Modified Differential Evolution Algorithm Based Neural Network for Nonlinear Discrete Time System

2020 ◽  
pp. 1598-1621
Author(s):  
Uday Pratap Singh ◽  
Sanjeev Jain ◽  
Rajeev Kumar Singh ◽  
Mahesh Parmar
Keyword(s):  
Neural Network ◽  
Nonlinear Systems ◽  
Differential Evolution ◽  
Differential Evolution Algorithm ◽  
Uncertain Nonlinear Systems ◽  
Discrete Time System ◽  
Brushless Dc ◽  
Unknown Disturbances ◽  
Evolution Algorithm ◽  
The Difference

Two main important features of neural networks are weights and bias connection, which is still a challenging problem for researchers. In this paper we select weights and bias connection of neural network (KN) using modified differential evolution algorithm (MDEA) i.e. MDEA-NN for uncertain nonlinear systems with unknown disturbances and compare it with KN using differential evolution algorithm (DEA) i.e. DEA-KN. In this work, MDEA is based on exploitation and exploration of capability, we have implemented differential evolution algorithm and modified differential evolution algorithm, which are based on the consideration of the three main operator's mutation, crossover and selection. MDEA-KN is applied on two different uncertain nonlinear systems, and one benchmark problem known as brushless dc (BDC) motor. Proposed method is validated through statistical testing's methods which demonstrate that the difference between target and output of proposed method are acceptable.

Modified Differential Evolution Algorithm Based Neural Network for Nonlinear Discrete Time System

2017 ◽  
pp. 397-420 ◽  
Author(s):  
Uday Pratap Singh ◽  
Sanjeev Jain ◽  
Rajeev Kumar Singh ◽  
Mahesh Parmar
Keyword(s):  
Neural Network ◽  
Nonlinear Systems ◽  
Differential Evolution ◽  
Differential Evolution Algorithm ◽  
Uncertain Nonlinear Systems ◽  
Discrete Time System ◽  
Brushless Dc ◽  
Unknown Disturbances ◽  
Evolution Algorithm ◽  
The Difference

Two main important features of neural networks are weights and bias connection, which is still a challenging problem for researchers. In this paper we select weights and bias connection of neural network (KN) using modified differential evolution algorithm (MDEA) i.e. MDEA-NN for uncertain nonlinear systems with unknown disturbances and compare it with KN using differential evolution algorithm (DEA) i.e. DEA-KN. In this work, MDEA is based on exploitation and exploration of capability, we have implemented differential evolution algorithm and modified differential evolution algorithm, which are based on the consideration of the three main operator's mutation, crossover and selection. MDEA-KN is applied on two different uncertain nonlinear systems, and one benchmark problem known as brushless dc (BDC) motor. Proposed method is validated through statistical testing's methods which demonstrate that the difference between target and output of proposed method are acceptable.

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.

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 Wavelet-Based Elman Neural Network with the Modified Differential Evolution Algorithm for Forecasting Foreign Exchange Rates

2021 ◽  
Vol 9 (4) ◽  
pp. 421-439
Author(s):  
Renquan Huang ◽  
Jing Tian
Keyword(s):  
Neural Network ◽  
Differential Evolution ◽  
Exchange Rates ◽  
Foreign Exchange ◽  
Differential Evolution Algorithm ◽  
Wavelet Function ◽  
Foreign Exchange Rates ◽  
Elman Neural Network ◽  
Evolution Algorithm ◽  
Hidden Layer

Abstract It is challenging to forecast foreign exchange rates due to the non-linear characters of the data. This paper applied a wavelet-based Elman neural network with the modified differential evolution algorithm to forecast foreign exchange rates. Elman neural network has dynamic characters because of the context layer in the structure. It makes Elman neural network suit for time series problems. The main factors, which affect the accuracy of the Elman neural network, included the transfer functions of the hidden layer and the parameters of the neural network. We applied the wavelet function to replace the sigmoid function in the hidden layer of the Elman neural network, and we found there was a “disruption problem” caused by the non-linear performance of the wavelet function. It didn’t improve the performance of the Elman neural network, but made it get worse in reverse. Then, the modified differential evolution algorithm was applied to train the parameters of the Elman neural network. To improve the optimizing performance of the differential evolution algorithm, the crossover probability and crossover factor were modified with adaptive strategies, and the local enhanced operator was added to the algorithm. According to the experiment, the modified algorithm improved the performance of the Elman neural network, and it solved the “disruption problem” of applying the wavelet function. These results show that the performance of the Elman neural network would be improved if both of the wavelet function and the modified differential evolution algorithm were applied integratedly.

Sign in / Sign up

Export Citation Format

Share Document