Speech Recognition Based on Fuzzy Neural Network and Chaotic Differential Evolution Algorithm

2015 ◽  
Vol 12 (14) ◽  
pp. 5451-5458 ◽  
Author(s):  
Xueqin Zhou
Keyword(s):  
Neural Network ◽  
Speech Recognition ◽  
Differential Evolution ◽  
Fuzzy Neural Network ◽  
Differential Evolution Algorithm ◽  
Fuzzy Neural ◽  
Evolution Algorithm

Embedded Differential Evolution Algorithm for Recurrent Fuzzy Neural Network Controller Optimization

2013 ◽  
Vol 321-324 ◽  
pp. 2141-2145
Author(s):  
Xiao Sheng Wang ◽  
Ying Li ◽  
Yan Hui Guo
Keyword(s):  
Neural Network ◽  
Differential Evolution ◽  
Fuzzy Neural Network ◽  
Differential Evolution Algorithm ◽  
Experimental Simulation ◽  
Neural Network Controller ◽  
Fuzzy Neural ◽  
Embedded Controller ◽  
Evolution Algorithm ◽  
Recurrent Fuzzy Neural Network

A chaos concise differential evolution algorithm (CcDE) is proposed for the embedded controller with limited memory, which introduces chaotic local search based on basic differential evolution algorithm to increase exploring and prevent premature convergence. Using virtual population and Gaussian sampling, the CcDE becomes simple and reduces the memory requirements at run time. Experimental simulation on optimizing parameters of the recurrent fuzzy neural network shows that the proposed CcDE can obtain better performance than other concise algorithm.

scholarly journals Text Classification of Public Feedbacks using Convolutional Neural Network Based on Differential Evolution Algorithm

2019 ◽  
Vol 14 (1) ◽  
pp. 124-134 ◽  
Author(s):  
Shuai Zhang ◽  
Yong Chen ◽  
Xiaoling Huang ◽  
Yishuai Cai
Keyword(s):  
Neural Network ◽  
Differential Evolution ◽  
Convolutional Neural Network ◽  
Text Classification ◽  
Differential Evolution Algorithm ◽  
Classification Performance ◽  
Classification Model ◽  
Evolution Algorithm ◽  
Classification Prediction

Online feedback is an effective way of communication between government departments and citizens. However, the daily high number of public feedbacks has increased the burden on government administrators. The deep learning method is good at automatically analyzing and extracting deep features of data, and then improving the accuracy of classification prediction. In this study, we aim to use the text classification model to achieve the automatic classification of public feedbacks to reduce the work pressure of administrator. In particular, a convolutional neural network model combined with word embedding and optimized by differential evolution algorithm is adopted. At the same time, we compared it with seven common text classification models, and the results show that the model we explored has good classification performance under different evaluation metrics, including accuracy, precision, recall, and F1-score.

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