Stationary points of single-layer feedback neural networks

Żurada, Jacek M.; Kang, Min Jae

doi:10.1109/iscas.1992.230015

Cited by 16 publications

(21 citation statements)

References 5 publications

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“…MLP technique of ANN was used for modeling the loading curve, and the Power law model was fitted in the unloading section curve using the least square optimization technique. Several available literatures cite that ANN provides better prediction than regression models [29][30][31][32]. To check the suitability of ANN, polynomial regression of degree-2, 3, and 4 was carried out on the loading section dataset followed by the ANN model.…”

Section: Materials and Methodologymentioning

confidence: 99%

Artificial neural network-based approach for prediction of nanomechanical properties of anodic coating on additively manufactured Al–10Si–Mg alloy

Ghosh,

Sahu,

Dey

et al. 2024

Modelling Simul. Mater. Sci. Eng.

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Nowadays, anodized coating on additively manufactured (AM) or 3D printed Al-10Si-Mg alloy are used for various components in spacecraft such as antenna feeds, wave guides, structural brackets, collimators, thermal radiators etc. In this study, Artificial Neural Network (ANN) and Power law-based models are developed from experimental nanoindentation data for predicting elastic modulus and hardness of anodized AM Al-10Si-Mg at any desired loads. Data from nanoindentation experiments conducted on plan- and cross-sections of anodized coating on AM Al-10Si-Mg alloy was considered for modelling. Apart from nanomechanical properties, load and displacement curves were predicted using Python software from ANN and the Power law model of nanoindentation. It is observed that the ANN model of 50 mN nanoindentation experimental data can accurately predict the loading pattern at any desired load below 50 mN. Elastic modulus and hardness of anodized AM Al-10Si-Mg computed from ANN and the power law model of the unloading curve are also comparable with the values obtained from Weibull distribution analysis reported elsewhere. The derived models were also used to predict nanomechanical properties at 25 and 35 mN, for which no experimental data was available. The computed hardness of plan section of the anodic coating is 3.99 and 4.02 GPa for 25 and 35 mN, respectively. The computed hardness of cross-section of the anodic coating of is 7.16 and 6.61 GPa for 25 and 35 mN, respectively. Thus, the ANN and Power law model of nanoindentation can predict elastic modulus and hardness at different loads by conducting the minimum number of experiments. The novel approach to predict nanomechanical properties using ANN resulted in determining realistic and design specific data on hardness and modulus of the anodized coating on AM Al-10Si-Mg alloy.

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Section: Materials and Methodologymentioning

confidence: 99%

Artificial neural network-based approach for prediction of nanomechanical properties of anodic coating on additively manufactured Al–10Si–Mg alloy

Ghosh,

Sahu,

Dey

et al. 2024

Modelling Simul. Mater. Sci. Eng.

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“…An adaptive system called an ANN modifies its structure in response to information passing through the network (Zurada, 1992). Each output acts as an input for the following function, and neurons within the network connect with neurons in the adjacent layer.…”

Section: Artificial Neural Network (Ann)mentioning

confidence: 99%

Explainable artificial intelligence (XAI) enabled anomaly detection and fault classification of an industrial asset

Sharma

Mittal

Soni

2023

Preprint

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Predictive maintenance helps organizations to reduce equipment downtime, optimize maintenance schedules, and enhance operational efficiency. By leveraging machine learning algorithms to predict when equipment failure will likely occur, maintenance teams can proactively schedule maintenance activities and prevent unexpected breakdowns. Anomaly detection and fault classification are essential components of predictive maintenance. Anomaly detection involves analyzing sensor data collected from equipment to identify deviations from normal behavior. Fault classification, on the other hand, involves identifying the root cause of a fault or failure. A dataset of an industrial asset is used to evaluate the proposed study. Four distinct data-driven anomaly detection methodologies were employed after the pre-processing of the data, with the deep learning-based autoencoder producing the best results of all the techniques. Implementing machine learning-based fault categorization approaches revealed that Random Forest had the best results. Bayesian optimization and sequential model-based hyperparameter optimization technique is used for greater accuracy and optimized hyperparameters. Significant progress has been made in anomaly detection and fault classification using machine learning, but the degree of their explainability is significantly limited by the ``black-box" character of some machine learning techniques. Less emphasis has been placed on explainable artificial intelligence (XAI) approaches in the domain of maintenance. Therefore, the XAI tools have been used to acknowledge the extent of the variables to analyze the influence of respective features. A stability metric has been included to improve the explanation's overall quality. The findings of this article suggest that the utilization of eXplainable Artificial Intelligence (XAI) can offer significant contributions in terms of insights and solutions for addressing critical maintenance issues. As a result, decision-making processes can become more informed and effective.

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“…Not as complex as CNNs, these networks can still use low resolution images and have been used in the past for self-driving vehicles. An example of this is ALVINN (Autonomous Land Vehicle in a Neural Network) which back in 1989, used a simple NN consisting on one hidden layer with 29 nodes, having as an input 30x32 video images as well as input from a range finder that was trained to yield 45 direction outputs [32]. ALVINN can be considered as a proof of concept and because of the accuracy importance of such application, is not up to now that DL is taking on commercial autonomous vehicles having reached much better results than the ones reported in [32].…”

Section: Has DL Been Used Recently In Agricultural Machinery Applicat...mentioning

confidence: 99%

“…An example of this is ALVINN (Autonomous Land Vehicle in a Neural Network) which back in 1989, used a simple NN consisting on one hidden layer with 29 nodes, having as an input 30x32 video images as well as input from a range finder that was trained to yield 45 direction outputs [32]. ALVINN can be considered as a proof of concept and because of the accuracy importance of such application, is not up to now that DL is taking on commercial autonomous vehicles having reached much better results than the ones reported in [32]. Usually such shallow networks take on inputs in the form of features which total number is much less than the total number of pixels in an image.…”

Section: Has DL Been Used Recently In Agricultural Machinery Applicat...mentioning

confidence: 99%

Intelligent Agricultural Machinery Using Deep Learning

Gabriel¹,

Balocco²,

Mann³

et al. 2021

IEEE Instrum. Meas. Mag.

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Artificial intelligence, deep learning, big data, self-driving cars …, these are words that have become familiar to most people and have captured the imagination of the public and have brought hopes as well as fears. We have been told that artificial intelligence will be a major part of our lives, and almost all of us witness this when decisions made by algorithms show us commercial adds that are specifically targeting our interests while using the web. In this paper, the conversation around artificial intelligence focuses on a particular application, agricultural machinery, but offering enough content so that the reader can have a very good idea on how to consider this technology for not only other agricultural applications such as sorting and grading produce, but also other areas in which this technology can be a part of a system that includes sensors, hardware and software that can make accurate decisions. Narrowing the application and also focusing on one specific artificial intelligence approach, that of deep learning, allow us to illustrate from start to end the steps that are usually considered and elaborate on recent developments on artificial intelligence. Agriculture importance as not only providing the world with food but also as an economic factorAgriculture plays a significant role in Canada's economy. From a recent release from Statistics Canada, we can highlight that the sector contributed $49.0 billion to Canada's gross domestic product (GDP) in 2015, accounting for 2.6% of total GDP. Agriculture industries contributed $25.1 billion or 51% of GDP in the sector, while agri-food manufacturing industries contributed $23.9 billion or 49%. In the future, the industry is expected to grow at a considerable rate. "Canada's agriculture industry can generate $11 billion for the country's GDP annually by 2030 if the government invests in people and technology" as indicated in [1]. Canada is a global leader in agricultural production and by taking on research and development on new technologies this can be of great benefit to the country and the world, as this activity is vital and will help the country to remain competitive.

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Stationary points of single-layer feedback neural networks

Cited by 16 publications

References 5 publications

Artificial neural network-based approach for prediction of nanomechanical properties of anodic coating on additively manufactured Al–10Si–Mg alloy

Artificial neural network-based approach for prediction of nanomechanical properties of anodic coating on additively manufactured Al–10Si–Mg alloy

Explainable artificial intelligence (XAI) enabled anomaly detection and fault classification of an industrial asset

Intelligent Agricultural Machinery Using Deep Learning

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