A New Back-Propagation Neural Network Optimized with Cuckoo Search Algorithm

2013 ◽  
pp. 413-426 ◽  
Author(s):  
Nazri Mohd. Nawi ◽  
Abdullah Khan ◽  
Mohammad Zubair Rehman
Keyword(s):  
Neural Network ◽  
Search Algorithm ◽  
Back Propagation ◽  
Cuckoo Search ◽  
Back Propagation Neural Network ◽  
Cuckoo Search Algorithm

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

2016 ◽  
Vol 2016 ◽  
pp. 1-28 ◽  
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.

scholarly journals Secure Intelligent Vehicular Network Using Fog Computing

Electronics ◽  
2019 ◽  
Vol 8 (4) ◽  
pp. 455 ◽  
Author(s):  
Samuel Kofi Erskine ◽  
Khaled M. Elleithy
Keyword(s):  
Neural Network ◽  
Ad Hoc ◽  
Search Algorithm ◽  
Fog Computing ◽  
Denial Of Service ◽  
Back Propagation ◽  
Cuckoo Search ◽  
Back Propagation Neural Network ◽  
Vehicular Network ◽  
Message Delivery

VANET (vehicular ad hoc network) has a main objective to improve driver safety and traffic efficiency. The intermittent exchange of real-time safety message delivery in VANET has become an urgent concern due to DoS (denial of service) and smart and normal intrusions (SNI) attacks. The intermittent communication of VANET generates huge amount of data which requires typical storage and intelligence infrastructure. Fog computing (FC) plays an important role in storage, computation, and communication needs. In this research, fog computing (FC) integrates with hybrid optimization algorithms (OAs) including the Cuckoo search algorithm (CSA), firefly algorithm (FA), firefly neural network, and the key distribution establishment (KDE) for authenticating both the network level and the node level against all attacks for trustworthiness in VANET. The proposed scheme is termed “Secure Intelligent Vehicular Network using fog computing” (SIVNFC). A feedforward back propagation neural network (FFBP-NN), also termed the firefly neural, is used as a classifier to distinguish between the attacking vehicles and genuine vehicles. The SIVNFC scheme is compared with the Cuckoo, the FA, and the firefly neural network to evaluate the quality of services (QoS) parameters such as jitter and throughput.

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

2020 ◽  
Vol 53 (7) ◽  
pp. 4993-5018
Author(s):  
R. Cristin ◽  
B. Santhosh Kumar ◽  
C. Priya ◽  
K. Karthick
Keyword(s):  
Neural Network ◽  
Deep Neural Network ◽  
Plant Disease ◽  
Search Algorithm ◽  
Cuckoo Search ◽  
Cuckoo Search Algorithm ◽  
Disease Detection

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.

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