Method of AC Servo System Based on Genetic Algorithm and Neural Network PID Control

2013 ◽  
Vol 717 ◽  
pp. 563-567 ◽  
Author(s):  
Wen Chun Chang ◽  
Cheng Chen
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Network Model ◽  
Network Structure ◽  
Bp Neural Network ◽  
Extreme Values ◽  
Learning Algorithm ◽  
Bp Algorithm ◽  
Bp Network ◽  
Neural Network Learning

BP network model has become one of the important neural network model, is used in many fields, but it has some defects. As from a mathematical perspective, it is a nonlinear optimization problem, which inevitably has the local minima problem; BP neural network learning algorithm has slow convergence rate, and the convergence speed and the initial weights of choice; network structure, namely the hidden layer nodes selection is still no theory until, but according to the experience. Based on the BP algorithm the local extreme values, considering the genetic algorithm and BP algorithm is combined with, on the BP neural network optimization. Neural network using genetic algorithm optimization mainly includes three aspects: the connection weights of evolution, evolutionary network structure, learning the rules of evolution.

Method of AC Servo System Based on Genetic Algorithm and Neural Network PID Control

2013 ◽  
Vol 422 ◽  
pp. 221-225
Author(s):  
Wen Chun Chang ◽  
Cheng Chen
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Network Model ◽  
Network Structure ◽  
Bp Neural Network ◽  
Extreme Values ◽  
Learning Algorithm ◽  
Bp Algorithm ◽  
Bp Network ◽  
Neural Network Learning

BP network model has become one of the important neural network model which is used in many fields, but it has some defects. From a mathematical perspective, it is a nonlinear optimization problem, which inevitably has the local minima problem; BP neural network learning algorithm has slow convergence rate, and the convergence speed and the initial weights of choice; network structure, namely the hidden layer nodes selection still has no theory, but according to the experience. Based on the BP algorithm local extreme values, considering the genetic algorithm, combining with BP algorithm, the BP neural network optimization is achieved. Neural network using genetic algorithm optimization mainly includes three aspects: the connection weights of evolution, evolutionary network structure, learning the rules of evolution.

BP Neural Network Learning Algorithm and its Software Implementation

2014 ◽  
Vol 513-517 ◽  
pp. 738-741 ◽  
Author(s):  
Ying Jian Lin ◽  
Xiao Ji Chen
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Learning Algorithm ◽  
Bp Algorithm ◽  
Software Implementation ◽  
Voice Conversion ◽  
Bp Network ◽  
Widespread Application ◽  
Neural Network Learning ◽  
Basic Work

BP neural network in character recognition, pattern classification, text and voice conversion, image compression, decision support and so on aspects has the widespread application, in view of the problems existing in the actual application, this paper researches learning algorithm and software implementation. Learning algorithm studies include three aspects, illustrates the basic thoughts of the BP algorithm, designed the three layers BP network structure, the mathematical model for the accurate description of algorithm. Software implementation studies include two aspects, the network model of all neurons become linked list structure and storage structure is designed, the design of the software process and will implement the process into four steps. BP algorithm of the software implementation is a basic work for the application of BP neural network, using the research results of this paper, the user can easily neural network design and simulation.

Application of a genetic algorithm to optimize BP neural networks

2020 ◽  
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Bp Neural Network ◽  
Local Minimum ◽  
Network Performance ◽  
Learning Algorithm ◽  
Simulation Data ◽  
Neural Network Learning ◽  
Network Learning ◽  
Fast Convergence Rate

A genetic algorithm is proposed to us to prevent a local minimum defect when using the BP neural network learning algorithm. The genetic algorithm is first used to optimize the weight and threshold of the BP neural network, and then obtained values are used to optimize the BP neural network. Optimized network performance is estimated using simulation data. The results of numerical simulations show that the BP neural network optimized by the genetic algorithm can effectively eliminate a local minimum defect, which is easy to find in the original BP neural network, and has the advantages of fast convergence rate and high accuracy. Keywords BP neural network; genetic algorithm; local minimum defect; optimization

scholarly journals The Research of Temperature Compensation for Thermopile Sensor Based on Improved PSO-BP Algorithm

2015 ◽  
Vol 2015 ◽  
pp. 1-6 ◽  
Author(s):  
Yuanjiang Li ◽  
Yuehua Li ◽  
Feng Li ◽  
Bin Zhao ◽  
QingQing Li
Keyword(s):  
Neural Network ◽  
Thermal Radiation ◽  
Bp Neural Network ◽  
Temperature Compensation ◽  
Safety Monitoring ◽  
Bp Algorithm ◽  
Bp Network ◽  
The Core ◽  
Thermopile Sensor ◽  
Improved Pso

When thermopile sensor is used for safety monitoring of equipment in industrial environments, particularly for measuring the thermal radiation information of device, the measured result of this kind of sensor is usually affected by ambient temperature due to its unique structure. An improved PSO-BP algorithm is proposed for temperature compensation of thermopile sensor and correcting the error in the condition of the system accuracy requirements reduced by temperature. The core of improved PSO-BP algorithm is to improve the certainty of initial weights and thresholds that belonged to BP neural network and then train the samples by using BP neural network for enhancing the generalization ability and stability of system. The experimental results show that the proposed PSO-BP network outperforms other similar algorithms with faster convergence speed, lower errors, and higher accuracy.

Transformer Fault Diagnosis Based on Improved Quantum Genetic Algorithm and BP Network

2010 ◽  
Vol 29-32 ◽  
pp. 1543-1549 ◽  
Author(s):  
Jie Wei ◽  
Hong Yu ◽  
Jin Li
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Fault Diagnosis ◽  
Bp Neural Network ◽  
Fuzzy Theory ◽  
Back Propagation ◽  
Gas Analysis ◽  
Bp Network ◽  
Quantum Genetic Algorithm ◽  
Transformer Fault

Three-ratio of the IEC is a convenient and effective approach for transformer fault diagnosis in the dissolved gas analysis (DGA). Fuzzy theory is used to preprocess the three-ratio for its boundary that is too absolute. As the same time, an improved quantum genetic algorithm IQGA (QGASAC) is used to optimize the weight and threshold of the back propagation (BP). The local and global searching ability of the QGASAC approach is utilized to find the BP optimization solution. It can overcome the slower convergence velocity and hardly getting the optimization of the BP neural network. So, aiming at the shortcoming of BP neural network and three-ratio, blurring the boundary of the gas ratio and the QGASAC algorithm is introduced to optimize the BP network. Then the QGASAC-IECBP method is proposed in this paper. Experimental results indicate that the proposed algorithm in this paper that both convergence velocity and veracity are all improved to some extent. And in this paper, the proposed algorithm is robust and practical.

Research on Dissolve Oxygen Modeling Based on Neural Network

2011 ◽  
Vol 287-290 ◽  
pp. 2640-2643
Author(s):  
Guo Dong Gao ◽  
Wen Xiao Zhang ◽  
Gong Zhi Yu ◽  
Jiang Hua Sui
Keyword(s):  
Neural Network ◽  
Dissolved Oxygen ◽  
Network Model ◽  
Neural Network Model ◽  
Bp Neural Network ◽  
Structure Learning ◽  
Learning Algorithm ◽  
Impact Factors ◽  
Function Learning ◽  
Bp Neural Network Model

The structure, characteristics and principles of BP neural network model are described in this paper. First, three impact factors of the dissolved oxygen are selected as the sample input of network, and then the parameters of BP neural network are selected, such as network structure, learning algorithm, output layer transfer function, learning rate and so on. Finally, the BP neural network model is established and trained, in order to approach compensate the effects of improves non-linearity. The simulation results show that BP neural network is practical and dependable in the field of dissolved oxygen modeling and has nice applied prospect.

Power Information System Risk Assessment Method Based on Genetic Algorithms and Neural Network

2014 ◽  
Vol 530-531 ◽  
pp. 429-433 ◽  
Author(s):  
Heng Yang ◽  
Ru Sen Fan ◽  
Dong Hui Xu
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Risk Assessment ◽  
Information System ◽  
Evaluation Method ◽  
Assessment Method ◽  
Local Extremum ◽  
Bp Algorithm ◽  
Bp Network ◽  
System Risk

In order to scientifically and accurately evaluate power information system, the new power information risk evaluation method based on the genetic algorithm and BP neural network is presented. The method combining the genetic algorithm and BP algorithm can be used to train the feedforward neural network , namely, first , to use the genetic algorithm to do the global training, then ,to use BP algorithm to do local precise training ,which not only overcomes the drawbacks of the traditional BP network (the training time is long, and the network is easy to fall to local extremum),but also improves the global convergence efficiency. The method was adopted to evaluate the power information system. And findings identify that the new method has distinctive convergence speed and high predicition accuracy, which provides a new concept for power information system risk assessment.

Modified BP Neural Network Model is Used for Odd-Even Discrimination of Integer Number

2013 ◽  
Vol 423-426 ◽  
pp. 2675-2678 ◽  
Author(s):  
Bao Long Hu ◽  
Ji Ren Xu ◽  
Huai Hui Gao ◽  
Ji Hai Liu ◽  
Ke Ren Wang
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Bp Neural Network ◽  
Network Models ◽  
Bp Algorithm ◽  
Integer Number ◽  
Neural Network Models ◽  
Bp Neural Network Model ◽  
Bp Model

This paper introduced the BP neural network model and the BP algorithm in detail, and pointed out the BP neural network existed the defects of local optimal tendency of local optimal, slowed convergence speed etc. Through the modified BP algorithm, we could solve the problems existing in the traditional BP algorithm successfully, simulation results for odd-even discrimination of integer number based on MATLAB BP algorithm show that modified BP model compared with BP model has faster training speed and high study accuracy. Modified BP neural network models is used in practice, as long as it is complementary with effective measures, and we can get satisfactory result completely.

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