The Pachycondyla Apicalis metaheuristic algorithm for parameters identification of chaotic electrical system

Fouzia, Maamri; Bououden, Sofiane; Chadli, Mohammed; Boulkaibet, Ilyes

doi:10.1080/17445760.2017.1401622

Cited by 10 publications

(7 citation statements)

References 21 publications

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“…For damage detection of regular dams and irregular plates, a new approach has been proposed based on a combination of ANFIS with wavelet transform (Hamidian et al, 2018). Several studies on the analysis of the stability and synthesis of control laws have been undertaken for multiple models (Chadli & Borne, 2013;Maamri et al, 2018). The aims of another study were diagnosing, analyzing and learning the purpose of the prostate cancer diseases using the Fuzzy Expert System.…”

Section: Other Fuzzy Expert Systems For Diagnosis Of Diseasementioning

confidence: 99%

A novel fuzzy expert system design to assist with peptic ulcer disease diagnosis

2021

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Section: Other Fuzzy Expert Systems For Diagnosis Of Diseasementioning

confidence: 99%

A novel fuzzy expert system design to assist with peptic ulcer disease diagnosis

2021

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“…The efficiency of the electric machine is modeled as a quasi-static map of its power and operating speed, with the assumption that the efficiency h is constant for bidirectional operation of the electric machine. Therefore, the power P m (k) flowing in and out of the electric machine in motoring and generating modes is represented in equations (12) and (13). The motor used in hybrid electric auto rickshaw is shown in Figure 8.…”

Section: Electric Machinementioning

confidence: 99%

Benchmark fuel economy for a parallel hybrid electric three-wheeler vehicle (rickshaw)

Asghar

Bhatti

Izhar

2018

Advances in Mechanical Engineering

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The core contribution to this work is the development of benchmark fuel economy for a three-wheeler hybrid electric rickshaw and its comparison with heuristics controllers designed with optimal and non-optimal rules. Dynamic programming is used as a feasible technique for powertrain benchmark analysis. A parallel hybrid electric three-wheeler vehicle is modeled in MATLAB/Simulink through forward facing simulator. The dynamic programming technique is employed through the backward facing simulator, ensuring optimal power sharing between two energy sources (engine and motor) while keeping the battery state of charge in the charge-sustaining mode. The extracted rules from dynamic programming forming near-optimal control strategies are playing a vital role in deciding overall fuel consumption. Unlike the dynamic programming control actions, these extracted rules are implementable through the forward facing simulator. From the simulation results, it can be concluded that a substantial improvement of fuel economy is achieved through the application of dynamic programming. Rule-based (near-optimal) strategy using dynamic programming results shows about 9% more fuel consumption as compared with the dynamic programming (benchmark solution), which is then compared with non-optimal rule-based heuristics controller. It is shown that non-optimal rule-based controller has 18% more fuel consumption than dynamic programming results.

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“…Applying optimization algorithms to UPF can also solve the problems mentioned above. However, from the perspective of the algorithm, the divide-and-conquer algorithm has more advantages than the optimization algorithm [ 40 , 41 , 42 ]. The particle swarm algorithm in the swarm intelligence algorithm is applied to find the optimal investment allocation of the stocks [ 40 ].…”

Section: Introductionmentioning

confidence: 99%

“…The divide-and-conquer algorithm divides the problem into many sub-problems, which not only guarantees the accuracy of the information, but also reduces the running time. The meta-heuristic algorithm is used to optimize the control parameters of the given chaotic systems [ 41 ]. The algorithm has been improved in terms of running time, but it is easy to prematurely fall into the local optimum during the optimization process.…”

Section: Introductionmentioning

confidence: 99%

Unscented Particle Filter Algorithm Based on Divide-and-Conquer Sampling for Target Tracking

Deng

2021

Sensors

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Unscented particle filter (UPF) struggles to completely cover the target state space when handling the maneuvering target tracing problem, and the tracking performance can be affected by the low sample diversity and algorithm redundancy. In order to solve this problem, the method of divide-and-conquer sampling is applied to the UPF tracking algorithm. By decomposing the state space, the descending dimension processing of the target maneuver is realized. When dealing with the maneuvering target, particles are sampled separately in each subspace, which directly prevents particles from degeneracy. Experiments and a comparative analysis were carried out to comprehensively analyze the performance of the divide-and-conquer sampling unscented particle filter (DCS-UPF). The simulation result demonstrates that the proposed algorithm can improve the diversity of particles and obtain higher tracking accuracy in less time than the particle swarm algorithm and intelligent adaptive filtering algorithm. This algorithm can be used in complex maneuvering conditions.

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The Pachycondyla Apicalis metaheuristic algorithm for parameters identification of chaotic electrical system

Cited by 10 publications

References 21 publications

A novel fuzzy expert system design to assist with peptic ulcer disease diagnosis

A novel fuzzy expert system design to assist with peptic ulcer disease diagnosis

Benchmark fuel economy for a parallel hybrid electric three-wheeler vehicle (rickshaw)

Unscented Particle Filter Algorithm Based on Divide-and-Conquer Sampling for Target Tracking

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