Desiccant wheel system modeling improvement using multiple population genetic algorithm training of neural network

Yang, Yongli; Cong, Hua; Jiang, Pengcheng; Feng, Fuzhou; Zhang, Ping; Li, Yaokai; Hao, Jinfeng

doi:10.1080/07373937.2016.1260031

Cited by 8 publications

(3 citation statements)

References 18 publications

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“…The error back-propagation (BP) algorithm has a simple network structure, a powerful parallel processing capability, and a strong fault tolerance, and can approximate the complex non-linear relationships well [36]. The BP neural network is a multilayer feedforward neural network with three or more layers, including an input layer, a hidden layer or layers, and an output layer.…”

Section: Back-propagation (Bp) Neural Networkmentioning

confidence: 99%

“…However, it has some disadvantages, such as slow learning-convergent velocity, difficulty in network structure determination, and inability to obtain accurate initial connection weights and thresholds. This study adopted the multiple population genetic algorithm (MPGA) to optimize the neural networks in an attempt to compensate for the aforementioned drawbacks of BP neural network [36].…”

Section: Bp Neural Network Parameter Optimizationmentioning

confidence: 99%

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Non-Invasive Continuous Blood-Pressure Monitoring Models Based on Photoplethysmography and Electrocardiography

2019

Sensors

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Blood pressure is an extremely important blood hemodynamic parameter. The pulse wave contains abundant blood-pressure information, and the convenience and non-invasivity of its measurement make it ideal for non-invasive continuous monitoring of blood pressure. Based on combined photoplethysmography and electrocardiogram signals, this study aimed to extract the waveform information, introduce individual characteristics, and construct systolic and diastolic blood-pressure (SBP and DBP) estimation models using the back-propagation error (BP) neural network. During the model construction process, the mean impact value method was employed to investigate the impact of each feature on the model output and reduce feature redundancy. Moreover, the multiple population genetic algorithm was applied to optimize the BP neural network and determine the initial weights and threshold of the network. Finally, the models were integrated for further optimization to generate the final individualized continuous blood-pressure monitoring models. The results showed that the predicted values of the model in this study correlated significantly with the measured values of the electronic sphygmomanometer. The estimation errors of the model met the Association for the Advancement of Medical Instrumentation (AAMI) criteria (the SBP error was 2.5909 ± 3.4148 mmHg, and the DBP error was 2.6890 ± 3.3117 mmHg) and the Grade A British Hypertension Society criteria.

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Section: Back-propagation (Bp) Neural Networkmentioning

confidence: 99%

Section: Bp Neural Network Parameter Optimizationmentioning

confidence: 99%

Non-Invasive Continuous Blood-Pressure Monitoring Models Based on Photoplethysmography and Electrocardiography

2019

Sensors

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“…Data-driven modeling can use a large amount of data from factory production history to train neural networks, so as establishing the statistical relationship between output variables and input variables. Fuzzy inference system [8], [9], support vector regression [10] and neural network [10], [11], can available to establish data-driven models. Due to the advantages of fuzzy system and neural network, namely comprehensiveness, transparency, adaptability and rapid convergence, fuzzy neural network has been successfully applied to solve the modeling problems in practical industrial engineering [12].…”

Section: Introductionmentioning

confidence: 99%

Modeling of tobacco loosening and conditioning system using LSTM recurrent neural networks

Song

Gao

et al. 2022

International Conference on Computer, Artificial Intelligence, and Control Engineering (CAICE 2022)

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In cut tobacco production line, the loosening and conditioning process is one of the most significant links affecting tobacco leaves quality. In order to solve the modeling difficulties of tobacco loosening and conditioning system due to time delay, strong coupling, nonlinearity and missing parameters, a data-driven model based on Long-Short-Term Memory networks is designed. Using the strong time series information learning ability and nonlinear fitting ability of the LSTM networks, it is trained only with the historical time series data of the outlet moisture and temperature of the loosening and conditioning cylinder, and the system model that can accurately predict the outlet moisture and temperature in output tobacco is obtained. The model predicts the output moisture and temperature values at the next time by inputting 60 consecutive historical output values. It is verified that the model has excellent fitting effect on both training set and verification set.

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New multiple regression and machine learning models of rotary desiccant wheel for unbalanced flow conditions

Güzelel

Olmuş

Çerçi

et al. 2022

International Communications in Heat and Mass Transfer

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Desiccant wheel system modeling improvement using multiple population genetic algorithm training of neural network

Cited by 8 publications

References 18 publications

Non-Invasive Continuous Blood-Pressure Monitoring Models Based on Photoplethysmography and Electrocardiography

Non-Invasive Continuous Blood-Pressure Monitoring Models Based on Photoplethysmography and Electrocardiography

Modeling of tobacco loosening and conditioning system using LSTM recurrent neural networks

New multiple regression and machine learning models of rotary desiccant wheel for unbalanced flow conditions

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