A Hybrid Master-Slave Genetic Algorithm-Neural Network Approach for Modeling a Piezoelectric Actuator

2012 ◽  
Author(s):  
Mohamed B. Trabia ◽  
Mohammad Y. Saadeh
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Input Voltage ◽  
Hysteresis Model ◽  
Neural Network Approach ◽  
Operational Conditions ◽  
Damping Matrix ◽  
Sinusoidal Input ◽  
Actuator System ◽  
Hidden Layer

This work presents an approach for developing the model of a smart fin dynamics that is activated by a fully-enclosed piezoelectric (PZT) bimorph actuator, which is created by bonding two Macro Fiber Composites (MFCs). Observing the dynamics of the fin indicates that the use of a linear dynamic model does not adequately describe its behavior. An earlier work proposed incorporating a proportional damping matrix as well as Bouc-Wen hysteresis model and backlash operators to create a more accurate model. However, the number of parameters describing the expanded model is large, which limits its use. Therefore, there is a need for a different approach for developing an alternative model of the fin. In this work, a hybrid master-slave Genetic Algorithm (GA)-Neural Network (NN) model is proposed to identify the optimal set of parameters for the damping matrix constants, the Bouc-Wen hysteresis model and the backlash operators. A total of nine sinusoidal input voltage cases that resemble a grid of three different amplitudes excited at three different frequencies are used to train and validate the model. Three input cases are considered for training the NN architecture, connection weights, bias weights and learning rules using GA. The NN consists of three layers: an input layer that has two nodes for the amplitude and the frequency of the input voltage, an output layer that has seven nodes for the backlash, hysteresis, and damping operators, and a hidden layer that is free to have any number of nodes between two and nine. The GA constantly performs natural selection of chromosomes that propagate best compilation of NN parameters. Simulation results show that the proposed model can predict the damping, hysteresis and backlash of the smart fin–actuator system under various operational conditions.

Modeling of Hysteresis and Backlash Within a Smart Fin With a Piezoelectric Actuator

2010 ◽  
Author(s):  
Mohamed B. Trabia ◽  
Woosoon Yim ◽  
Mohammad Saadeh
Keyword(s):  
Genetic Algorithm ◽  
Piezoelectric Actuator ◽  
Fiber Composites ◽  
Piezoelectric Bimorph ◽  
Hysteresis Model ◽  
Operational Conditions ◽  
Damping Matrix ◽  
Realistic Approach ◽  
Actuator System ◽  
Optimal Set

This work presents a dynamic model of a smart fin, which is activated by a piezoelectric bimorph actuator that is made by bonding two Macro Fiber Composites (MFCs). The actuator is completely enclosed within the fin. Earlier research has indicated that the use of a linear model for the fin dynamics does not fully describe the fin. This work presents a more realistic approach to the problem by incorporating additional components into the model. Therefore, a proportional damping matrix is introduced. It was also observed that experimental results exhibit hysteresis and backlash. A Bouc-Wen hysteresis model is combined with two backlash operators to model the observed saturation and the non-symmetry of the response. Hybrid Fuzzy Simplex Genetic Algorithm (HFSGA) is used to identify the optimal set of parameters for the damping matrix constants, the Bouc-Wen model, and the backlash operators. The results show that proposed method can predict the hysteresis of the smart fin–actuator system under various operational conditions.

scholarly journals Crude Palm Oil Prediction Based on Backpropagation Neural Network Approach

2019 ◽  
Vol 2 (1) ◽  
pp. 1
Author(s):  
Hijratul Aini ◽  
Haviluddin Haviluddin
Keyword(s):  
Neural Network ◽  
Palm Oil ◽  
Good Accuracy ◽  
Activation Function ◽  
Crude Palm Oil ◽  
Backpropagation Neural Network ◽  
Neural Network Approach ◽  
Learning Function ◽  
Hidden Layer ◽  
Accuracy Of Prediction

Crude palm oil (CPO) production at PT. Perkebunan Nusantara (PTPN) XIII from January 2015 to January 2018 have been treated. This paper aims to predict CPO production using intelligent algorithms called Backpropagation Neural Network (BPNN). The accuracy of prediction algorithms have been measured by mean square error (MSE). The experiment showed that the best hidden layer architecture (HLA) is 5-10-11-12-13-1 with learning function (LF) of trainlm, activation function (AF) of logsig and purelin, and learning rate (LR) of 0.5. This architecture has a good accuracy with MSE of 0.0643. The results showed that this model can predict CPO production in 2019.

Neuronet-Based Approach for Assessing Liquefaction Potential of Soils

1998 ◽  
Vol 1633 (1) ◽  
pp. 3-8 ◽  
Author(s):  
Hossam Eldin Ali ◽  
Yacoub M. Najjar
Keyword(s):  
Neural Network ◽  
Numerical Approach ◽  
Soil Liquefaction ◽  
Data Sets ◽  
Ann Model ◽  
Neural Network Approach ◽  
Liquefaction Potential ◽  
Network Training ◽  
Artificial Neural Network Ann ◽  
Hidden Layer

A backpropagation artificial neural network (ANN) algorithm with one hidden layer was used as a new numerical approach to characterize the soil liquefaction potential. For this purpose, 61 field data sets representing various earthquake sites from around the world were used. To develop the most accurate prediction model for liquefaction potential, alternating combinations of input parameters were used during the training and testing phases of the developed network. The accuracy of the designed network was validated against an additional 44 records not used previously in either the network training or testing stages. The prediction accuracy of the neural network approach–based model is compared with predictions obtained by using fuzzy logic and statistically based approaches. Overall, the ANN model outperformed all other investigated approaches.

Genetic‐algorithm/neural‐network approach to seismic attribute selection for well‐log prediction

Geophysics ◽  
2004 ◽  
Vol 69 (1) ◽  
pp. 212-221 ◽  
Author(s):  
Kevin P. Dorrington ◽  
Curtis A. Link
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Reservoir Characterization ◽  
Seismic Attributes ◽  
Optimal Number ◽  
Attribute Selection ◽  
Well Log ◽  
Seismic Attribute ◽  
Neural Network Approach ◽  
Data Set

Neural‐network prediction of well‐log data using seismic attributes is an important reservoir characterization technique because it allows extrapolation of log properties throughout a seismic volume. The strength of neural‐networks in the area of pattern recognition is key in its success for delineating the complex nonlinear relationship between seismic attributes and log properties. We have found that good neural‐network generalization of well‐log properties can be accomplished using a small number of seismic attributes. This study presents a new method for seismic attribute selection using a genetic‐algorithm approach. The genetic algorithm attribute selection uses neural‐network training results to choose the optimal number and type of seismic attributes for porosity prediction. We apply the genetic‐algorithm attribute‐selection method to the C38 reservoir in the Stratton field 3D seismic data set. Eleven wells with porosity logs are used to train a neural network using genetic‐algorithm selected‐attribute combinations. A histogram of 50 genetic‐algorithm attribute selection runs indicates that amplitude‐based attributes are the best porosity predictors for this data set. On average, the genetic algorithm selected four attributes for optimal porosity log prediction, although the number of attributes chosen ranged from one to nine. A predicted porosity volume was generated using the best genetic‐algorithm attribute combination based on an average cross‐validation correlation coefficient. This volume suggested a network of channel sands within the C38 reservoir.

Genetic Programming for Letters Identification Based on Neural Network

2015 ◽  
Vol 734 ◽  
pp. 642-645
Author(s):  
Yan Hui Liu ◽  
Zhi Peng Wang
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Neural Networks ◽  
Genetic Programming ◽  
Network Structure ◽  
High Accuracy ◽  
Identification System ◽  
Neural Network Structure ◽  
Hidden Layer

According to the problem that the letters identification is not high accuracy using neural networks, in this paper, an optimal neural network structure is designed based on genetic algorithm to optimize the number of hidden layer. The English letters can be identified by optimal neural network. The results obtained in the genetic programming optimizations are very satisfactory. Experiments show that the identification system has higher accuracy and achieved good ideal letters identification effect.

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