Least Means Squares Research Articles

ANFIS soft sensing model of SMB chromatographic separation process based on new adaptive population evolution particle swarm optimization algorithm

Aiming at predicting the purity of the extract and raffinate components in the simulated moving bed (SMB) chromatographic separation process, a soft-sensor modeling method was proposed by adoptig the hybrid learning algorithm based on an improved particle swarm optimization (PSO) algorithm and the least means squares (LMS) method to optimize the adaptive neural fuzzy inference system (ANFIS) parameters. The hybrid learning algorithm includes a premise parameter learning phase and a conclusion parameter learning phase. In the premise parameter learning stage, the input data space division of the SMB chromatographic separation process and the initialization of the premise parameters are realized based on the fuzzy C-means (FCM) clustering algorithm. Then, the improved PSO algorithm is used to calculate the excitation intensity and normalized excitation intensity of all the rules for each individual in the population. In the conclusion parameter learning phase, these linear parameters are identified by the LMS method. In order to improve population diversity and convergence accuracy, the population evolution rate function was defined. According to the relationship between population diversity, population fitness function and particle position change, a new adaptive population evolution particle swarm optimization (NAPEPSO) algorithm was proposed. The inertia weight is adaptively adjusted according to the evolution of the population and the change of the particle position, thereby improving the diversity of the particle swarm and the ability of the algorithm to jump out of the local optimal solution. The simulation results show that the proposed soft-sensor model can effectively predict the key economic and technical indicators of the SMB chromatographic separation process so as to meet the real-time and efficient operation of the SMB chromatographic separation process.

Optimal preconditioned regularization of least mean squares algorithm for robust online learning1

Despite its low computational cost, and steady state behavior, some well known drawbacks of the least means squares (LMS) algorithm are: slow rate of convergence and unstable behaviour for ill conditioned autocorrelation matrices of input signals. Several modified algorithms have been presented with better convergence speed, however most of these algorithms are expensive in terms of computational cost and time, and sometimes deviate from optimal Wiener solution that results in a biased solution of online estimation problem. In this paper, the inverse Cholesky factor of the input autocorrelation matrix is optimized to pre-whiten input signals and improve the robustness of the LMS algorithm. Furthermore, in order to have an unbiased solution, mean squares deviation (MSD) is minimized by improving convergence in misalignment. This is done by regularizing step-size adaptively in each iteration that helps in developing a highly efficient optimal preconditioned regularized LMS (OPRLMS) algorithm with adaptive step-size. Comparison of OPRLMS algorithm with other LMS based algorithms is given for unknown system identification and noise cancelation from ECG signal, that results in preference of the proposed algorithm over the other variants of LMS algorithm.

Adaptive beamforming for moving targets using Genetic Algorithms

Beamforming is a property of smart antennas that provides improvements on all sorts of wireless communications but it still requires studies. Generally, Least Means Squares (LMS) algorithm provides good adaptive beamforming and target tracking capability for an array of antennas since a reference signal is known. However, Genetic Algorithms (GA) have proven to perform very well in global optimization tasks, including blind DOA estimation. This works investigates the use of GA for beamforming on a CDMA environment under different Signal-to-Noise Ratios (SNR), considering a reference signal is known. Computational experiments on static targets have shown that, when compared to LMS, GA allow dramatically reducing the threshold SNR of performance for both correlated and uncorrelated interfering sources. On the other hand, LMS provided zero Root-Mean-Square-Errors (RMSE) at high SNR. Additionally, the performance has shown to be acceptable even for moving targets.

The Integrated Simulation on Power Source of Hydraulic Hybrid Vehicle

In order to improve the fuel economy and to reduce emission of the hybrid vehicle, according to between the power flow distribution and energy utilization with two or more than two of energy flow of the hybrid vehicle, was put forward to use adaptive fuzzy control law and LMS (Least Means Squares) to integrated simulate for power source. Pinciple of control algorithm was introduced, and control rules have been setup. Through the case to verify, the simulation results show that the control method is reliable and effective, and the disturbance input of the same torque, fluctuation of the engine rotation is from the 15% by traditional method down to 1.2%~3% and the operation area of the power source is mainly located in the economic area.

High Speed FPGA-Based Implementations of Delayed-LMS Filters

A variation of the least means squares (LMS) algorithm, called the delayed LMS (DLMS) algorithm is ideally suited for highly pipelined, adaptive digital filter implementations. In this paper, we present an efficient method to determine the delays in the DLMS filter. Furthermore, in order to achieve fully pipelined circuit architectures for FPGA implementation, we transfer these delays using retiming. The method has been used to derive a series of retimed delayed LMS (RDLMS) architectures, which allow a 66.7% reduction in delays and 5 times faster convergence time thereby giving superior performance in terms of throughput rate when compared to previous work. Three circuit architectures and three hardware shared versions are presented which have been implemented using the Virtex-II FPGA technology resulting in a throughput rate of 182 Msample/s.

Adaptive multiuser interference suppression for CDM satellite systems

This paper deals with the application of linear multiuser detectors in mobile terminals of code division multiplex (CDM) satellite systems. Generally, the interference caused by other users can dramatically reduce the performance of a CDM-based satellite communication system. Since no spreading information of other users is given and since there are no trainings sequences, a blind adaptive interference detector with low complexity is needed. In order to implement a detector for suppressing the interference from other users, different adaptation algorithms can be applied. Comparing them in terms of complexity, adaptation speed and bit error rate, the LMS (least means squares) algorithm with adaptive step size shows the best performance. However, the implementation of this algorithm in a satellite system still leads to high processing times. For this reason, two new schemes with reduced complexity are presented. A very important point is, that the adaptive algorithms are basically restricted to short spreading codes (code period equals symbol period) whereas CDM-based satellite systems use long codes (one-code period spans over multiple symbols). In this paper, a new scheme is presented which can be applied to long codes. By cascading multiple separated interference detectors the adaptation is done every time when the same part of the spreading sequence appears. Copyright © 2005 John Wiley & Sons, Ltd.

Echocardiographic Image Sequence Segmentation and Analysis Using Self-Organizing Maps

This paper presents a new approach for echocardiographic image sequence segmentation and a practical application of the results. The proposed method uses the self-organizing maps to approximate the probability density function of the image patterns. The map is post-processed, by the k-means clustering algorithm, in order to detect groups of neurons whose weights are similar. Each segmented image of the sequence is generated by correlating its pixels and clusters found in the map. The image sequence segmented was used to measure fetal heart structures. To refine the measurements we used a border detection technique based on the least-means squares error. The segmentation procedure was validated successfully by physicians.