scholarly journals Nonlinear Friction Estimation in Elastic Drive Systems Using a Dynamic Neural Network-Based Observer

2013 ◽  
Vol 17 (4) ◽  
pp. 637-646 ◽  
Author(s):  
Amir Hossein Jafari ◽  
◽  
Rached Dhaouadi ◽  
Ali Jhemi ◽  
Keyword(s):  
Neural Network ◽  
Viscous Friction ◽  
Nonlinear Observer ◽  
Training Algorithm ◽  
Mass Model ◽  
Nonlinear Characteristics ◽  
The Neural Network ◽  
Neural Network Structure ◽  
Drive Systems ◽  
Nonlinear Elastic

This paper presents a neural-network based observer for nonlinear elastic drive systems. The proposed nonlinear observer uses a Diagonal Recurrent Neural Network (DRNN) combined with the dynamics of a linear Two-Mass-Model (2MM) system to identify nonlinear characteristics of the drive system such as Coulomb and nonlinear viscous friction torques. Theoretical analysis of the proposed neural-network based observer, including the neural network structure and the training algorithm convergence, are presented and discussed. Simulation results are confirmed experimentally using a 2MM system setup.

Neural Network-Based Speed Control of A Two-Mass-Model System

1999 ◽  
Vol 3 (5) ◽  
pp. 427-430 ◽  
Author(s):  
Rached Dhaouadi ◽  
◽  
Khaled Nouri
Keyword(s):  
Neural Network ◽  
Inverse Dynamics ◽  
Mass Model ◽  
Neural Network Learning ◽  
Inverse Control ◽  
Network Learning ◽  
The Neural Network ◽  
Control Scheme ◽  
On Line ◽  
Neural Network Structure

We present an application of artificial neural networks to the problem of controlling the speed of an elastic drive system. We derive a neural network structure to simulate the inverse dynamics of the system, then implement the direct inverse control scheme in a closed loop. The neural network learning is done on-line to adaptively control the speed to follow a stepwise changing reference. The experimental results with a two-mass-model analog board confirm the effectiveness of the proposed neurocontrol scheme.

Methods for evaluation the manufacturability of a vehicle

2020 ◽  
pp. 152-160
Keyword(s):  
Neural Network ◽  
Initial Data ◽  
Comparative Data ◽  
Training Algorithm ◽  
Neural Network Method ◽  
The Neural Network ◽  
Network Method ◽  
Calculation Results ◽  
Production And Operation

Methods for evaluation the manufacturability of a vehicle in the field of production and operation based on an energy indicator, expert estimates and usage of a neural network are stated. By using the neural network method the manufacturability of a car in a complex and for individual units is considered. The preparation of the initial data at usage a neural network for predicting the manufacturability of a vehicle is shown; the training algorithm and the architecture for calculating the manufacturability of the main units are given. According to the calculation results, comparative data on the manufacturability vehicles of various brands are given.

scholarly journals The implementation of optimized neural network to improve the accuracy of flow measurement by ultrasonic flowmeter

2019 ◽  
pp. 193-194
Author(s):  
Oleksandr Ihorovich Parfeniuk ◽  
Oleksandr Mykolaiovych Naumchuk ◽  
Olena Olehivna Poliukhovych ◽  
Pawel Mazurek
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Flow Measurement ◽  
Ultrasonic Flowmeter ◽  
Structural Scheme ◽  
Nonlinear Characteristics ◽  
The Neural Network ◽  
Artificial Neural

It is proposed the technology of intellectual measurement of expenses with the use of an artificial neural network for overcoming the constraints caused by nonlinear characteristics of ultrasonic flowmeters. It is presented structural scheme of the proposed technology and structure of the model of the neural network

Research on the Optimized Algorithms on Neural Network

2012 ◽  
Vol 605-607 ◽  
pp. 2175-2178
Author(s):  
Xiao Qin Wu
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Simulated Annealing Algorithm ◽  
Fitness Function ◽  
Strong Stability ◽  
Training Algorithm ◽  
Network Training ◽  
The Neural Network ◽  
Annealing Algorithm ◽  
Proper Operation

In order to overcome the disadvantage of neural networks that their structure and parameters were decided stochastically or by one’s experience, an improved BP neural network training algorithm based on genetic algorithm was proposed.In this paper,genetic algorithms and simulated annealing algorithm that optimizes neural network is proposed which is used to scale the fitness function and select the proper operation according to the expected value in the course of optimization,and the weights and thresholds of the neural network is optimized. This method is applied to the stock prediction system.The experimental results show that the proposed approach have high accuracy,strong stability and improved confidence.

Sunlight Greenhouse Temperature Prediction Model Based on Bayesian Regularization BP Neural Network

2015 ◽  
Vol 740 ◽  
pp. 871-874
Author(s):  
Hui Zhao ◽  
Li Rong Shi ◽  
Hong Jun Wang
Keyword(s):  
Neural Network ◽  
Network Structure ◽  
Bp Neural Network ◽  
Principal Component ◽  
Coupling Factor ◽  
Bayesian Regularization ◽  
The Neural Network ◽  
Neural Network Structure ◽  
Input Variables ◽  
Bp Neural Network Model

Directing against the problems of too large size of the neural network structure due to the existence of a complex relationship between the input coupling factor and too many input factors in establishing model for predicting temperature of sunlight greenhouse. This article chose the environmental factors that affect the sunlight greenhouse temperature as data sample. Through the principal component analysis of data samples, three main factors were extracted. These selected principal component values were taken as the input variables of BP neural network model. Use the Bayesian regularization algorithm to improve the BP neural network. The empirical results show that this method is utilized modify BP neural network, which can simplify network structure and smooth fitting curve, has good generalization capability.

STDP Provides the Substrate for Igniting Synfire Chains by Spatiotemporal Input Patterns

2008 ◽  
Vol 20 (2) ◽  
pp. 415-435 ◽  
Author(s):  
Ryosuke Hosaka ◽  
Osamu Araki ◽  
Tohru Ikeguchi
Keyword(s):  
Neural Network ◽  
Experimental Studies ◽  
Spike Timing ◽  
Inhibitory Neurons ◽  
Spatiotemporal Pattern ◽  
Spiking Neural Network ◽  
Synfire Chains ◽  
The Neural Network ◽  
Stdp Rule ◽  
Neural Network Structure

Spike-timing-dependent synaptic plasticity (STDP), which depends on the temporal difference between pre- and postsynaptic action potentials, is observed in the cortices and hippocampus. Although several theoretical and experimental studies have revealed its fundamental aspects, its functional role remains unclear. To examine how an input spatiotemporal spike pattern is altered by STDP, we observed the output spike patterns of a spiking neural network model with an asymmetrical STDP rule when the input spatiotemporal pattern is repeatedly applied. The spiking neural network comprises excitatory and inhibitory neurons that exhibit local interactions. Numerical experiments show that the spiking neural network generates a single global synchrony whose relative timing depends on the input spatiotemporal pattern and the neural network structure. This result implies that the spiking neural network learns the transformation from spatiotemporal to temporal information. In the literature, the origin of the synfire chain has not been sufficiently focused on. Our results indicate that spiking neural networks with STDP can ignite synfire chains in the cortices.

Application of Neural Network in Bioactive Prediction of Inhibitor of Thymidylic Acid-Based Synthetase

2014 ◽  
Vol 937 ◽  
pp. 308-312
Author(s):  
Xi Hua Du ◽  
Xiao Hui Wang
Keyword(s):  
Neural Network ◽  
Biological Activity ◽  
Molecular Design ◽  
Structural Parameters ◽  
Regression Method ◽  
New Drugs ◽  
The Neural Network ◽  
Neural Network Structure ◽  
Electrical State ◽  
Neural Network Input

Based on the molecular topology information and adjacency matrix, the 38 electrical state indices of molecules of inhibitor of thymidylic acid-based synthetase as five-membered heterocyclic pyrimidine derivatives were calculated to provide theoretical basis for molecular design of new drugs. By using variable regression method, the best subset of structural parameters ofE1,E2,E7,E16andE31were optimized. When the five structural parameters were used as the BP neural network input neurons and the neural network structure of 5:3:1 was used, an ideal prediction model of biological activity was obtained. Its total correlation coefficientrand average relative error were 0.972 and 2.13%, respectively. The result showed that the biological activity andE1,E2,E7,E16andE31have a good non-linear relationship with the biological activity, and the results predicted by neural networks was better than that by multiple regression method. The test proved that the model had good robust and predictive capabilities. Our research would provide theoretical guidance for the development of new drugs of inhibitor of thymidylic acid-based synthetase with efficient and low toxicity.

scholarly journals CHOOSING A NEURAL NETWORK STRUCTURE FOR SIGNAL PROCESSING AS AN EXPERIMENT PLANNING

2021 ◽  
pp. 123-132
Author(s):  
Leonid A. Slavutskii ◽  
Elena V. Slavutskaya
Keyword(s):  
Neural Network ◽  
Signal Processing ◽  
Network Structure ◽  
Electric Power Industry ◽  
Planning Theory ◽  
Experiment Planning ◽  
Harmonic Amplitude ◽  
The Neural Network ◽  
Nonlinear Distortions ◽  
Neural Network Structure

The paper is devoted to the use of artificial neural networks for signal processing in electrical engineering and electric power industry. Direct propagation neural network (perceptron) is considered as an object in the theory of experiment planning. The variants of the neural network structure empirical choice, the quality criteria of its training and testing are analyzed. It is shown that the perceptron structure choice, the training sample, and the training algorithms require planning. Variables and parameters of neuro algorithm that can act as factors, state parameters, and disturbing influences in the framework of the experimental planning theory are discussed. The proposed approach is demonstrated by the example of neural network analysis of the industrial frequency signal of 50 Hz nonlinear distortions. The possibility of using an elementary perceptron with one hidden layer and a minimum number of neurons to correct the transformer saturation current is analyzed. The conditions under which the neuro algorithm allows one to restore the values of the main harmonic amplitude, frequency and phase with an error of no more than one percent are revealed. The signal processing in a «sliding window» with a duration of a fraction of the fundamental frequency period is proposed, and the neuro algorithm accuracy characteristics are estimated. The possibility to automate the neural network structure choosing for signal processing is discussed.

scholarly journals Moisture Estimation in Cabinet Dryer with Thin-Layer Relationships Using Genetic Algorithm and Neural Network

2019 ◽  
Author(s):  
Behzad Maleki ◽  
Mahyar Ghazvini ◽  
Mohammad Hossein Ahmadi ◽  
Heydar Maddah ◽  
Shahab Shamshirband
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Thin Layer ◽  
Experimental Models ◽  
Moisture Loss ◽  
Squared Error ◽  
The Neural Network ◽  
Quality Product ◽  
Neural Network Structure ◽  
Moisture Estimation

Nowadays industrial dryers are used instead of traditional methods for drying. In designing dryers suitable for controlling the process of drying and reaching a high quality product, it is necessary to predict the instantaneous moisture loss during drying. For this purpose, ten mathematical-experimental models with a neural network model based on the kinetic data of pistachio drying are studied. The data obtained from the cabinet dryer will be evaluated at four temperatures of inlet air and different air velocities. The pistachio seeds will be placed in a thin layer on an aluminum sheet on a drying tray and weighed by a scale attached to the computer at different times. In the neural network, data are divided into three parts: educational (60%), validation (20%) and test (20%). Finally, the best mathematical-experimental model using genetic algorithm and the best neural network structure for predicting instantaneous moisture are selected based on the least squared error and the highest correlation coefficient.

scholarly journals Improved Feature Learning: A Maximum-Average-Out Deep Neural Network for the Game Go

2020 ◽  
Vol 2020 ◽  
pp. 1-6
Author(s):  
Xiali Li ◽  
Zhengyu Lv ◽  
Bo Liu ◽  
Licheng Wu ◽  
Zheng Wang
Keyword(s):  
Neural Network ◽  
Low Cost ◽  
Computer Game ◽  
Feature Learning ◽  
Superior Performance ◽  
Computing Power ◽  
Unit Structure ◽  
The Neural Network ◽  
Neural Network Structure ◽  
Hidden Layer

Computer game-playing programs based on deep reinforcement learning have surpassed the performance of even the best human players. However, the huge analysis space of such neural networks and their numerous parameters require extensive computing power. Hence, in this study, we aimed to increase the network learning efficiency by modifying the neural network structure, which should reduce the number of learning iterations and the required computing power. A convolutional neural network with a maximum-average-out (MAO) unit structure based on piecewise function thinking is proposed, through which features can be effectively learned and the expression ability of hidden layer features can be enhanced. To verify the performance of the MAO structure, we compared it with the ResNet18 network by applying them both to the framework of AlphaGo Zero, which was developed for playing the game Go. The two network structures were trained from scratch using a low-cost server environment. MAO unit won eight out of ten games against the ResNet18 network. The superior performance of the MAO unit compared with the ResNet18 network is significant for the further development of game algorithms that require less computing power than those currently in use.

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