Heuristic Algorithms for Recognition of Some Cubic Hypersurfaces

Селиверстов, А. В.

doi:10.1134/s0361768821010096

Cited by 5 publications

(2 citation statements)

References 27 publications

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“…However, BPNN have slow convergence speeds, are sensitive to initial weights and thresholds, and require improvements in accuracy. 25,30 To address these limitations, three categories of enhancement methods have emerged: heuristic algorithms, 31 intelligent optimization algorithms, 32 and changing activation functions. 33,34 Heuristic algorithms are iterative methods inspired by nature, simulating various biological behaviors and physical laws to achieve optimization.…”

Section: Motivations and Technical Challengesmentioning

confidence: 99%

An Improved Self-Adaptive Flower Pollination-Backpropagation Neural Network for the State of Charge Estimation of Lithium-Ion Batteries with Output Sliding Average Window Strategy

Zou,

Wang,

Hai

et al. 2024

J. Electrochem. Soc.

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With the rapid development of electric vehicles and green energy sources, the use of backpropagation neural network (BPNN) to precisely estimate the state of charge (SOC) in lithium-ion batteries has become a popular research topic. However, traditionally BPNN has low prediction accuracy and large output fluctuations. To address the shortcomings of BPNN, self-adaptive flower pollination algorithm (SFPA) was proposed to optimize the initial weights and thresholds of BPNN, and an output sliding average window (OSAW) strategy is proposed to smooth SOC outputs in this research, which SOC estimation method is named SFPA-BP-OSAW. In addition, the performance of the newly-proposed method is compared with other common related algorithms under different working conditions to verify the effectiveness of SFPA-BP-OSAW. The experimental results show that the mean absolute error of SFPA-BP-OSAW is 0.771% and 0.897%, and the root mean square error is 0.236% and 0.37%, respectively, under HPPC and BBDST working conditions. Experimental data and error analysis show that the method proposed in this paper has fast convergence, high prediction accuracy, and curve smoothness.

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Section: Motivations and Technical Challengesmentioning

confidence: 99%

An Improved Self-Adaptive Flower Pollination-Backpropagation Neural Network for the State of Charge Estimation of Lithium-Ion Batteries with Output Sliding Average Window Strategy

Zou,

Wang,

Hai

et al. 2024

J. Electrochem. Soc.

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show abstract

“…In this review, we consider only a small area of MathPartner application, namely the calculation of definite and indefinite integrals. Symbolic computations and estimates of the computational complexity are of the greatest interest [5]- [9]. However, in some cases, symbolic computations need to be supplemented with numerical methods.…”

Section: Introductionmentioning

confidence: 99%

Calculation of integrals in MathPartner

Malaschonok

Селиверстов

2021

Discrete and Continuous Models

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We present the possibilities provided by the MathPartner service of calculating definite and indefinite integrals. MathPartner contains software implementation of the Risch algorithm and provides users with the ability to compute antiderivatives for elementary functions. Certain integrals, including improper integrals, can be calculated using numerical algorithms. In this case, every user has the ability to indicate the required accuracy with which he needs to know the numerical value of the integral. We highlight special functions allowing us to calculate complete elliptic integrals. These include functions for calculating the arithmetic-geometric mean and the geometric-harmonic mean, which allow us to calculate the complete elliptic integrals of the first kind. The set also includes the modified arithmetic-geometric mean, proposed by Semjon Adlaj, which allows us to calculate the complete elliptic integrals of the second kind as well as the circumference of an ellipse. The Lagutinski algorithm is of particular interest. For given differentiation in the field of bivariate rational functions, one can decide whether there exists a rational integral. The algorithm is based on calculating the Lagutinski determinant. This year we are celebrating 150th anniversary of Mikhail Lagutinski.

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Effective Lower Bounds on the Matrix Rank and Their Applications

Zverkov,

Seliverstov

2023

Program Comput Soft

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Heuristic Algorithms for Recognition of Some Cubic Hypersurfaces

Cited by 5 publications

References 27 publications

An Improved Self-Adaptive Flower Pollination-Backpropagation Neural Network for the State of Charge Estimation of Lithium-Ion Batteries with Output Sliding Average Window Strategy

An Improved Self-Adaptive Flower Pollination-Backpropagation Neural Network for the State of Charge Estimation of Lithium-Ion Batteries with Output Sliding Average Window Strategy

Calculation of integrals in MathPartner

Effective Lower Bounds on the Matrix Rank and Their Applications

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