Seismic Pattern Recognition of Nuclear Explosion Based on Generalization Learning Algorithm of BP Network and Genetic Algorithm

2004 ◽  
pp. 959-964
Author(s):  
Daizhi Liu ◽  
Renming Wang ◽  
Xihai Li ◽  
Zhigang Liu
Keyword(s):  
Genetic Algorithm ◽  
Pattern Recognition ◽  
Learning Algorithm ◽  
Nuclear Explosion ◽  
Bp Network

Design and Algorithm of Intelligent Manufacturing Evaluation Model Based on Machine Learning

2021 ◽  
Author(s):  
Xianwang Li ◽  
Zhongxiang Huang ◽  
Wenhui Ning
Keyword(s):  
Machine Learning ◽  
Genetic Algorithm ◽  
Manufacturing System ◽  
Learning Algorithm ◽  
Intelligent Manufacturing ◽  
System Model ◽  
Machine Learning Algorithm ◽  
Model Design ◽  
Bp Network ◽  
Intelligent Manufacturing System

Abstract Machine learning is gradually developed and applied to more and more fields. Intelligent manufacturing system is also an important system model that many companies and enterprises are designing and implementing. The purpose of this study is to evaluate and analyze the model design of Intelligent Manufacturing System Based on machine learning algorithm. The method of this study is to first obtain all the relevant attributes of the intelligent manufacturing system model, and then use machine learning algorithm to delete irrelevant attributes to prevent redundancy and deviation of neural network fitting, make the original probability distribution as close as possible to the distribution when using the selected attributes, and use the ratio of industry average to quantitative expression for measurable and obvious data indicators. As a result, the average running time of the intelligent manufacturing system is 17.35 seconds, and the genetic algorithm occupies 15.63 seconds. The machine learning network takes up 1.72 seconds. Under the machine learning algorithm, the training speed is very high, obviously higher than that of the genetic algorithm, and the BP network is 2.1% higher than the Elman algorithm. The evaluation running speed of the system model design is fast and the accuracy is high. This study provides a certain value for the model design evaluation and algorithm of various systems in the intelligent era.

Gearbox Fault Detection Using Neural Networks Technology

1999 ◽  
Author(s):  
Qiao Sun ◽  
Xiaolei Li ◽  
Baoyun Xu
Keyword(s):  
Genetic Algorithm ◽  
Neural Networks ◽  
Feature Extraction ◽  
Fault Diagnosis ◽  
Learning Algorithm ◽  
Extraction Methods ◽  
Bp Network ◽  
Speed Increase ◽  
Learning Speed ◽  
Hidden Layer

Abstract This paper describes the application of neural networks to gearbox fault diagnosis. In order to increase learning speed of BP network, a modified learning algorithm was presented. Considering of the difficulty of choosing neural networks’ architecture, genetic algorithm was employed. The discussion of the effect of hidden layer nodes shows that with the increase of the number of nodes, the learning speed increase also yet result in poor generalization ability. The test of fault tolerance ability tells that neural networks have ‘bench type’ tolerance ability. This ensures that when signals were contaminated by noise or feature extraction methods were not effective, the result can still be acceptable. To test the performance of the application of neural networks on gearbox fault diagnosis, experiments of single fault and multi-faults were both implemented and diagnosed by neural networks. The results were satisfied.

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.

scholarly journals Computational Design of Modular Robots Based on Genetic Algorithm and Reinforcement Learning

Symmetry ◽  
2021 ◽  
Vol 13 (3) ◽  
pp. 471
Author(s):  
Jai Hoon Park ◽  
Kang Hoon Lee
Keyword(s):  
Genetic Algorithm ◽  
Reinforcement Learning ◽  
Design Space ◽  
Learning Algorithm ◽  
Computational Design ◽  
Computational Method ◽  
Learning Ability ◽  
Modular Robots ◽  
Control Mechanisms ◽  
Candidate Structure

Designing novel robots that can cope with a specific task is a challenging problem because of the enormous design space that involves both morphological structures and control mechanisms. To this end, we present a computational method for automating the design of modular robots. Our method employs a genetic algorithm to evolve robotic structures as an outer optimization, and it applies a reinforcement learning algorithm to each candidate structure to train its behavior and evaluate its potential learning ability as an inner optimization. The size of the design space is reduced significantly by evolving only the robotic structure and by performing behavioral optimization using a separate training algorithm compared to that when both the structure and behavior are evolved simultaneously. Mutual dependence between evolution and learning is achieved by regarding the mean cumulative rewards of a candidate structure in the reinforcement learning as its fitness in the genetic algorithm. Therefore, our method searches for prospective robotic structures that can potentially lead to near-optimal behaviors if trained sufficiently. We demonstrate the usefulness of our method through several effective design results that were automatically generated in the process of experimenting with actual modular robotics kit.

Pattern recognition using multilayer neural-genetic algorithm

2003 ◽  
Vol 51 ◽  
pp. 237-247 ◽  
Author(s):  
Yas Abbas Alsultanny ◽  
Musbah M. Aqel
Keyword(s):  
Genetic Algorithm ◽  
Pattern Recognition

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.

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