Bioinspired multiobjective synthesis of X-band FSS via general regression neural network and cuckoo search algorithm

2015 ◽  
Vol 57 (10) ◽  
pp. 2400-2405 ◽  
Author(s):  
M. C. Alcantara Neto ◽  
J. P. L. Araújo ◽  
F. J. B. Barros ◽  
A. N. Silva ◽  
G. P. S. Cavalcante ◽  
...  
Keyword(s):  
Neural Network ◽  
Search Algorithm ◽  
Cuckoo Search ◽  
Cuckoo Search Algorithm ◽  
General Regression Neural Network ◽  
X Band ◽  
General Regression

scholarly journals Novel Back Propagation Optimization by Cuckoo Search Algorithm

2014 ◽  
Vol 2014 ◽  
pp. 1-8 ◽  
Author(s):  
Jiao-hong Yi ◽  
Wei-hong Xu ◽  
Yuan-tao Chen
Keyword(s):  
Search Algorithm ◽  
Back Propagation ◽  
Cuckoo Search ◽  
Cuckoo Search Algorithm ◽  
Prediction Performance ◽  
General Regression Neural Network ◽  
Parameter Study ◽  
Local Minima ◽  
Bp Network ◽  
General Regression

The traditional Back Propagation (BP) has some significant disadvantages, such as training too slowly, easiness to fall into local minima, and sensitivity of the initial weights and bias. In order to overcome these shortcomings, an improved BP network that is optimized by Cuckoo Search (CS), called CSBP, is proposed in this paper. In CSBP, CS is used to simultaneously optimize the initial weights and bias of BP network. Wine data is adopted to study the prediction performance of CSBP, and the proposed method is compared with the basic BP and the General Regression Neural Network (GRNN). Moreover, the parameter study of CSBP is conducted in order to make the CSBP implement in the best way.

scholarly journals Optimization and synthesis of multilayer frequency selective surfaces via bioinspired hybrid techniques

2020 ◽  
Vol 8 (5) ◽  
pp. 542-561
Author(s):  
Wirlan Gomes Lima ◽  
Jasmine Priscyla Leite Leite de Araújo ◽  
Fabrício José Brito Barros ◽  
Gervásio Protásio Dos Santos Cavalcante ◽  
Cássio da Cruz Nogueira ◽  
...  
Keyword(s):  
Search Algorithm ◽  
Cuckoo Search ◽  
Cuckoo Search Algorithm ◽  
General Regression Neural Network ◽  
Electromagnetic Properties ◽  
Frequency Selective Surfaces ◽  
Hybrid Techniques ◽  
Operating Frequency Range ◽  
Frequency Selective ◽  
General Regression

In this study, two bioinspired computation (BIC) techniques are discussed and applied to the project and synthesis of multilayer frequency selective surfaces (FSS) within the microwave band, specifically for C, X and Ku bands. The proposed BIC techniques consist of combining an artificial, general regression neural network to a genetic algorithm (GA) and a cuckoo search algorithm, respectively. The objective is to find the optimal values of separation between the investigated FSS. Numerical analysis of the electromagnetic properties of the device is made possible with the finite integration method (FIT) and validated through the finite element method (FEM), utilizing both softwares CST Microwave Studio and Ansys HFSS respectively. Thus, the BIC-optimized devices present good phase / angular stability for angles 10°, 20°, 30° and 40°, as well as being polarization independent. The cutoff frequencies to control the operating frequency range of the FSS, referring to transmission coefficient in decibels (dB), were obtained at a threshold of –10dB. Numerical results denote good accordance with measured data.

scholarly journals A novel nonlinear observer for fault diagnosis of induction motor

2020 ◽  
Vol 14 ◽  
pp. 174830262092272
Author(s):  
Lingzhi Yi ◽  
Yue Liu ◽  
Wenxin Yu ◽  
Jian Zhao
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Induction Motor ◽  
Bp Neural Network ◽  
Search Algorithm ◽  
Cuckoo Search ◽  
Cuckoo Search Algorithm ◽  
Adaptive Observer ◽  
Nonlinear Observer ◽  
Feedback Gain

In order to accurately diagnose the fault of induction motor, a fault diagnosis of nonlinear observer method based on BP neural network and Cuckoo Search algorithm is proposed. It is a new method which mixes analytical model and artificial neural network; firstly, the induction motor model is divided into linear and nonlinear parts, and BP neural network is used to approximate the nonlinear part. Then an adaptive observer is established, in which a simple and effective method for selecting the feedback gain matrix is offered. Cuckoo Search algorithm is utilized to improve the convergence speed and approximation accuracy in BP Neural Network. Compared with some other algorithms, the simulation results show that the proposed method has higher prediction accuracy. The designed nonlinear observer can estimate the current and speed accurately. Finally, the experiment of winding fault is implemented, and the online fault detection of induction motor is realized by analyzing the current residual errors.

Optimized Radial Basis Neural Network for the Classification of Breast Cancer Images

2020 ◽  
Vol 16 ◽  
Author(s):  
G. M. Rajathi
Keyword(s):  
Breast Cancer ◽  
Neural Network ◽  
Breast Feeding ◽  
Search Algorithm ◽  
Cuckoo Search ◽  
Cuckoo Search Algorithm ◽  
Feature Reduction ◽  
Radial Basis Neural Network ◽  
Fitness Value ◽  
Radial Basis

Background: The breast cancer is not such a dreadful if the detection is not performed at an early. The chances of having breast cancer is the married woman highly after the breast-feeding phase because, the cancer is formed from the blocked milk ducts. Introduction: Recent days, the cancer is the major issue for human death. The women are mostly affected by breast cancer. This leads to deadliest life of most of the women. The breast cancer is caused while breast-feeding phase. The early detection technique uses the mammography image analysis. Various researchers are used the artificial intelligence based mammogram techniques. This process of mammography will reduce the death rate of the patients affected breast cancer. This process is improved by image analysing, detection, screening, diagnosing, and other performance measures. Methods: The radial basis neural network will be used for the classification purpose. The radial basis neural network is designed with the help of the optimization algorithm. The optimization is to tune the classifier to reduce the error rate with the minimum time for training process. The cuckoo search algorithm will be used for this purpose. Results: Thus, the proposed optimum RBNN is determined to classify the breast cancer images. In this, the three set of properties were classified by performing the feature extraction and feature reduction. In this breast cancer MRI image, the normal, benign, and malignant is taken to perform the classification. The minimum fitness value is determined to evaluate the optimum value of possible locations. The radial basis function is evaluated with the cuckoo search algorithm to optimize the feature reduction process. The proposed methodology is compared with the traditional radial basis neural network using the evaluation parameter like accuracy, precision, recall and f1-score. The whole system model is done by using Matrix Laboratory (MATLAB) with the adaptation of 2018a. Since the proposed system is most efficient than most recent related literatures. Conclusion: Thus, it concluded with the efficient classification process of RBNN using cuckoo search algorithm for breast cancer images. The mammogram images are taken into the recent research because the breast cancer is the major issue for women. This process is carried to classify the various features for three set of properties. The optimized classifier improves the performance and provides the better result. In this proposed research work, the input image is filtered using wiener filter and the classifier extracts the feature based on the breast image.

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