How do you train artificial neural networks?
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| This technique will allow to train artificial neural networks; identify effective measures to improve the performance of artificial neural networks. | |
05 Jun 2013 10 Citations | In order to yield better results Artificial Neural Networks need to have an optimal architecture and be trained in a suitable way. |
149 Citations | Artificial neural networks (ANNs) are systems that can learn. |
| This will be easy, because there are currently quite a few ready-made computer programs, easily available, which allow their user to quickly and effortlessly create artificial neural networks, run them, train and use in practice. | |
107 Citations | Artificial neural networks (ANN), have shown to be an effective, general-purpose approach for pattern recognition, classification, clustering, and prediction. |
Open access•Journal Article 28 Dec 2012-International Journal of Advanced Research in Computer Science and Electronics Engineering 31 Citations | Hence, they are well suited to the problem of training and optimize weights of Artificial Neural Networks. |
Open access•Journal Article 38 Citations | This training set can also guarantee effective learning andcomprehension of Artificial Neural Networks (ANN). |
41 Citations | As a result, the artificial neural networks are able to predict the behavior of the system with good accuracy in each scenario. |
| It illustrates how neural network concepts can be integrated into traditional artificial intelligence course material. |
Related Questions
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How to train an artificial neural network on matlab?5 answersStep 1: The process of training an artificial neural network in MATLAB involves designing the network with the appropriate number of nodes and transfer functions, selecting the optimization routine, and then optimizing the error function to determine the best set of weights and biases for the dataset. Finally, the network needs to be tested to ensure a good balance between accuracy and generalization ^[Salahalddin Abusalah, Ali Zilouchian]. Additionally, the training process may involve stages such as weight initialization, weight vector creation, and testing the network using the initialization parameters and training vector created in the previous stages ^[Mahabad]. The use of the nntool feature in MATLAB R2020a for training and testing the artificial neural network model has also been highlighted ^[Unnamed].
Step 2:
- "The process of training an artificial neural network in MATLAB involves designing the network with the appropriate number of nodes and transfer functions, selecting the optimization routine, and then optimizing the error function to determine the best set of weights and biases for the dataset."
- "Additionally, the training process may involve stages such as weight initialization, weight vector creation, and testing the network using the initialization parameters and training vector created in the previous stages."
- "The use of the nntool feature in MATLAB R2020a for training and testing the artificial neural network model has also been highlighted."
Step 3:
The process of training an artificial neural network in MATLAB involves designing the network with the appropriate number of nodes and transfer functions, selecting the optimization routine, and then optimizing the error function to determine the best set of weights and biases for the dataset ^[Salahalddin Abusalah, Ali Zilouchian]. Additionally, the training process may involve stages such as weight initialization, weight vector creation, and testing the network using the initialization parameters and training vector created in the previous stages ^[Mahabad]. The use of the nntool feature in MATLAB R2020a for training and testing the artificial neural network model has also been highlighted ^[Unnamed].
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