Training a Multi-Layered Perceptron using Moth Swarm Algorithm for Predicting Energy Demand of a Data Centre and Weights-Based Analysis of Input Parameters

Ajayi, Oluwafemi; Heymann, Reolyn

doi:10.1109/africon51333.2021.9570996

Cited by 2 publications

(1 citation statement)

References 13 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Thus, analyzing the validation error during the training of the neural network can be an appropriate action. One non-classical approach to training neural networks is based on the application of meta-heuristic algorithms [24,25]. The methods mentioned are often based on swarm observations, where a population is a group of potential solutions and while processing is iteratively repeated, modifications are introduced according to the minimization of the objective function.…”

Section: Preliminaries and Short Description Of Methodologymentioning

confidence: 99%

Neural Network Applications in Electrical Drives—Trends in Control, Estimation, Diagnostics, and Construction

Kaminski

Tarczewski

2023

Energies

View full text Add to dashboard Cite

Currently, applications of the algorithms based on artificial intelligence (AI) principles can be observed in various fields. This can be also noticed in the wide area of electrical drives. Consideration has been limited to neural networks; however, the tasks for the models can be defined as follows: control, state variable estimation, and diagnostics. In the subsequent sections of this paper, electrical machines, as well as power electronic devices, are assumed as the main objects. This paper describes the basics, issues, and possibilities related to the used tools and explains the growing popularity of neural network applications in automatic systems with electrical drives. The paper begins with the overall considerations; following that, the content proceeds with the details, and two specific examples are shown. The first example deals with a neural network-based speed controller tested in a structure with a synchronous reluctance motor. Then, the implementation of recurrent neural networks as state variable estimators is analyzed. The achieved results present a precise estimation of the load speed and the shaft torque signals from a two-mass system. All descriptions in the article are considered in the context of the trends and perspectives in modern algorithm applications for electrical drives.

show abstract

Section: Preliminaries and Short Description Of Methodologymentioning

confidence: 99%

Neural Network Applications in Electrical Drives—Trends in Control, Estimation, Diagnostics, and Construction

Kaminski

Tarczewski

2023

Energies

View full text Add to dashboard Cite

show abstract

The Optimization of Path Planning for Express Delivery Based on Clone Adaptive Ant Colony Optimization

Zhang

Liu

et al. 2022

Journal of Advanced Transportation

View full text Add to dashboard Cite

In recent years, China's express delivery market has developed rapidly in the context of a booming economy. However, logistics costs are still high, which will affect the decision-making and policy making of relevant departments. Therefore, it is essential to optimize the last-mile assignment problem (LMAP) to meet the consumer’s demand for delivery time and reduce economic expenditure. The LMAP of express delivery requires multiple packages to be delivered to different destinations. Finding the path with the minimum delivery cost and time is an NP-hard problem, and it is impossible to obtain the optimal solution by enumerating all possible answers. This study proposes a new express delivery path planning method based on a clone adaptive ant colony optimization (CAACO) to find suboptimal solutions. Moreover, a new distribution cost fitness function constructed by weighing the economic expenditure and time of express delivery is designed. Specifically, a new adaptive operator and a novel clone operator are also designed to accelerate the speed of convergence. Finally, by comparing the performance of CAACO with ant colony optimization (ACO), simulated annealing (SA), and genetic algorithm (GA), the effectiveness of CAACO in solving the express LAMP is verified. In the simulation results, it is obvious that the economic expenditure and time of express delivery based on the CAACO are lower than ACO, SA, and GA, and the convergence speed is also faster than the SA and GA. It can be seen that CAACO has valuable benefits in solving LMAP.

show abstract

Training a Multi-Layered Perceptron using Moth Swarm Algorithm for Predicting Energy Demand of a Data Centre and Weights-Based Analysis of Input Parameters

Cited by 2 publications

References 13 publications

Neural Network Applications in Electrical Drives—Trends in Control, Estimation, Diagnostics, and Construction

Neural Network Applications in Electrical Drives—Trends in Control, Estimation, Diagnostics, and Construction

The Optimization of Path Planning for Express Delivery Based on Clone Adaptive Ant Colony Optimization

Contact Info

Product

Resources

About