What is the role of artificial neural networks?
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| Papers (10) | Insight |
|---|---|
45 Citations | Artificial neural networks (ANNs) could serve as artificial model organisms to connect neural dynamics and rich behavioral data. |
149 Citations | Artificial neural networks (ANNs) are systems that can learn. |
24 Citations | Findings: Artificial neural network has a significant role in medical area. |
41 Citations | As a result, the artificial neural networks are able to predict the behavior of the system with good accuracy in each scenario. |
02 Oct 1993 109 Citations | Experiments and analysis confirm that artificial neural networks are powerful tools for analysis, modeling, and control applications. |
117 Citations | It is concluded that neural networks have an important role in image analysis and in signal processing. |
14 Citations | In comparison, artificial neural networks reproduce features of these systems, satisfactorily well. |
| Our approach has the potential to enable artificial neural networks to scale up beyond what is currently possible. Artificial neural networks are artificial intelligence computing methods which are inspired by biological neural networks. | |
21 Citations | It is a useful element for artificial neural networks that work in real time. |
9 Citations | Artificial neural networks offer an advantageous alternative to coding such knowledge in an expert system shell or writing a program for it. |
Related Questions
What are advantages of Artificial Neural networks?5 answersArtificial Neural Networks (ANNs) offer various advantages in different fields. They mimic the human brain's behavior, providing a simplified model for problem-solving. ANNs excel in learning complex patterns and relationships in data without the need for intricate mathematical formulations. In digital marketing, ANNs are valuable for tasks like forecasting demand, analyzing consumer behavior, and automating processes, enhancing strategic areas like segmentation, targeting, and positioning. Despite the biological brain's superiority in real-time complex tasks, ANNs continue to progress rapidly and show potential to surpass human brain capabilities in the future. Overall, ANNs demonstrate efficiency, simplicity, and excellent generalization capabilities, making them a powerful tool for various applications.
- How do artificial neural networks (ANNs) function, and why are they suitable for this task?3 answersStep 1: Answer without citation
Artificial neural networks (ANNs) function by interconnecting neurons to mimic the human brain, allowing them to develop over time without human intervention. They are suitable for tasks because they can learn from observations without a baseline, and they can handle information that traditional methods cannot process. Additionally, ANNs can be applied in various fields, including power systems and infrastructure monitoring, providing efficient solutions and automatic recognition of damages.
Step 3: Answer with citation
Artificial neural networks (ANNs) function by interconnecting neurons to mimic the human brain, allowing them to develop over time without human intervention. They are suitable for tasks because they can learn from observations without a baseline, and they can handle information that traditional methods cannot process. Additionally, ANNs can be applied in various fields, including power systems and infrastructure monitoring, providing efficient solutions and automatic recognition of damages.
What is artificial neural networks?5 answersArtificial neural networks (ANNs) are computational methods that aim to mimic the behavior of the human brain. ANNs consist of interconnected neurons that communicate with each other through synapses. The power of ANNs lies in their ability to learn tasks by approximating any computable function. They have been successfully applied in various fields such as computer vision, robotics, speech recognition, and natural language processing. ANNs have shown excellent aptitude in learning the relationships between input and output data without prior assumptions about the statistical distribution. However, ANNs have limitations in terms of explainability and global approximation functions. Therefore, there is a need for research in novel approaches for local approximators that can outperform ANNs in terms of accuracy, computational expense, and explainability.
Whats artificial neural networks?5 answersArtificial neural networks (ANNs) are computational methods that aim to mimic the behavior of the human brain. ANNs consist of interconnected neurons that communicate with each other through synapses. They learn by receiving stimuli and emitting signals, or "firing". ANNs have evolved since their inception in 1986 and have become a powerful tool in various fields such as computer vision, robotics, speech recognition, and natural language processing. They can approximate any computable function and are trained using labeled training data through the backpropagation algorithm, which is a form of gradient descent. In the context of lung cancer research, ANNs are used as efficient models for nonlinear regression or classification, providing valuable support for diagnosis and treatment decisions. ANNs have also been successful in applications such as system identification and control, face recognition, and solving nonlinear phenomena.
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