Electric Vehicle Relay Lifetime Prediction Model Using the Improving Fireworks Algorithm–Grey Neural Network Model

The relay reliability has an impact on the reliability of the entire electric vehicle system. This paper contributes to propose the improving fireworks algorithm optimizing the grey neural network model to predict the relay lifetime. This paper shows how the mutation operation and mapping operation in the fireworks algorithm are used to improve the convergence ability and running speed; the convergence performance and running speed of improved fireworks algorithm are tested with standard test function and compared with fireworks algorithm; and the grey neural network model–improved fireworks algorithm is used to predict the relay life and compared with grey model, grey neural network, and grey neural network model–fireworks algorithm. The results show that the convergence accuracy of the improved fireworks algorithm is better than the fireworks algorithm. The running time of improved fireworks algorithm is the shortest; the improved fireworks algorithm–grey neural network model has the best prediction effect and the root mean square error value is 6.75% smaller than the fireworks algorithm–grey neural network model.

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Assessing Dry Weight of Hemodialysis Patients via Sparse Laplacian Regularized RVFL Neural Network with L2,1-Norm

BioMed Research International ◽

10.1155/2021/6627650 ◽

2021 ◽

Vol 2021 ◽

pp. 1-10

Author(s):

Xiaoyi Guo ◽

Wei Zhou ◽

Qun Lu ◽

Aiyan Du ◽

Yinghua Cai ◽

...

Keyword(s):

Neural Network ◽

Network Model ◽

Neural Network Model ◽

Dry Weight ◽

Hemodialysis Patients ◽

Normal Weight ◽

The Body ◽

Functional Link ◽

Patient Will ◽

Better Than

Dry weight is the normal weight of hemodialysis patients after hemodialysis. If the amount of water in diabetes is too much (during hemodialysis), the patient will experience hypotension and shock symptoms. Therefore, the correct assessment of the patient’s dry weight is clinically important. These methods all rely on professional instruments and technicians, which are time-consuming and labor-intensive. To avoid this limitation, we hope to use machine learning methods on patients. This study collected demographic and anthropometric data of 476 hemodialysis patients, including age, gender, blood pressure (BP), body mass index (BMI), years of dialysis (YD), and heart rate (HR). We propose a Sparse Laplacian regularized Random Vector Functional Link (SLapRVFL) neural network model on the basis of predecessors. When we evaluate the prediction performance of the model, we fully compare SLapRVFL with the Body Composition Monitor (BCM) instrument and other models. The Root Mean Square Error (RMSE) of SLapRVFL is 1.3136, which is better than other methods. The SLapRVFL neural network model could be a viable alternative of dry weight assessment.

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Sound Quality Evaluation of the Interior Noise of Pure Electric Vehicle Based on Neural Network Model

IEEE Transactions on Industrial Electronics ◽

10.1109/tie.2017.2711554 ◽

2017 ◽

Vol 64 (12) ◽

pp. 9442-9450 ◽

Cited By ~ 18

Author(s):

Conggan Ma ◽

Chaoyi Chen ◽

Qinghe Liu ◽

Haibo Gao ◽

Qing Li ◽

...

Keyword(s):

Neural Network ◽

Electric Vehicle ◽

Network Model ◽

Neural Network Model ◽

Quality Evaluation ◽

Sound Quality ◽

Interior Noise

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An Optimized Hybrid Neural Network Model for Detecting Depression among Twitter Users

International Journal of Innovative Technology and Exploring Engineering - Special Issue ◽

10.35940/ijitee.j9590.0881019 ◽

2019 ◽

Vol 8 (10) ◽

pp. 2781-2795

Keyword(s):

Neural Network ◽

Network Model ◽

Neural Network Model ◽

Research Work ◽

Hybrid Neural Network ◽

The Neural Network ◽

Twitter Users ◽

Trained Neural Network ◽

User Friendly ◽

Better Than

The proposed work is to extensively evaluate if a user is depressed or not using his Tweets on Twitter. With the omni presence of social media, this method should help in identifying the depression of users. We propose an Optimized Hybrid Neural Network model to evaluate the user tweets on Twitter to analyze if a user is depressed or not. Where Neural Network is trained using Tweets to predict the polarity of Tweets. The Neural Network is trained in such a way that at any point when presented with a Tweet the model outputs the polarity associated with the Tweet. Also, a user-friendly GUI is presented to the user that loads the trained neural network in no time and can be used to analyze the users’ state of depression. The aim of this research work is to provide an algorithm to evaluate users’ sentiment on Twitter in a way better than all other existing techniques

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Improved Accuracy of an Ultrasonic Based Neuro-Navigation System Using Neural Networks

Dynamic Systems and Control, Parts A and B ◽

10.1115/imece2005-79775 ◽

2005 ◽

Author(s):

Jennifer Akers ◽

Sanjeevi Chitikeshi ◽

Ajay Mahajan ◽

Sumeer Lal

Keyword(s):

Neural Network ◽

Network Model ◽

Neural Network Model ◽

Navigation Systems ◽

The Neural Network ◽

Neuronavigation System ◽

Electronic Hardware ◽

Improved Accuracy ◽

2D And 3D ◽

Better Than

This paper presents the design and development of an ultrasonic based neuronavigation system to be used for real time surgery. The system formulation, hardware and a neural network model is presented that improves the accuracy of the system considerably. 1D, 2D and 3D results from the neural network model are presented along with designs for the physical and electronic hardware. The 3D system presented in this paper eliminates the space intensive camera, has an accuracy better than 1.0 mm in the operating range of about 20–40 cm, makes the system independent of line-of-sight occlusion problems, and is expected to pave the way for accurate fusion models of the future that may account for brain shifts during surgery. The results show that the performance of the proposed system provides many advantages over existing neuro-navigation systems without compromising on the accuracy.

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Research on Aviation Material with Unsafe Events Prediction Based on BP Neural Network with PCA Feature Extraction

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.540.488 ◽

2014 ◽

Vol 540 ◽

pp. 488-491 ◽

Cited By ~ 1

Author(s):

Xu Sheng Gan ◽

Hua Ping Li ◽

Jing Shun Duanmu

Keyword(s):

Neural Network ◽

Feature Extraction ◽

Network Model ◽

Neural Network Model ◽

Bp Neural Network ◽

Aviation Material ◽

Training Samples ◽

Basic Features ◽

Bp Neural Network Model ◽

Better Than

In order to better predict the aviation material unsafe events, a BP neural network model based on PCA feature extraction is proposed. Firstly, the training samples of aviation material unsafe events are used to carry out the PCA feature extraction, and then using the extracted basic features, BP neural network model is established. The numerical example shows that, the hybrid model proposed is better than that of alone BP neural network model, and it is effective and feasible to establish the unsafe events model for aviation material.

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