Subclasses of Univalent Functions Involving Modified Sigmoid Function

Fadipe-Joseph, Olubunmi A.; Oluwayemi, Matthew Olanrewaju; Titiloye, E. O.

doi:10.37622/ijde/16.1.2021.81-93

Cited by 3 publications

(2 citation statements)

References 3 publications

(3 reference statements)

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“…They are calculated differently in the two directions of back propagation [27]. Sigmoid function and the tach function are calculated as shown in equations ( 5) and ( 6), respectively [28,29].…”

Section: Journal Of Applied Mathematicsmentioning

confidence: 99%

A Study of Deep Learning Neural Network Algorithms and Genetic Algorithms for FJSP

Shang

2023

Journal of Applied Mathematics

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Flexible job-shop scheduling problem (FJSP) is a new research hotspot in the field of production scheduling. To solve the multiobjective FJSP problem, the production of flexible job shop can run normally and quickly. This research takes into account various characteristics of FJSP problems, such as the need to ensure the continuity and stability of processing, the existence of multiple objectives in the whole process, and the constant complexity of changes. It starts with deep learning neural networks and genetic algorithms. Long short-term memory (LSTM) and convolutional neural networks (CNN) are combined in deep learning neural networks. The new improved algorithm is based on the combination of deep learning neural networks LSTM and CNN with genetic algorithm (GA), namely, CNN-LSTM-GA algorithm. Simulation results showed that the accuracy of the CNN-LSTM-GA algorithm was between 85.2% and 95.3% in the test set. In the verification set, the minimum accuracy of the CNN-LSTM-GA algorithm was 84.6%, both of which were higher than the maximum accuracy of the other two algorithms. In the FJSP simulation experiment, the AUC value of the CNN-LSTM-GA algorithm was 0.92. After 40 iterations, the F1 value of the CNN-LSTM-GA algorithm remained above 0.8, which was significantly higher than the other two algorithms. CNN-LSTM-GA is superior to the other two algorithms in terms of prediction accuracy and overall performance of FJSP. It is more suitable for solving the discrete manufacturing job scheduling problem with FJSP characteristics. This study significantly raises the utilisation rate of the assembly shop’s equipment, optimises the scheduling of FJSP, and fully utilises each processing device’s versatile characteristics, which are quite useful for the production processes of domestic vehicle manufacturing companies.

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Section: Journal Of Applied Mathematicsmentioning

confidence: 99%

A Study of Deep Learning Neural Network Algorithms and Genetic Algorithms for FJSP

Shang

2023

Journal of Applied Mathematics

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“…Timothy Oloyede OPOOLA, Ezekiel Abiodun OYEKAN, Seyi Deborah OLUWASEGUN and Peter Oluwafemi ADEPOJU with γ(s) = 1 for s = 0 be the modified Sigmoid function. ( See details in [6], [7], [10], [5], [11], [15]). Additionally, let T ∈ A be the class of functions of the form…”

Section: Introductionmentioning

confidence: 99%

New subfamilies of univalent functions defined by Opoola differential operator and connected with modified Sigmoid function

TIMOTHY OLOYEDE OPOOLA,

EZEKIEL ABIODUN OYEKAN,

SEYI DEBORAH OLUWASEGUN

et al. 2023

Malaya J. Mat.

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In this exploration, by making use of the Hadamard product of Opoola differential operator and modified sigmoid function, we define new subclasses of analytical and univalent functions $\mathcal{T}_n \, S^k(\phi, \beta, \xi, \lambda, \delta, L, M, \mu, \rho, \omega) \:$ and $\mathcal{T}_n \, \mathcal{V}^k(\phi, \beta, \xi, \lambda, \delta, L, M, \mu, \rho, \omega)$ and discussed some properties of the classes; such as the coefficient estimates, Growth and Closure theorems.

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On a Class of Function of Bounded Turning with Negative Coefficients Associated with a Generalized Multiplier Transformation

Oluwayemi

Moses

Hamza

2022

Wseas Transactions on Mathematics

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The study of univalent functions and its applications is an hallmark of geometric function theory. Since univalent functions are analytic and has one-to-one mapping, it has a wide range of applications in the fields of studies where transformations (enlargements and reductions) are done. The functions also have angle and orientation preserving properties among other uses. Many authors have defined and studied various classes of univalent functions using different approaches and tools. In this study however, the authors used a generalized multiplier transformation to define a and investigate a new class of functions T_σ^n (λ). Various properties of the class of functions were investigated. The results extend some known results in literature.

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Subclasses of Univalent Functions Involving Modified Sigmoid Function

Cited by 3 publications

References 3 publications

A Study of Deep Learning Neural Network Algorithms and Genetic Algorithms for FJSP

A Study of Deep Learning Neural Network Algorithms and Genetic Algorithms for FJSP

New subfamilies of univalent functions defined by Opoola differential operator and connected with modified Sigmoid function

On a Class of Function of Bounded Turning with Negative Coefficients Associated with a Generalized Multiplier Transformation

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