Application of RBF Neural Network in WEDM

2012 ◽  
Vol 468-471 ◽  
pp. 607-612
Author(s):  
Shi Ping Zhang ◽  
Yi Chao Ding ◽  
Jing Wang ◽  
Yuan Hui Li
Keyword(s):  
Neural Network ◽  
Complex System ◽  
Process Modeling ◽  
Rbf Neural Network ◽  
Machining Process ◽  
Self Organized ◽  
The Neural Network ◽  
Nonlinear Relation ◽  
Self Learning ◽  
Parallel Type

It is difficult to build a strict mathematical model for WEDM due to the complication of the machining process and the nonlinear relation between process parameters and process targets. The neural network is suited to the modeling of complex system, because it has the functions of self-organized, self-learning and associative memory, and properties of distributed parallel type and high robustness. Therefore, this paper attempts to use the RBF neural network for the process modeling of WEDM.

The Optimization of Adaptive PID Control Algorithm Based on RBF Neural Network

2014 ◽  
Vol 998-999 ◽  
pp. 943-946
Author(s):  
Jing Liu ◽  
Guo Xin Wang
Keyword(s):  
Neural Network ◽  
Pid Controller ◽  
Rbf Neural Network ◽  
High Reliability ◽  
Simple Algorithm ◽  
Poor Performance ◽  
Learning Ability ◽  
The Neural Network ◽  
Pid Controller Tuning ◽  
Deterministic Control

As the earliest practical controller, PID controller has more than 50 years of history, and it is still the most widely used and most common industrial controllers. PID controller is simple to understand and use, without a prerequisite for an accurate model of the physical system, thus become the most popular, the most common controller. The reason why PID controller is the first developed one is that its simple algorithm, robustness and high reliability. It is widely used in process control and motion control, especially for accurate mathematical model that can be established deterministic control system. But the conventional PID controller tuning parameters are often poor performance, poor adaptability to the operating environment. The neural network has a strong nonlinear mapping ability, competence, self-learning ability of associative memory, and has a viable quantities of information processing methods and good fault tolerance.

A Graph Grammar Approach to Generate Neural Network Topologies

2007 ◽  
Author(s):  
Chaitanya Vempati ◽  
Matthew I. Campbell
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Process Modeling ◽  
Graph Transformation ◽  
Graph Grammar ◽  
Transformation Rules ◽  
The Neural Network ◽  
Network Topologies ◽  
Novel Method ◽  
Defining Graph

Neural networks are increasingly becoming a useful and popular choice for process modeling. The success of neural networks in effectively modeling a certain problem depends on the topology of the neural network. Generating topologies manually relies on previous neural network experience and is tedious and difficult. Hence there is a rising need for a method that generates neural network topologies for different problems automatically. Current methods such as growing, pruning and using genetic algorithms for this task are very complicated and do not explore all the possible topologies. This paper presents a novel method of automatically generating neural networks using a graph grammar. The approach involves representing the neural network as a graph and defining graph transformation rules to generate the topologies. The approach is simple, efficient and has the ability to create topologies of varying complexity. Two example problems are presented to demonstrate the power of our approach.

Fault Diagnosis of Transformer Based on RBF Neural Network

2014 ◽  
Vol 571-572 ◽  
pp. 201-204
Author(s):  
Jian Li Yu ◽  
Zhe Zhang
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Rbf Neural Network ◽  
High Energy ◽  
Ratio Method ◽  
Matlab Simulation ◽  
Medium Temperature ◽  
The Neural Network ◽  
Transformer Fault ◽  
Network Diagnosis

According to the characteristics of fault types of the transformer ,RBF neural network is used to diagnose transformer fault. The paper regards six gases as inputs of the neural network and establishes RBF neural network model which can diagnose six transformer faults: low temperature overheat, medium temperature overheat, high temperature overheat, low energy discharge, high energy discharge and partial discharge . The Matlab simulation studies show that transformer fault diagnosis model based on RBF neural network diagnosis for failure beyond the traditional three-ratio method. The rate of the transformer fault diagnosis accuracy reaches 91.67% which is also much higher than the traditional three ratio method.

Eccentricity of Rotor Prediction of Aero-Engine Rotor Based on Image Identification and Machine Learning

2019 ◽  
Author(s):  
Zihao Zhang ◽  
Junkang Guo ◽  
Yanhui Sun ◽  
Jun Hong
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Rbf Neural Network ◽  
Engineering Application ◽  
Direction Error ◽  
Rotor Eccentricity ◽  
The Neural Network ◽  
Aero Engine ◽  
Feature Information ◽  
Engine Rotor

Abstract The eccentricity of rotor seriously affect the vibration and reliability of aero-engine. Due to the machining error of parts, it is very important to accurately predict the error propagation in assembly. A method based on image recognition and machine learning is proposed to predict the eccentricity of rotor. Firstly, by analyzing and calculating the axial and radial runout error data, the error is mainly concentrated in the first 30 orders of the Fourier series. Secondly, based on the mapping relationship between profile trajectory and eccentricity of rotor, the feature information of the profile trajectory is extracted by constructing the complex domain autoregressive (CAR) model for the radial and axial direction error profile trajectory. Then use the finite element method to calculate the rotor eccentricity. Using the feature information as the input of the neural network, the rotor eccentricity is assembled as the output of the neural network, and the radial basis function (RBF) neural network is built to predict the rotor eccentricity. Theoretical and experimental results show that the proposed method has good enforceability, high accuracy, short calculation time and high engineering application value. In addition, this method can not only be applied to predict the eccentricity of aero-engine rotor flange assembly, but also can be used in the general field of interference fit of assembly.

Applying RBF Neural Networks and Genetic Algorithms to Nonlinear System Optimization

2012 ◽  
Vol 605-607 ◽  
pp. 2457-2460 ◽  
Author(s):  
Hong Fa Wang ◽  
Xin Ai Xu
Keyword(s):  
Neural Network ◽  
Nonlinear System ◽  
Rbf Neural Network ◽  
System Optimization ◽  
Rbf Neural Networks ◽  
Optimal Solutions ◽  
Base Function ◽  
The Neural Network ◽  
Engineering Practices ◽  
Radial Base Function

Nonlinear system optimization is always an issue that needs to be considered in engineering practices and management. In order to obtain optimal solutions without analysis formulas to nonlinear systems, we first construct a radial-base-function (RBF) neural network using the newrb() function in MALTAB 7.0, then train the neural network according to input and output, and finally obtain the solution using a genetic algorithm. Simulated experimental results show that the proposed algorithm is able to achieve optimal solutions with a relatively fast speed of convergence.

Machine Fault Diagnosis Using MLPs and RBF Neural Networks

2011 ◽  
Vol 110-116 ◽  
pp. 5021-5028 ◽  
Author(s):  
Gholam Hassan Payganeh ◽  
Mehrdad Nouri Khajavi ◽  
Reza Ebrahimpour ◽  
Ebrahim Babaei
Keyword(s):  
Neural Network ◽  
Rbf Neural Network ◽  
Velocity Spectrum ◽  
Financial Assets ◽  
Rbf Neural Networks ◽  
Good Decision ◽  
Mass Unbalance ◽  
The Neural Network ◽  
Characteristic Features ◽  
Rotor Assembly

-Fault detection and elimination in industrial machineries can help prevent loss of life and financial assets. In this study four common faults in rotating machineries namely: 1) Mass Unbalance 2) Angular Misalignment 3) Bearing Faults and 4) Mechanical Looseness have been considered. Each of these defects has been created separately on a test rig comprising of an electrical motor coupled to a rotor assembly. A Vibrotest 60 vibration spectrum analyzer has been used to collect velocity spectrum of the vibration on the bearings. Eleven characteristic features have been chosen to distinguish different faults. Based on the acquired data an Artificial Neural Network Multi Layer Perceptrons (MLPs) and Radial Basis Functions (RBF) Neural Network has been designed to recognize each one of the aforementioned defects. After training the Neural Network, it was checked by new data gathered by new experiments and the results showed that the designed network can predict the faults with more than 75% reliability, and it can be a good assistance to an ordinary machine operator to guess the problem and hence make a good decision.

PID control of an omnidirectional mobile platform based on an RBF neural network controller

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Niu Zijie ◽  
Zhang Peng ◽  
Yongjie Cui ◽  
Zhang Jun
Keyword(s):  
Neural Network ◽  
Real Time ◽  
Network Model ◽  
Neural Network Model ◽  
Control Algorithm ◽  
Rbf Neural Network ◽  
Mobile Platform ◽  
Content Type ◽  
The Neural Network ◽  
Omnidirectional Mobile Platform

Purpose Omnidirectional mobile platforms are still plagued by the problem of heading deviation. In four-Mecanum-wheel systems, this problem arises from the phenomena of dynamic imbalance and slip of the Mecanum wheels while driving. The purpose of this paper is to analyze the mechanism of omnidirectional motion using Mecanum wheels, with the aim of enhancing the heading precision. A proportional-integral-derivative (PID) setting control algorithm based on a radial basis function (RBF) neural network model is introduced. Design/methodology/approach In this study, the mechanism of omnidirectional motion using Mecanum wheels is analyzed, with the aim of enhancing the heading precision. A PID setting control algorithm based on an RBF neural network model is introduced. The algorithm is based on a kinematics model for an omnidirectional mobile platform and corrects the driving heading in real time. In this algorithm, the neural network RBF NN2 is used for identifying the state of the system, calculating the Jacobian information of the system and transmitting information to the neural network RBF NN1. Findings The network RBF NN1 calculates the deviations ?Kp, ?Ki and ?Kd to regulate the three coefficients Kp, Ki and Kd of the heading angle PID controller. This corrects the driving heading in real time, resolving the problems of low heading precision and unstable driving. The experimental data indicate that, for a externally imposed deviation in the heading angle of between 34º and ∼38°, the correction time for an omnidirectional mobile platform applying the algorithm during longitudinal driving is reduced by 1.4 s compared with the traditional PID control algorithm, while the overshoot angle is reduced by 7.4°; for lateral driving, the correction time is reduced by 1.4 s and the overshoot angle is reduced by 4.2°. Originality/value In this study, the mechanism of omnidirectional motion using Mecanum wheels is analyzed, with the aim of enhancing the heading precision. A PID setting control algorithm based on an RBF neural network model is introduced. The algorithm is based on a kinematics model for an omnidirectional mobile platform and corrects the driving heading in real time. In this algorithm, the neural network RBF NN2 is used for identifying the state of the system, calculating the Jacobian information of the system and transmitting information to the neural network RBF NN1. The method is innovative.

Self-Organized Maps on Continuous Bump Attractors

2013 ◽  
Vol 8 (1) ◽  
pp. 234-247 ◽  
Author(s):  
К.П. Соловьева ◽  
K.P. Solovyeva
Keyword(s):  
Neural Network ◽  
Recurrent Neural Network ◽  
Recurrent Network ◽  
External Signal ◽  
Stable States ◽  
Factors Affecting ◽  
One Dimensional ◽  
Self Organized ◽  
The Neural Network ◽  
Winner Takes All

In this article, we describe a simple binary neuron system, which implements a self-organized map. The system consists of R input neurons (R receptors), and N output neurons of a recurrent neural network. The neural network has a quasi-continuous set of attractor states (one-dimensional “bump attractor”). Due to the dynamics of the network, each external signal (i.e. activity state of receptors) imposes transition of the recurrent network into one of its stable states (points of its attractor). That makes our system different from the “winner takes all” construction of T.Kohonen. In case, when there is a one-dimensional cyclical manifold of external signals in R-dimensional input space, and the recurrent neural network presents a complete ring of neurons with local excitatory connections, there exists a process of learning of connections between the receptors and the neurons of the recurrent network, which enables a topologically correct mapping of input signals into the stable states of the neural network. The convergence rate of learning and the role of noises and other factors affecting the described phenomenon has been evaluated in computational simulations.

Based on RBF Neural Network Modeling of Well Test System

2014 ◽  
Vol 535 ◽  
pp. 606-609
Author(s):  
Jia Tian
Keyword(s):  
Neural Network ◽  
Rbf Neural Network ◽  
Effective Means ◽  
Test System ◽  
Neural Network Modeling ◽  
Well Test ◽  
Fast Learning ◽  
The Neural Network ◽  
Neural Network System ◽  
Neural Network Toolbox

The Neural Network Toolbox in MATLAB is a powerful instrument of analyzing and designing a neural network system. RBF Neural Network has small computational burden and fast learning rate and is not liable to be trapped by local minimal points etc. So it is an effective means to identify and model a system. In this paper, the Neural Network Toolbox in MATLAB and RBF Neural Network are combined to solve the problem of modeling the pressure in oilfield test well systems and the result is excellent.

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