Deep neural network based Rider-Cuckoo Search Algorithm for plant disease detection

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.

Download Full-text

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

Current Medical Imaging Formerly Current Medical Imaging Reviews ◽

10.2174/1573405616666200516172118 ◽

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.

Download Full-text

Vibration Fault Diagnosis for Hydroelectric Generating Units Based on the Modified Cuckoo Search Algorithm and Evidence Theory

Recent Patents on Engineering ◽

10.2174/1872212112666180815130319 ◽

2019 ◽

Vol 13 (3) ◽

pp. 281-288

Author(s):

Jiatang Cheng ◽

Li Ai ◽

Yan Xiong

Keyword(s):

Neural Network ◽

Fault Diagnosis ◽

Bp Neural Network ◽

Search Algorithm ◽

Evidence Theory ◽

Cuckoo Search ◽

Cuckoo Search Algorithm ◽

System Structure ◽

Hybrid Scheme ◽

Independent Evidence

Background: In view of the complex system structure and uncertain factors in the fault diagnosis of hydroelectric generating units (HGU), it is a difficult problem to design the diagnosis method rationally. Objective: An attempt is made to employ multi-source feature information to improve the accuracy of fault diagnosis, and the effectiveness of the proposed scheme is verified by using a diagnostic example. Methods: Through the research on recent papers and patents related to fault diagnosis of the HGU, a hybrid scheme based on the modified cuckoo search algorithm, back-propagation (BP) neural network and evidence theory are proposed. For this modified version named cuckoo search with fitness information (CSF), the step factor is adaptively tuned using the fitness value. Next, three diagnostic models based on BP neural network trained by CSF are used for primary diagnosis. These diagnostic results are then used as the independent evidence, and the fusion decision is made by using evidence theory. Results: Experimental results show that CSF algorithm is better than the original cuckoo search (CS) and its three variants, and the hybrid method has the highest diagnostic accuracy. Conclusion: The proposed hybrid scheme has strong robustness and fault tolerance, and can effectively classify the vibration faults of hydroelectric generating units

Download Full-text

Features Extraction of Flotation Froth Images and BP Neural Network Soft-Sensor Model of Concentrate Grade Optimized by Shuffled Cuckoo Searching Algorithm

The Scientific World JOURNAL ◽

10.1155/2014/208094 ◽

2014 ◽

Vol 2014 ◽

pp. 1-17 ◽

Cited By ~ 2

Author(s):

Jie-sheng Wang ◽

Shuang Han ◽

Na-na Shen ◽

Shu-xia Li

Keyword(s):

Neural Network ◽

Bp Neural Network ◽

Search Algorithm ◽

Cuckoo Search ◽

Cuckoo Search Algorithm ◽

Soft Sensor ◽

Features Extraction ◽

Image Processing Technique ◽

Flotation Process ◽

Sensor Model

For meeting the forecasting target of key technology indicators in the flotation process, a BP neural network soft-sensor model based on features extraction of flotation froth images and optimized by shuffled cuckoo search algorithm is proposed. Based on the digital image processing technique, the color features in HSI color space, the visual features based on the gray level cooccurrence matrix, and the shape characteristics based on the geometric theory of flotation froth images are extracted, respectively, as the input variables of the proposed soft-sensor model. Then the isometric mapping method is used to reduce the input dimension, the network size, and learning time of BP neural network. Finally, a shuffled cuckoo search algorithm is adopted to optimize the BP neural network soft-sensor model. Simulation results show that the model has better generalization results and prediction accuracy.

Download Full-text

A Hybrid Forecasting Model Based on Empirical Mode Decomposition and the Cuckoo Search Algorithm: A Case Study for Power Load

Mathematical Problems in Engineering ◽

10.1155/2016/3205396 ◽

2016 ◽

Vol 2016 ◽

pp. 1-28 ◽

Cited By ~ 3

Author(s):

Jiani Heng ◽

Chen Wang ◽

Xuejing Zhao ◽

Jianzhou Wang

Keyword(s):

Neural Network ◽

Search Algorithm ◽

Back Propagation ◽

Cuckoo Search ◽

Back Propagation Neural Network ◽

Cuckoo Search Algorithm ◽

Mode Decomposition ◽

Power Load ◽

Hybrid Forecasting

Power load forecasting always plays a considerable role in the management of a power system, as accurate forecasting provides a guarantee for the daily operation of the power grid. It has been widely demonstrated in forecasting that hybrid forecasts can improve forecast performance compared with individual forecasts. In this paper, a hybrid forecasting approach, comprising Empirical Mode Decomposition, CSA (Cuckoo Search Algorithm), and WNN (Wavelet Neural Network), is proposed. This approach constructs a more valid forecasting structure and more stable results than traditional ANN (Artificial Neural Network) models such as BPNN (Back Propagation Neural Network), GABPNN (Back Propagation Neural Network Optimized by Genetic Algorithm), and WNN. To evaluate the forecasting performance of the proposed model, a half-hourly power load in New South Wales of Australia is used as a case study in this paper. The experimental results demonstrate that the proposed hybrid model is not only simple but also able to satisfactorily approximate the actual power load and can be an effective tool in planning and dispatch for smart grids.

Download Full-text