Study on Steady Condition Control of Hybrid Turbocharging System

2010 ◽  
Vol 139-141 ◽  
pp. 1941-1944
Author(s):  
Fu Zhou Zhao ◽  
Rong Liang ◽  
Xiao Ping Chen
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
High Speed ◽  
Linear Interpolation ◽  
Control Model ◽  
Steady Condition ◽  
Operation Condition ◽  
Engine Operation ◽  
Mapping Algorithm ◽  
Turbocharging System

This paper analyzes the principle of hybrid turbocharging system in a vehicle diesel engine, and proposes motor control model about hybrid turbocharging system in steady engine operation condition according to energy imbalance of the exhaust gas. The high-speed motor can work as a motor or a generator in this control model of different engine condition. Then mapping algorithms about n-dimensional linear interpolation and BP neural network are presented to solve steady condition control problem of the hybrid turbocharging system. Each algorithm is applied to map same sample data, the simulation results reveal that BP neural network mapping algorithm is more suitable for the mapping control of hybrid turbocharging system because BP neural network has better generalization ability and faster processing speed.

Study on Lidar Data Interpolation Method Based on GA-BP

2012 ◽  
Vol 588-589 ◽  
pp. 1312-1315
Author(s):  
Yi Kun Zhang ◽  
Ming Hui Zhang ◽  
Xin Hong Hei ◽  
Deng Xin Hua ◽  
Hao Chen
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Interpolation Method ◽  
Linear Interpolation ◽  
Lidar Data ◽  
Data Interpolation ◽  
Interpolation Model ◽  
Genetic Method ◽  
Interpolation Accuracy ◽  
Linear Interpolation Method

Aiming at building a Lidar data interpolation model, this paper designs and implements a GA-BP interpolation method. The proposed method uses genetic method to optimize BP neural network, which greatly improves the calculation accuracy and convergence rate of BP neural network. Experimental results show that the proposed method has a higher interpolation accuracy compared with BP neural network as well as linear interpolation method.

A Short-Term Power Load Forecasting Method Based on BP Neural Network

2014 ◽  
Vol 494-495 ◽  
pp. 1647-1650 ◽  
Author(s):  
Ling Juan Li ◽  
Wen Huang
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
High Speed ◽  
Load Forecasting ◽  
High Accuracy ◽  
Back Propagation Algorithm ◽  
Short Term ◽  
Power Load ◽  
Forecasting Method ◽  
Power Load Forecasting

Short-term power load forecasting is very important for the electric power market, and the forecasting method should have high accuracy and high speed. A three-layer BP neural network has the ability to approximate any N-dimensional continuous function with arbitrary precision. In this paper, a short-term power load forecasting method based on BP neural network is proposed. This method uses the three-layer neural network with single hidden layer as forecast model. In order to improve the training speed of BP neural network and the forecasting efficiency, this method firstly reduces the factors which affect load forecasting by using rough set theory, then takes the reduced data as input variables of the BP neural network model, and gets the forecast value by using back-propagation algorithm. The forecasting results with real data show that the proposed method has high accuracy and low complexity in short-term power load forecasting.

A BP Neural Network-Based Automatic Windshield Wiper Controller

2012 ◽  
Vol 482-484 ◽  
pp. 31-34
Author(s):  
Hang Fei Xu ◽  
Hong Yan Chen ◽  
Kun Yuan
Keyword(s):  
Neural Network ◽  
Mathematical Model ◽  
Pattern Recognition ◽  
Bp Neural Network ◽  
Control Model ◽  
Windshield Wiper ◽  
Model Based

This paper introduces a method of constructing the control model of automatic windshield wiper based on BP neural network. A model of pattern recognition based on BP neural network is built and train it with specialists’ experience data, and then tested it. The result indicates that this model based on BP neural network is effective to handle uncertainties and nonlinearities of the automatic windshield wiper system, without use of a sophisticated mathematical model.

scholarly journals Research on Thermal Error Modeling of Motorized Spindle Based on BP Neural Network Optimized by Beetle Antennae Search Algorithm

Machines ◽  
2021 ◽  
Vol 9 (11) ◽  
pp. 286
Author(s):  
Zhaolong Li ◽  
Bo Zhu ◽  
Ye Dai ◽  
Wenming Zhu ◽  
Qinghai Wang ◽  
...  
Keyword(s):  
Neural Network ◽  
Prediction Model ◽  
Bp Neural Network ◽  
High Speed ◽  
Search Algorithm ◽  
Thermal Error ◽  
Error Prediction ◽  
Machining Accuracy ◽  
Motorized Spindle ◽  
Thermal Error Model

High-speed motorized spindle heating will produce thermal error, which is an important factor affecting the machining accuracy of machine tools. The thermal error model of high-speed motorized spindles can compensate for thermal error and improve machining accuracy effectively. In order to confirm the high precision thermal error model, Beetle antennae search algorithm (BAS) is proposed to optimize the thermal error prediction model of motorized spindle based on BP neural network. Through the thermal characteristic experiment, the A02 motorized spindle is used as the research object to obtain the temperature and axial thermal drift data of the motorized spindle at different speeds. Using fuzzy clustering and grey relational analysis to screen temperature-sensitive points. Beetle antennae search algorithm (BAS) is used to optimize the weights and thresholds of the BP neural network. Finally, the BAS-BP thermal error prediction model is established. Compared with BP and GA-BP models, the results show that BAS-BP has higher prediction accuracy than BP and GA-BP models at different speeds. Therefore, the BAS-BP model is suitable for prediction and compensation of spindle thermal error.

scholarly journals Optimal design of noise reduction and shape modification for traction gears of EMU based on improved BP neural network

2021 ◽  
Vol 69 (4) ◽  
pp. 373-388
Author(s):  
Zhaoping Tang ◽  
Min Wang ◽  
Xiaoying Xiong ◽  
Manyu Wang ◽  
Jianping Sun ◽  
...  
Keyword(s):  
Neural Network ◽  
Prediction Model ◽  
Bp Neural Network ◽  
High Speed ◽  
Transmission System ◽  
Gear Transmission ◽  
Noise Prediction ◽  
Vibration Acceleration ◽  
Acceleration Level ◽  
Gear Transmission System

Under high-speed operating conditions, the noise caused by the vibration of the traction gear transmission system of the Electric Multiple Units (EMU) will distinctly reduce the comfort of passengers. Therefore, analyzing the dynamic characteristics of traction gears and reducing noise from the root cause through comprehensive modification of gear pairs have become a hot research topic. Taking the G301 traction gear transmission system of the CRH380A high-speed EMU as the research object and then using Romax software to establish a parametric modification model of the gear transmission system, through dynamics, modal and Noise Vibration Harshness (NVH) simulation analysis, the law of howling noise of gear pair changes with modification parameters is studied. In the small sample training environment, the noise prediction model is constructed based on the priority weighted Back Propagation (BP) neural network of small noise samples. Taking the minimum noise of high-speed EMU traction gear transmission as the optimization goal, the simulated annealing (SA) algorithm is introduced to solve the model, and the optimal combination of modification parameters and noise data is obtained. The results show that the prediction accuracy of the prediction model is as high as 98.9%, and it can realize noise prediction under any combination of modification parameters. The optimal modification parameter combination obtained by solving the model through the SA algorithm is imported into the traction gear transmission system model. The vibration acceleration level obtained by the simulation is 89.647 dB, and the amplitude of the vibration acceleration level is reduced by 25%. It is verified that this modification optimization design can effectively reduce the gear transmission.

The Fuzzy Comprehensive Evaluation of Hydropower Plant Production and Operating Conditions Based on the BP Neural Network Improved

2011 ◽  
Vol 71-78 ◽  
pp. 4170-4173 ◽  
Author(s):  
Shu Liang Liu ◽  
Yun Xia Song ◽  
Peng Liang Hao
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Indicator System ◽  
Operating Conditions ◽  
Plant Production ◽  
Quality Analysis ◽  
Production Operation ◽  
Operation Condition ◽  
Hydropower Plants ◽  
Hydropower Production

With the rapid economic growth, the resources and environment condition gradually tightens. We need to accelerate the development of hydropower resources. The electric power produced by the hydropower plants is a kind of green power energy. The quality analysis of hydropower production operation condition will become the focus of our researching. In paper we construct a reasonable set of evaluating indicator system of hydropower production operation and have used the BP neural network and fuzzy quality synthetic to analyze the production and operation condition of several hydropower plants. Help hydropower to summarize experience.

scholarly journals Forward Kinematics and Workspace Determination of a Novel Redundantly Actuated Parallel Manipulator

2019 ◽  
Vol 2019 ◽  
pp. 1-14 ◽  
Author(s):  
Haiqiang Zhang ◽  
Hairong Fang ◽  
Dan Zhang ◽  
Xueling Luo ◽  
Qi Zou
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Parallel Manipulator ◽  
High Speed ◽  
High Efficiency ◽  
Screw Theory ◽  
Back Propagation ◽  
Forward Kinematics ◽  
Redundantly Actuated ◽  
Five Axis

This paper presents a novel redundantly actuated 2RPU-2SPR parallel manipulator that can be employed to form a five-axis hybrid kinematic machine tool for large heterogeneous complex structural component machining in aerospace field. On the contrary to series manipulators, the parallel manipulator has the potential merits of high stiffness, high speed, excellent dynamic performance, and complicated surface processing capability. First, by resorting to the screw theory, the degree of freedom of the proposed parallel manipulator is briefly addressed with general configuration and verified by Grübler-Kutzbach (G-K) criteria as well. Next, the inverse kinematics solution for such manipulator is deduced in detail; however, the forward kinematics is mathematically intractable. To deal with such problem, the forward kinematics is solved by means of three back propagation (BP) neural network optimization strategies. The remarkable simulation results of the parallel manipulator demonstrate that the BP neural network with position compensation is an appropriate method for solving the forward kinematics because of its various advantages, such as high efficiency and high convergence ratios. Simultaneously, workspaces, including joint space and workspace of the proposed parallel manipulator, are graphically depicted based on the previous research, which illustrate that the proposed manipulator is a good candidate for engineering practical application.

A new sub-pixel mapping algorithm based on a BP neural network with an observation model

2008 ◽  
Vol 71 (10-12) ◽  
pp. 2046-2054 ◽  
Author(s):  
Liangpei Zhang ◽  
Ke Wu ◽  
Yanfei Zhong ◽  
Pingxiang Li
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Observation Model ◽  
Mapping Algorithm ◽  
Pixel Mapping

License Plate Recognition Based on Neural Network Algorithm to Improve Research

2013 ◽  
Vol 860-863 ◽  
pp. 2892-2897 ◽  
Author(s):  
De Yong Liu ◽  
Hong Song ◽  
Quan Pan
Keyword(s):  
Neural Network ◽  
Real Time ◽  
Bp Neural Network ◽  
High Speed ◽  
Recognition Rate ◽  
Training Transfer ◽  
License Plate ◽  
License Plate Recognition ◽  
The Real ◽  
Time Performance

with the development of intelligent transportation technology, which all countries are suitable for their own license plate recognition system is developed. But because of the CCD camera Angle problem will make license plate image tilt; Segmentation after do not match the characters in size and character discontinuity, led to license plate recognition rate is not high, speed slow, reduce the real-time performance of the system. In order to improve the rate of convergence, the recognition rate presents a license plate recognition algorithm based on BP neural network. First put the image correction, segmentation of character normalization processing and eliminate the unfavorable factors, then puts forward characteristics of characters input for training the BP neural network. By setting the network weights and training transfer function, improved algorithm to improve the recognition rate of the system, as well as the real-time performance.

Sign in / Sign up

Export Citation Format

Share Document