Multilayer Perceptron Method to Estimate Real-World Fuel Consumption Rate of Light Duty Vehicles

Li, Yawen; Tang, Guangcan; Du, Jiameng; Zhou, Nan; Zhao, Yue; Wu, Tian

doi:10.1109/access.2019.2914378

Cited by 32 publications

(24 citation statements)

References 24 publications

(24 reference statements)

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“…Artificial neural networks are today widely used for solving classification and regression tasks in cancer recognition [25,26], sepsis prediction [27], recognition of marine objects [28], prediction of marine diesel engine parameters [29], etc. MLP can be considered as the most popular ANN subclass, due to its high classification [30,31] and regression performances [32,33] in problems similar to condition-based maintenance of marine CODLAG propulsion system components.…”

Section: Introductionmentioning

confidence: 99%

Multilayer Perceptron approach to Condition-Based Maintenance of Marine CODLAG Propulsion System Components

et al. 2019

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In this paper multilayer perceptron (MLP) approach to condition-based maintenance of combined diesel-electric and gas (CODLAG) marine propulsion system is presented. By using data available in UCI, online machine learning repository, MLPs for prediction of gas turbine (GT) and GT compressor decay state coefficients are designed. Aforementioned MLPs are trained and tested by using 11 934 samples, of which 9 548 samples are used for training and 2 386 samples are used testing. In the case of GT decay state coefficient prediction, the lowest mean relative error of 0.622 % is achieved if MLP with one hidden layer of 50 artificial neurons (AN) designed with Tanh activation function is utilized. This configuration achieves the best results if it is trained by using L-BFGS solver. In the case of GT compressor decay state coefficient, the best results are achieved if MLP is designed with four hidden layers of 100, 50, 50 and 20 ANs, respectively. This configuration is designed by using Logistic sigmoid activation function. The lowest mean relative error of 1.094 % is achieved if MLP is trained by using L-BFGS solver.

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Section: Introductionmentioning

confidence: 99%

Multilayer Perceptron approach to Condition-Based Maintenance of Marine CODLAG Propulsion System Components

et al. 2019

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“…One machine-learning approach, the multilayer perceptron method, can handle the complicated properties of different types of data. 13 The application of AI to diabetes diagnosis is attracting much interest. 14 This requires the comprehensive analysis of several parameters that include heart rate variability and arterial blood glucose alterations.…”

Section: Ai-based Comprehensive Analysis Of Multiple Informationmentioning

confidence: 99%

Artificial intelligence-aided decision support in paediatrics clinical diagnosis: development and future prospects

Zhang

Yang

et al. 2020

J Int Med Res

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Artificial intelligence (AI)-aided decision support has developed rapidly to meet the needs for effective analysis of substantial data sets from electronic medical records and medical images generated daily, and computer-assisted intelligent drug design. In clinical practice, paediatricians make medical decisions after obtaining a large amount of information about symptoms, physical examinations, laboratory test indicators, special examinations and treatments. This information is used in combination with paediatricians’ knowledge and experience to form the basis of clinical decisions. This diagnosis and therapeutic strategy development based on large amounts of information storage can be applied to both large clinical databases and data for individual patients. To date, AI applications have been of great value in intelligent diagnosis and treatment, intelligent image recognition, research and development of intelligent drugs and intelligent health management. This review aims to summarize recent advances in the research and clinical use of AI in paediatrics.

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“…MLP is an ANN made up of multiple neuronal layers in a feed-through architecture. A multi-layer sensor consists of three or more layers: one input, one output, and one or more hidden layers [48,49].…”

Section: Machine Learning Algorithmsmentioning

confidence: 99%

Modeling of Heat Transfer Coefficient in Solar Greenhouse Type Drying Systems

Çerçi

Daş

2019

Sustainability

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As a sustainable energy source, solar energy is used in many applications. A greenhouse type dryer, which is a food drying system, directly benefits from solar energy. Convective heat transfer coefficient (hc) is an important parameter in food drying systems, in terms of system design and performance. Many parameters and equations are used to determine hc. However, as it is difficult to manually process and analyze large amounts of data and different formulations, machine learning algorithms are preferred. In this study, natural and forced convective solar greenhouse type dryers were designed. In a solar greenhouse type dryer, grape is dried in natural (GDNC) and forced convection (GDFC). For convective heat transfer coefficient (hc), predictive models were created using a multilayer perceptron (MLP)—which has many uses in drying applications, as mentioned in the literature—and decision tree (DT), which has not been used before in food drying applications. The machine learning algorithms and results of the estimated models are compared in this study. Error analyses were performed to determine the accuracy rates of the obtained models. As a result, the hc value of the dried grape product in a natural convective solar greenhouse type dryer was 11.3% higher than that of the forced type. The DT algorithm was found to be a more successful model than the MLP algorithm in estimating hc values in HDFC according to Root Mean Square Error. (RMSE = 0.0903). On the contrary, the MLP algorithm was more successful than the DT algorithm in estimating hc values in GDNC (RMSE = 0.0815).

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Multilayer Perceptron Method to Estimate Real-World Fuel Consumption Rate of Light Duty Vehicles

Cited by 32 publications

References 24 publications

Multilayer Perceptron approach to Condition-Based Maintenance of Marine CODLAG Propulsion System Components

Multilayer Perceptron approach to Condition-Based Maintenance of Marine CODLAG Propulsion System Components

Artificial intelligence-aided decision support in paediatrics clinical diagnosis: development and future prospects

Modeling of Heat Transfer Coefficient in Solar Greenhouse Type Drying Systems

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