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.

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.

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.

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

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.

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