Optimal Design of Gears by Means of Genetic Algorithm and Neural Network

2013 ◽  
Vol 397-400 ◽  
pp. 816-820
Author(s):  
Yong Gang Li, ◽  
Yong Mei Ma
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Optimal Design ◽  
Bp Neural Network ◽  
Optimization Design ◽  
Optimal Solution ◽  
Constraint Equation ◽  
Relative Parameter

Optimal design of gears was complicated with much difficulty to determine the parameter of strength constraint equation, and find the optimal solution. Used BP Neural Network to approximate the relative parameter of gears optimization design which was shown by chart. Used Genetic Algorithm to search the optimal solution. The result shows that the application of Genetic Algorithm and Neural Network in gear optimization is effective.

The Missile Fault Diagnosis Expert System Based on GA-BPNN

2011 ◽  
Vol 255-260 ◽  
pp. 2164-2168
Author(s):  
Jian Xiao Zou ◽  
Yao Zhang ◽  
Gang Zheng
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Fault Diagnosis ◽  
Expert System ◽  
Bp Neural Network ◽  
Optimal Solution ◽  
Convergence Speed ◽  
Diagnosis Accuracy ◽  
Simulation Results ◽  
Locally Optimal Solution

To improve the performance of fault diagnosis expert system based on ANN IN fields of convergence speed, locally optimal solution and the low accuracy, an missile fault diagnosis expert system based on GA-BPNN is proposed in this paper. The genetic algorithm (GA) is adopt to optimize the weight and threshold of matrix while BP neural network realizes the non-linear map relations between failure feature and failure cause. The simulation results indicate that the method proposed in this paper significantly increase the convergence speed and globally optimal solution of neural network, the fault diagnosis accuracy of expert system for a missile has been improved also.

Verifying Three Nodes Optimization Based on LM-BP Neural Network and Hybrid Genetic Algorithm

2013 ◽  
Vol 385-386 ◽  
pp. 348-351
Author(s):  
Xin Li ◽  
Zhong Peng Zhang ◽  
Shi Lin Shen ◽  
Fei Xie
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Bp Neural Network ◽  
Thrust Force ◽  
Hybrid Genetic Algorithm ◽  
Optimal Solution ◽  
Pareto Optimal Solution ◽  
Optimal Position ◽  
Main Method ◽  
Formula Derivation

Taking the minimum to cylinder thrust force, turntable force and boom force as the objective function then establish the optimization mathematical model of the verifying three nodes, taking BP Neural Network as the main method instead of the cumbersome formula derivation. This article puts forward a Hybrid Genetic Algorithm flow set of solving pareto optimal solution, It is achieved by mixed-using Niche Technology, Groups Sorting Technology. The optimal position of the arm verifying three nodes is conformed by programming using Matlab genetic algorithm toolbox. And the force of the fuel tank , boom and turntable is effectively mitigated. This gives a appropriate reference for the next boom verifying three nodes position to determine and the optimal design of similar structures in other engineering machinery.

A Hybrid of Back Propagation Neural Network and Genetic Algorithm for Modeling and Simulation of Standard Involute Spur Gear

2013 ◽  
Vol 823 ◽  
pp. 111-114
Author(s):  
Jian Lan Luo ◽  
Xiao Kai Wang
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Modeling And Simulation ◽  
Bp Neural Network ◽  
Mechanical Design ◽  
Back Propagation ◽  
Optimal Solution ◽  
3D Models ◽  
Spur Gear ◽  
Element Analysis

A method is presented for gear modeling and simulation based on a hybrid of back propagation (BP) neural network and genetic algorithm (GA). Generally, the method of mechanical design includes building up 3D models and gets them checked in corresponding software, which is complicated and takes much time. This proposed method offers a quick way for designers, which combines finite element analysis (FEA) with a hybrid of BP neural network and an intelligence global optimization algorithm, i.e. genetic algorithm (GA). ANSYS APDL is applied to build up the FEA database, which offers data for study and training of BP neural network. Then, the GA is applied to optimize the process parameters that would result in optimal solution of the BP neural network goals. The case study demonstrates that the proposed method can get accurate results in a short time.

Precision Power System Harmonic Analysis Algorithm Based on ABC-BPNN

2013 ◽  
Vol 483 ◽  
pp. 630-634
Author(s):  
Shu Chuan Gan ◽  
Ling Tang ◽  
Li Cao ◽  
Ying Gao Yue
Keyword(s):  
Neural Network ◽  
Power System ◽  
Harmonic Analysis ◽  
Bp Neural Network ◽  
Artificial Bee Colony Algorithm ◽  
Artificial Bee Colony ◽  
Optimal Solution ◽  
Bee Colony ◽  
Network Algorithm ◽  
Neural Network Algorithm

An algorithm of artificial colony algorithm to optimize the BP neural network algorithm was presented and used to analyze the harmonics of power system. The artificial bee colony algorithm global searching ability, convergence speed for the BP neural network algorithm for harmonic analysis is easy to fall into local optimal solution of the disadvantages, and the initial weights of the artificial bee colony algorithm also greatly enhance whole algorithm model generalization capability. This algorithm using MATLAB for Artificial bee colony algorithm and BP neural network algorithm simulation training toolbox found using artificial bee colony algorithm to optimize BP neural network algorithm converges faster results with greater accuracy, with better harmonic analysis results.

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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