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Issues in artificial neural networks 


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Artificial Neural Networks (ANNs) have several issues that need to be addressed. Firstly, the simulation of neural networks is a challenging and tedious process . Secondly, ANNs can generate outputs for input patterns that have no similar training patterns, which raises concerns about the reliability of extrapolations . Additionally, there is a lack of generalized rules for optimizing the design parameters of ANNs, as different problems may require different parameters for accurate results . Furthermore, when using ANNs in Model Predictive Controls (MPCs) for building modeling, specific issues can arise, such as difficulty in training the ANN or managing energy flexibility . Overall, addressing these issues is crucial for improving the effectiveness and reliability of ANNs in various applications.

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Proceedings ArticleDOI
18 Apr 2019
3 Citations
The paper discusses various design issues in artificial neural networks, including the lack of generalized rules and the need for optimization of design parameters for robust performance and accuracy.
The paper discusses the issue of uncertainties in artificial neural networks and the need for these systems to provide an indication of the training sample related uncertainty associated with their output.
The paper discusses various issues that need to be addressed while using artificial neural networks, but does not explicitly list them.
The paper discusses some issues related to the use of artificial neural networks (ANNs) in model predictive controls (MPCs) for building modeling, such as difficulty in training the ANN or in managing flexibility by the MPC.
The paper does not specifically discuss issues in artificial neural networks. The paper focuses on the process of intelligent modeling of pedagogical situations using artificial neural networks.

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