Robert bosch gmbh (20240177004). METHOD FOR TRAINING AN ARTIFICIAL NEURAL NETWORK simplified abstract
Contents
- 1 METHOD FOR TRAINING AN ARTIFICIAL NEURAL NETWORK
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 METHOD FOR TRAINING AN ARTIFICIAL NEURAL NETWORK - A simplified explanation of the abstract
- 1.4 Simplified Explanation
- 1.5 Potential Applications
- 1.6 Problems Solved
- 1.7 Benefits
- 1.8 Potential Commercial Applications
- 1.9 Possible Prior Art
- 1.10 Unanswered Questions
- 1.11 Original Abstract Submitted
METHOD FOR TRAINING AN ARTIFICIAL NEURAL NETWORK
Organization Name
Inventor(s)
METHOD FOR TRAINING AN ARTIFICIAL NEURAL NETWORK - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240177004 titled 'METHOD FOR TRAINING AN ARTIFICIAL NEURAL NETWORK
Simplified Explanation
The abstract describes a method for training an artificial neural network by considering available resources and cost functions.
- Providing training data for the artificial neural network
- Detecting specifications regarding available resources
- Ascertaining a cost function that considers both the learning task and available resources
- Training the artificial neural network based on the provided data using the cost function
Potential Applications
This technology could be applied in various fields such as:
- Machine learning
- Data analysis
- Pattern recognition
Problems Solved
This method addresses the following issues:
- Efficient resource utilization
- Cost-effective training of neural networks
Benefits
The benefits of this technology include:
- Improved performance of artificial neural networks
- Optimal resource allocation
- Cost savings in training processes
Potential Commercial Applications
The technology could be commercially applied in industries such as:
- Finance
- Healthcare
- Manufacturing
Possible Prior Art
Prior art in the field of artificial neural network training methods may include:
- Gradient descent algorithms
- Backpropagation techniques
Unanswered Questions
How does this method compare to existing cost function optimization techniques in neural network training?
This article does not provide a direct comparison with other optimization methods, leaving room for further exploration of its effectiveness in comparison to existing techniques.
What impact could the consideration of available resources have on the overall training process and performance of the neural network?
While the abstract mentions the consideration of available resources in training, the specific impact of this consideration on the training process and neural network performance is not detailed. Further research could delve into this aspect to understand its significance.
Original Abstract Submitted
a method for training an artificial neural network. the method includes the following steps: providing training data for training the artificial neural network; detecting at least one specification regarding available resources; ascertaining a cost function that, in addition to an actual learning task, also takes into account the at least one specification regarding available resources; and training the artificial neural network on the basis of the provided training data using the ascertained cost function.
