A hybrid approach based on tissue P systems and artificial bee colony for IIR system identification

2016 ◽  
Vol 28 (9) ◽  
pp. 2675-2685 ◽  
Author(s):  
Hong Peng ◽  
Jun Wang
Keyword(s):  
System Identification ◽  
Artificial Bee Colony ◽  
Hybrid Approach ◽  
P Systems ◽  
Bee Colony

scholarly journals Modelling of Horizontal Flexible Plate Structure Using Artificial Bee Colony Algorithm

2018 ◽  
Vol 7 (4.36) ◽  
pp. 415
Author(s):  
Muhamad Sukri Hadi ◽  
Sukri Hadi Zaurah Mat Darus
Keyword(s):  
System Identification ◽  
Artificial Bee Colony Algorithm ◽  
Artificial Bee Colony ◽  
Mean Squared Error ◽  
Least Square ◽  
Flexible Plate ◽  
Plate Structure ◽  
Bee Colony ◽  
Squared Error ◽  
Plate System

This paper presents the performance of system identification for modeling the horizontal flexible plate system using artificial bee colony and recursive least square algorithms. Initially, the experimental rig of flexible plate was designed and fabricated with all edges clamped boundary condition at the horizontal position. Then, the instrumentation and data acquisition systems were integrated into the rig for acquiring the input-output vibration experimentally. The collected data in the experiment will be used later for modeling the dynamic system of horizontal flexible plate system using system identification. The effectiveness of the developed model will be validated using mean squared error, one step ahead prediction, correlation tests and pole zero diagram stability. The estimated of the developed models were found are acceptable and possible to be used as a platform of controller development for vibration suppression of the undesirable vibration in the flexible plate structure. It was found that the artificial bee colony algorithm has performed better in this study by achieving the lowest mean squared error, good correlation test and high stability in the pole zero diagram.  

scholarly journals Chaotic Artificial Bee Colony Algorithm for System Identification of a Small-Scale Unmanned Helicopter

2015 ◽  
Vol 2015 ◽  
pp. 1-11 ◽  
Author(s):  
Li Ding ◽  
Hongtao Wu ◽  
Yu Yao
Keyword(s):  
System Identification ◽  
Artificial Bee Colony Algorithm ◽  
Artificial Bee Colony ◽  
State Space Model ◽  
Global Optimum ◽  
Small Scale ◽  
Local Optimum ◽  
Unmanned Helicopter ◽  
Bee Colony ◽  
Chaotic Operator

The purpose of this paper is devoted to developing a chaotic artificial bee colony algorithm (CABC) for the system identification of a small-scale unmanned helicopter state-space model in hover condition. In order to avoid the premature of traditional artificial bee colony algorithm (ABC), which is stuck in local optimum and can not reach the global optimum, a novel chaotic operator with the characteristics of ergodicity and irregularity was introduced to enhance its performance. With input-output data collected from actual flight experiments, the identification results showed the superiority of CABC over the ABC and the genetic algorithm (GA). Simulations are presented to demonstrate the effectiveness of our proposed algorithm and the accuracy of the identified helicopter model.

scholarly journals Prognostic of RUL based on Echo State Network Optimized by Artificial Bee Colony

2020 ◽  
Vol 7 (1) ◽  
Author(s):  
Edgar J. Amaya ◽  
Alberto J. Alvares
Keyword(s):  
Artificial Bee Colony ◽  
Hybrid Approach ◽  
Remaining Useful Life ◽  
Data Repository ◽  
Echo State Network ◽  
Health State ◽  
Future Health ◽  
Large Space ◽  
Abc Algorithm ◽  
Bee Colony

Prognostic is an engineering technique used to predict the future health state or behavior of an equipment or system. In this work, a data-driven hybrid approach for prognostic is presented. The approach based on Echo State Network (ESN) and Artificial Bee Colony (ABC) algorithm is used to predict machine’s Remaining Useful Life (RUL). ESN is a new paradigm that establishes a large space dynamic reservoir to replace the hidden layer of Recurrent Neural Network (RNN). Through the application of ESN is possible to overcome the shortcomings of complicated computing and difficulties in determining the network topology of traditional RNN. This approach describes the ABC algorithm as a tool to set the ESN with optimal parameters. Historical data collected from sensors are used to train and test the proposed hybrid approach in order to estimate the RUL. To evaluate the proposed approach, a case study was carried out using turbofan engine signals show that the proposed method can achieve a good collected from physical sensors (temperature, pressure, speed, fuel flow, etc.). The experimental results using the engine data from NASA Ames Prognostics Data Repository RUL estimation precision. The performance of this model was compared using prognostic metrics with the approaches that use the same dataset. Therefore, the ESN-ABC approach is very promising in the field of prognostics of the RUL.

scholarly journals An artificial bee colony-based hybrid approach for waste collection problem with midway disposal pattern

2019 ◽  
Vol 76 ◽  
pp. 629-637 ◽  
Author(s):  
Qu Wei ◽  
Zhaoxia Guo ◽  
Hoong Chuin Lau ◽  
Zhenggang He
Keyword(s):  
Artificial Bee Colony ◽  
Hybrid Approach ◽  
Waste Collection ◽  
Bee Colony

scholarly journals Feature Selection Method Based on Artificial Bee Colony Algorithm and Support Vector Machines for Medical Datasets Classification

2013 ◽  
Vol 2013 ◽  
pp. 1-10 ◽  
Author(s):  
Mustafa Serter Uzer ◽  
Nihat Yilmaz ◽  
Onur Inan
Keyword(s):  
Feature Selection ◽  
Support Vector Machines ◽  
Artificial Bee Colony ◽  
Hybrid Approach ◽  
Feature Selection Method ◽  
Support Vector ◽  
Svm Classifier ◽  
Bee Colony ◽  
Vector Machines

This paper offers a hybrid approach that uses the artificial bee colony (ABC) algorithm for feature selection and support vector machines for classification. The purpose of this paper is to test the effect of elimination of the unimportant and obsolete features of the datasets on the success of the classification, using the SVM classifier. The developed approach conventionally used in liver diseases and diabetes diagnostics, which are commonly observed and reduce the quality of life, is developed. For the diagnosis of these diseases, hepatitis, liver disorders and diabetes datasets from the UCI database were used, and the proposed system reached a classification accuracies of 94.92%, 74.81%, and 79.29%, respectively. For these datasets, the classification accuracies were obtained by the help of the 10-fold cross-validation method. The results show that the performance of the method is highly successful compared to other results attained and seems very promising for pattern recognition applications.

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