Novel Spiking Neural Network Model for Gear Fault Diagnosis

Ali, Yasir Hassan; Ahmed, Falah Y. H.; Abdelrhman, Ahmed M.; Ali, Salah M.; Borhana, Abdoulhdi A.; Hamzah, Rohana

doi:10.1109/esmarta56775.2022.9935414

Cited by 2 publications

(1 citation statement)

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“…This makes it limited to be implemented in some domains [28]. They require extensive data and computing resources, limiting their scalability and applicability in specific settings [29]. There are still various challenges and limitations that these technologies currently need to overcome.…”

Section: Introductionmentioning

confidence: 99%

Critical Review of Neural Network Generations and Models Design

Yousif,

Yousif

2023

Preprint

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In recent years, Neural networks are increasingly deployed in various fields to learn complex patterns and make accurate predictions. However, designing an effective neural network model is a challenging task that requires careful consideration of various factors, including architecture, optimization method, and regularization technique. This paper aims to comprehensively overview the state-of-the-art artificial neural network (ANN) generation and highlight key challenges and opportunities in machine learning applications. It provides a critical analysis of current neural network model design methodologies, focusing on the strengths and weaknesses of different approaches. Also, it explores the use of different learning approaches, including convolutional neural networks (CNN), deep neural networks (DNN), and recurrent neural networks (RNN) in image recognition, natural language processing, and time series analysis. Besides, it discusses the benefits of choosing the ideal values for the different components of ANN, such as the number of Input/output layers, hidden layers number, activation function type, epochs number, and model type selection, which help improve the model performance and generalization. Furthermore, it identifies some common pitfalls and limitations of existing design methodologies, such as overfitting, lack of interpretability, and computational complexity. Finally, it proposes some directions for future research, such as developing more efficient and interpretable neural network architectures, improving the scalability of training algorithms, and exploring the potential of new paradigms, such as Spiking Neural Networks, quantum neural networks, and neuromorphic computing.

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Section: Introductionmentioning

confidence: 99%