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Records with Subject: Intelligent Systems
Showing records 1 to 25 of 116. [First] Page: 1 2 3 4 5 Last
Deep Learning-Based Pose Estimation of Apples for Inspection in Logistic Centers Using Single-Perspective Imaging
Lino Antoni Giefer, Juan Daniel Arango Castellanos, Mohammad Mohammadzadeh Babr, Michael Freitag
September 13, 2019 (v1)
Keywords: deep learning, lie algebra, logistic centers, pose estimation, quality inspection
Fruit packaging is a time-consuming task due to its low automation level. The gentle handling required by some kinds of fruits and their natural variations complicates the implementation of automated quality controls and tray positioning for final packaging. In this article, we propose a method for the automatic localization and pose estimation of apples captured by a Red-Green-Blue (RGB) camera using convolutional neural networks. Our pose estimation algorithm uses a cascaded structure composed of two independent convolutional neural networks: one for the localization of apples within the images and a second for the estimation of the three-dimensional rotation of the localized and cropped image area containing an apple. We used a single shot multi-box detector to find the bounding boxes of the apples in the images. Lie algebra is used for the regression of the rotation, which represents an innovation in this kind of application. We compare the performances of four different network ar... [more]
Gaussian Process-Based Hybrid Model for Predicting Oxygen Consumption in the Converter Steelmaking Process
Sheng-Long Jiang, Xinyue Shen, Zhong Zheng
August 8, 2019 (v1)
Keywords: GPR, oxygen consumption, prediction model, steelmaking
Oxygen is one of the most important energies used in converter steelmaking processes of integrated iron and steel works. Precisely forecasting oxygen consumption before processing can benefit process control and energy optimization. This paper assumes there is a linear relationship between the oxygen consumption and input materials, and random noises are caused by other unmeasurable materials and unobserved reactions. Then, a novel hybrid prediction model integrating multiple linear regression (MLR) and Gaussian process regression (GPR) is introduced. In the hybrid model, the MLR method is developed to figure the global trend of the oxygen consumption, and the GPR method is applied to explore the local fluctuation caused by noise. Additionally, to accelerate the computational speed on the practical data set, a K-means clustering method is devised to respectively train a number of GPR models. The proposed hybrid model is validated with the actual data collected from an integrated iron a... [more]
Multi-Label Classification Based on Random Forest Algorithm for Non-Intrusive Load Monitoring System
Xin Wu, Yuchen Gao, Dian Jiao
August 7, 2019 (v1)
Keywords: multi-label classification, non-intrusive load monitoring, random forest
Non-intrusive load monitoring (NILM) is an effective method to optimize energy consumption patterns. Since the concept of NILM was proposed, extensive research has focused on energy disaggregation or load identification. The traditional method is to disaggregate mixed signals, and then identify the independent load. This paper proposes a multi-label classification method using Random Forest (RF) as a learning algorithm for non-intrusive load identification. Multi-label classification can be used to determine which categories data belong to. This classification can help to identify the operation states of independent loads from mixed signals without disaggregation. The experiments are conducted in real environment and public data set respectively. Several basic electrical features are selected as the classification feature to build the classification model. These features are also compared to select the most suitable features for classification by feature importance parameters. The clas... [more]
Day-Ahead Prediction of Microgrid Electricity Demand Using a Hybrid Artificial Intelligence Model
Yuan-Jia Ma, Ming-Yue Zhai
August 5, 2019 (v1)
Keywords: Artificial Intelligence, electricity demand, feedforward artificial neural network, forecasting, microgrid, simulated annealing, smart grid, wavelet transform
Improved-performance day-ahead electricity demand forecast is important to deliver necessary information for right decision of energy management of microgrids. It supports microgrid operators and stakeholders to have better decisions on microgrid flexibility, stability and control. The available conventional forecasting methods for electricity demand at national or regional level are not effective for electricity demand forecasting in microgrids. This is due to the fact that the electricity consumption in microgrids is many times less than the regional or national demands and it is highly volatile. In this paper, an integrated Artificial Intelligence (AI) based approach consisting of Wavelet Transform (WT), Simulated Annealing (SA) and Feedforward Artificial Neural Network (FFANN) is devised for day-ahead prediction of electric power consumption in microgrids. The FFANN is the basic forecasting engine of the proposed model. The WT is utilized to extract relevant features of the target... [more]
Designing, Developing and Validating a Forecasting Method for the Month Ahead Hourly Electricity Consumption in the Case of Medium Industrial Consumers
Dana-Mihaela Petroșanu
July 31, 2019 (v1)
Keywords: artificial neural networks (ANNs), electricity consumption forecasting, long short-term memory (LSTM) neural networks, medium industrial consumers, non-linear autoregressive with exogenous inputs (NARX) model, smart meter device, timestamps dataset
An accurate forecast of the electricity consumption is particularly important to both consumers and system operators. The purpose of this study is to develop a forecasting method that provides such an accurate forecast of the month-ahead hourly electricity consumption in the case of medium industrial consumers, therefore assuring an intelligent energy management and an efficient economic scheduling of their resources, having the possibility to negotiate in advance appropriate billing tariffs relying on accurate hourly forecasts, in the same time facilitating an optimal energy management for the dispatch operator. The forecasting method consists of developing first non-linear autoregressive, with exogenous inputs (NARX) artificial neural networks (ANNs) in order to forecast an initial daily electricity consumption, a forecast that is being further processed with custom developed long short-term memory (LSTM) neural networks with exogenous variables support in order to refine the daily f... [more]
Heat Flux Estimation at Pool Boiling Processes with Computational Intelligence Methods
Erdem Alic, Mehmet Das, Onder Kaska
July 31, 2019 (v1)
Keywords: boiling, computational intelligence techniques, heat flux, Optimization
It is difficult to manually process and analyze large amounts of data. Therefore, to solve a given problem, it is easier to reach the solution by studying the data obtained from the environment of the problem with computational intelligence methods. In this study, pool boiling heat flux was estimated in the isolated bubble regime using two optimization methods (genetic and artificial bee colony algorithm) and three machine learning algorithms (decision tree, artificial neural network, and support vector machine). Six boiling mechanisms containing eighteen different parameters in the genetic and the artificial bee colony (ABC) algorithms were used to calculate overall heat flux of the isolated bubble regime. Support vector machine regression (SVMReg), alternating model tree (ADTree), and multilayer perceptron (MLP) regression only used the heat transfer equation input parameters without heat transfer equations for prediction of pool boiling heat transfer over a horizontal tube. The perf... [more]
CDL4CDRP: A Collaborative Deep Learning Approach for Clinical Decision and Risk Prediction
Mingrui Sun, Tengfei Min, Tianyi Zang, Yadong Wang
July 30, 2019 (v1)
Keywords: clinical diagnosis, decision support systems, Machine Learning, prediction algorithms, recommender systems
(1) Background: Recommendation algorithms have played a vital role in the prediction of personalized recommendation for clinical decision support systems (CDSSs). Machine learning methods are powerful tools for disease diagnosis. Unfortunately, they must deal with missing data, as this will result in data error and limit the potential patterns and features associated with obtaining a clinical decision; (2) Methods: Recent years, collaborative filtering (CF) have proven to be a valuable means of coping with missing data prediction. In order to address the challenge of missing data prediction and latent feature extraction, neighbor-based and latent features-based CF methods are presented for clinical disease diagnosis. The novel discriminative restricted Boltzmann machine (DRBM) model is proposed to extract the latent features, where the deep learning technique is adopted to analyze the clinical data; (3) Results: Proposed methods were compared to machine learning models, using two diffe... [more]
DA-Based Parameter Optimization of Combined Kernel Support Vector Machine for Cancer Diagnosis
Tao Xie, Jun Yao, Zhiwei Zhou
July 29, 2019 (v1)
Keywords: cancer, classification, combined kernel function, dragonfly algorithm, parameter optimization, support vector machine (SVM)
As is well known, the correct diagnosis for cancer is critical to save patients’ lives. Support vector machine (SVM) has already made an important contribution to the field of cancer classification. However, different kernel function configurations and their parameters will significantly affect the performance of SVM classifier. To improve the classification accuracy of SVM classifier for cancer diagnosis, this paper proposed a novel cancer classification algorithm based on the dragonfly algorithm and SVM with a combined kernel function (DA-CKSVM) which was constructed from a radial basis function (RBF) kernel and a polynomial kernel. Experiments were performed on six cancer data sets from University of California, Irvine (UCI) machine learning repository and two cancer data sets from Cancer Program Legacy Publication Resources to evaluate the validity of the proposed algorithm. Compared with four well-known algorithms: dragonfly algorithm-SVM (DA-SVM), particle swarm optimization-SVM... [more]
Prediction of CO2 Solubility in Ionic Liquids Based on Multi-Model Fusion Method
Luyue Xia, Jiachen Wang, Shanshan Liu, Zhuo Li, Haitian Pan
July 29, 2019 (v1)
Keywords: Carbon Dioxide, ionic liquids, multi-model fusion, prediction, solubility
Reducing the emissions of greenhouse gas is a worldwide problem that needs to be solved urgently for sustainable development in the future. The solubility of CO2 in ionic liquids is one of the important basic data for capturing CO2. Considering the disadvantages of experimental measurements, e.g., time-consuming and expensive, the complex parameters of mechanism modeling and the poor stability of single data-driven modeling, a multi-model fusion modeling method is proposed in order to predict the solubility of CO2 in ionic liquids. The multiple sub-models are built by the training set. The sub-models with better performance are selected through the validation set. Then, linear fusion models are established by minimizing the sum of squares of the error and information entropy method respectively. Finally, the performance of the fusion model is verified by the test set. The results showed that the prediction effect of the linear fusion models is better than that of the other three optima... [more]
Extracting Valuable Information from Big Data for Machine Learning Control: An Application for a Gas Lift Process
Ana Carolina Spindola Rangel Dias, Felipo Rojas Soares, Johannes Jäschke, Maurício Bezerra de Souza Jr, José Carlos Pinto
July 28, 2019 (v1)
Keywords: echo state network, gas lift, Machine Learning, model predictive control (MPC)
The present work investigated the use of an echo state network for a gas lift oil well. The main contribution is the evaluation of the network performance under conditions normally faced in a real production system: noisy measurements, unmeasurable disturbances, sluggish behavior and model mismatch. The main pursued objective was to verify if this tool is suitable to compose a predictive control scheme for the analyzed operation. A simpler model was used to train the neural network and a more accurate process model was used to generate time series for validation. The system performance was investigated with distinct sample sizes for training, test and validation procedures and prediction horizons. The performance of the designed ESN was characterized in terms of slugging, setpoint changes and unmeasurable disturbances. It was observed that the size and the dynamic content of the training set tightly affected the network performance. However, for data sets with reasonable information co... [more]
Power Quality Disturbance Classification Using the S-Transform and Probabilistic Neural Network
Huihui Wang, Ping Wang, Tao Liu
July 26, 2019 (v1)
Keywords: feature extraction, probabilistic neural network (PNN), S-transform, transient power quality, width factor
This paper presents a transient power quality (PQ) disturbance classification approach based on a generalized S-transform and probabilistic neural network (PNN). Specifically, the width factor used in the generalized S-transform is feature oriented. Depending on the specific feature to be extracted from the S-transform amplitude matrix, a favorable value is determined for the width factor, with which the S-transform is performed and the corresponding feature is extracted. Four features obtained this way are used as the inputs of a PNN trained for performing the classification of 8 disturbance signals and one normal sinusoidal signal. The key work of this research includes studying the influence of the width factor on the S-transform results, investigating the impacts of the width factor on the distribution behavior of features selected for disturbance classification, determining the favorable value for the width factor by evaluating the classification accuracy of PNN. Simulation result... [more]
Ensemble Prediction Model with Expert Selection for Electricity Price Forecasting
Bijay Neupane, Wei Lee Woon, Zeyar Aung
July 26, 2019 (v1)
Keywords: electricity price forecasting, ensemble model, expert selection
Forecasting of electricity prices is important in deregulated electricity markets for all of the stakeholders: energy wholesalers, traders, retailers and consumers. Electricity price forecasting is an inherently difficult problem due to its special characteristic of dynamicity and non-stationarity. In this paper, we present a robust price forecasting mechanism that shows resilience towards the aggregate demand response effect and provides highly accurate forecasted electricity prices to the stakeholders in a dynamic environment. We employ an ensemble prediction model in which a group of different algorithms participates in forecasting 1-h ahead the price for each hour of a day. We propose two different strategies, namely, the Fixed Weight Method (FWM) and the Varying Weight Method (VWM), for selecting each hour’s expert algorithm from the set of participating algorithms. In addition, we utilize a carefully engineered set of features selected from a pool of features extracted from the p... [more]
Short-Term Forecasting of Electric Loads Using Nonlinear Autoregressive Artificial Neural Networks with Exogenous Vector Inputs
Jaime Buitrago, Shihab Asfour
July 26, 2019 (v1)
Keywords: artificial neural networks, closed-loop forecasting, nonlinear autoregressive exogenous input, short-term load forecasting
Short-term load forecasting is crucial for the operations planning of an electrical grid. Forecasting the next 24 h of electrical load in a grid allows operators to plan and optimize their resources. The purpose of this study is to develop a more accurate short-term load forecasting method utilizing non-linear autoregressive artificial neural networks (ANN) with exogenous multi-variable input (NARX). The proposed implementation of the network is new: the neural network is trained in open-loop using actual load and weather data, and then, the network is placed in closed-loop to generate a forecast using the predicted load as the feedback input. Unlike the existing short-term load forecasting methods using ANNs, the proposed method uses its own output as the input in order to improve the accuracy, thus effectively implementing a feedback loop for the load, making it less dependent on external data. Using the proposed framework, mean absolute percent errors in the forecast in the order of... [more]
A Systematic Grey-Box Modeling Methodology via Data Reconciliation and SOS Constrained Regression
José Luis Pitarch, Antonio Sala, César de Prada
July 25, 2019 (v1)
Keywords: grey-box model, Machine Learning, process modeling, SOS programming
Developing the so-called grey box or hybrid models of limited complexity for process systems is the cornerstone in advanced control and real-time optimization routines. These models must be based on fundamental principles and customized with sub-models obtained from process experimental data. This allows the engineer to transfer the available process knowledge into a model. However, there is still a lack of a flexible but systematic methodology for grey-box modeling which ensures certain coherence of the experimental sub-models with the process physics. This paper proposes such a methodology based in data reconciliation (DR) and polynomial constrained regression. A nonlinear optimization of limited complexity is to be solved in the DR stage, whereas the proposed constrained regression is based in sum-of-squares (SOS) convex programming. It is shown how several desirable features on the polynomial regressors can be naturally enforced in this optimization framework. The goodnesses of the... [more]
Mold Level Predict of Continuous Casting Using Hybrid EMD-SVR-GA Algorithm
Zhufeng Lei, Wenbin Su
July 25, 2019 (v1)
Keywords: continuous cast, empirical mode decomposition, Genetic Algorithm, mold level, support vector regression
The prediction of mold level is a basic and key problem of continuous casting production control. Many current techniques fail to predict the mold level because of mold level is non-linear, non-stationary and does not have a normal distribution. A hybrid model, based on empirical mode decomposition (EMD) and support vector regression (SVR), is proposed to solve the mold level in this paper. Firstly, the EMD algorithm, with adaptive decomposition, is used to decompose the original mold level signal to many intrinsic mode functions (IMFs). Then, the SVR model optimized by genetic algorithm (GA) is used to predict the IMFs and residual sequences. Finally, the equalization of the predict results is reconstructed to obtain the predict result. Several hybrid predicting methods such as EMD and autoregressive moving average model (ARMA), EMD and SVR, wavelet transform (WT) and ARMA, WT and SVR are discussed and compared in this paper. These methods are applied to mold level prediction, the exp... [more]
Evaluation of the Difficulties in the Internet of Things (IoT) with Multi-Criteria Decision-Making
Buse Uslu, Tamer Eren, Şeyda Gür, Evrencan Özcan
July 25, 2019 (v1)
Keywords: analytic hierarchy process, analytic network process, Internet of Things, IoT, multi-criteria decision-making
The rapid development of technology has increased the desire of all to be on the Internet. The discovery that objects born of the Internet communicate with each other without external factors revealed, with the fourth industrial revolution, the concept of the Internet of Things (IoT). The communication of objects with each other means minimum labor and minimum cost for enterprises. Enterprises that want to transition to the Internet of Things face many difficulties. Identifying and correcting these difficulties can lead to both lost time and high cost. In this study, we investigated the difficulties encountered in the Internet of Things. As a result of the study, the degree of importance of the factors causing these difficulties was determined by multi-criteria decision-making methods and was presented to the enterprises. The main criteria, and the sub-criteria related to these main criteria, were determined. The main purpose of the enterprises transitioning to Industry 4.0 is the comm... [more]
Evaluating the Factors that are Affecting the Implementation of Industry 4.0 Technologies in Manufacturing MSMEs, the Case of Peru
Chung-Jen Huang, Elisa Denisse Talla Chicoma, Yi-Hsien Huang
July 25, 2019 (v1)
Keywords: analytic hierarchy process, developing countries, Industry 4.0, micro, small, and medium enterprises
The micro, small, and medium enterprises (MSMEs) sector plays a very crucial role in the economic and social development of Peru. Unfortunately, the tough access to the use of technologies is one of the weaknesses of this type of enterprises, which implies a low technological intensity production, according to the new technological trends. This study analyzes the factors that are affecting the implementation of Industry 4.0 technologies in Peruvian micro, small, and medium enterprises. According to the findings from the semi-structured interviews, it has identified four factors that respond to the main question of this research—lack of advanced technology, lack of financial investment, poor management vision, and lack of skilled workers. Data from 49 enterprises from the manufacturing sector were used for the assessment. The surveys conducted on business managers were evaluated using a multi-criterion decision-making method by the analytic hierarchy process. The findings of the study g... [more]
Drivers and Barriers in Using Industry 4.0: A Perspective of SMEs in Romania
Mirela Cătălina Türkeș, Ionica Oncioiu, Hassan Danial Aslam, Andreea Marin-Pantelescu, Dan Ioan Topor, Sorinel Căpușneanu
July 25, 2019 (v1)
Keywords: barriers, business, cloud computing, cyber-physical systems, digitalization, drivers, flexible manufacturing, implementation, Industry 4.0, managers, SMEs, systems
Considering the worldwide evolutionary stage of Industry 4.0, this study wants to fill in a lack of information and decision-making, trying to answer a question about the level of preparation of Romanian Small and Medium-sized Enterprises (SMEs) regarding the implementation of the new technology. The main purpose of this article is to identify the opinions and perceptions of SME managers in Romania on the drivers and barriers of implementing Industry 4.0 technology for business development. The research method used in the study was analyzed by sampling using the questionnaire as a data collection tool. It includes closed questions, measured with a nominal and orderly scale. 176 managers provided complete and useful answers to this research. The collected data were analyzed with the Statistical Package for the Social Sciences (SPSS) package using frequency tables, contingency tables, and main component analysis. Major contributions from research have highlighted the fact that Romania is... [more]
An Intelligent Fault Diagnosis Method Using GRU Neural Network towards Sequential Data in Dynamic Processes
Jing Yuan, Ying Tian
July 25, 2019 (v1)
Keywords: dynamic process, fault diagnosis, gate recurrent unit (GRU), moving horizon
Intelligent fault diagnosis is a promising tool to deal with industrial big data due to its ability in rapidly and efficiently processing collected signals and providing accurate diagnosis results. In traditional static intelligent diagnosis methods, however, the correlation between sequential data is neglected, and the features of raw data cannot be effectively extracted. Therefore, this paper proposes a three-stage fault diagnosis method based on a gate recurrent unit (GRU) network. The raw data is divided into several sequence units by first using a moving horizon as the input of GRU. In this way, we can intercept the sequence to get information as needed. Then, the GRU deep network is established through batch normalization (BN) algorithm to extract the dynamic feature from the sequence units effectively. Finally, the softmax regression is employed to classify faults based on dynamic features. Thus, the diagnosis result is obtained with a probabilistic explanation. Two chemical pro... [more]
Data-Mining for Processes in Chemistry, Materials, and Engineering
Hao Li, Zhien Zhang, Zhe-Ze Zhao
July 25, 2019 (v1)
Keywords: chemistry, data-mining, Energy, engineering, Machine Learning, materials, neural networks
With the rapid development of machine learning techniques, data-mining for processes in chemistry, materials, and engineering has been widely reported in recent years. In this discussion, we summarize some typical applications for process optimization, design, and evaluation of chemistry, materials, and engineering. Although the research and application targets are various, many important common points still exist in their data-mining. We then propose a generalized strategy based on the philosophy of data-mining, which should be applicable for the design and optimization targets for processes in various fields with both scientific and industrial purposes.
Determination of KOSGEB Support Models for Small- and Medium-Scale Enterprises by Means of Data Envelopment Analysis and Multi-Criteria Decision Making Methods
Ali Sevinç, Tamer Eren
July 11, 2019 (v1)
Keywords: AHP, data envelopment analysis, KOSGEB, productivity, SME, TOPSIS
Small- and Medium-Scale Enterprises (SMEs) act as catalysts in the general economy with regard to their added value. Support programs have been designed by the government through the Small and Medium Enterprises Development and Support Administration KOSGEB) and other institutions in order to further the general economic contributions of such enterprises. However, there is no method for using support models according to a productivity and effectiveness principle. This causes serious wastes of both resources and time. In this study, the problem of applying support models to improve the most critical problems of SMEs was discussed. As a place of application, 82 firms registered to the Konya Chamber of Industry were selected for the automotive supplier industry. Firstly, a productivity evaluation of companies was performed by a data envelopment analysis (DEA). Firms were grouped into A, B1, B2, C1, and C2 according to their activity scores. Using an Analytical Hierarchy Process (AHP), the... [more]
A Hybrid Energy Feature Extraction Approach for Ship-Radiated Noise Based on CEEMDAN Combined with Energy Difference and Energy Entropy
Yuxing Li, Xiao Chen, Jing Yu
May 16, 2019 (v1)
Keywords: complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), energy difference (ED), energy entropy (EE), hybrid energy feature extraction, ship-radiated noise (S-RN)
Influenced by the complexity of ocean environmental noise and the time-varying of underwater acoustic channels, feature extraction of underwater acoustic signals has always been a difficult challenge. To solve this dilemma, this paper introduces a hybrid energy feature extraction approach for ship-radiated noise (S-RN) based on complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) combined with energy difference (ED) and energy entropy (EE). This approach, named CEEMDAN-ED-EE, has two main advantages: (i) compared with empirical mode decomposition (EMD) and ensemble EMD (EEMD), CEEMDAN has better decomposition performance by overcoming mode mixing, and the intrinsic mode function (IMF) obtained by CEEMDAN is beneficial to feature extraction; (ii) the classification performance of the single energy feature has some limitations, nevertheless, the proposed hybrid energy feature extraction approach has a better classification performance. In this paper, we first deco... [more]
Application of Data Mining in an Intelligent Early Warning System for Rock Bursts
Xuejun Zhu, Xiaona Jin, Dongdong Jia, Naiwei Sun, Pu Wang
May 16, 2019 (v1)
Keywords: clustering analysis, data mining, data warehouse, intelligent early warning, rock burst
In view of rock burst accidents frequently occurring, a basic framework for an intelligent early warning system for rock bursts (IEWSRB) is constructed based on several big data technologies in the computer industry, including data mining, databases and data warehouses. Then, a data warehouse is modeled with regard to monitoring the data of rock bursts, and the effective application of data mining technology in this system is discussed in detail. Furthermore, we focus on the K-means clustering algorithm, and a data visualization interface based on the Browser/Server (B/S) mode is developed, which is mainly based on the Java language, supplemented by Cascading Style Sheets (CSS), JavaScript and HyperText Markup Language (HTML), with Tomcat, as the server and Mysql as the JavaWeb project of the rock burst monitoring data warehouse. The application of data mining technology in IEWSRB can improve the existing rock burst monitoring system and enhance the prediction. It can also realize real... [more]
Ultrasonic-Assisted Extraction and Swarm Intelligence for Calculating Optimum Values of Obtaining Boric Acid from Tincal Mineral
Bahdisen Gezer, Utku Kose
April 15, 2019 (v1)
Keywords: Artificial Intelligence, boric acid, central composite design, Optimization, swarm intelligence, tincal, ultrasound assisted extraction
The objective of this study is to focus on boric acid extraction from the mineral tincal, in order to determine the optimum conditions thanks to the ultrasonic-assisted extraction (UAE) technique (with the response surface methodology (RSM) for the first time), and artificial intelligence based swarm intelligence. Characterization of the tincal were done by using thermo-gravimetric assay (TG-DTA), X-ray diffraction (XRD), and Fourier transform infrared spectroscopy (FTIR) analyses. In detail, a central composite design (CCD) was used for determining the effects of different solvent/solid ratios, pH, extraction time, and extraction temperature on the yield, which was determined by the conductometric method. The optimum values regarding the best extraction process was calculated by using five different swarm intelligence techniques: Particle swarm optimization (PSO), cuckoo search (CS), genetic algorithms (GA), Differential evolution (DE), and the vortex optimization algorithm (VOA). In... [more]
FFANN Optimization by ABC for Controlling a 2nd Order SISO System’s Output with a Desired Settling Time
Aydın Mühürcü
April 9, 2019 (v1)
Keywords: ABC, buck converter, control, FFANN, Modelling, Optimization, settling time
In this study, a control strategy is aimed to ensure the settling time of a 2nd order system’s output value while its input reference value is changed. Here, Feed Forward Artificial Neural Network (FFANN) nonlinear structure has been chosen as a control algorithm. In order to implement the intended control strategy, FFANN’s normalization coefficient (K), learning coefficients (ŋ), momentum coefficients (μ) and the sampling time (Ts) were optimized by Artificial Bee Colony (ABC) but FFANN’s values of weights were chosen arbitrary on start time of control system. After optimization phase, the FFANN behaves as an adaptive optimal discrete time non-linear controller that forces the system output to take the same value with the input reference for a desired settling time (ts). The success of the optimization algorithm was proved with close loop feedback control simulations on Matlab’s Simulink platform based on 2nd order transfer functions. Also, the success was proved with a 2nd order phys... [more]
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