Distance Evaluated Simulated Kalman Filter Algorithm for Peak Classification of EEG Signals

Adam, Asrul; Muhammad, Badaruddin

doi:10.5013/ijssst.a.19.05.06

Cited by 3 publications

(2 citation statements)

References 25 publications

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“…In order to eradicate this problem, various binary-based SKF algorithms were introduced such as Angle modulated SKF (AMSKF) [56], Binary SKF (BSKF) [57], Local Optimum Distance evaluated SKF (LocalDESKF) [58], and Global Distance Evaluated SKF (GlobalDESKF) [59] algorithms. Based on the capability of the Binary-based SKF algorithms, they have potential to be developed as a feature selection method [61][62].…”

Section: Feature Selection Methods Using Optimization Algorithms For mentioning

confidence: 99%

A Review on Time-domain Peak Detection and Classification Algorithms for Electroencephalogram Signals

Adam¹,

Abidin²,

Yusof³

et al. 2019

MEKATRONIKA

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In this paper, the developments in the field of EEG signals peaks detection and classification methods based on time-domain analysis have been discussed. The use of peak classification algorithm has end up the most significant approach in several applications. Generally, the peaks detection and classification algorithm is a first step in detecting any event-related for the variation of signals. A review based on the variety of peak models on their respective classification methods and applications have been investigated. In addition, this paper also discusses on the existing feature selection algorithms in the field of peaks classification.

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Section: Feature Selection Methods Using Optimization Algorithms For mentioning

confidence: 99%

A Review on Time-domain Peak Detection and Classification Algorithms for Electroencephalogram Signals

Adam¹,

Abidin²,

Yusof³

et al. 2019

MEKATRONIKA

Self Cite

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“…The SKF has also been applied to solve engineering problems. For example, the SKF have employed as feature selector [30][31][32][33], algorithms in adaptive beamforming [34][35][36][37], routing algorithm in manufacturing process [38][39][40] and airport gate allocation [41], tuning algorithm in control engineering [42][43][44][45], and matching algorithm in image processing [46][47][48].…”

Section: Simulated Kalman Filtermentioning

confidence: 99%

Enhancing Simulated Kalman Filter Algorithm using Current Optimum Opposition-based Learning

Azmi¹,

Ibrahim²,

Pebrianti³

et al. 2019

mekatronika

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Simulated Kalman filter (SKF) is a new population-based optimization algorithm inspired by estimation capability of Kalman filter. Each agent in SKF is regarded as a Kalman filter. Based on the mechanism of Kalman filtering, the SKF includes prediction, measurement, and estimation process to search for global optimum. The SKF has been shown to yield good performance in solving benchmark optimization problems. However, the exploration capability of SKF could be further improved. From literature, current optimum opposition-based learning (COOBL) has been employed to increase the diversity (exploration) of search algorithm by allowing current population to be compared with an opposite population. By employing this concept, more potential agents are generated to explore more promising regions that exist in the solution domain. Therefore, this paper intends to improve the exploration capability of SKF through the application of COOBL. The COOBL is employed after the estimation process of SKF. Experimental results over the IEEE Congress on Evolutionary Computation (CEC) 2014 benchmark functions indicate that current optimum opposition-based simulated Kalman filter (COOBSKF) improved the exploration capability of SKF significantly. The COOBSKF also has been compared with five other optimization algorithms and outperforms them all.

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A brief review of simulated Kalman Filter Algorithm – variants and applications

et al. 2021

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Simulated Kalman Filter (SKF) solves optimization problems by finding the estimate of the optimum solution. As a multi-agent algorithm, every agent in the population acts as a Kalman filter by using a standard Kalman filter framework, which includes a simulated measurement process and a best-so-far solution as a reference. This paper presented an overview of the research progress in SKF from the day it was introduced until the present day, discussing the progress, improvements, modifications, and applications of SKF. The fundamental and standard algorithm were first introduced. Then, the work on the algorithm improvements was surveyed. Finally, the remaining unresolved problems and some directions of SKF research were discussed. We reviewed 57 SKF papers. 16 of them on fundamental improvements, 9 on extension of the algorithm to discrete problems and 25 on their applications. Researchers have worked on ideas to improve exploration capability to prevent premature convergence by trying prediction operators, opposition-based learning, and different iteration strategies. There were also attempts to hybridize SKF with other famous algorithms such as Particle Swarm Optimization (PSO), Gravitational Search Algorithm (GSA), and Sine Cosine Algorithm (SCA) to improve its performance. Lastly, a single-agent variant of SKF and a multi-objective SKF were introduced. SKF algorithms and its variants have been implemented in at least nine areas of applications: drill path optimization, airport gate allocation problem (AGAP), assembly sequence planning (ASP), system identification, feature selection, image template matching, controller tuning, wireless sensor network, and engineering design problem. The literature reviewed solely depended on the keyword search that contained the terms simulated Kalman filter from December 2015 to the present date. This is the first review paper on SKF. It is hoped that this survey would be beneficial for the researchers of this area and attracting interest towards the algorithm.

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Distance Evaluated Simulated Kalman Filter Algorithm for Peak Classification of EEG Signals

Cited by 3 publications

References 25 publications

A Review on Time-domain Peak Detection and Classification Algorithms for Electroencephalogram Signals

A Review on Time-domain Peak Detection and Classification Algorithms for Electroencephalogram Signals

Enhancing Simulated Kalman Filter Algorithm using Current Optimum Opposition-based Learning

A brief review of simulated Kalman Filter Algorithm – variants and applications

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