scholarly journals Dynamic System Identification by Neural Network : A New Fast Learning Method Based on Error Back Propagation

1995 ◽  
Vol 38 (4) ◽  
pp. 686-692
Author(s):  
Chinmoy Pal ◽  
Naoki Kayaba ◽  
Shin Morishita ◽  
Ichiro Hagiwara
Keyword(s):  
Neural Network ◽  
System Identification ◽  
Dynamic System ◽  
Back Propagation ◽  
Learning Method ◽  
Error Back Propagation ◽  
Fast Learning

Neural network-based hardware classifier using CORDIC algorithm

2020 ◽  
Vol 34 (15) ◽  
pp. 2050161
Author(s):  
Vipin Tiwari ◽  
Ashish Mishra
Keyword(s):  
Neural Network ◽  
Programming Languages ◽  
Back Propagation ◽  
Activation Function ◽  
Data Repository ◽  
Cordic Algorithm ◽  
Back Propagation Algorithm ◽  
Error Back Propagation ◽  
Benchmark Datasets ◽  
Error Back Propagation Algorithm

This paper designs a novel classification hardware framework based on neural network (NN). It utilizes COordinate Rotation DIgital Computer (CORDIC) algorithm to implement the activation function of NNs. The training was performed through software using an error back-propagation algorithm (EBPA) implemented in C++, then the final weights were loaded to the implemented hardware framework to perform classification. The hardware framework is developed in Xilinx 9.2i environment using VHDL as programming languages. Classification tests are performed on benchmark datasets obtained from UCI machine learning data repository. The results are compared with competitive classification approaches by considering the same datasets. Extensive analysis reveals that the proposed hardware framework provides more efficient results as compared to the existing classifiers.

Identification of Multivariable Nonlinear Dynamic System Based on PID Neural Network

2015 ◽  
Vol 719-720 ◽  
pp. 475-481
Author(s):  
Hua Shu ◽  
Huai Lin Shu
Keyword(s):  
Neural Network ◽  
System Identification ◽  
Dynamic System ◽  
Nonlinear Dynamic ◽  
Linear Time ◽  
Nonlinear Dynamic System ◽  
Identification Methods ◽  
Linear Time Invariant ◽  
Linear Time Invariant Systems ◽  
Pid Neural Network

System identification is the basis for control system design. For linear time-invariant systems have a variety of identification methods, identification methods for nonlinear dynamic system is still in the exploratory stage. Nonlinear identification method based on neural network is a simple and effective general method that does not require too much priori experience about the system to be identified. Through training and learning, the network weights are corrected to achieve the purpose of system identification. The paper is about the identification of multivariable nonlinear dynamic system based on PID neural network. The structure and algorithm of PID neural network are introduced and the properties and characteristics are analyzed. The system identification is completed and the results are fast convergence.

scholarly journals The method of array antenna constructive synthesis on the basis of neural network approach

2019 ◽  
Vol 30 ◽  
pp. 05001
Author(s):  
Sergey Mishchenko ◽  
Vitaliy Shatskiy ◽  
Alexey Litvinov ◽  
Denis Eliseev
Keyword(s):  
Neural Network ◽  
Active Element ◽  
Back Propagation ◽  
Array Antenna ◽  
Antenna Element ◽  
Network Approach ◽  
Passive Element ◽  
Neural Network Approach ◽  
Neural Network Learning ◽  
Error Back Propagation

The method to decision constructive synthesis of array antennas was conducted. The method usefull when antenna elements can be in discreste states (for example: active element, passive element, excluded item, active element with discrete nominal of output power e.t.c). The method is based on neural network approach. The structure of a neural network consist of a classifying neural network and several approximating neural networks is substantiated. Input signals correspond to phase centers of array antenna elements. Number of output signals in classifying part is equal to discrete status of antenna element. Each approximating part of network has one output signal wich correspond to continious meaning. Separate parts of network preliminary learning with error back propagation method. The genetic algorithm of neural network learning with limited number of training coefficients is proposed. Examples of solving problems of constructive synthesis, with different indicators of the quality of neural network training are given.

scholarly journals PV Maximum Power-Point Tracking by Using Artificial Neural Network

2012 ◽  
Vol 2012 ◽  
pp. 1-10 ◽  
Author(s):  
Farzad Sedaghati ◽  
Ali Nahavandi ◽  
Mohammad Ali Badamchizadeh ◽  
Sehraneh Ghaemi ◽  
Mehdi Abedinpour Fallah
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Back Propagation ◽  
Maximum Power ◽  
Atmospheric Conditions ◽  
Maximum Power Point ◽  
Error Back Propagation ◽  
Point Tracking ◽  
Artificial Neural ◽  
Power Point

In this paper, using artificial neural network (ANN) for tracking of maximum power point is discussed. Error back propagation method is used in order to train neural network. Neural network has advantages of fast and precisely tracking of maximum power point. In this method neural network is used to specify the reference voltage of maximum power point under different atmospheric conditions. By properly controling of dc-dc boost converter, tracking of maximum power point is feasible. To verify theory analysis, simulation result is obtained by using MATLAB/SIMULINK.

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