Hasil untuk "Logic"

L. Zadeh

Chuen-Chien Lee

Robert C. Moore

T. Nipkow, M. Wenzel, Lawrence C. Paulson

J. Jaffar, J. Lassez

Shmuel Sagiv, T. Reps, R. Wilhelm

A. Anderson, N. Belnap, J. Dunn

R. Zimmermann, W. Fichtner

F. Baader, Diego Calvanese, Deborah L. McGuinness et al.

W. Thomas

R. Koymans

A. García, Guillermo R. Simari

H. Chesbrough

C. Grönroos

Piro Siuti, John Yazbek, T. Lu

G. Hegel, G. D. Giovanni

M. Genesereth, E. Kao

Pablo Catota-Ocapana, Cesar Minaya-Andino, Paul Astudillo et al.

In recent years, agriculture has significantly evolved with the integration of technology, enabling the development of new cultivation techniques that respond to the growing demand for food and the need to conserve natural resources. In this context, we conducted a comprehensive review of models of intelligent control for managing nutrients in hydroponic systems by analyzing studies from the last five years. The selection of articles was based on the guidelines of PRISMA and research questions, focusing on control techniques based on fuzzy Logic, Artificial Intelligence and artificial Vision. These models are essential to automatically adjust the concentrations of nutrients, adapting to the needs of the plants at each stage of their growth. The review results highlight essential advances but also identify significant challenges, such as the need for precise sensors, the management of large volumes of data, and adapting the models to different crops and conditions. Despite these challenges, the benefits include a more efficient use of nutrients, a reduction in the consumption of water, and increased crop yields. Continuous research in this field is essential to improve the sustainability and productivity of hydroponic systems, offering new opportunities for agriculture in the future. The findings of this review provide a solid basis for evaluating the effectiveness of the control models and their application in real agricultural scenarios.

Salah Mahdi Thajeel, Doğu Çağdaş Atilla

A reinforcement neural network-based grid-integrated photovoltaic (PV) system with a battery management system (BMS) was developed to enhance the efficiency and reliability of renewable energy systems. In such a setup, the PV system generates electricity, which can be used immediately, stored in batteries, or fed into the grid. The challenge lies in dynamically optimizing the power flow between these components to minimize energy costs, maximize the use of renewable energy, and maintain grid stability. Reinforcement learning (RL) combined with NNs offers a powerful solution by enabling the system to learn and adapt its energy management strategy in real time. By using the proposed techniques, the convergence time was decreased with lower complexity compared with existing approaches. The RL agent interacts with the environment (i.e., the grid, PV system, and battery), continuously improving its decisions regarding when to store energy, draw from the battery, and supply power to the grid. This intelligent control approach ensures optimal performance, contributing to a more sustainable and resilient energy system.