Artificial neural networks (ANNs) are a type of machine learning algorithm that is designed to mimic the structure and function of the human brain. They consist of interconnected processing nodes, with each node having adjustable weights for learning and making predictions based on input data. ANNs are used in a wide range of applications, such as computer vision, natural language processing, intelligent control, autonomous decision-making, optimal estimation, and more. Neural networks have been used to achieve state-of-the-art performance on a variety of complex tasks. In the fields of control and decision-making, ANNs play a critical role in learning and approximating elusive solutions and complicated unknowns. These have enabled the fast development of autonomous technology. ANNs have even become one of the key tools to realize high-level autonomous systems. Ongoing research is likely to reveal more applications of this powerful tool in autonomous systems in the coming years.
The goal of this topic is to explore the theoretical foundations of artificial neural networks (ANNs) and their practical applications in the control and decision-making of autonomous systems. This research topic also provides a platform for exchanging research works, technical trends, and practical experience about ANNs. Though significant breakthroughs in deep neural networks have greatly promoted the development of artificial intelligence, shallow neural networks should also be paid more attention. Shallow neural networks can still be very effective for certain tasks in autonomous systems, especially when the dataset is smaller, or the problem is simpler. Through this topic, the full view of the development of ANNs in the fields of control and decision-making of autonomous systems will be showcased.
The theory and application of artificial neural networks (ANNs) has been rapidly advancing in recent years. The purpose of this research topic is to present the latest research about ANNs in the fields of control and decision-making of autonomous systems. Papers that address new methods, advanced algorithms, and innovative applications related to ANN-based control and decision-making are welcome for this research topic. We welcome submissions of original research and review articles. The topics of interest include, but are not limited to:
- Neural attitude control of autonomous systems
- Neural guidance of autonomous systems
- Neural decision-making of autonomous systems
- ANN-based mission planning
- ANN-based multi-agent games
Artificial neural networks (ANNs) are a type of machine learning algorithm that is designed to mimic the structure and function of the human brain. They consist of interconnected processing nodes, with each node having adjustable weights for learning and making predictions based on input data. ANNs are used in a wide range of applications, such as computer vision, natural language processing, intelligent control, autonomous decision-making, optimal estimation, and more. Neural networks have been used to achieve state-of-the-art performance on a variety of complex tasks. In the fields of control and decision-making, ANNs play a critical role in learning and approximating elusive solutions and complicated unknowns. These have enabled the fast development of autonomous technology. ANNs have even become one of the key tools to realize high-level autonomous systems. Ongoing research is likely to reveal more applications of this powerful tool in autonomous systems in the coming years.
The goal of this topic is to explore the theoretical foundations of artificial neural networks (ANNs) and their practical applications in the control and decision-making of autonomous systems. This research topic also provides a platform for exchanging research works, technical trends, and practical experience about ANNs. Though significant breakthroughs in deep neural networks have greatly promoted the development of artificial intelligence, shallow neural networks should also be paid more attention. Shallow neural networks can still be very effective for certain tasks in autonomous systems, especially when the dataset is smaller, or the problem is simpler. Through this topic, the full view of the development of ANNs in the fields of control and decision-making of autonomous systems will be showcased.
The theory and application of artificial neural networks (ANNs) has been rapidly advancing in recent years. The purpose of this research topic is to present the latest research about ANNs in the fields of control and decision-making of autonomous systems. Papers that address new methods, advanced algorithms, and innovative applications related to ANN-based control and decision-making are welcome for this research topic. We welcome submissions of original research and review articles. The topics of interest include, but are not limited to:
- Neural attitude control of autonomous systems
- Neural guidance of autonomous systems
- Neural decision-making of autonomous systems
- ANN-based mission planning
- ANN-based multi-agent games
