Applications of General Regression Neural Networks in Dynamic Systems

Al-Mahasneh, Ahmad Jobran; Anavatti, Sreenatha G.; Garratt, Matthew A.; Pratama, Mahardhika

doi:10.5772/intechopen.80258

Cited by 39 publications

(19 citation statements)

References 41 publications

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“…Adly et al in [57] proposed a novel algorithm that combines a general regression network-based (GRN) consensus learning model with randomization technique to detect defective patterns in semiconductor wafers, where the combination results in randomized general regression network (RGRN). GRN are singlepass associative memory feed-forward type ANNs which use normalized Gaussian kernels in the hidden layer as activation functions [381]. The randomization technique was applied by implementing randomized bootstrap to the original data.…”

Section: E: Clusteringmentioning

confidence: 99%

A Review and Analysis of Automatic Optical Inspection and Quality Monitoring Methods in Electronics Industry

2020

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Electronics industry is one of the fastest evolving, innovative, and most competitive industries. In order to meet the high consumption demands on electronics components, quality standards of the products must be well-maintained. Automatic optical inspection (AOI) is one of the non-destructive techniques used in quality inspection of various products. This technique is considered robust and can replace human inspectors who are subjected to dull and fatigue in performing inspection tasks. A fully automated optical inspection system consists of hardware and software setups. Hardware setup include image sensor and illumination settings and is responsible to acquire the digital image, while the software part implements an inspection algorithm to extract the features of the acquired images and classify them into defected and non-defected based on the user requirements. A sorting mechanism can be used to separate the defective products from the good ones. This article provides a comprehensive review of the various AOI systems used in electronics, microelectronics , and opto-electronics industries. In this review the defects of the commonly inspected electronic components, such as semiconductor wafers, flat panel displays, printed circuit boards and light emitting diodes, are first explained. Hardware setups used in acquiring images are then discussed in terms of the camera and lighting source selection and configuration. The inspection algorithms used for detecting the defects in the electronic components are discussed in terms of the preprocessing, feature extraction and classification tools used for this purpose. Recent articles that used deep learning algorithms are also reviewed. The article concludes by highlighting the current trends and possible future research directions.

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Section: E: Clusteringmentioning

confidence: 99%

A Review and Analysis of Automatic Optical Inspection and Quality Monitoring Methods in Electronics Industry

2020

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“…Both algorithms are well-known for their ability of regression, function modeling, and prediction. More details about GRNN and SVR can be found in [27][28][29] and [30], respectively.…”

Section: Machine Learning-based Reference Voltage Estimatormentioning

confidence: 99%

Maximum Power Point Tracking of PV System Based on Machine Learning

et al. 2020

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This project studies the conditions at which the maximum power point of a photovoltaic (PV) panel is obtained. It shows that the maximum power point is very sensitive to external disturbances such as temperature and irradiation. It introduces a novel method for maximizing the output power of a PV panel when connected to a DC/DC boost converter under variable load conditions. The main contribution of this work is to predict the optimum reference voltage of the PV panel at all-weather conditions using machine learning strategies and to use it as a reference for a Proportional-Integral-Derivative controller that ensures that the DC/DC boost converter provides a stable output voltage and maximum power under different weather conditions and loads. Evaluations of the proposed system, which uses an experimental photovoltaic dataset gathered from Spain, prove that it is robust against internal and external disturbances. They also show that the system performs better when using support vector machines as the machine learning strategy compared to the case when using general regression neural networks.

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“…The most successful applications of ANNs are: image and voice processing, pattern recognition, planning, adaptive interfaces for man/machine systems, prediction, control and optimization, signal filtering [12][13][14][15][16][17][18][19][20].…”

Section: Artificial Intelligence and Neural Networkmentioning

confidence: 99%

Synapse. A Neutron Spectrum Unfolding Code Based on Generalized Regression Artificial Neural Networks

Martínez-Blanco¹,

Serrano-Muñoz²,

Vega-Carrillo³

et al. 2020

EAI Endorsed Transactions on Industrial Networks and Intelligen

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In its broadest sense, the term artificial intelligence indicates the ability of an artifact to perform the same types of functions that characterize human thought. The goal of AI is to use algorithms, heuristics and methodologies based on the ways in which the human brain solves problems. Artificial neural networks recreate the structure of the human brain imitating the learning process. The Artificial neural networks theory has provided an alternative to classical computing for those problems in which traditional methods have delivered results that are not very convincing or not very convenient such as in the case of the neutron spectrometry and dosimetry problem for radiation protection purposes, using the Bonner spheres spectrometer as measurement system, mainly because many problems are encountered when trying to determine the neutron energy spectrum of a measured data. The most delicate part of the spectrometry based on this system is the unfolding process, for which several neutron spectrum unfolding codes have being developed. However, these codes require an initial guess spectrum in order to initiate the unfolding process. Their poor availability and their not easy management for the end user are other associated problems. Artificial Intelligence technology, is an alternative technique that is gaining popularity among researchers in neutron spectrometry research area, since it offers better results compared with the traditional solution methods. In this work, "Synapse", a neutron spectrum unfolding code based on Generalized Regression Artificial Neural Networks technology is presented. The Synapse code is capable to unfold the neutron spectrum and to calculate 15 dosimetric quantities using the count rates, coming from a BSS as the only entrance information. The results obtained show that the Synapse code, based on GRANN technology, is a promising and innovative technological alternative for solving the neutron spectrometry and dosimetry problems.

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Applications of General Regression Neural Networks in Dynamic Systems

Cited by 39 publications

References 41 publications

A Review and Analysis of Automatic Optical Inspection and Quality Monitoring Methods in Electronics Industry

A Review and Analysis of Automatic Optical Inspection and Quality Monitoring Methods in Electronics Industry

Maximum Power Point Tracking of PV System Based on Machine Learning

Synapse. A Neutron Spectrum Unfolding Code Based on Generalized Regression Artificial Neural Networks

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