Introductory Chapter: Artificial Neural Networks

El-Shahat, Adel

doi:10.5772/intechopen.73530

Cited by 24 publications

(3 citation statements)

References 11 publications

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“…The applications of neural networks are numerous and include many various fields, among which is engineering. ANNs have been used for the prediction of manufacturing systems' performance [41], manufacturing process costs [42], photovoltaic solar integrated system efficiencies [43], space weather [44], outdoor sound transmission [45], stream flow [46] and wind waves [47].…”

Section: Artificial Neural Network (Anns)mentioning

confidence: 99%

“…The applications of neura works are numerous and include many various fields, among which is engineering. A have been used for the prediction of manufacturing systems' performance [41], man turing process costs [42], photovoltaic solar integrated system efficiencies [43], s weather [44], outdoor sound transmission [45], stream flow [46] and wind waves [47 In this study, we deal with modelling the fatigue experimental data of fibreforced plastics using ANNs. The objective of the ANN model is to determine missin perimental information which would improve the accuracy of the model.…”

Section: Artificial Neural Network (Anns)mentioning

confidence: 99%

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Enhancing the Design of Experiments on the Fatigue Life Characterisation of Fibre-Reinforced Plastics by Incorporating Artificial Neural Networks

Witzgall,

Ashhab,

Wartzack

2024

Materials

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Fatigue life testing is a complex and costly matter, especially in the case of fibre-reinforced thermoplastics, where other parameters in addition to force alone must be taken into account. The number of tests required therefore increases significantly, especially if the influence of different fibre orientations is to be taken into account. It is therefore important to gain the greatest possible amount of knowledge from the limited number of available tests. In order to achieve this, this study aims to utilise adaptive sampling, which is used in numerous areas of computational engineering, for the design of experiments on fatigue life testing. Artificial neural networks (ANNs) are therefore trained on data for the short-fibre-reinforced material PBT GF30, and their areas of greatest model uncertainty are queried. This was undertaken with ANNs from various numbers of hidden layers, which were analysed for their performance. The ideal case turned out to be four hidden layers, for which a squared error as small as 1 × 10−3 was recorded. Locally resolved, the ANN was used to identify the region of greatest uncertainty for samples of vertical orientation and small numbers of cycles. With information such as this, additional data can be obtained in such uncertain regions in order to improve the model prediction—almost halving the recorded error to only 0.55 × 10−3. In this way, a model of comparable value can be found with less experimental effort, or a model of better quality can be set up with the same experimental effort.

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Section: Artificial Neural Network (Anns)mentioning

confidence: 99%

Section: Artificial Neural Network (Anns)mentioning

confidence: 99%

Enhancing the Design of Experiments on the Fatigue Life Characterisation of Fibre-Reinforced Plastics by Incorporating Artificial Neural Networks

Witzgall,

Ashhab,

Wartzack

2024

Materials

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“…Various sentences with similar word frequency and meanings are analyzed. It also identifies the meaning of the sentence and makes command in a single line [44]. Various locations with similar lexical method are connected to the dissimilar test.…”

Section: Finding Meaningmentioning

confidence: 99%

Optimal Learning Behavior Prediction System Based on Cognitive Style Using Adaptive Optimization-Based Neural Network

et al. 2020

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Widespread development of system software, the process of learning, and the excellence in profession of teaching are the formidable challenges faced by the learning behavior prediction system. The learning styles of teachers have different kinds of content designs to enhance their learning. In this learning environment, teachers can work together with the students, but the learning materials are designed by the teachers. The cognitive style deals with mental activities such as learning, remembering, thinking, and the usage of language. Therefore, being motivated by the problems mentioned above, this paper proposes the concept of adaptive optimization-based neural network (AONN). The learning behavior and browsing behavior features are extracted and incorporated into the input of artificial neural network (ANN). Hence, in this paper, the neural network weights are optimized with the use of grey wolf optimizer (GWO) algorithm. The output operation of e-learning with teaching equipment is chosen based on the cognitive style predicted by AONN. In experimental section, the measures of accuracy, sensitivity, specificity, time (sec), and memory (bytes) are carried out. Each of the measure is compared with the proposed AONN and existing fuzzy logic methodologies. Ultimately, the proposed AONN method produces higher accuracy, specificity, and sensitivity results. The results demonstrate that the algorithm proposed in this study can automatically learn network structures competitively, unlike those achieved for neural networks through standard approaches.

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Machine Learning-Based Automated Methods for Brain Tumor Segmentation, Subtype Classification, Tracking and Patient Survival Prediction

Pei

Iftekharuddin

2021

Molecular Pathology Library

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Introductory Chapter: Artificial Neural Networks

Cited by 24 publications

References 11 publications

Enhancing the Design of Experiments on the Fatigue Life Characterisation of Fibre-Reinforced Plastics by Incorporating Artificial Neural Networks

Enhancing the Design of Experiments on the Fatigue Life Characterisation of Fibre-Reinforced Plastics by Incorporating Artificial Neural Networks

Optimal Learning Behavior Prediction System Based on Cognitive Style Using Adaptive Optimization-Based Neural Network

Machine Learning-Based Automated Methods for Brain Tumor Segmentation, Subtype Classification, Tracking and Patient Survival Prediction

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