FIR digital filter design using improved particle swarm optimization based on refraction principle

Shao, Peng; Wu, Zhijian; Zhou, Xuanyu; Tran, Dang Cong

doi:10.1007/s00500-015-1963-3

Cited by 36 publications

(16 citation statements)

References 25 publications

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“…Once the mHMI enters a testing stage, in EOG mode, EOG are firstly filtered using a zero-phase FIR lowpass filter (hamming window) with a lower cutoff frequency 0.05 Hz and a higher cutoff frequency 45 Hz (Shao et al, 2017). Next, the results are obtained by filtered EOG based on wavelet transform using the 4th order of Daubechies Wavelet with 10 decomposition levels.…”

Section: Methodsmentioning

confidence: 99%

An EEG/EMG/EOG-Based Multimodal Human-Machine Interface to Real-Time Control of a Soft Robot Hand

et al. 2019

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Brain-computer interface (BCI) technology shows potential for application to motor rehabilitation therapies that use neural plasticity to restore motor function and improve quality of life of stroke survivors. However, it is often difficult for BCI systems to provide the variety of control commands necessary for multi-task real-time control of soft robot naturally. In this study, a novel multimodal human-machine interface system (mHMI) is developed using combinations of electrooculography (EOG), electroencephalography (EEG), and electromyogram (EMG) to generate numerous control instructions. Moreover, we also explore subject acceptance of an affordable wearable soft robot to move basic hand actions during robot-assisted movement. Six healthy subjects separately perform left and right hand motor imagery, looking-left and looking-right eye movements, and different hand gestures in different modes to control a soft robot in a variety of actions. The results indicate that the number of mHMI control instructions is significantly greater than achievable with any individual mode. Furthermore, the mHMI can achieve an average classification accuracy of 93.83% with the average information transfer rate of 47.41 bits/min, which is entirely equivalent to a control speed of 17 actions per minute. The study is expected to construct a more user-friendly mHMI for real-time control of soft robot to help healthy or disabled persons perform basic hand movements in friendly and convenient way.

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Section: Methodsmentioning

confidence: 99%

An EEG/EMG/EOG-Based Multimodal Human-Machine Interface to Real-Time Control of a Soft Robot Hand

et al. 2019

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“…Quantum-behaved PSO [229], adaptive PSO [251], genetic PSO with dynamically varying neighborhood technique [252] Hybrid Cellular PSO [220], single-swarm PSO [221], adaptive PSO [222] Self-adaptive PSO with adaptive filtering technique [253] Adaptive PSO [223], PSO with wavelet mutation [224], improved boolean PSO [225], hybrid enhanced PSO, and DE [116] Binary PSO [262], modified quantum PSO [263] Image processing…”

Section: Design Of Electromagnetic Devicesmentioning

confidence: 99%

Particle Swarm Optimization: A Comprehensive Survey

et al. 2022

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Particle swarm optimization (PSO) is one of the most well-regarded swarm-based algorithms in the literature. Although the original PSO has shown good optimization performance, it still severely suffers from premature convergence. As a result, many researchers have been modifying it resulting in a large number of PSO variants with either slightly or significantly better performance. Mainly, the standard PSO has been modified by four main strategies: modification of the PSO controlling parameters, hybridizing PSO with other well-known meta-heuristic algorithms such as genetic algorithm (GA) and differential evolution (DE), cooperation and multi-swarm techniques. This paper attempts to provide a comprehensive review of PSO, including the basic concepts of PSO, binary PSO, neighborhood topologies in PSO, recent and historical PSO variants, remarkable engineering applications of PSO, and its drawbacks. Moreover, this paper reviews recent studies that utilize PSO to solve feature selection problems. Finally, eight potential research directions that can help researchers further enhance the performance of PSO are provided.

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“…ROL strategy combining the refraction principle [ 28 , 34 ] from physics with an OBL strategy is a strong method to strengthen MAs [ 28 , 29 , 34 , 35 ]. In this paper, the ROL strategy is applied to augment the performance of the BROMLDE algorithm.…”

Section: The Proposed Bromlde Algorithmmentioning

confidence: 99%

An Enhanced Differential Evolution Algorithm with Bernstein Operator and Refracted Oppositional-Mutual Learning Strategy

Zhang

et al. 2022

Entropy

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Numerical optimization has been a popular research topic within various engineering applications, where differential evolution (DE) is one of the most extensively applied methods. However, it is difficult to choose appropriate control parameters and to avoid falling into local optimum and poor convergence when handling complex numerical optimization problems. To handle these problems, an improved DE (BROMLDE) with the Bernstein operator and refracted oppositional-mutual learning (ROML) is proposed, which can reduce parameter selection, converge faster, and avoid trapping in local optimum. Firstly, a new ROML strategy integrates mutual learning (ML) and refractive oppositional learning (ROL), achieving stochastic switching between ROL and ML during the population initialization and generation jumping period to balance exploration and exploitation. Meanwhile, a dynamic adjustment factor is constructed to improve the ability of the algorithm to jump out of the local optimum. Secondly, a Bernstein operator, which has no parameters setting and intrinsic parameters tuning phase, is introduced to improve convergence performance. Finally, the performance of BROMLDE is evaluated by 10 bound-constrained benchmark functions from CEC 2019 and CEC 2020, respectively. Two engineering optimization problems are utilized simultaneously. The comparative experimental results show that BROMLDE has higher global optimization capability and convergence speed on most functions and engineering problems.

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FIR digital filter design using improved particle swarm optimization based on refraction principle

Cited by 36 publications

References 25 publications

An EEG/EMG/EOG-Based Multimodal Human-Machine Interface to Real-Time Control of a Soft Robot Hand

An EEG/EMG/EOG-Based Multimodal Human-Machine Interface to Real-Time Control of a Soft Robot Hand

Particle Swarm Optimization: A Comprehensive Survey

An Enhanced Differential Evolution Algorithm with Bernstein Operator and Refracted Oppositional-Mutual Learning Strategy

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