Showing papers on "Recursive least squares filter published in 2021"

Hierarchical recursive signal modeling for multifrequency signals based on discrete measured data

Abstract: This paper studies the problem of parameter estimation for the multifrequency sine signals, which have multiple characteristic parameters such as the amplitudes, phases, and frequencies. I...

Data-Driven Recursive Least Squares Estimation for Model Predictive Current Control of Permanent Magnet Synchronous Motors

Abstract: The performance of model predictive controllers (MPC) strongly depends on the quality of their models. In the field of electric drive control, white-box (WB) modeling approaches derived from first-order physical principles are most common. This procedure typically does not cover parasitic effects and often comes with parameter deviations. These issues are particularly crucial in the domain of self-commissioning drives where a hand-tailored, accurate WB plant model is not available. In order to compensate for such modeling errors and, consequently, to improve the control performance during transients and steady state, this article proposes a data-driven, real-time capable recursive least squares estimation method for the current control of a permanent magnet synchronous motor. Following this machine learning approach, the effect of the flux linkage and voltage harmonics due to the winding scheme can also be taken into account through suitable feature engineering. Moreover, a compensating scheme for the interlocking time of the inverter is proposed. The resulting algorithm is investigated using the well-known finite-control-set MPC (FCS-MPC) in the rotor-oriented coordinate system. The extensive experimental results show the superior performance of the presented scheme compared to a FCS-MPC-based on a state-of-the-art WB motor model using look-up tables for adressing (cross-)saturation.

Adaptive fuzzy modeling of interval-valued stream data and application in cryptocurrencies prediction

Abstract: This paper introduces an adaptive interval fuzzy modeling method using participatory learning and interval-valued stream data. The model is a collection of fuzzy functional rules in which the rule base structure and the parameters of the rules evolve simultaneously as data are input. The evolving nature of the method allows continuous model adaptation using the stream interval input data. The method employs participatory learning to cluster the interval input data recursively, constructs a fuzzy rule for each cluster, uses the weighted recursive least squares to update the parameters of the rule consequent intervals, and returns an interval-valued output. The method is evaluated using actual data to model and forecast the daily lowest and highest prices of the four most traded cryptocurrencies, BitCoin, Ethereum, XRP, and LiteCoin. The performance of the adaptive interval fuzzy modeling is compared with the adaptive neuro-fuzzy inference system, long short-term memory neural network, autoregressive integrated moving average, exponential smoothing state model, and the naive random walk methods. Results show that the suggested interval fuzzy model outperforms all these methods in predicting prices in the digital coin market, especially when considering directional accuracy measure.

A Vehicle Rollover Evaluation System Based on Enabling State and Parameter Estimation

Abstract: There is an increasing awareness of the need to reduce the traffic accidents and fatality rates due to vehicle rollover incidents. The accurate detection of impending rollover is necessary to effectively implement vehicle rollover prevention. To this end, a real-time rollover index and a rollover tendency evaluation system are needed. These should give high accuracy and be of a low application cost. In this article, we propose a rollover evaluation system taking lateral load transfer ratio (LTR) as the rollover index with inertial measurement unit as the system input. A nonlinear suspension model and a rolling plane vehicle model are established for the state and parameter estimation. An adaptive extended Kalman filter is utilized to estimate the roll angle and rate, which adjusts noise covariance matrices to accommodate the nonlinear model characteristic and the unknown noise characteristic. In the meantime, the forgetting factor recursive least squares method is utilized to identify the height of the center of gravity. The Butterworth filter is used to filter out the high-frequency noise of the acceleration signal and the index of LTR is accordingly calculated based on the estimation results. The proposed scheme is verified and compared through hardware-in-loop tests. The results show that the developed scheme performs well in a variety of operating conditions.

Filtering-based recursive least squares estimation approaches for multivariate equation-error systems by using the multiinnovation theory

Abstract: Summary This article researches the filtering‐based parameter estimation issues for a class of multivariate control systems with colored noise. A filtering‐based recursive generalized extended leas...

A novel one-way transmitted co-estimation framework for capacity and state-of-charge of lithium-ion battery based on double adaptive extended Kalman filters

Abstract: Precise capacity and state-of-charge (SOC) estimation is crucial to assure safe and reliable operation of lithium-ion battery. To lower the influence of cross interference between these two estimated states and possible divergence existing in two-way transmitted co-estimation framework, a novel double adaptive extended Kalman filters (AEKFs) based one-way transmitted co-estimation framework for capacity and SOC is proposed in this paper. Firstly, the model parameters of the first-order RC model and open-circuit-voltage (OCV) are online obtained by forgetting factor recursive least squares. With the first derivative of OCV versus SOC, the SOC inferred through OCV-SOC table and identified parameters are inputted into AEKF1 to online estimate capacity. Subsequently, estimated capacity is further transmitted into AEKF2 to predict SOC. By simulation driving cycles, the proposed co-estimation framework is compared with AEKF based SOC algorithm without capacity calibration, whose results indicate that the presented algorithm can lower the impact of inaccurate initial capacity value on SOC estimation to more effectively track SOC. Moreover, through robustness analysis results, it is clearly found that initial erroneous SOC values will not influence capacity estimation results due to the one-way transmitted characteristic of the proposed co-estimation framework and SOC can still be estimated accurately and robustly.

An Enhanced Equivalent Circuit Model with Real-Time Parameter Identification for Battery State-of-Charge Estimation

Abstract: This paper introduces an efficient modeling approach based on Wiener structure to reinforce the capacity of the classical Equivalent Circuit Models (ECMs) in capturing the nonlinearities of Lithium-ion (Li-ion) batteries. The proposed block-oriented modeling architecture is composed of a simple linear ECM followed by a static output nonlinearity block, which helps achieving a superior nonlinear mapping property while maintaining the real-time efficiency. The observability of the established battery model is analytically proven. This paper also introduces an efficient parameter estimator based on extended-kernel iterative recursive least squares algorithm for real-time estimation of the parameters of the proposed Wiener model. The proposed approach is applied for state-of-charge (SoC) estimation of 3.4 Ah 3.6 V NMC-based Li-ion cells using the extended Kalman filter (EKF). The results show about 1.5% improvement in SoC estimation accuracy compared with the EKF algorithm based on second-order ECM. A series of real-time tests are also carried out to demonstrate the computational efficiency of the proposed method.

Parameter Identification and State-of-Charge Estimation for Lithium-Ion Batteries Using Separated Time Scales and Extended Kalman Filter

Abstract: With the development of new energy vehicle technology, battery management systems used to monitor the state of the battery have been widely researched. The accuracy of the battery status assessment to a great extent depends on the accuracy of the battery model parameters. This paper proposes an improved method for parameter identification and state-of-charge (SOC) estimation for lithium-ion batteries. Using a two-order equivalent circuit model, the battery model is divided into two parts based on fast dynamics and slow dynamics. The recursive least squares method is used to identify parameters of the battery, and then the SOC and the open-circuit voltage of the model is estimated with the extended Kalman filter. The two-module voltages are calculated using estimated open circuit voltage and initial parameters, and model parameters are constantly updated during iteration. The proposed method can be used to estimate the parameters and the SOC in real time, which does not need to know the state of SOC and the value of open circuit voltage in advance. The method is tested using data from dynamic stress tests, the root means squared error of the accuracy of the prediction model is about 0.01 V, and the average SOC estimation error is 0.0139. Results indicate that the method has higher accuracy in offline parameter identification and online state estimation than traditional recursive least squares methods.

FPGA-Based Sliding-Mode Predictive Control for PMSM Speed Regulation System Using an Adaptive Ultralocal Model

Abstract: An adaptive ultralocal surface-mounted permanent-magnet synchronous motor (SPMSM)-model-based continuous control set sliding-mode predictive speed control (UL-MPSC) is proposed in this article to promote the performance of conventional continuous control set model-based predictive speed control (CCS-MPSC). First, an ultralocal SPMSM model that represents the lumped disturbance as the sum of rotor speed, and $q$ -axis current is provided. A forgetting factor recursive least squares identification technique is utilized to identify the coefficients of the rotor speed, and $q$ -axis current, simultaneously. Consequently, the ultralocal SPMSM model is updated at each sampling time, according to I/O data. Second, unlike the error-prediction-based cost function, a fast terminal sliding-mode-based cost function is designed by applying a linear sliding-mode surface, and a fast terminal reaching law. Finally, a discrete-time integral sliding-mode predictive observer is developed to realize a one-step prediction of the reference speed, which is fed forward to the predefined cost function. Experiments of the minimum controller synthesis algorithm, discrete-time integral sliding-mode load torque observer-based CCS-MPSC, and UL-MPSC have been carried out on a field-programmable gate array-based hardware prototype, and the experimental results validate the excellent performance of the UL-MPSC strategy.

Co-estimation for capacity and state of charge for lithium-ion batteries using improved adaptive extended Kalman filter

Abstract: Accurate state of charge (SOC) plays a dominant role in safety control and energy management of battery system. In this paper, an improved adaptive extended Kalman filter (IAEKF) is proposed to realize co-estimation for battery capacity and SOC. Firstly, the online OCV identified by forgetting factor recursive least squares (FFRLS) is innovatively regarded as the observation state, so battery capacity and SOC can be integrated into a second-order filter to realize co-estimation. Secondly, the polynomial relationship between OCV, SOC and T is established to enhance the temperature adaptability of IAEKF. Notably, compared with adaptive extended Kalman filter (AEKF), IAEKF can improve estimation process by producing a fast convergence speed under big OCV errors and assure a slower slope when the OCV errors turn small. Besides, the forgetting factor can simplify the moving window of the adaptive update for the process and measurement noise. With sophisticated Federal Urban Driving Schedule test (FUDS), the estimation accuracy of the proposed algorithm is verified with SOC error band controlled between ±1.2% within the first 50s and relative capacity error limited within 2% after convergence. Furthermore, the verification results also prove that IAEKF based co-estimation algorithm has strong anti-interference ability when facing disturbance, including erroneous initial SOC settings, unknown battery capacity and various ambient temperatures.

An extended Kalman filter based data-driven method for state of charge estimation of Li-ion batteries

Abstract: Accurate state-of-charge (SOC) estimation, which can effectively prevent battery overcharge and over-discharge, provide accurate driving range and extend battery life, is challenging due to complicated battery dynamics and ever-changing ambient conditions In this paper, an extended Kalman filter (EKF) based data-driven method for SOC estimation of Li-ion Batteries is proposed First, the model characteristic parameters are dynamically tracked through the recursive least squares method Then the filtered output result of the EKF algorithm reflecting the battery's dynamic characteristics is used as the training data of the extreme gradient boosting (XGBoost) model Benefit from the XGBoost's excellent machine learning and predictive capabilities, which can realize the battery's SOC high-precision prediction based on the EKF algorithm The simulation results show that the proposed XGBoost algorithm, especially in the low SOC range, has good convergence and robustness, compared with the EKF algorithm and the library for support vector machines (LIBSVM) algorithm The algorithm is validated under different driving conditions and achieves accurate SOC estimation with a maximum absolute error of less than 2% It can be easily concluded that the proposed method can achieve accurate and stable SOC estimation, effectively avoiding the open-loop risk of data-driven algorithms

Online Parameter Estimation Methods for Adaptive Cruise Control Systems

Abstract: Modeling Adaptive Cruise Control (ACC) vehicles enables the understanding of the impact of these vehicles on traffic flow. In this work, two online methods are used to provide real time system identification of ACC enabled vehicles. The first technique is a recursive least squares (RLS) approach, while the second method solves a nonlinear joint state and parameter estimation problem via particle filtering (PF). We provide a parameter identifiability analysis for both methods to analytically show that the model parameters are not identifiable using equilibrium driving. The accuracy and computational runtime of the online methods are compared to a commonly used offline simulation-based optimization (i.e., batch optimization) approach. The methods are tested on synthetic data as well as on empirical data collected directly from a 2019 model year ACC vehicle using data from sensors that are part of the stock ACC system. The online methods are scalable and provide comparable accuracy to the batch method. RLS runs in real time and is two orders of magnitude faster than the batch method for modest sized (e.g., 15 min) datasets. The particle filter also runs in real-time, and is also suitable in streaming applications in which the datasets can grow arbitrarily large.

A Lagrange multiplier and sigma point Kalman filter based fused methodology for online state of charge estimation of lithium-ion batteries

Abstract: In this paper, a fused online approach consisting of the Lagrange multiplier technique and sigma point Kalman filter (SPKF) is proposed for the lithium-ion battery model identification and state of charge (SOC) estimation, respectively. The Lagrange multiplier technique minimized the error between the reference and estimated SOC by estimating the accurate battery parameters, whereas SPKF helps to calculate non-linear system dynamics more precisely. The effectiveness of the proposed technique is evaluated using different publicly available experimental profiles such as the Beijing dynamic stress test, dynamic stress test, and hybrid pulse power characteristics. The effect of sensor accuracy on the SOC estimation is also analyzed. The comparative analysis reveals that the proposed methodology yields better performance than recursive least squares (RLS)-SPKF and forgetting factor RLS-SPKF. The maximum noted errors for the proposed technique were 0.2%, 0.4%, and 0.9% for hybrid pulse power characteristics, dynamic stress test, and Beijing dynamic stress test, respectively. The improvement in SOC estimation accuracy shows the effectiveness, superiority, and distinctiveness of the proposed approach.

State-of-charge estimation for the lithium-ion battery based on adaptive extended Kalman filter using improved parameter identification

Abstract: The state of charge(SOC) of lithium-ion battery is an essential parameter of battery management system. Accurate estimation of SOC is conducive to give full play to the capacity and performance of the battery. For the problems of selection of forgetting factor and poor robustness and susceptibility to the noise of extended Kalman filtering algorithm, this paper proposes a SOC estimation method for the lithium-ion battery based on adaptive extended Kalman filter using improved parameter identification. Firstly, the Thevenin equivalent circuit model is established and the recursive least squares with forgetting factor(FFRLS) method is used to achieve the parameter identification. Secondly, an evaluation factor is defined, and fuzzy control is used to realize the mapping between the evaluation factor and the correction value of forgetting factor, so as to realize the adaptive adjustment of forgetting factor. Finally, the noise adaptive algorithm is introduced into the extended Kalman filtering algorithm(AEKF) to estimate the SOC based on the identification results, which is applied to the parameter identification at the next time and executed circularly, so as to realize the accurate estimation of SOC. The experimental results show that the proposed method has good robustness and estimation accuracy compared with other filtering algorithms under different working conditions, state of health(SOH) and temperature.

Online Smart Meter Measurement Error Estimation Based on EKF and LMRLS Method

Abstract: This paper presents an online smart meter measurement error estimation algorithm. Extended Kalman filter (EKF) and limit memory recursive least square (LMRLS) methods are used for remote calibration of a large amount of user-side smart meters. Then, a modified joint estimation model is obtained by selecting the estimation step that conforms to the actual working condition and filtering the abnormal estimation value according to the line loss rate characteristics. Finally, based on the experimental data obtained by the program-controlled load simulation system, the precision of metering error estimation is verified. The results show that the method improves the precision of error estimation by analyzing the coupling between line loss rates and metering error estimation. By using the limited memory RLS algorithm, the influence of old measured data on error parameter estimation is reduced so that new data can be added to correct error parameter estimation to enhance the precision of the real-time smart meter error estimation.

On the Identification of Electrical Equivalent Circuit Models Based on Noisy Measurements

Abstract: Real-time identification of electrical equivalent circuit models (ECMs) is a critical requirement in many practical systems, such as batteries and electric motors. Significant work has been done in the past developing different types of algorithms for system identification using reduced-order ECMs. However, little work was done in analyzing the theoretical performance bounds of these system identification approaches. Given that both voltage and current are measured with error, proper understanding of theoretical bounds will help in designing a system that is economical in cost and robust in performance. In this article, we analyze the performance of a linear recursive least squares (RLS) approach to ECM identification and show that the LS approach is both unbiased and efficient when the signal-to-noise ratio is high enough. However, we show that when the signal-to-noise ratio is low–resembling the case in many practical applications–the LS estimator becomes significantly biased. Consequently, we develop a parameter estimation approach based on the total LS method and show it to be asymptotically unbiased and efficient at practically low signal-to-noise ratio regions. Further, we develop a recursive implementation of the total least square algorithm and find it to be slow to converge; for this, we employ a Kalman filter to improve the convergence speed of the total LS method. The resulting total Kalman filter (TKF) is shown to be both unbiased and efficient in ECM identification. The performance of this filter is analyzed using real-world current profiles under fluctuating signal-to-noise ratios. Finally, the applicability of the algorithms and analysis in this article in identifying higher-order electrical ECMs is explained.

Lithium-Sulfur Cell State of Charge Estimation Using a Classification Technique

Abstract: Lithium-Sulfur (Li-S) batteries are a promising next-generation technology providing high gravimetric energy density compared to existing lithium-ion (Li-ion) technologies in the market. The literature shows that in Li-S, estimation of state of charge (SoC) is a demanding task, in particular due to a large flat section in the voltage-SoC curve. This study proposes a new SoC estimator using an online parameter identification method in conjunction with a classification technique. This study investigates a new prototype Li-S cell. Experimental characterization tests are conducted under various conditions; the duty cycle – intended to represent a real-world application – is based on an electric city bus. The characterization results are then used to parameterize an equivalent-circuit-network (ECN) model, which is then used to relate real-time parameter estimates derived using a Recursive Least Squares (RLS) algorithm to state of charge using a Support Vector Machine (SVM) classifier to estimate an approximate SoC range. The estimate is used together with a conventional coulomb-counting technique to achieve continuous SoC estimation in real-time. It is shown that this method can provide an acceptable level of accuracy with less than 3% error under realistic driving conditions.

Channel State Information Prediction for Adaptive Underwater Acoustic Downlink OFDMA System: Deep Neural Networks Based Approach

Abstract: In underwater acoustic (UWA) adaptive communication system, due to time-varying channel, the transmitter often has outdated channel state information (CSI), which results in low efficiency. UWA channels are much more difficult to estimate and predict than terrestrial wireless channels, given the more severe multipath environments with varying propagation speeds in different locations, non-linear propagation paths, several-order higher propagation latency, mobile transceiver and obstacles in the sea, etc. To handle the complexity, this paper proposes an efficient online CSI prediction model for UWA CSI prediction considering the complicated correlationship of UWA channels in both the time and frequency domains. This paper designs a learning model called CsiPreNet, which is an integration of a one-dimensional convolutional neural network (CNN) and a long short term memory (LSTM) network. The performance is compared with the widely used recursive least squares (RLS) predictor, the approximate linear dependency recursive kernel least-squares (ALD-KRLS), and two common conventional deep neural networks (DNN) predictors, i.e., back propagation neural network (BPNN) and LSTM network using the measured data recorded in the South China Sea. To validate the efficacy of prediction, we investigate the prediction of CSI in simulated downlink UWA orthogonal frequency division multiple access (OFDMA) systems. Specifically, the measured UWA channel is used in the OFDMA system. A joint subcarrier-bit-power adaptive allocation scheme is used for resource allocation. To further improve the performance, we develop an offline-online prediction scheme, enabling the prediction results to be more stable. Simulation and experimental results show that the CsiPreNet has superior performance than the existing solutions, thanks to its capability in capturing both the temporal and frequency correlation of the UWA CSIs.

State of charge estimation of power lithium-ion battery based on an adaptive time scale dual extend Kalman filtering

Abstract: In this paper, we introduce the Unsymmetrical Thevenin model, an improved equivalent circuit model to obtain a more precise SOC estimation. We first propose an Auto-tuning Multiple Forgetting Factors Recursive Least Squares (AMFFRLS) for model parameter identification, then, we proposed an Adaptive Time Scale Dual Extend Kalman Filtering (ATSDEKF) to update the model parameters and Sliding Window Forgetting Factor Approximate Total Recursive Least Squares (SWFFATRLS) to update the maximum available capacity of a lithium-ion battery to obtain more accurate state of charge (SOC) estimation. Numerical experiments demonstrate that the proposed method can get better SOC estimation results compare to the traditional ones. Except for extreme temperatures, such as at 0 °C, the root mean square error (RMSE) of the Unsymmetrical Thevenin model is below 1.2%, which is much smaller than the most common Thevenin model with fixed parameters based on Extend Kalman Filtering (EKF).

Comparing Methods of DC Motor Control for UUVs

Abstract: Adaptive and learning methods are proposed and compared to control DC motors actuating control surfaces of unmanned underwater vehicles. One type of adaption method referred to as model-following is based on algebraic design, and it is analyzed in conjunction with parameter estimation methods such as recursive least squares, extended least squares, and batch least squares. Another approach referred to as deterministic artificial intelligence uses the process dynamics defined by physics to control output to track a necessarily specified autonomous trajectory (sinusoidal versions implemented here). In addition, one instantiation of deterministic artificial intelligence uses 2-norm optimal feedback learning of parameters to modify the control signal, while another instantiation is presented with proportional plus derivative adaption. Model-following and deterministic artificial intelligence are simulated, and respective performance metrics for transient response and input tracking are evaluated and compared. Deterministic artificial intelligence outperformed the model-following approach in minimal peak transient value by a percent range of approximately 2–70%, but model-following achieved at least 29% less error in input tracking than deterministic artificial intelligence. This result is surprising and not in accordance with the recently published literature, and the explanation of the difference is theorized to be efficacy with discretized implementations.

Parameter Identification Based Online Noninvasive Estimation of Rotor Temperature in Induction Motors

Abstract: This article proposes a rotor temperature estimation method for in-service induction machine (IM) based on parameter identification, which combines the advantage of recursive least squares (RLS) and model reference adaptive system (MRAS). The RLS with forgetting factor is firstly adopted to identify the parameters of motor inductances. Then, the online identification of rotor resistance can be realized via the MRAS based on the instantaneous reactive power (IRP) and the proportional integral (PI) regulation adaptive law designed by the Popov hyper-stable theory. Thereby, the rotor temperature is computed by the resistance-temperature relationship of metals. To achieve a fast convergence of the parameter identification, rotor slot harmonics are extracted from the stator current and used to determine the rotor speed in real time. Furthermore, to obtain a more accurate initial value of the stator and rotor leakage inductances and resistances, the first 5–15 cycles of the IM starting process are used to mimic the locked-rotor test condition. A merit of the proposed method is that it requires significantly less time to estimate the rotor resistance than the traditional methods. With a novel test bench, experimental validation is performed on a 22-kW IM. In the test, the rotor temperature is measured in three different ways: 1) wireless sensors inserted in the rotating rotor core and rotor end rings, 2) infrared sensor for rotor end ring temperature, and 3) PT100 installed in stator end winding. With this test bench, the real rotor temperature was revealed, and the effectiveness of the presented method is also verified.

A Battery SOC Estimation Method Based on AFFRLS-EKF.

Abstract: The lithium-ion battery is the key power source of a hybrid vehicle. Accurate real-time state of charge (SOC) acquisition is the basis of the safe operation of vehicles. In actual conditions, the lithium-ion battery is a complex dynamic system, and it is tough to model it accurately, which leads to the estimation deviation of the battery SOC. Recursive least squares (RLS) algorithm with fixed forgetting factor is widely used in parameter identification, but it lacks sufficient robustness and accuracy when battery charge and discharge conditions change suddenly. In this paper, we proposed an adaptive forgetting factor regression least-squares–extended Kalman filter (AFFRLS–EKF) SOC estimation strategy by designing the forgetting factor of least squares algorithm to improve the accuracy of SOC estimation under the change of battery charge and discharge conditions. The simulation results show that the SOC estimation strategy of the AFFRLS–EKF based on accurate modeling can effectively improve the estimation accuracy of SOC.

Hybrid Dy-NFIS & RLS equalization for ZCC code in optical-CDMA over multi-mode optical fiber

Abstract: For long haul coherent optical fiber communication systems, it is significant to precisely monitor the quality of transmission links and optical signals. The channel capacity beyond Shannon limit of Single-mode optical fiber (SMOF) is achieved with the help of Multi-mode optical fiber (MMOF), where the signal is multiplexed in different spatial modes. To increase single-mode transmission capacity and to avoid a foreseen “capacity crunch”, researchers have been motivated to employ MMOF as an alternative. Furthermore, different multiplexing techniques could be applied in MMOF to improve the communication system. One of these techniques is the Optical Code Division Multiple Access (Optical-CDMA), which simplifies and decentralizes network controls to improve spectral efficiency and information security increasing flexibility in bandwidth granularity. This technique also allows synchronous and simultaneous transmission medium to be shared by many users. However, during the propagation of the data over the MMOF based on Optical-CDMA, an inevitable encountered issue is pulse dispersion, nonlinearity and MAI due to mode coupling. Moreover, pulse dispersion, nonlinearity and MAI are significant aspects for the evaluation of the performance of high-speed MMOF communication systems based on Optical-CDMA. This work suggests a hybrid algorithm based on nonlinear algorithm (Dynamic evolving neural fuzzy inference (Dy-NFIS)) and linear algorithm (Recursive least squares (RLS)) equalization for ZCC code in Optical-CDMA over MMOF. Root mean squared error (RMSE), mean squared error (MSE) and Structural Similarity index (SSIM) are used to measure performance results.