scholarly journals Performance Analysis of Wireless Location and Velocity Tracking of Digital Broadcast Signals Based on Extended Kalman Filter Algorithm

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Yukai Hao ◽  
Xin Qiu
Keyword(s):  
Neural Network ◽  
Measurement Data ◽  
Nonlinear Dynamic System ◽  
The State ◽  
Statistical Characteristics ◽  
State Variables ◽  
Bias Estimation ◽  
Wireless Location ◽  
Network Training ◽  
Artificial Neural Network Ann

In order to improve the accuracy and reliability of wireless location in NLOS environment, a wireless location algorithm based on artificial neural network (ANN) is proposed for NLOS positioning error caused by non-line-of-sight (NLOS) propagation, such as occlusion and signal reflection. The mapping relationship between TOA and TDOA measurement data and coordinates is established. The connection weights of neural network are estimated as the state variables of nonlinear dynamic system. The multilayer perceptron network is trained by the real-time neural network training algorithm based on extended Kalman (EKF). Combined with the statistical characteristics of NLOS error, the state component NLOS bias estimation is modified to realize TDOA data reconstruction. Simulation and experimental data analysis show that the algorithm can effectively weaken the influence of NLOS error. The localization method does not depend on the specific NLOS error distribution, nor does it need LOS and NLOS recognition. It can significantly improve the mobile positioning accuracy.

scholarly journals PATTERN RECOGNITION BASED SPEED FORECASTING METHODOLOGY FOR URBAN TRAFFIC NETWORK

Transport ◽  
2018 ◽  
Vol 33 (4) ◽  
pp. 959-970 ◽  
Author(s):  
Tamás Tettamanti ◽  
Alfréd Csikós ◽  
Krisztián Balázs Kis ◽  
Zsolt János Viharos ◽  
István Varga
Keyword(s):  
Neural Network ◽  
Input Data ◽  
Road Traffic ◽  
Urban Traffic ◽  
Traffic Network ◽  
Data Handling ◽  
Urban Road ◽  
Traffic Sensors ◽  
Network Training ◽  
Artificial Neural Network Ann

A full methodology of short-term traffic prediction is proposed for urban road traffic network via Artificial Neural Network (ANN). The goal of the forecasting is to provide speed estimation forward by 5, 15 and 30 min. Unlike similar research results in this field, the investigated method aims to predict traffic speed for signalized urban road links and not for highway or arterial roads. The methodology contains an efficient feature selection algorithm in order to determine the appropriate input parameters required for neural network training. As another contribution of the paper, a built-in incomplete data handling is provided as input data (originating from traffic sensors or Floating Car Data (FCD)) might be absent or biased in practice. Therefore, input data handling can assure a robust operation of speed forecasting also in case of missing data. The proposed algorithm is trained, tested and analysed in a test network built-up in a microscopic traffic simulator by using daily course of real-world traffic.

Neuronet-Based Approach for Assessing Liquefaction Potential of Soils

1998 ◽  
Vol 1633 (1) ◽  
pp. 3-8 ◽  
Author(s):  
Hossam Eldin Ali ◽  
Yacoub M. Najjar
Keyword(s):  
Neural Network ◽  
Numerical Approach ◽  
Soil Liquefaction ◽  
Data Sets ◽  
Ann Model ◽  
Neural Network Approach ◽  
Liquefaction Potential ◽  
Network Training ◽  
Artificial Neural Network Ann ◽  
Hidden Layer

A backpropagation artificial neural network (ANN) algorithm with one hidden layer was used as a new numerical approach to characterize the soil liquefaction potential. For this purpose, 61 field data sets representing various earthquake sites from around the world were used. To develop the most accurate prediction model for liquefaction potential, alternating combinations of input parameters were used during the training and testing phases of the developed network. The accuracy of the designed network was validated against an additional 44 records not used previously in either the network training or testing stages. The prediction accuracy of the neural network approach–based model is compared with predictions obtained by using fuzzy logic and statistically based approaches. Overall, the ANN model outperformed all other investigated approaches.

scholarly journals Particle filter in power system state estimation – bad measurement data and branch disconnection

2015 ◽  
Vol 64 (2) ◽  
pp. 237-248
Author(s):  
Piotr Kozierski ◽  
Marcin Lis ◽  
Adam Owczarkowski ◽  
Dariusz Horla
Keyword(s):  
Power System ◽  
Particle Filter ◽  
State Estimation ◽  
Measurement Data ◽  
The State ◽  
Filter Method ◽  
System State ◽  
State Variables ◽  
Zero Bias ◽  
Power System State Estimation

Abstract An approach to power system state estimation using a particle filter has been proposed in the paper. Two problems have been taken into account during research, namely bad measurements data and a network structure modification with rapid changes of the state variables. For each case the modification of the algorithm has been proposed. It has also been observed that anti-zero bias modification has a very positive influence on the obtained results (few orders of magnitude, in comparison to the standard particle filter), and additional calculations are quite symbolic. In the second problem, used modification also improved estimation quality of the state variables. The obtained results have been compared to the extended Kalman filter method

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.

Investigation of Neural-Network-Based Inverse Kinematics for a 6-DOF Serial Manipulator With Non-Spherical Wrist

2018 ◽  
Author(s):  
Benjamin E. Hargis ◽  
Wesley A. Demirjian ◽  
Matthew W. Powelson ◽  
Stephen L. Canfield
Keyword(s):  
Neural Network ◽  
Inverse Kinematics ◽  
Computational Time ◽  
Robot Arm ◽  
Training Set ◽  
Test Set ◽  
Network Training ◽  
Inverse Kinematics Problem ◽  
Spherical Wrist ◽  
Artificial Neural Network Ann

This study proposes using an Artificial Neural Network (ANN) to train a 6-DOF serial manipulator with a non-spherical wrist to solve the inverse kinematics problem. In this approach, an ANN has been trained to determine the configuration parameters of a serial manipulator that correspond to the position and pose of its end effector. The network was modeled after the AUBO-i5 robot arm, and the experimental results have shown the ability to achieve millimeter accuracy in tool space position with significantly reduced computational time relative to an iterative kinematic solution when applied to a subset of the workspace. Furthermore, a separate investigation was conducted to quantify the relationship between training example density, training set error, and test set error. Testing indicates that, for a given network, sufficient example point density may be approximated by comparing the training set error with test set error. The neural network training was performed using the MATLAB Neural Network Toolbox.

Nonlinear dynamic system identification using neuro-fractional-order Hammerstein model

2017 ◽  
Vol 40 (13) ◽  
pp. 3872-3883 ◽  
Author(s):  
Mohammad-Reza Rahmani ◽  
Mohammad Farrokhi
Keyword(s):  
Fractional Order ◽  
Nonlinear Dynamic ◽  
Nonlinear Dynamic System ◽  
Lyapunov Stability Theory ◽  
The State ◽  
Order System ◽  
Hammerstein Model ◽  
Identification Algorithm ◽  
State Variables ◽  
State Matrix

This paper presents a neuro-fractional-order Hammerstein model with a systematic identification algorithm for identifying unknown nonlinear dynamic systems. The proposed model consists of a Radial Basis Function Neural Network (RBF NN) followed by a Fractional-Order System (FOS). The proposed identification scheme is performed in two stages. First, the fractional-order and the number of state variables (or degree) of the state-space realization of the FOS are estimated in the frequency domain. Then, the parameters of the RBF NN (the weights, centers and widths of the Gaussian functions) and the state matrix of the FOS are determined using the time domain data via the Lyapunov stability theory. Simulating as well as experimental examples are provided to verify the effectiveness of the proposed method. The identification results show that the proposed neuro-fractional-order Hammerstein modeling is superior as compared with the existing Hammerstein modeling in literature.

Identification and State Observation of Uncertain Chaotic Systems Using Projectional Differential Neural Networks

2011 ◽  
pp. 42-67
Author(s):  
Alejandro García ◽  
Isaac Chairez ◽  
Alexander Poznyak
Keyword(s):  
Neural Network ◽  
State Estimation ◽  
Chaotic Systems ◽  
Chaotic Behavior ◽  
The State ◽  
State Variables ◽  
Neural Network Approach ◽  
State Observation ◽  
The Neural Network ◽  
Uncertain Chaotic Systems

The following chapter tackles the nonparametric identification and the state estimation for uncertain chaotic systems by the dynamic neural network approach. The developed algorithms consider the presence of additive noise in the state, for the case of identification, and in the measurable output, for the state estimation case. Mathematical model of the chaotic system is considered unknown, only the chaotic behavior as well as the maximal and minimal bound for each one of state variables are taking into account in the algorithm. Mathematical analysis and simulation results are presented. Application considering the so-called electronic Chua’s circuit is carried out; particularly a scheme of information encryption by the neural network observer with a noisy transmission is showed. Formal mathematical proofs and figures, illustrate the robustness of proposed algorithms mainly in the presence of noises with high magnitude.

Application of Preprocessed Classifier Type Neural Network for Searching of Faulty Components of Power Cycles in Case of Incomplete Measurement Data

2002 ◽  
Author(s):  
Jerzy Głuch ◽  
Jerzy Krzyz˙anowski
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Steam Turbine ◽  
Measurement Data ◽  
Diagnostic System ◽  
Multiple Faults ◽  
Power Cycles ◽  
Thermal Measurements ◽  
Incomplete Measurement ◽  
Artificial Neural Network Ann

Thermal and flow diagnostics of power units makes use of diagnostic relations i.e. relations between fault signatures (sets of symptoms) and geometry degradation of its components. Determining symptoms may base on thorough thermal measurements of the cycle. However, numerous apparatuses in the cycle are not or cannot be properly equipped for necessary measurements. Examples of such apparatuses in a steam turbine are external glands and nozzle box sealings. The paper studies the applicability of a selected type of Artificial Neural Network, ANN, as a diagnostic relation for locating faulty apparatuses in HP and IP turbine casings, including their sealing systems. The obtained results can be assessed as good for single faults, and satisfactory for multiple faults of the cycle components. The examined type of ANN can be used e.g. in a modular hierarchical diagnostic system proposed by Gluch & Krzyzanowski, 1998, 1999.

A Methodology to Evaluate Artificial Neural Network Training Used to Estimate Transformer Failure Probability

2007 ◽  
Author(s):  
Enrique A. Susemihl ◽  
Shuzhen Xu
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Failure Probability ◽  
Transformer Oil ◽  
Network Training ◽  
Insulation Degradation ◽  
Artificial Neural ◽  
Predicted Values ◽  
Artificial Neural Network Ann ◽  
Paper Insulation

In a previous paper [1] the authors presented a methodology to estimate the probability of failure of power transformers due to paper insulation degradation. The methodology was based on the identification of patterns in indirect measurements by means of an artificial neural network (ANN). The parameters measured were the amounts of dissolved gases and other chemical in the transformer oil. The failure probability was then estimated from the population life data. The methods presented in this paper are useful to estimate the quantitity of cases required for the training of the ANN to achieve acceptable predicted values, which is particularly important when the available data is limited.

Sign in / Sign up

Export Citation Format

Share Document