Intelligent feature recognition for STEP-NC-compliant manufacturing based on artificial bee colony algorithm and back propagation neural network

2021 ◽  
Author(s):  
Yu Zhang ◽  
Yongsheng Zhang ◽  
Kaiwen He ◽  
Dongsheng Li ◽  
Xun Xu ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Bee Colony Algorithm ◽  
Artificial Bee Colony ◽  
Feature Recognition ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Bee Colony

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.

Optimization Artificial Neural Network Using Artificial Bee Colony in Letter Recognition Classification

2020 ◽  
Vol 8 (4) ◽  
pp. 469
Author(s):  
I Gusti Ngurah Alit Indrawan ◽  
I Made Widiartha
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Classification Accuracy ◽  
Artificial Bee Colony Algorithm ◽  
Artificial Bee Colony ◽  
Letter Recognition ◽  
Bee Colony ◽  
Artificial Neural ◽  
Hidden Layer

Artificial Neural Networks or commonly abbreviated as ANN is one branch of science from the field of artificial intelligence which is often used to solve various problems in fields that involve grouping and pattern recognition. This research aims to classify Letter Recognition datasets using Artificial Neural Networks which are weighted optimally using the Artificial Bee Colony algorithm. The best classification accuracy results from this study were 92.85% using a combination of 4 hidden layers with each hidden layer containing 10 neurons.

scholarly journals Dynamic Stress Measurement with Sensor Data Compensation

Electronics ◽  
2019 ◽  
Vol 8 (8) ◽  
pp. 859
Author(s):  
Jingjing Gu ◽  
Zhiteng Dong ◽  
Cai Zhang ◽  
Xiaojiang Du ◽  
Mohsen Guizani
Keyword(s):  
Neural Network ◽  
Dynamic Stress ◽  
Stress Measurement ◽  
Rapid Change ◽  
Back Propagation ◽  
Environmental Parameters ◽  
Back Propagation Neural Network ◽  
Sensor Data ◽  
Bee Colony ◽  
Stability Of Solution

Applying parachutes-deployed Wireless Sensor Network (WSN) in monitoring the high-altitude space is a promising solution for its effectiveness and cost. However, both the high deviation of data and the rapid change of various environment factors (air pressure, temperature, wind speed, etc.) pose a great challenge. To this end, we solve this challenge with data compensation in dynamic stress measurements of parachutes during the working stage. Specifically, we construct a data compensation model to correct the deviation based on neural network by taking into account a variety of environmental parameters, and name it as Data Compensation based on Back Propagation Neural Network (DC-BPNN). Then, for improving the speed and accuracy of training the DC-BPNN, we propose a novel Adaptive Artificial Bee Colony (AABC) algorithm. We also address its stability of solution by deriving a stability bound. Finally, to verify the real performance, we conduct a set of real implemented experiments of airdropped WSN.

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