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A class of pso-tuned controllers in Lorenz chaotic system

MATHEMATICS AND COMPUTERS IN SIMULATION 2023年 204卷 430-449页

作者： Dali, Ali Abdelmalek, Samir Bakdi, Azzeddine Bettayeb, Maamar CDER Ctr Dev Energies Renouvelables BP 62 Route Observ Bouzareah 16340 Alger Algeria Univ Medea Fac Technol Dept Elect Engn Medea Algeria Univ Oslo Dept Math N-0851 Oslo Norway Univ Sharjah Dept Elect Engn Sharjah U Arab Emirates King Abdulaziz Univ Ctr Excellence Intelligent Engn Syst CEIES Jeddah Saudi Arabia

This paper considers the optimal robust control of chaotic systems subject to parameter perturbations and measurement noise. Three novel dynamic tracking controllers are designed herein to suppress the chaotic behaviour in Lorenz systems under practical constraints. The dynamic controllers allow for extra improvements towards optimal and robust tracking but their design is challenging in the chaotic system. Given a single measured state only, the single-state feedback controllers are optimally tuned using high-performance heuristic Particle Swarm Optimization (pso) algorithm with a constrained multiobjective function focusing mainly on the Integral Absolute Error (IAE). The controlled system stability is ensured through Routh-Hurwitz stability criteria where control parameters are further tuned using a metaheuristic pso algorithm to ensure faster transients with minimum errors when tracking desired state trajectories and also to ensure robustness to parameter variations and external disturbances in the chaotic system. Different designs are tested through extensive simulations and comparisons where the proposed pso-tuned PD-tracking controller is proved superior for not only suppressing the chaotic behaviour and reducing the transients but also for their high robustness to parameter uncertainties and external disturbances. (c) 2022 International Association for Mathematics and Computers in Simulation (IMACS). Published by Elsevier B.V. All rights reserved.

关键词： Chaos Lorenz systems Tracking control pso algorithm Robustness

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An Effective pso-LSSVM-Based Approach for Surface Roughness Prediction in High-Speed Precision Milling

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IEEE ACCESS 2021年 9卷 80006-80014页

作者： Li, Bo Tian, Xitian Northwestern Polytech Univ Sch Mech Engn Xian 710072 Peoples R China Northwestern Polytech Univ Inst Intelligent Mfg Xian 710072 Peoples R China

Surface roughness is one of the important indicators to measure the surface quality of parts processed, in addition to the cutting parameters affecting the surface roughness, the inevitable tool wear during the cutting process also makes the surface roughness constantly changing. In order to achieve high-precision prediction of machined surface roughness, a high-speed precision milling surface roughness prediction method based on particle swarm optimization least squares support vector machine (pso-LSSVM) is proposed in this paper. The prediction method uses standardized cutting parameters and tool wear as the input variables, and uses the LSSVM algorithm to model the relationship between the input variables and the surface roughness, the improved pso algorithm is used to optimize the hyperparameters of LSSVM so as to improve the generalization ability. In order to verify the effectiveness and superiority of pso-LSSVM predictive performance, two surface roughness prediction models have been developed based on support vector machine (SVM) and response surface method (RSM), respectively. With the same sample conditions, average relative error and root mean square error of pso-LSSVM prediction model are the smallest, and the correlation coefficient of pso-LSSVM method is the largest. It is indicate that the surface roughness prediction accuracy and generalization ability based on pso-LSSVM are the best under different cutting parameters.

关键词： Surface roughness Rough surfaces Surface treatment Predictive models Tools Response surface methodology Milling Surface roughness prediction least squares support vector machine pso algorithm precision milling

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Automatic speech patterns recognition of commands using SVM and pso

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MULTIMEDIA TOOLS AND APPLICATIONS 2019年第22期78卷 31709-31731页

作者： Batista, Gracieth Cavalcanti Silva, Washington Luis Santos de Oliveira, Duarte Lopes Saotome, Osamu Technol Inst Aeronaut 50 Vila Acacias Sao Jose Dos Campos SP Brazil Fed Inst Maranhao 4 Monte Castelo Sao Luis MA Brazil

This paper proposes the implementation of an Automatic Speech Recognition (ASR) process through extraction of Mel-Frequency Cepstral Coefficients (MFCCs) from voice signal commands, application of the Discrete Cosine Transform (DCT) in these coefficients, Support Vector Machine (SVM) training optimized by the Particle Swarm Optimization (pso) technique in order to speed up the whole process and using One Against All (OAA) multiclass SVM classification. The main contribution is in training phase that it is the combination of SVM with pso algorithm, resulting in computational load and processing time reduction. This novel algorithm is called here as pso-SVM hybrid training application and its performance is shown as the experimental results of voice signal commands in Brazilian Portuguese language. Such commands comprise 10 isolated digits (from zero to nine) and 20 action commands such as "go ahead", "finish", "pause", etc.;that is, there are 30 different pattern types (classes) to be separated (recognized). The process is speaker independent type, that is, the voice bank used in training is different from the one used in tests. The obtained results presented success rates of 92% to 99% during the tests for the classifier using RBF kernel function. Besides, the comparison section shows that this technique is 25 times faster than the recognition without optimization and also, it presents 10% of improvement in recognition success rate when compared to the well-known technique, Gaussian Mixture Models (GMM) algorithm. In addition, the proposed algorithm can be applied in any data processing board for voice signals (DSP, FPGA, DSPIC, ...).

关键词： pso-SVM hybrid training pso algorithm SVM multiclass Speech patterns recognition

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The optimal control synchronization of complex dynamical networks with time-varying delay using pso

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NEUROCOMPUTING 2019年 333卷 1-10页

作者： Chang, Qi Yang, Yongqing Sui, Xin Shi, Zhicheng Jiangnan Univ Wuxi Engn Res Ctr Biocomp Sch Sci Wuxi 214122 Peoples R China Jiangnan Univ Sch IoT Engn Wuxi 214122 Peoples R China

The paper mainly deals with the exponential synchronization of complex dynamical networks with a time-varying delay. Based on the event-triggered control, some sufficient conditions for exponential synchronization of complex dynamical networks can be achieved by using the Lyapunov function method, along with the Linear Matrix Inequality (LMI) and matrix analysis. The Zeno-behavior is excluded for the designed event-trigger controller. The optimal control parameters satisfying a minimal integral square error (ISE) index and control energy can be computed by using the particle swarm optimization (pso) algorithm. A numerical example is given to demonstrate the effectiveness and applicability of the proposed approach. (C) 2018 Published by Elsevier B.V.

关键词： Exponential synchronization Complex dynamical networks Event-triggered control Optimal control pso algorithm

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Development of GWO-DSO and pso-DSO hybrid models to redesign the optimal dimensions of labyrinth spillway

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SOFT COMPUTING 2019年第15期23卷 6391-6406页

作者： Tabari, Mahmoud Mohammad Rezapour Hashempour, Masoumeh Shahrekord Univ Dept Engn Shahrekord Iran

In this study, considering the importance of dams spillways redesign to provide optimal dimensions, the hybrid of particle swarm optimization, gray wolf optimization (GWO) and direct search optimization meta-heuristic approach were proposed. In this methodology, the total volume of spillway body, which indicates the amount of concrete and cost, is considered as the objective function and height of spillway, the number of cycles, apex length and angle of wall are defined as decision variables. By implementing the proposed model based on the data of the Ute dam labyrinth spillway, the optimal dimensions of spillway were determined and compared with the other studies. The results indicated that the hybrid of meta-heuristic algorithms has a very good performance in generating global optimal values, except that the GWO algorithm has a higher convergence rate. In the proposed method, the optimal dimensions provided by the hybrid algorithm led to saving in concrete and reduction of costs to 64% as compared to the existing design of dam. Investigating the optimal dimension of trapezoidal labyrinth spillway also indicates that these dimensions increase the flow capacity of the spillway to 15,760 m3/s. Finally, comparison of the optimum dimensions obtained with two trapezoidal and triangular sections showed that the optimal form for labyrinth spillway is triangular and leads to improved spillway hydraulic performance.

关键词： Labyrinth spillway Optimization of labyrinth dimensions pso algorithm Gray wolves algorithm Ute dam

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Self-tuning vibration reduction study of magnetic fluid rolling-ball damper based on pso-FUZZY-PID

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PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART C-JOURNAL OF MECHANICAL ENGINEERING SCIENCE 2024年第1期238卷 29-47页

作者： Yang, Wenrong Zhang, Dalei Zhang, Yumeng Sun, Changxing Hou, Lizheng Wei, Dejie Hebei Univ Technol Sch Elect Engn State Key Lab Reliabil & Intelligence Elect Equipm Tianjin Peoples R China Beihang Univ Sch Automat Sci & Elect Engn Beijing Peoples R China Hebei Univ Technol Sch Elect Engn State Key Lab Reliabil & Intelligence Elect Equipm Tianjin 300130 Peoples R China

For towering structures under complex time-varying excitation, the tuned magnetic fluid rolling-ball dampers (TMFRBD) cannot achieve self-tuning damping. In this paper, considering the operating characteristics of the TMFRBD, a damper self-tuning control algorithm, namely particle swarm optimization (pso)-FUZZY-PID, is proposed for real-time adjustment of the intrinsic frequency of TMFRBD, which can greatly improve the damping effect, namely self-tuning magnetic fluid rolling-ball dampers (STMFRBD). Firstly, the equivalent dynamics model of the damping system is established, deriving the structural dynamic amplitude response as a function of the excitation frequency to structural frequency ratio. Different frequency tracking schemes of the damper are compared and analyzed. Then magnetic field simulations and magnetic fluid magnetic property measurements are performed for the dampers, respectively. Secondly, the self-tuning pso-FUZZY-PID control algorithm is designed to improve the damping performance of STMFRBD under time-varying excitation by optimizing factors of the FUZZY-PID controller with pso. To confirm the effectiveness of the damper self-tuning control algorithm, the frequency tracking of dampers with different control algorithms under time-varying excitation is compared. The results show that the damper's frequency can be tuned quickly and accurately tracking excitation frequency by pso-FUZZY-PID. Finally, the simulation and experimental results of STMFRBD and passive mass damper (PMD) under different loads are analyzed, and the structural amplitude response is analyzed using the Hilbert-Huang transform (HHT). In addition, the damping of STMFBD and TMFBD under different loads is compared. The results show that STMFRBD has better vibration-damping performance than PMD and TMFRBD.

关键词： Magnetic fluid rolling-ball damper pso algorithm time-varying excitation FUZZY-PID control frequency tracking scheme

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pso (Particle Swarm Optimization) for Interpretation of Magnetic Anomalies Caused by Simple Geometrical Structures

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PURE AND APPLIED GEOPHYSICS 2018年第10期175卷 3539-3553页

作者： Essa, Khalid S. Elhussein, Mahmoud Cairo Univ Dept Geophys Fac Sci PO 12613 Giza Egypt

A new efficient approach to estimate parameters that controlled the source dimensions from magnetic anomaly profile data in light of pso algorithm (particle swarm optimization) has been presented. The pso algorithm has been connected in interpreting the magnetic anomaly profiles data onto a new formula for isolated sources embedded in the subsurface. The model parameters deciphered here are the depth of the body, the amplitude coefficient, the angle of effective magnetization, the shape factor and the horizontal coordinates of the source. The model parameters evaluated by the present technique, generally the depth of the covered structures were observed to be in astounding concurrence with the real parameters. The root mean square (RMS) error is considered as a criterion in estimating the misfit between the observed and computed anomalies. Inversion of noise-free synthetic data, noisy synthetic data which contains different levels of random noise (5, 10, 15 and 20%) as well as multiple structures and in additional two real-field data from USA and Egypt exhibits the viability of the approach. Thus, the final results of the different parameters are matched with those given in the published literature and from geologic results.

关键词： Magnetic anomaly pso algorithm the depth RMS

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SDpso: Spark Distributed pso-based approach for feature selection and cancer disease prognosis

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JOURNAL OF BIG DATA 2021年第1期8卷 1-22页

作者： Tadist, Khawla Mrabti, Fatiha Nikolov, Nikola S. Zahi, Azeddine Najah, Said Sidi Mohammed Ben Abdellah Univ Fac Sci & Technol Lab Intelligent Syst & Applicat Fes Morocco

The Dimensionality Curse is one of the most critical issues that are hindering faster evolution in several fields broadly, and in bioinformatics distinctively. To counter this curse, a conglomerate solution is needed. Among the renowned techniques that proved efficacy, the scaling-based dimensionality reduction techniques are the most prevalent. To insure improved performance and productivity, horizontal scaling functions are combined with Particle Swarm Optimization (pso) based computational techniques. Optimization algorithms are an interesting substitute to traditional feature selection methods that are both efficient and relatively easier to scale. Particle Swarm Optimization (pso) is an iterative search algorithm that has proved to achieve excellent results for feature selection problems. In this paper, a composite Spark Distributed approach to feature selection that combines an integrative feature selection algorithm using Binary Particle Swarm Optimization (Bpso) with Particle Swarm Optimization (pso) algorithm for cancer prognosis is proposed;hence Spark Distributed Particle Swarm Optimization (SDpso) approach. The effectiveness of the proposed approach is demonstrated using five benchmark genomic datasets as well as a comparative study with four state of the art methods. Compared with the four methods, the proposed approach yields the best in average of purity ranging from 0.78 to 0.97 and F-measure ranging from 0.75 to 0.96.

关键词： Big Data Spark Feature selection pso algorithm Clustering Genomics Prognosis

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Segmentation of Tissues from MRI Biomedical Images Using Kernel Fuzzy pso Clustering Based Level Set Approach

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CURRENT MEDICAL IMAGING 2018年第3期14卷 389-400页

作者： Ramudu, Kama Babu, Tummala Ranga Acharya Nagarjuna Univ Dept Elect & Commun Engn Guntur India Acharya Nagarjuna Univ RVR&JC Coll Engn Dept ECE Guntur India

Background: We propose a novel global region based method for segmentation of biomedical images with a fast level set model using Kernel Fuzzy Particles Swarm Optimization (KFpso) clustering. In the field of medical study, segmentation of biomedical images is one of the most important approaches for the diagnosis of a patient. Segmentation of these images is still a tedious task and cumbersome due to weak contrast and poor resolution of images etc. The automatic segmentation of such images is very difficult. The main reason is a large amount of inhomogeneity present in the background and foreground of real world image. The conventional methods like C-V model and Distance Regularized Level Set (DRLS) method lead to getting improper segmentation with unconvinced results. Methods: We proposed an efficient segmentation method for MRI biomedical images with level set approach using KFpso clustering. In the pre-processing step, Kernel Fuzzy C-Means (KFCM) clustering is combined with pso Initialization algorithm called KFpso clustering for improving the clustering efficiency. In KFCM clustering algorithm, the initial cluster center is chosen randomly, and with the help of pso, the optimal cluster centers are chosen. Results: The Membership Function (MF) of this algorithm is less sensitive to noise and consideration of the spatial information. In the post processing step, the resulting Kernel fuzzy pso clustering is modified to the level set model for faster and accurate segmentation. Conclusion: The Proposed segmentation results are effective, superior and accurate compared to conventional methods. This new approach is very helpful for accurate detection of the white matter and gray matter, cancerous cells in brain and bone images.

关键词： Biomedical images image segmentation KFCM clustering level sets pso algorithm DRLS

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ZEBRA battery SOC estimation using pso-optimized hybrid neural model considering aging effect

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IEICE ELECTRONICS EXPRESS 2012年第13期9卷 1115-1121页

作者： Gharavian, Davood Pardis, Reza Sheikhan, Mansour Shahid Abbaspour Univ Technol EE Dept Tehran Iran Islamic Azad Univ S Tehran Branch EE Dept Tehran Iran

The state of charge (SOC) estimation for electric vehicles (EVs) is important and helps to optimize the utilization of the battery energy storage in EVs. In this way, aging is also a key parameter impacting the performance of batteries. In this paper, a hybrid neural model is proposed for the SOC estimation of ZEBRA (Zero Emission Battery Research Activities) battery considering the aging effect through the state of health (SOH) and the discharge efficiency (DE) parameters. The number of hidden nodes in neural modules is also optimized using particle swarm optimization (pso) algorithm. The SOC estimation error of the proposed system is 1.7% when compared with the real SOC obtained from a discharge test.

关键词： hybrid neural networks state of charge estimation pso algorithm

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