Evolving spiking neural network (ESNN) and harmony search algorithm (HSA) for parameter optimization

2017 ◽  
Author(s):  
Zulhairi Mi Yusuf ◽  
Haza Nuzly Abdull Hamed ◽  
Lizawati Mi Yusuf ◽  
Mohd Adham Isa
Keyword(s):  
Neural Network ◽  
Parameter Optimization ◽  
Search Algorithm ◽  
Harmony Search ◽  
Harmony Search Algorithm ◽  
Spiking Neural Network ◽  
Evolving Spiking Neural Network

Optimal hybrid scheme of dynamic neural network and PID controller based on harmony search algorithm to control a PWM-driven pneumatic actuator position

2017 ◽  
Vol 24 (16) ◽  
pp. 3538-3554 ◽  
Author(s):  
Mahmood Mazare ◽  
Mostafa Taghizadeh ◽  
Mohammad Ghasem Kazemi
Keyword(s):  
Neural Network ◽  
Pid Controller ◽  
Pulse Width Modulation ◽  
Search Algorithm ◽  
Harmony Search ◽  
Harmony Search Algorithm ◽  
Pneumatic Actuator ◽  
Hybrid Scheme ◽  
Dynamic Neural Network ◽  
Switching Valve

In this paper, the position of a pulse width modulation (PWM)-driven pneumatic actuator has been controlled using a dynamic neural network (DNN) and Proportional Integral Derivative (PID) controller. The harmony search algorithm (HSA) has been used to unravel the optimization problem. The DNN controller is optimally designed to control the position of the actuator. As to the performance of the PID controller, it can assist the DNN controller to give better results. Therefore, an optimal hybrid scheme with both DNN and PID controllers based on HSA is suggested. A pneumatic circuit containing a fast-switching valve is used to reduce the complexity of the PWM-driven servo pneumatic system along with its cost price.

Hybrid Data Mining Approach for Image Segmentation Based Classification

Biometrics ◽  
2017 ◽  
pp. 1543-1561 ◽  
Author(s):  
Mrutyunjaya Panda ◽  
Aboul Ella Hassanien ◽  
Ajith Abraham
Keyword(s):  
Neural Network ◽  
Data Mining ◽  
Image Segmentation ◽  
Search Algorithm ◽  
Rbf Neural Network ◽  
Harmony Search ◽  
Optimal Solution ◽  
Solution Space ◽  
Harmony Search Algorithm ◽  
Data Mining Technique

Evolutionary harmony search algorithm is used for its capability in finding solution space both locally and globally. In contrast, Wavelet based feature selection, for its ability to provide localized frequency information about a function of a signal, makes it a promising one for efficient classification. Research in this direction states that wavelet based neural network may be trapped to fall in a local minima whereas fuzzy harmony search based algorithm effectively addresses that problem and able to get a near optimal solution. In this, a hybrid wavelet based radial basis function (RBF) neural network (WRBF) and feature subset harmony search based fuzzy discernibility classifier (HSFD) approaches are proposed as a data mining technique for image segmentation based classification. In this paper, the authors use Lena RGB image; Magnetic resonance image (MR) and Computed Tomography (CT) Image for analysis. It is observed from the obtained simulation results that Wavelet based RBF neural network outperforms the harmony search based fuzzy discernibility classifiers.

Optimizing Surface Roughness in Face Milling Using a New Meta-Heuristic Method of Harmony Search

2010 ◽  
Author(s):  
M. R. Razfar ◽  
R. Farshbaf Zinati ◽  
M. Haghshenas
Keyword(s):  
Neural Network ◽  
Surface Roughness ◽  
Artificial Neural Network ◽  
Stainless Steel ◽  
Search Algorithm ◽  
Harmony Search ◽  
Harmony Search Algorithm ◽  
Face Milling ◽  
Statistical Experimental Design ◽  
Artificial Neural

The focus of this study is on a new approach for determination of the optimal cutting parameters leading to minimum surface roughness in face milling of X20Cr13 stainless steel by coupling Neural Network (NN) and Harmony Search (HS) algorithm. In this regard, advantages of statistical experimental design technique, experimental measurements, artificial neural network and Harmony Search algorithm were exploited in an integrated manner. For this purpose, numerous experiments on X20Cr13 stainless steel were conducted to obtain surface roughness values. A predictive model for surface roughness was created using a feed forward artificial neural network exploiting experimental data. The optimization problem was solved by Harmony Search algorithm. Additional experiments were performed to validate optimum surface roughness value predicted by HS algorithm. From the obtained results, it is clearly seen that the Harmony Search algorithm is an efficient and accurate method in approaching the global minimum of surface roughness in face milling.

scholarly journals Application of HS-LMBP Hybrid Neural Network Algorithm in Image Denoising

2018 ◽  
Vol 173 ◽  
pp. 03056
Author(s):  
Hai jun Wang ◽  
Menke Neimule ◽  
Jin Tao
Keyword(s):  
Neural Network ◽  
Image Denoising ◽  
High Speed ◽  
Search Algorithm ◽  
Harmony Search ◽  
Harmony Search Algorithm ◽  
Hybrid Neural Network ◽  
Existing Problems ◽  
Network Algorithm ◽  
Neural Network Algorithm

In order to overcome the problems such as poor global search ability, slow convergence rate, and easy to fall into local minimum values in the image denoising process of traditional BP neural networks, the HS-LMBP hybrid neural network image denoising algorithm is proposed which combines the harmony search algorithm and the LMBP algorithm. The HS-LMBP hybrid neural network algorithm combines the high speed of the LMBP algorithm and the global nature of the HS algorithm, which can be a good improvement to the existing problems of the BP algorithm model. Compared with the Wiener filtering, BP, LMBP and PSO-LMBP model image denoising effects, the denoising model using HS-LMBP neural network algorithm has a better denoising effect.

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