a primary response signal is firstly constructed; driving an actuator by using the reference signal to obtain a mixed signal superposed with the primary response; predicting primary response based on the mapping relation, and separating secondary response; and secondary channel modeling is completed by using the LMS algorithm again. The method breaks through the limitation of traditional modeling, can achieve high-precision modeling without shutdown, effectively reduces the calculation burden, is not interfered by the change of the structural mode of a controlled object, greatly improves the debugging efficiency of an active control system of large marine equipment, and brings an innovative technical breakthrough to the field of ship vibration noise control.

The application discloses a kind of active control methods for containing unknown harmonic broadband microvibration, it is related to spacecraft microvibration control technical field, including the following steps: using acceleration sensor obtains the residual acceleration response at spacecraft load;Based on residual acceleration response, using PID controller obtains first control force;Using reference signal generator, harmonic frequency is roughly estimated, refined estimation and fine adjustment are carried out, and reference signal is generated;Based on reference signal, using parallel filter-based least mean square algorithm obtains second control force;According to first control force and second control force, total control force is obtained, and is output by executing mechanism, realizes the efficient suppression to containing multiple unknown harmonic broadband microvibration.The application solves the problem that traditional vibration control method is insufficient for harmonic disturbance control ability, and filter-based least mean square algorithm is difficult to obtain the reference signal meeting the requirements under the condition that disturbance characteristics are unknown.

The application relates to an electrical signal parameter estimation method and device, electronic equipment and a storage medium, and relates to the technical field of electrical signal processing. The method comprises the following steps: adopting a variable step size least mean square algorithm, performing cyclic estimation on a fundamental wave signal based on a received signal until the fundamental wave signal meets a preset condition; wherein the fundamental wave signal is a difference value of an error signal and the received signal, the error signal is an output signal of the received signal after a notch filter; a step size factor of the variable step size least mean square algorithm is adjustable, and the step size factor is adjusted based on a square of a time average value of an autocorrelation function of error signals at two adjacent moments; and a signal parameter of the fundamental wave signal is acquired. When the frequency of the electrical signal rapidly changes, good response speed and estimation accuracy can be simultaneously obtained, and the method is more suitable for parameter estimation of a non-stationary signal.

The invention discloses a nonlinear secondary channel modeling method with extremely low additional operand, and belongs to the field of active control of vibration noise. The method aims at solving the problems that in active control over strong response objects such as a vibration source, secondary channel nonlinearity causes insufficient modeling precision, and an existing method is large in additional calculation amount. The method comprises the following steps: firstly, determining a target frequency, selecting N kinds of sinusoidal signals with gradual increase amplitudes to identify a secondary channel in an off-line manner, and obtaining N groups of pulse response sequences; constructing an M-row N-column two-dimensional array storage sequence; during control, a round instruction is built in a digital processor to round the output to obtain an index number, and a corresponding secondary channel coefficient is called from an array so as to provide a precise model for a self-adaptive algorithm. The method is high in modeling precision and low in additional operand, guarantees the real-time performance and control stability of the system, and is suitable for a vibration noise active control scene of a filtering least mean square (FxLMS) algorithm.

The invention discloses a self-adaptive narrowband interference suppression method and device for power line communication, and the method comprises the following steps: S1, receiving a mixed signal, and carrying out the decorrelation processing of the mixed signal, so as to obtain a decorrelation signal; s2, inputting the decorrelated signal into a filter, and estimating the narrowband interference signal at the current moment by the filter according to the filter coefficient at the current moment to obtain a narrowband interference estimation signal; s3, calculating the energy of the decorrelation signal; s4, subtracting the received signal at the current moment from the narrowband interference estimation signal to obtain an error signal; and S5, according to the filter coefficient at the current moment, the error signal and the energy of the decorrelation signal, calculating the filter coefficient at the next moment by using a power normalized least mean square algorithm, so that the algorithm maintains the characteristic of insensitivity to the power change of the input signal, the calculation resource consumption is greatly reduced, and the calculation efficiency is improved. And the feasibility and efficiency of the algorithm in practical application are improved.

The invention relates to the technical field of signal processing, and particularly discloses an adaptive graph signal temperature online reconstruction method based on a maximum dustpan line criterion, which comprises the following steps: taking a meteorological station as a node of a graph, taking temperature as a signal value of the node on the graph, and constructing a graph signal model according to an observation signal influenced by impulse noise; based on the graph signal model, establishing an optimization model by using a maximum dustpan line function criterion and a minimum mean square algorithm and using an L2 norm and an L1 norm; constructing an MGLMS robust algorithm based on the optimization model, and converting the MGLMS robust algorithm into a time domain update equation by using inverse Fourier transform of a graph; and performing on-line reconstruction on the signal values of the nodes on the graph by using the time domain update equation to obtain reconstructed graph signals, thereby obtaining the temperature on the unknown node. According to the adaptive graph signal temperature online reconstruction method based on the maximum dustpan line criterion, data reconstruction and processing of a GSP in an impulse noise environment are improved.

The present invention belongs to the field of oil fluid abrasive monitoring, and particularly relates to a signal detection method for an abrasive detection device with a U-shaped channel reflux oil circuit. The device includes an inductive abrasive detection sensor; the inductive abrasive detection sensor includes a U-shaped tube detection component, an excitation coil, an iron core, two detection coils, two oil pipe locking devices, and two magnetic poles; two detection signals of the oil fluid are collected, the movement rate of the abrasive is preliminarily estimated according to the detection signals, and the cut-off frequency of low-pass filtering is set; the detection signals are filtered according to the cut-off frequency, and the filtered signals are denoised to obtain two new detection signals, and the volume size of the abrasive is estimated according to the new detection signals; the present invention can accurately calculate the real-time movement speed of ferromagnetic wear particles in the oil fluid based on the time delay of the collected abrasive signals during oil fluid abrasive monitoring, and use the multi-dimensional least mean square algorithm to realize signal adaptive denoising, and realize abrasive signal recognition and abrasive size estimation.

The invention relates to an electrical signal parameter estimation method and device, electronic equipment and a storage medium, and relates to the technical field of electrical signal processing. The method comprises the following steps: carrying out cyclic estimation on a fundamental wave signal based on a received signal by adopting a variable step size minimum mean square algorithm until the fundamental wave signal meets a preset condition; wherein the fundamental wave signal is a difference value between the error signal and the receiving signal, and the error signal is an output signal after the receiving signal passes through the wave trap; the step length factor of the variable step length minimum mean square algorithm is adjustable, and the step length factor is adjusted based on the square of the autocorrelation function time average value of the error signals at two adjacent moments; and acquiring a signal parameter of the fundamental wave signal. When the electrical signal frequency changes rapidly, good response speed and estimation precision can be obtained at the same time, and the method is more suitable for parameter estimation of non-stationary signals.

The invention relates to an infrared image stripe correction method and apparatus, an electronic device and a storage medium. The method comprises the steps of obtaining a to-be-corrected image; performing edge detection on the to-be-corrected image to obtain an edge part and a non-edge part of the to-be-corrected image; respectively calculating updated error values of the edge part and the non-edge part according to different error calculation rules; updating a preset stripe correction model based on a least mean square algorithm and the update error value to obtain a target stripe correction model; and processing the to-be-corrected image according to the target stripe correction model to obtain a target image. The correction algorithm provided by the invention is low in complexity and good in real-time performance, does not need to cache multiple rows of data, and is more favorable for being realized through hardware. According to the method, the edge part and the non-edge part of the image are divided, so that the edge structure of the image can be fully protected, and the transverse and vertical stripes of the infrared image can be corrected more quickly and accurately.

The invention discloses a feed-forward equalizer manufacturing optimization method for a clock data recovery circuit, which relates to the field of high-speed serial communication, and comprises the following steps: based on a short-tap feed-forward equalizer, setting a tap coefficient, and outputting a normalized output discrete signal amplitude; taking the normalized output discrete signal amplitude of the short-tap feed-forward equalizer as an input signal in a constant modulus algorithm updating process, correcting a tap coefficient based on a constant modulus criterion, and optimizing the short-tap feed-forward equalizer under the constant modulus algorithm; based on a short-tap feed-forward equalizer under an optimized constant modulus algorithm, a normalized expected signal amplitude judged by a receiving end judgment device and an instantaneous error between a normalized output discrete signal amplitude and the normalized expected signal amplitude are calculated, and a tap coefficient is further corrected through a minimum mean square algorithm; and optimizing the short-tap feed-forward equalizer under the minimum mean square algorithm.

The application discloses a high-precision torque control method for a permanent magnet motor of a new energy vehicle, constructs a neural network torque observer by taking motor voltage, current, temperature and electric angular velocity as input, performs online parameter identification on the motor by introducing historical data of the motor under different working conditions, and calculates partial derivative information of torque to current by using the identified parameters. The application substitutes the partial derivative information of torque to current and the observed torque of the torque observer into a Taylor formula of the historical data to determine whether the network output is correct, and adjusts the network interior by using a least mean square algorithm, which improves the torque observation precision of the network, solves the shortcoming that the fixed parameters in the traditional network cannot adapt to time-varying working conditions, improves the accuracy and robustness of the network, and is suitable for working conditions of parameter change caused by motor temperature change, torque control precision reduction and MTPA trajectory deviation.

This application relates to the technical field of roll angle measurement, and specifically relates to a dynamic roll angle measurement method based on the change trend of antenna intensity. The method includes: designing a phased array radar beam to track the target of the projectile, obtaining the desired signal in real time, and obtaining the received signal reflected by the antenna on the projectile for each array element after the signal is transmitted; calculating the disorder coefficient of each modal component; determining the interference coefficient of each array element at the current moment; determining the step size parameter of the least mean square algorithm at the current moment; based on the desired signal and the step size parameter, combining the least mean square algorithm, adjusting the beam direction of the transmitted signal of each array element in real time; each array element transmits a signal with the adjusted transmitted signal, and when the signal intensity received by the antenna on the projectile shows a minimum value, the roll angle of the projectile is measured. This application dynamically adjusts the step size parameter in the least mean square algorithm to improve the accurate measurement of the roll angle of the projectile.

The present invention discloses an echo cancellation device based on frequency-domain block IPNLMS, which includes a microphone array, a speech preprocessing module, and an echo cancellation module; based on the microphone array architecture, a beamforming method is used to preprocess the speech signal; the echo cancellation module adopts a block-by-block update method and executes an improved proportionate normalized least mean square algorithm in the frequency domain. The echo cancellation method of the present invention uses the fast Fourier transform to achieve time-domain linear convolution and linear correlation, solves the disadvantage of large computational complexity caused by point-by-point update in the original time-domain calculation, and at the same time introduces a time-varying step factor to have a superior algorithm convergence speed for the echo path with sparse characteristics. In the signal preprocessing stage of the present invention, the microphone array architecture is adopted as the speech acquisition module to achieve the spatial selectivity of the signal, effectively reducing the acoustic echo interference caused by the coupling between the microphone and the speaker.

The invention provides a mechanical arm visual servo control method and system based on a self-learning disturbance observer, and relates to the technical field of robot control. Extracting an image feature vector; performing state estimation through extended Kalman filtering to obtain a filtered image feature state; carrying out online learning through a kernel least mean square algorithm to obtain a disturbance estimation value; generating a nominal control sequence and a nominal state trajectory based on the undisturbed ideal model; and generating a final control instruction according to the deviation and the disturbance estimation value and driving the robotic arm to move. According to the method, the extended Kalman filtering and the kernel minimum mean square algorithm are combined to construct the composite disturbance observer, adaptive online learning and high-precision estimation of unknown disturbance are realized, and a double-layer robust control structure is formed in combination with tubular model predictive control. And the control precision and robust stability of the visual servo system of the mechanical arm in a complex disturbance environment are remarkably improved.

The invention relates to a fault judgment method and system for a wind turbine generator. The method comprises the steps that core electrical parameters and auxiliary environment parameters in the wind turbine generator are collected to serve as an initial data set; preprocessing data in the initial data set by adopting a data abnormal value judgment and data alignment method to obtain a target data set; performing noise reduction on the target data set by adopting an improved minimum mean square algorithm to obtain noise-reduced data; a time-frequency domain feature extraction algorithm is adopted to extract time-frequency domain features of the noise reduction data, and aligned time-frequency features are obtained; a dynamic weight generator based on a long short-term memory network carries out fault discrimination on the time-frequency characteristics; and performing fault judgment processing based on the overall feature data of the wind turbine generator to obtain a predicted fault type corresponding to a power grid where the target wind turbine generator is located. According to the method, the influence of factors such as data acquisition, noise reduction processing and time domain feature fusion is fully considered, the reliability degree of the result is determined, and the effect of accurately reflecting the fault state of the wind turbine generator is achieved.

Wander magnitude measurement by inputting a first input wander frequency into a timing phase locked loop circuit to produce a first output wander frequency, determining two parameters based on the first input wander frequency and the first output wander frequency, and using a least mean square (LMS) algorithm to estimate a wander transfer function based on the two parameters.

The present disclosure provides systems and methods for adjusting the audio output of a wearable device based on a user's audio gain profile. The wearable device may receive an audiogram indicating one or more frequency ranges associated with hearing loss. The wearable device may determine the audio gain profile based on the audiogram. The wearable device may use one or more adjustment modules to determine a gain to apply to the audio output based on the audio gain profile. The adjustment modules may include an active noise control module, a hearing aid module, and a transparency control module. The wearable device may use a least mean square algorithm and / or a machine learning model to determine the amount of gain to apply.

Aiming at the problem of signal nonlinear distortion caused by nonlinear characteristics of a power amplifier in satellite communication, the invention provides a generalized linear dual-mode switching blind equalization algorithm based on a third-order truncated Volterra structure, and the technical scheme and the application effect are expanded according to the following steps S1 to S3: S1: initializing parameters of a generalized linear blind equalizer based on the third-order Volterra structure; s2, taking a modified constant modulus algorithm and a decision-oriented least mean square algorithm in the linear blind equalization field as theoretical basis, combining signal processing characteristics of a third-order truncated Volterra nonlinear model, deriving to obtain two generalized linear equalizer tap updating formulas suitable for the nonlinear model, and providing algorithm support for a dual-mode equalization mechanism; s3, adopting a staged adaptive equalization strategy to construct a switching mechanism: in an equalization initial stage, using a generalized linear correction constant modulus algorithm of a nonlinear channel, and utilizing a rapid convergence characteristic of the algorithm to realize rapid reduction of a mean square error; and when the equalization process reaches a preset judgment condition, automatically switching to a generalized linear judgment guide algorithm of a nonlinear channel, further reducing the steady-state error through judgment feedback, and considering both the convergence speed and the equalization precision.

The invention relates to the technical field of voice processing, in particular to a voice signal enhancement method and system, and the method comprises the steps: obtaining a real-time voice signal and a historical voice signal of a target region, and carrying out the filtering of the real-time voice signal of the target region through an improved least mean square algorithm, so as to enhance the voice quality; a concept of self-adaptive initial step length is introduced, and the step length is dynamically adjusted according to the maximum characteristic value of an autocorrelation matrix of a real-time signal and the step length adjustment degree so as to adapt to the change of a voice signal; the step length adjustment reflects the convergence trend of the voice signal filtering process; the difference degree is used for quantifying the difference between the real-time voice signal and the historical voice signal, the characteristics of the voice signal can be effectively evaluated, and the filtering process is dynamically adjusted. According to the invention, the problem that the existing algorithm is difficult to realize ideal convergence efficiency and filtering effect in different scenes is solved.

Wander magnitude measurement by inputting a first input wander frequency into a timing phase locked loop circuit to produce a first output wander frequency, determining two parameters based on the first input wander frequency and the first output wander frequency, and using a least mean square (LMS) algorithm to estimate a wander transfer function based on the two parameters.