Feed-forward neural network modeling and optimization using genetic algorithm: Enzymatic hydrolysis of xylose production

2013 ◽  
Author(s):  
Nur'Atiqah Norhalim ◽  
Zainal Ahmad ◽  
Mashitah Mat Don
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Enzymatic Hydrolysis ◽  
Network Modeling ◽  
Neural Network Modeling ◽  
Feed Forward Neural Network ◽  
Feed Forward ◽  
Modeling And Optimization ◽  
Xylose Production ◽  
Hydrolysis Of

A study on global optimization and deep neural network modeling method in performance-seeking control

2019 ◽  
Vol 234 (1) ◽  
pp. 46-59 ◽  
Author(s):  
Qiangang Zheng ◽  
Dawei Fu ◽  
Yong Wang ◽  
Haoying Chen ◽  
Haibo Zhang
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Global Optimization ◽  
Interval Analysis ◽  
Sequential Quadratic Programming ◽  
Deep Neural Network ◽  
Network Modeling ◽  
Engine Performance ◽  
Neural Network Modeling ◽  
Swarm Optimization

In this article, a novel performance-seeking control method based on deep neural network and interval analysis is proposed to obtain a better engine performance. A deep neural network modeling method which has stronger representation capability than conventional neural network and can deal with big training data is adopted to establish an on-board model in the subsonic and supersonic cruising envelops. Meanwhile, a global optimization algorithm interval analysis is applied here to get a better engine performance. Finally, two simulation experiments are conducted to verify the effectiveness of the proposed methods. One is the on-board model modeling which compares the deep neural network with the conventional neural network, and the other is the performance-seeking control simulations comparing interval analysis with feasible sequential quadratic programming, particle swarm optimization, and genetic algorithm, respectively. These two experiments show that the deep neural network has much higher precision than the conventional neural network and the interval analysis gets much better engine performance than feasible sequential quadratic programming, particle swarm optimization, and genetic algorithm.

scholarly journals Feed Forward Neural Network Modeling for Rainfall Prediction

2018 ◽  
Vol 73 ◽  
pp. 05017
Author(s):  
Yasin Hasbi ◽  
Warsito Budi ◽  
Santoso Rukun
Keyword(s):  
Neural Network ◽  
Autocorrelation Function ◽  
Seasonal Pattern ◽  
Optimization Method ◽  
Rainfall Data ◽  
Neural Network Modeling ◽  
Least Mean Square ◽  
Feed Forward Neural Network ◽  
Feed Forward ◽  
Hidden Layer

Prediction of rainfall data by using Feed Forward Neural Network (FFNN) model is proposed. FFNN is a class of neural network which has three layers for processing. In time series prediction, including in case of rainfall data, the input layer is the past values of the same series up to certain lag and the output layer is the current value. Beside a few lagged times, the seasonal pattern also considered as an important aspect of choosing the potential input. The autocorrelation function and partial autocorrelation function patterns are used as aid of selecting the input. In the second layer called hidden layer, the logistic sigmoid is used as activation function because of the monotonic and differentiable. Processing is done by the weighted summing of the input variables and transfer process in the hidden layer. Backpropagation algorithm is applied in the training process. Some gradient based optimization methods are used to obtain the connection weights of FFNN model. The prediction is the output resulting of the process in the last layer. In each optimization method, the looping process is performed several times in order to get the most suitable result in various composition of separating data. The best one is chosen by the least mean square error (MSE) criteria. The least of in-sample and out-sample predictions from the repeating results been the base of choosing the best optimization method. In this study, the model is applied in the ten-daily rainfall data of ZOM 136 Cokrotulung Klaten. Simulation results give a consecution that the more complex architecture is not guarantee the better prediction.

scholarly journals Modeling of multicomponent diffraction structures based on optimization approaches and numerical methods

2019 ◽  
Vol 30 ◽  
pp. 05013
Author(s):  
Igor Lvovich ◽  
Yakov Lvovich ◽  
Andrey Preobrazhenskiy ◽  
Oleg Choporov
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Numerical Methods ◽  
Network Modeling ◽  
Methodological Approach ◽  
Neural Network Modeling ◽  
Expert Evaluation ◽  
Two Dimensional ◽  
Modeling Expert

The paper proposes a methodological approach in which the representation of objects in the form of a set of diffraction structures is their Association into groups. Using neural network modeling, expert evaluation and application of optimization based on genetic algorithm, there is a formation of the object with the desired scattering properties. An example of modeling an object presented as a set of two-dimensional cylinders is given.

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