Research on State-Space Mapping Algorithm from the Vehicle Unsteady Constraint Test System to the Independent Vehicle System

2011 ◽  
Vol 130-134 ◽  
pp. 326-331 ◽  
Author(s):  
Guo Ye Wang ◽  
Juan Li Zhang
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
State Space ◽  
Test System ◽  
Space Mapping ◽  
The State ◽  
Dynamics Simulation ◽  
Mapping Algorithm ◽  
Vehicle System ◽  
Artificial Neural

Project the vehicle unsteady constraint test system for testing vehicle ESP control performances safely and efficiently, set up the test system dynamics model. Based on the Matlab/Simulink establish the dynamics simulation system of the vehicle unsteady constraint test system for the Chery A3 car. Using the simulation model, we respectively simulate the stability control performances of the test system and the independent vehicle system on steady-state conditions of under steering and over steering. Research and verify the state-space mapping algorithm from the test system to the independent vehicle system using the artificial neural network. The study results indicate that the state-space mapping algorithm from the vehicle unsteady constraint test system to the independent vehicle system using the artificial neural network has ideal mapping performance, it will provide a theoretical basis and technical support for researching the vehicle ESP control performances based on the vehicle unsteady constraint test system.

Modified ANN Inverse Compensating Method for Two-Dimensional Sensor

2013 ◽  
Vol 303-306 ◽  
pp. 266-269
Author(s):  
Yu Han Ding ◽  
Guo Hai Liu ◽  
Xian Zhong Dai
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
State Space ◽  
Dynamic Performance ◽  
The State ◽  
Two Dimensional ◽  
Modified Method ◽  
Artificial Neural ◽  
Space Equation

To improve the dynamic performance of the two-dimensional sensors, we presented a modified ANN (artificial neural network) inverse compensating method. The modified method is based on the state-space equation, which can fully describe the complex sensor and make the obtained inverse compensator more accurate, as well as decrease the derivative orders appeared in the inverse compensator. Simulation result verifies the modified compensator is more suitable to be used to compensate the complex two-dimensional sensor and the compensating result of the modified method is better that of the unmodified one.

A Non - Intrusive Load Identification Algorithm Combined with Event Detection

2021 ◽  
Vol 14 ◽  
Author(s):  
Runhai Jiao ◽  
Qihang Zhou ◽  
Liangqiu Lyu ◽  
Guangwei Yan
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Event Detection ◽  
The State ◽  
Event Recognition ◽  
Identification Algorithm ◽  
Load Identification ◽  
Fusion Model ◽  
Public Data ◽  
Artificial Neural

Background: The traditional state-based non-intrusive load monitoring method mainly deploys the aggregate load as the characteristic to identify the states of every electrical appliance. Each identification is relatively independent, and there is no correlation between the identification results. Objective: This paper combines the event detection results with the state-based non-intrusive load identification algorithm to improve accuracy. Methods: Firstly, the load recognition model based on an artificial neural network is constructed, and the state-based recognition results are obtained. An event recognition and detection model is then built to identify electrical state transitions, that is, the current moment based on the event recognition results obtained from the previous moment. Finally, a reasonable decision method is constructed to determine the identification result of the electrical states. Result: Experimental results on the public data set REDD show that in the Long Short-Term Memory (LSTM) fusion model, the macro-F1 is increased by an average of 6%, and the macro-F1 of the Artificial Neural Network (ANN) fusion model is increased by an average of 5.3% compared with LSTM and ANN. Conclusion: The proposed model can effectively improve the accuracy of identification compared with the state-based load identification method.

Artificial neural network approach for air brake pushrod stroke prediction in heavy commercial road vehicles

2018 ◽  
Vol 233 (10) ◽  
pp. 2467-2478 ◽  
Author(s):  
Radhika Raveendran ◽  
Apoorva Suresh ◽  
Vignesh Rajaram ◽  
Shankar C Subramanian
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Confidence Interval ◽  
Prediction Error ◽  
Performance Degradation ◽  
Dynamics Simulation ◽  
Brake System ◽  
Road Vehicles ◽  
Prediction Scheme ◽  
Artificial Neural

In heavy commercial road vehicles, the air brake system is a critical vehicle safety system whose performance degradation increases the risk of accidents and hence requires periodic inspection and maintenance. The wear of brake pad lining and brake drum during operation leads to increase in the stroke of a component called pushrod whose ‘out-of-adjustment’ creates severe brake performance degradation. The fact that the driver does not receive a corresponding tactile feedback till it is too severe adds to the complexity of manual detection. Motivated by the increase in onboard sensing, electronics, and computation capabilities, this study proposes an artificial neural network–based approach to predict pushrod stroke based on measurement of brake chamber pressure. Here, a back propagation algorithm was used to train the multilayer feed-forward network. The effect of excessive pushrod stroke on vehicle braking response was first studied using a Hardware-in-Loop system that consists of brake system hardware and a commercial vehicle dynamics simulation software (IPG TruckMaker®). Experimental data collected from this system with manual slack adjuster and automatic slack adjuster have then been used to train and test the artificial neural network for pushrod stroke prediction. The performance of the prediction scheme has been tested over the entire range of brake operating conditions. The prediction error corresponding to manual slack adjuster was found to be within ±15% in 322 out of the entire test set of 328 instances (98.17%) and automatic slack adjuster within ±8% in all 57 test sets (100%). Statistical analysis based on confidence interval revealed a prediction error between −1.62% and −3.05% for manual slack adjuster and 0.43% and −1.62% for automatic slack adjuster for 99% confidence interval, which demonstrated the efficacy of the proposed prediction scheme.

Estimation of monthly global solar irradiation using the Hargreaves–Samani model and an artificial neural network for the state of Alagoas in northeastern Brazil

2015 ◽  
Vol 125 (3-4) ◽  
pp. 743-756 ◽  
Author(s):  
Gustavo Bastos Lyra ◽  
Sidney Sára Zanetti ◽  
Anderson Amorim Rocha Santos ◽  
José Leonaldo de Souza ◽  
Guilherme Bastos Lyra ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
The State ◽  
Northeastern Brazil ◽  
Solar Irradiation ◽  
Global Solar Irradiation ◽  
Artificial Neural

Artificial Neural Network Application in Optimal Coordination of Directional Overcurrent Protective Relays in Electrical Mesh Distribution Network

2015 ◽  
Vol 785 ◽  
pp. 48-52 ◽  
Author(s):  
Osaji Emmanuel ◽  
Mohammad Lutfi Othman ◽  
Hashim Hizam ◽  
Muhammad Murtadha Othman
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Response Time ◽  
Short Circuit ◽  
Distribution Networks ◽  
Test System ◽  
Wrong Response ◽  
Optimal Coordination ◽  
Artificial Neural ◽  
Short Circuit Fault

Directional Overcurrent relays (DOCR) applications in meshed distribution networks (MDN), eliminate short circuit fault current due to the topographical nature of the system. Effective and reliable coordination’s between primary and secondary relay pairs ensures effective coordination achievement. Otherwise, the risk of safety of lives and installations may be compromised alongside with system instability. This paper proposes an Artificial Neural Network (ANN) approach of optimizing the system operation response time of all DOCR within the network to address miscoordination problem due to wrong response time among adjacent DOCRs to the same fault. A modelled series of DOCRs in a simulated IEEE 8-bus test system in DigSilent Power Factory with extracted data from three phase short circuit fault analysis adapted in training a custom ANN. Hence, an improved optimized time is produced from the network output to eliminate miscoordination among the DOCRs.

scholarly journals Hybrid GA-PSO Optimization of Artificial Neural Network for Forecasting Electricity Demand

2017 ◽  
Author(s):  
Atul Anand ◽  
L Suganthi
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Tamil Nadu ◽  
Consumer Price Index ◽  
Optimization Methods ◽  
Electricity Demand ◽  
The State ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Optimizing Algorithm

In  the present study, a hybrid  optimizing algorithm has been proposed using Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) for Artificial Neural Network (ANN) to improve the estimation of  electricity demand of  the state of Tamil Nadu in India. The GA-PSO model optimizes  the coefficients of factors of  gross state domestic product (GSDP), per capita demand, income and  consumer price index (CPI) that affect the electricity demand. Based on historical data of 25 years from 1991 till 2015 , the simulation results of GA-PSO models  are having greater accuracy and reliability than single optimization methods based on either PSO or GA. The forecasting results of ANN-GA-PSO are better than models based on single optimization such as  ANN-BP, ANN-GA, ANN-PSO models. Further  the paper also forecasts the electricity demand of Tamil Nadu  based on two scenarios. First scenario is the "as-it-is" scenario , the second scenario  is based on milestones set for achieving goals of "Vision 2023" document for the state. The present research also explores the causality between the economic growth and electricity demand in case of Tamil Nadu. The research indicates that the direct causality exists between  GSDP and the electricity demand of the state.

scholarly journals Hybrid GA-PSO Optimization of Artificial Neural Network for Forecasting Electricity Demand

2018 ◽  
Author(s):  
Atul Anand ◽  
L Suganthi
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Tamil Nadu ◽  
Consumer Price Index ◽  
Electricity Consumption ◽  
Optimization Methods ◽  
Electricity Demand ◽  
The State ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

In the present study, a hybrid optimizing algorithm has been proposed using Genetic Algorithm (GA)and Particle Swarm Optimization (PSO) for Artificial Neural Network (ANN) to improve the estimation of electricity demand of the state of Tamil Nadu in India. The GA-PSO model optimizes the coefficients of factors of gross state domestic product (GSDP) , electricity consumption per capita, income growth rate and consumer price index (CPI) that affect the electricity demand. Based on historical data of 25 years from 1991 till 2015 , the simulation results of GA-PSO models are having greater accuracy and reliability than single optimization methods based on either PSO or GA. The forecasting results of ANN-GA-PSO are better than models based on single optimization such as ANN-BP, ANN-GA, ANN-PSO models. Further the paper also forecasts the electricity demand of Tamil Nadu based on two scenarios. First scenario is the "as-it-is" scenario , the second scenario is based on milestones set for achieving goals of "Vision 2023" document for the state. The present research also explores the causality between the economic growth and electricity demand in case of Tamil Nadu. The research indicates that a direct causality exists between GSDP and the electricity demand of the state.

scholarly journals Improvement of the method of estimation and forecasting of the state of the monitoring object in intelligent decision support systems

2021 ◽  
Vol 4 (3(112)) ◽  
pp. 43-55
Author(s):  
Areej Adnan Abed ◽  
Iurii Repilo ◽  
Ruslan Zhyvotovskyi ◽  
Andrii Shyshatskyi ◽  
Spartak Hohoniants ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Decision Support ◽  
Decision Support Systems ◽  
Support Systems ◽  
The State ◽  
Intelligent Decision ◽  
Intelligent Decision Support Systems ◽  
Artificial Neural ◽  
Intelligent Decision Support

In order to objectively and completely analyze the state of the monitored object with the required level of efficiency, the method for estimating and forecasting the state of the monitored object in intelligent decision support systems was improved. The essence of the method is to provide an analysis of the current state of the monitored object and short-term forecasting of the state of the monitored object. Objective and complete analysis is achieved using advanced fuzzy temporal models of the object state, taking into account the type of uncertainty and noise of initial data. The novelty of the method is the use of an improved procedure for processing initial data in conditions of uncertainty, an improved procedure for training artificial neural networks and an improved procedure for topological analysis of the structure of fuzzy cognitive models. The essence of the training procedure is the training of synaptic weights of the artificial neural network, the type and parameters of the membership function and the architecture of individual elements and the architecture of the artificial neural network as a whole. The procedure of forecasting the state of the monitored object allows for multidimensional analysis, accounting and indirect influence of all components of the multidimensional time series with their different time shifts relative to each other in conditions of uncertainty. The method allows increasing the efficiency of data processing at the level of 12–18 % using additional advanced procedures. The proposed method can be used in decision support systems of automated control systems (ACS DSS) for artillery units, special-purpose geographic information systems. It can also be used in ACS DSS for aviation and air defense and ACS DSS for logistics of the Armed Forces of Ukraine

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