Neural Network Analysis of the Magnetic Bearing Systems

2010 ◽  
Vol 29-32 ◽  
pp. 190-196
Author(s):  
Hong Ya Fu ◽  
Ping Fan Liu ◽  
Qing Chun Zhang ◽  
Guo Dong Li
Keyword(s):  
Neural Network ◽  
Complete System ◽  
Magnetic Bearing ◽  
Neural Network Analysis ◽  
Robust Performance ◽  
Learning Capability ◽  
The Neural Network ◽  
Simulation Results ◽  
Pid Neural Network ◽  
Neural Network Controllers

In order to overcome the system nonlinear instability and uncertainty inherent in magnetic bearing systems, two PID neural network controllers (BP-based and GA-based) are designed and trained to emulate the operation of a complete system. Through the theoretical deduction and simulation results, the principles for the parameters choice of two neural network controllers are given. The feasibility of using the neural network to control nonlinear magnetic bearing systems with un-known dynamics is demonstrated. The robust performance and reinforcement learning capability in controlling magnetic bearing systems are compared between two PID neural network controllers.

The Design of a Wavelet-Based Neural Network Adaptive Filter

2013 ◽  
Vol 860-863 ◽  
pp. 2791-2795
Author(s):  
Qian Xiao ◽  
Yu Shan Jiang ◽  
Ru Zheng Cui
Keyword(s):  
Neural Network ◽  
Wavelet Transform ◽  
Real Time ◽  
Adaptive Filter ◽  
Adaptive Algorithm ◽  
Distributed Storage ◽  
Hopfield Neural Network ◽  
Lms Algorithm ◽  
The Neural Network ◽  
Simulation Results

Aiming at the large calculation workload of adaptive algorithm in adaptive filter based on wavelet transform, affecting the filtering speed, a wavelet-based neural network adaptive filter is constructed in this paper. Since the neural network has the ability of distributed storage and fast self-evolution, use Hopfield neural network to implement adaptive filter LMS algorithm in this filter so as to improve the speed of operation. The simulation results prove that, the new filter can achieve rapid real-time denoising.

scholarly journals ARCHITECTURE OF A NEURAL NETWORK ANALYZER OF BIOMETRIC PARAMETERS OF LISTENERS OF THE DISTANCE LEARNING SYSTEM

2020 ◽  
pp. 91-99
Author(s):  
Liudmyla Tereikovska
Keyword(s):  
Neural Network ◽  
Distance Learning ◽  
Network Analysis ◽  
Learning System ◽  
Integrated Analysis ◽  
Network Analyzer ◽  
Neural Network Analysis ◽  
Biometric Parameters ◽  
The Neural Network ◽  
Distance Learning System

The urgency of the task of developing tools for neural network analysis of biometric parameters for recognizing the personality and emotions of students of the distance learning system has been substantiated. The necessity of formalizing the architectural solutions used in the creation of software for neural network analysis of biometric parameters is shown. As a result of the research carried out in terms of the UML modeling language, the architecture of the neural network analyzer of biometric parameters has been developed. Diagrams of options for using the neural network analyzer have been developed both for recognizing the personality of a student when entering the system, and for recognizing the personality and emotions of a student in the process of his interaction with the distance learning system. Also, based on the developed use case diagrams, a structural diagram of the analyzer is built. The necessity of including subsystems for determining the functional parameters of the analyzer, registration of biometric parameters, neural network analysis of registered biometric parameters, personality recognition and emotion recognition is substantiated. An original feature of the proposed architectural solutions is the introduction into the neural network analysis subsystem of an integrated analysis module designed to summarize the results of neural network analysis separately for each of the biometric parameters. A rule for making an integrated decision has been developed, taking into account the results of a neural network analysis of each of the registered biometric parameters and the corresponding weight coefficients determined by expert evaluation. The introduction of the integrated analysis module makes it possible to increase the accuracy of recognition of emotions and personality of a student, since the final classification is realized through a generalized assessment of several guaranteed significant biometric parameters. In addition, the use of this module makes it possible to increase the reliability of the neural network analyzer in case of difficulties associated with the registration of a particular biometric parameter. It has been established that the decision-making rule can be improved by using one or more neural networks in the integrated analysis module, designed to generalize the results of the neural network analysis of all registered biometric parameters. It is proposed to correlate the directions of further research with the development of appropriate neural network solutions.

Modeling of Uncertain Nonlinear System With Z-Numbers

2021 ◽  
pp. 290-314
Author(s):  
Raheleh Jafari ◽  
Sina Razvarz ◽  
Alexander Gegov ◽  
Satyam Paul
Keyword(s):  
Neural Network ◽  
Nonlinear System ◽  
Nonlinear Systems ◽  
Network Approach ◽  
Uncertain Nonlinear System ◽  
Neural Network Approach ◽  
The Neural Network ◽  
Simulation Results ◽  
Fuzzy Equations

In order to model the fuzzy nonlinear systems, fuzzy equations with Z-number coefficients are used in this chapter. The modeling of fuzzy nonlinear systems is to obtain the Z-number coefficients of fuzzy equations. In this work, the neural network approach is used for finding the coefficients of fuzzy equations. Some examples with applications in mechanics are given. The simulation results demonstrate that the proposed neural network is effective for obtaining the Z-number coefficients of fuzzy equations.

End Effect Processing for Empirical Mode Decomposition Using Fuzzy Inductive Reasoning

2011 ◽  
Vol 55-57 ◽  
pp. 407-412 ◽  
Author(s):  
Ye Yuan ◽  
Zhong Kai Yang ◽  
Qing Fu Li
Keyword(s):  
Neural Network ◽  
Empirical Mode Decomposition ◽  
Input Signal ◽  
Inductive Reasoning ◽  
Qualitative Modeling ◽  
End Effect ◽  
Predictive Capability ◽  
Mode Decomposition ◽  
The Neural Network ◽  
Simulation Results

This paper focuses on the end effect problem of the empirical mode decomposition (EMD) algorithm, which results in a serious distortion in the EMD sifting process. A new method based on fuzzy inductive reasoning (FIR) is proposed to overcome the end effect. Fuzzy inductive reasoning method has simple inferring rules and strong predictive capability. The fuzzy inductive reasoning based method uses the sequence near the end as the input signal of fuzzy inductive reasoning model. This predictive value can be obtained after fuzzification, qualitative modeling ,qualitative simulation and debluring. The simulation results have shown that the fuzzy inductive reasoning based method has equivalent performance to the neural network based method.

Neural Network Emulation of a Magnetically Suspended Rotor

2004 ◽  
Vol 126 (2) ◽  
pp. 373-384 ◽  
Author(s):  
A. Escalante ◽  
V. Guzma´n ◽  
M. Parada ◽  
L. Medina ◽  
S. E. Diaz
Keyword(s):  
Neural Network ◽  
Virtual Instrument ◽  
High Speed ◽  
Magnetic Bearing ◽  
Magnetic Bearings ◽  
System Response ◽  
The Neural Network ◽  
Artificial Neurons ◽  
Position Data ◽  
System 2

The use of magnetic bearings in high speed/low friction applications is increasing in industry. Magnetic bearings are sophisticated electromechanical systems, and modeling magnetic bearings using standard techniques is complex and time consuming. In this work a neural network is designed and trained to emulate the operation of a complete system (magnetic bearing, PID controller, and power amplifiers). The neural network is simulated and integrated into a virtual instrument that will be used in the laboratory both as a teaching and a research tool. The main aims in this work are: (1) determining the minimum amount of artificial neurons required in the neural network to emulate the magnetic bearing system, (2) determining the more appropriate ANN training method for this application, and (3) determining the errors produced when a neural network trained to emulate system operation with a balanced rotor is used to predict system response when operating with an unbalanced rotor. The neural network is trained using as input the position data from the proximity sensors; neural network outputs are the control signals to the coil amplifiers.

scholarly journals A Compact Gradient Based Neural Network for Capon Spectral Estimation

2020 ◽  
Vol 7 ◽  
Keyword(s):  
Neural Network ◽  
Network Architecture ◽  
Spectral Estimation ◽  
Original Method ◽  
Vector Product ◽  
Neural Network Architecture ◽  
Computational Burden ◽  
The Neural Network ◽  
Gradient Based ◽  
Simulation Results

This paper describes the use of a novel gradient based recurrent neural network to perform Capon spectral estimation. Nowadays, in the fastest algorithm proposed by Marple et al., the computational burden still remains significant in the calculation of the autoregressive (AR) Parameters. In this paper we propose to use a gradient based neural network to compute the AR parameters by solving the Yule-Walker equations. Furthermore, to reduce the complexity of the neural network architecture, the weights matrixinputs vector product is performed efficiently using the fast Fourier transform. Simulation results show that proposed neural network and its simplified architecture lead to the same results as the original method which prove the correctness of the proposed scheme.

scholarly journals Neural Network Analysis of Follow-Up Data in Primary Breast Cancer

2000 ◽  
Vol 15 (1) ◽  
pp. 116-122 ◽  
Author(s):  
N. Harbeck ◽  
R. Kates ◽  
K Ulm ◽  
H. Graeff ◽  
M. Schmitt
Keyword(s):  
Breast Cancer ◽  
Neural Network ◽  
Risk Group ◽  
Neural Network Analysis ◽  
Relapse Free Survival ◽  
Free Survival ◽  
Kaplan Meier ◽  
The Neural Network ◽  
Pai 1

This paper reports on the performance of a recently developed neural network environment incorporating likelihood-based optimization and complexity reduction techniques in the analysis of breast cancer follow-up data with the goal of building up a clinical decision support system. The inputs to the neural network include classical factors such as grading, age, tumor size, estrogen and progesterone receptor measurements, as well as tumor biological markers such as PAI-1 and uPA. The network learns the structural relationship between these factors and the follow-up data. Examples of neural models for relapse-free survival are presented, which are based on data from 784 breast cancer patients who received their primary therapy at the Department of Obstetrics and Gynecology, Technische Universität München, Germany. The performance of the neural analysis as quantified by various indicators (likelihood, Kaplan-Meier curves, log-rank tests) was very high. For example, dividing the patients into two equally sized groups based on the neural score (i.e., cutoff = median score) leads to an estimated difference in relapse-free survival of 40% or better (80% vs. 40%) after 10 years in Kaplan-Meier analysis. Evidence for factor interactions as well as for time-varying impacts is presented. The neural network weights included in the models are significant at the 5% level. The use of neural network analysis and scoring in combination with strong tumor biological factors such as uPA and PAI-1 appears to result in a very effective risk group discrimination. Considerable additional comparison of data from different patient series will be required to establish the generalization capability more firmly. Nonetheless, the improvement of risk group discrimination represents an important step toward the use of neural networks for decision support in a clinical framework and in making the most of biological markers.

Analyzing the dynamics of administrative indicators in Russia

2021 ◽  
Vol 17 (2) ◽  
pp. 361-384
Author(s):  
Valerii V. SMIRNOV
Keyword(s):  
Neural Network ◽  
Network Analysis ◽  
Systems Approach ◽  
Political Stability ◽  
World Bank Group ◽  
Neural Network Analysis ◽  
Governance Indicators ◽  
The Neural Network ◽  
Bank Group ◽  
General Aspects

Subject. The article discusses the dynamics of the Russian indicators of administration. Objectives. I identify constraints that determine administrative criteria. Methods. The study is based on the systems approach and methods of statistical, cluster and neural network analysis. Results. The article spotlights the stewardship of Russia’s economy today and theoretical considerations on general administrative criterion. I analyzed trends in the Russian administrative indicators, referring to six general aspects of management (Worldwide Governance Indicators, World Bank Group). Using the cluster analysis of growth rates of the Russian administrative indicators, I found major and crucial clusters. Conducting the neural network analysis, I understood the hierarchy of priorities, with the governmental efficiency being the most important one. The supremacy of law, political stability and no violence/terrorism were found to of the least significance. Having evaluated the asymmetry of trends in the Russian administrative indicators against the average, I identified the priority, that us the governmental efficiency, which turns to be a determining criterion of management. Conclusions and Relevance. As a result of the study, I understood what hampers the dynamics of the Russian administrative indicators by determining administrative criteria. I especially point out the possibility of raising the governmental efficiency to maintain the political stability and prevent violence/terrorism by neglecting the supremacy of law, regulatory quality and simulating the anti-corruption activity. The findings contribute to the necessary scope of governmental authorities’ competence to make administrative decisions on the effective stewardship of Russia.

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