Neural network and fuzzy system for the tuning of Gravitational Search Algorithm parameters

2018 ◽  
Vol 102 ◽  
pp. 234-244 ◽  
Author(s):  
Danilo Pelusi ◽  
Raffaele Mascella ◽  
Luca Tallini ◽  
Janmenjoy Nayak ◽  
Bighnaraj Naik ◽  
...  
Keyword(s):  
Neural Network ◽  
Fuzzy System ◽  
Search Algorithm ◽  
Gravitational Search Algorithm ◽  
Gravitational Search

scholarly journals Inverse calculation of demolition robot based on gravitational search algorithm and differential evolution neural network

2020 ◽  
Vol 17 (3) ◽  
pp. 172988142092529
Author(s):  
Jianzhong Huang ◽  
Yuwan Cen ◽  
Nenggang Xie ◽  
Xiaohua Ye
Keyword(s):  
Neural Network ◽  
Convergence Rate ◽  
Differential Evolution ◽  
Search Algorithm ◽  
Gravitational Search Algorithm ◽  
Population Diversity ◽  
Measuring Point ◽  
Mapping Model ◽  
Gravitational Search ◽  
Set Up

For the inverse calculation of laser-guided demolition robot, its global nonlinear mapping model from laser measuring point to joint cylinder stroke has been set up with an artificial neural network. Due to the contradiction between population diversity and convergence rate in the optimization of complex neural networks by using differential evolution, a gravitational search algorithm and differential evolution is proposed to accelerate the convergence rate of differential evolution population driven by gravity. Gravitational search algorithm and differential evolution is applied to optimize the inverse calculation neural network mapping model of demolition robot, and the algorithm simulation shows that gravity can effectively regulate the convergence process of differential evolution population. Compared with the standard differential evolution, the convergence speed and accuracy of gravitational search algorithm and differential evolution are significantly improved, which has better optimization stability. The calculation results show that the output accuracy of this gravitational and differential evolution neural network can meet the calculation requirements of the positioning control of demolition robot’s manipulator. The optimization using gravitational search algorithm and differential evolution is done with the connection weights of a neural network in this article, and as similar techniques can be applied to the other hyperparameter optimization problem. Moreover, such an inverse calculation method can provide a reference for the autonomous positioning of large hydraulic series manipulator, so as to improve the robotization level of construction machinery.

scholarly journals QSAR Study for Prediction of HIV-1 Protease Inhibitor Using the Gravitational Search Algorithm–Neural Network (GSA-NN) Methods

2021 ◽  
Vol 12 (2) ◽  
pp. 62
Author(s):  
Isman Kurniawan ◽  
Reina Wardhani ◽  
Maya Rosalinda ◽  
Nurul Ikhsan
Keyword(s):  
Neural Network ◽  
Protease Inhibitor ◽  
Highly Active Antiretroviral Therapy ◽  
Immune Cell ◽  
Search Algorithm ◽  
Gravitational Search Algorithm ◽  
Coefficient Of Determination ◽  
Qsar Study ◽  
Gravitational Search ◽  
Hiv 1

Human immunodeficiency virus (HIV) is a virus that infects an immune cell and makes the patient more susceptible to infections and other diseases. HIV is also a factor that leads to acquired immune deficiency syndrome (AIDS) disease. The active target that is usually used in the treatment of HIV is HIV-1 protease. Combining HIV-1 protease inhibitors and reverse-transcriptase inhibitors in highly active antiretroviral therapy (HAART) is typically used to treat this virus. However, this treatment can only reduce the viral load, restore some parts of the immune system, and failed to overcome the drug resistance. This study aimed to build a QSAR model for predicting HIV-1 protease inhibitor activity using the gravitational search algorithm-neural network (GSA-NN) method. The GSA method is used to select molecular descriptors, while NN was used to develop the prediction model. The improvement of model performance was found after performing the hyperparameter tuning procedure. The validation results show that model 3, containing seven descriptors, shows the best performance indicated by the coefficient of determination (r2) and cross-validation coefficient of determination (Q2) values. We found that the value of r2 for train and test data are 0.84 and 0.82, respectively, and the value of Q2 is 0.81.  

RF MEMS for Reconfigurable Antenna Using Gravitational Search Optimization and Artificial Neural Network

2018 ◽  
Vol 27 (08) ◽  
pp. 1850132
Author(s):  
T. A. Balarajuswamy ◽  
R. Nakkeeran
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Rf Mems ◽  
Search Algorithm ◽  
Gravitational Search Algorithm ◽  
Beam Width ◽  
Optimization Method ◽  
Gravitational Search ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

The projected method explains about the problems occurred in the combination of the MEMS switches and the complete scheme plan is resolved through choosing the finest devise limits for the plan. The devise limits, namely, length of beam, width of beam, torsion arm length, switch thickness, holes and gap were measured. At this point, the finest value of the devise limit is forecast by the aid of artificial neural network (ANN). Furthermore, the method contains the optimization method of Gravitational Search Algorithm (GSA) to optimize the input signal and so dropping the Mean Square Error (MSE). The complete scheme is executed in the operational platform of MATLAB and the outcomes were examined.

scholarly journals Kombinasi Algoritma Backpropagation Neural Network dengan Gravitational Search Algorithm Dalam Meningkatkan Akurasi

2021 ◽  
Vol 5 (1) ◽  
pp. 90
Author(s):  
Miftahul Falah ◽  
Dian Palupi Rini ◽  
Iwan Pahendra
Keyword(s):  
Neural Network ◽  
Search Algorithm ◽  
Gravitational Search Algorithm ◽  
Disease Prediction ◽  
Backpropagation Algorithm ◽  
Cancer Data ◽  
Long Time ◽  
Gravitational Search ◽  
Artificial Neural ◽  
Hidden Layer

Predicting disease is usually done based on the experience and knowledge of the doctor. Diagnosis of such a disease is traditionally less effective. The development of medical diagnosis based on machine learning in terms of disease prediction provides a more accurate diagnosis than the traditional way. In terms of predicting disease can use artificial neural networks. The artificial neural network consists of various algorithms, one of which is the Backpropagation Algorithm. In this paper it is proposed that disease prediction systems use the Backpropagation algorithm. Backpropagation algorithms are often used in disease prediction, but the Backpropagation algorithm has a slight drawback that tends to take a long time in obtaining optimum accuracy values. Therefore, a combination of algorithms can overcome the shortcomings of the Backpropagation algorithm by using the success of the Gravitational Search Algorithm (GSA) algorithm, which can overcome the slow convergence and local minimum problems contained in the Backpropagation algorithm. So the authors propose to combine the Backpropagation algorithm using the Gravitational Search Algorithm (GSA) in hopes of improving accuracy results better than using only the Backpropagation algorithm. The results resulted in a higher level of accuracy with the same number of iterations than using Backpropagation only. Can be seen in the first trial of breast cancer data with parameters namely hidden layer 5, learning rate of 2 and iteration as much as 5000 resulting in accuracy of 99.3 % with error 0.7% on Backpropagation Algorithm, while in combination BP & GSA got accuracy of 99.68 % with error of 0.32%.

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