A speech recognition system based on fuzzy neural network trained by artificial bee colony algorithm

2011 ◽  
Author(s):  
Aiping Ning ◽  
Xueying Zhang
Keyword(s):  
Neural Network ◽  
Speech Recognition ◽  
Artificial Bee Colony Algorithm ◽  
Fuzzy Neural Network ◽  
Artificial Bee Colony ◽  
Recognition System ◽  
Speech Recognition System ◽  
Bee Colony ◽  
Fuzzy Neural

scholarly journals Using an Adaptive Fuzzy Neural Network Based on a Multi-Strategy-Based Artificial Bee Colony for Mobile Robot Control

Mathematics ◽  
2020 ◽  
Vol 8 (8) ◽  
pp. 1223
Author(s):  
Cheng-Hung Chen ◽  
Shiou-Yun Jeng ◽  
Cheng-Jian Lin
Keyword(s):  
Neural Network ◽  
Mobile Robot ◽  
Fuzzy Neural Network ◽  
Artificial Bee Colony ◽  
Mobile Robot Navigation ◽  
Adaptive Fuzzy Neural Network ◽  
Adaptive Fuzzy ◽  
Bee Colony ◽  
Fuzzy Neural ◽  
Navigation Control

This study proposes an adaptive fuzzy neural network (AFNN) based on a multi-strategy artificial bee colony (MSABC) algorithm for achieving an actual mobile robot navigation control. During the navigation control process, the AFNN inputs are the distance between the ultrasonic sensors and the angle between the mobile robot and the target, and the AFNN outputs are the robot’s left- and right-wheel speeds. A fitness function in reinforcement learning is defined to evaluate the navigation control performance of AFNN. The proposed MSABC algorithm improves the poor exploitation disadvantage in the traditional artificial bee colony (ABC) and adopts the mutation strategies of a differential evolution to balance exploration and exploitation. To escape in special environments, a manual wall-following fuzzy logic controller (WF-FLC) is designed. The experimental results show that the proposed MSABC method has improved the performance of average fitness, navigation time, and travel distance by 79.75%, 33.03%, and 10.74%, respectively, compared with the traditional ABC method. To prove the feasibility of the proposed controller, experiments were carried out on the actual PIONEER 3-DX mobile robot, and the proposed navigation control method was successfully completed.

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.

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