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Articles 661 - 690 of 839

Full-Text Articles in Operations Research, Systems Engineering and Industrial Engineering

A Robust Controller For The Manipulation Of Micro Scale Objects, Qinmin Yang, Jagannathan Sarangapani Jan 2005

A Robust Controller For The Manipulation Of Micro Scale Objects, Qinmin Yang, Jagannathan Sarangapani

Electrical and Computer Engineering Faculty Research & Creative Works

A suite of novel robust controllers is presented for the manipulation and handling of micro-scale objects in a micro-electromechanical system (MEMS) where adhesive, surface tension, friction and van der Waals forces are dominant. Moreover, these forces are typically unknown. The robust controller overcomes the unknown system dynamics and ensures the performance in the presence of actuator constraints by assuming that the upper bounds on these forces are known. On the other hand, for the robust adaptive controller, the unknown forces are estimated online. Using the Lyapunov approach, the uniformly ultimate boundedness (UUB) of the closed-loop manipulation error is shown for …

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Decentralized Discrete-Time Neural Network Controller For A Class Of Nonlinear Systems With Unknown Interconnections, Jagannathan Sarangapani Jan 2005

Decentralized Discrete-Time Neural Network Controller For A Class Of Nonlinear Systems With Unknown Interconnections, Jagannathan Sarangapani

Electrical and Computer Engineering Faculty Research & Creative Works

A novel decentralized neural network (NN) controller in discrete-time is designed for a class of uncertain nonlinear discrete-time systems with unknown interconnections. Neural networks are used to approximate both the uncertain dynamics of the nonlinear systems and the unknown interconnections. Only local signals are needed for the decentralized controller design and the stability of the overall system can be guaranteed using the Lyapunov analysis. Further, controller redesign for the original subsystems is not required when additional subsystems are appended. Simulation results demonstrate the effectiveness of the proposed controller. The NN does not require an offline learning phase and the weights …

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Energy-Efficient Rate Adaptation Mac Protocol For Ad Hoc Wireless Networks, Maciej Jan Zawodniok, Jagannathan Sarangapani Jan 2005

Energy-Efficient Rate Adaptation Mac Protocol For Ad Hoc Wireless Networks, Maciej Jan Zawodniok, Jagannathan Sarangapani

Electrical and Computer Engineering Faculty Research & Creative Works

Resource constraints in ad hoc wireless networks require that they are energy efficient during both transmission and rate adaptation. In this paper, we propose a novel energy-efficient rate adaptation protocol that selects modulation schemes online to maximize throughput based on channel state while saving energy. This protocol uses the distributed power control (DPC) algorithm (M. Zawodniok et al., 2004) to accurately determine the necessary transmission power and to reduce the energy consumption. Additionally, the transmission rate is altered using energy efficiency as a constraint to meet the required throughput, which is estimated with queue fill ratio. Moreover, back-off scheme is …

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Neural Network -Based Nearly Optimal Hamilton-Jacobi-Bellman Solution For Affine Nonlinear Discrete-Time Systems, Jagannathan Sarangapani, Zheng Chen Jan 2005

Neural Network -Based Nearly Optimal Hamilton-Jacobi-Bellman Solution For Affine Nonlinear Discrete-Time Systems, Jagannathan Sarangapani, Zheng Chen

Electrical and Computer Engineering Faculty Research & Creative Works

In this paper, we consider the use of nonlinear networks towards obtaining nearly optimal solutions to the control of nonlinear discrete-time systems. The method is based on least-squares successive approximation solution of the Generalized Hamilton-Jacobi-Bellman (HJB) equation. Since successive approximation using the GHJB has not been applied for nonlinear discrete-time systems, the proposed recursive method solves the GHJB equation in discrete-time on a well-defined region of attraction. The definition of GHJB, Pre-Hamiltonian function, HJB equation and method of updating the control function for the affine nonlinear discrete time systems are proposed. A neural network is used to approximate the GHJB …

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Neural Network-Based Control Of Nonlinear Discrete-Time Systems In Non-Strict Form, Jagannathan Sarangapani, Zheng Chen, Pingan He Jan 2005

Neural Network-Based Control Of Nonlinear Discrete-Time Systems In Non-Strict Form, Jagannathan Sarangapani, Zheng Chen, Pingan He

Electrical and Computer Engineering Faculty Research & Creative Works

A novel reinforcement learning-based adaptive neural network (NN) controller, also referred as the adaptive-critic NN controller, is developed to deliver a desired tracking performance for a class of non-strict feedback nonlinear discrete-time systems in the presence of bounded and unknown disturbances. The adaptive critic NN controller architecture includes a critic NN and two action NNs. The critic NN approximates certain strategic utility function whereas the action neural networks are used to minimize both the strategic utility function and the unknown dynamics estimation errors. The NN weights are tuned online so as to minimize certain performance index. By using gradient descent-based …

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Predictive Congestion Control Mac Protocol For Wireless Sensor Networks, Maciej Jan Zawodniok, Jagannathan Sarangapani Jan 2005

Predictive Congestion Control Mac Protocol For Wireless Sensor Networks, Maciej Jan Zawodniok, Jagannathan Sarangapani

Electrical and Computer Engineering Faculty Research & Creative Works

Available congestion control schemes, for example transport control protocol (TCP), when applied to wireless networks results in a large number of packet drops, unfairness with a significant amount of wasted energy due to retransmissions. To fully utilize the hop by hop feedback information, a suite of novel, decentralized, predictive congestion control schemes are proposed for wireless sensor networks in concert with distributed power control (DPC). Besides providing energy efficient solution, embedded channel estimator in DPC predicts the channel quality. By using the channel quality and node queue utilizations, the onset of network congestion is predicted and congestion control is initiated. …

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Block Phase Correlation-Based Automatic Drift Compensation For Atomic Force Microscopes, Qinmin Yang, Eric W. Bohannan, Jagannathan Sarangapani Jan 2005

Block Phase Correlation-Based Automatic Drift Compensation For Atomic Force Microscopes, Qinmin Yang, Eric W. Bohannan, Jagannathan Sarangapani

Electrical and Computer Engineering Faculty Research & Creative Works

Automatic nanomanipulation and nanofabrication with an Atomic Force Microscope (AFM) is a precursor for nanomanufacturing. In ambient conditions without stringent environmental controls, nanomanipulation tasks require extensive human intervention to compensate for the many spatial uncertainties of the AFM. Among these uncertainties, thermal drift is especially hard to solve because it tends to increase with time and cannot be compensated simultaneously by feedback. In this paper, an automatic compensation scheme is introduced to measure and estimate drift. This information can be subsequently utilized to compensate for the thermal drift so that a real-time controller for nanomanipulation can be designed as if …

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A "Theory Of Action" Perspective On Effective Organizational Change, Ray Luechtefeld Jan 2005

A "Theory Of Action" Perspective On Effective Organizational Change, Ray Luechtefeld

Engineering Management and Systems Engineering Faculty Research & Creative Works

No abstract provided.

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Feedback Linearization Based Power System Stabilizer Design With Control Limits, Wenxin Liu, Ganesh K. Venayagamoorthy, Donald C. Wunsch, Jagannathan Sarangapani Aug 2004

Feedback Linearization Based Power System Stabilizer Design With Control Limits, Wenxin Liu, Ganesh K. Venayagamoorthy, Donald C. Wunsch, Jagannathan Sarangapani

Electrical and Computer Engineering Faculty Research & Creative Works

In power system controls, simplified analytical models are used to represent the dynamics of power system and controller designs are not rigorous with no stability analysis. One reason is because the power systems are complex nonlinear systems which pose difficulty for analysis. This paper presents a feedback linearization based power system stabilizer design for a single machine infinite bus power system. Since practical operating conditions require the magnitude of control signal to be within certain limits, the stability of the control system under control limits is also analyzed. Simulation results under different kinds of operating conditions show that the controller …

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Successfully Blending Distance Students Into The On-Campus Classroom, Susan L. Murray, David Lee Enke, Sreeram Ramakrishnan Jun 2004

Successfully Blending Distance Students Into The On-Campus Classroom, Susan L. Murray, David Lee Enke, Sreeram Ramakrishnan

Engineering Management and Systems Engineering Faculty Research & Creative Works

As universities are increasingly embracing distance education technology, it is useful to examine the challenges and opportunities of technology in the classroom. This is especially true when the course contains on-campus local students in addition to students learning at a distance. A significant challenge commonly faced is how to remain flexible in presenting course materials while still having notes and other handouts in electronic format available before the lecture. Other challenges include creating and using lecture material that can be viewed at low resolution and low bandwidth, and getting distance students to interact with the instructor, on-campus students, and fellow …

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Cost Allocation For Transmission Investment Using Agent-Based Game Theory, Jakapun Mepokee, David Lee Enke, Badrul H. Chowdhury Jan 2004

Cost Allocation For Transmission Investment Using Agent-Based Game Theory, Jakapun Mepokee, David Lee Enke, Badrul H. Chowdhury

Engineering Management and Systems Engineering Faculty Research & Creative Works

Due to electrical power restructuring, a dramatic change has been made to the generation and transmission sectors of the power industry. Rules and legislation are continuously changing. To promote more competition, transmission has to be expanded or upgraded to remove congestion and market power. The cost allocation of new investment in transmission has to be recalculated. The socialization methods of the past have been shown to be unfair to some market and network participants. The decentralization of cost allocation must be considered. The proposed paper provides a comparison between traditional cost allocation methods and a new cost allocation method based …

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Forecasting Series-Based Stock Price Data Using Direct Reinforcement Learning, H. Li, Cihan H. Dagli, David Lee Enke Jan 2004

Forecasting Series-Based Stock Price Data Using Direct Reinforcement Learning, H. Li, Cihan H. Dagli, David Lee Enke

Engineering Management and Systems Engineering Faculty Research & Creative Works

A significant amount of work has been done in the area of price series forecasting using soft computing techniques, most of which are based upon supervised learning. Unfortunately, there has been evidence that such models suffer from fundamental drawbacks. Given that the short-term performance of the financial forecasting architecture can be immediately measured, it is possible to integrate reinforcement learning into such applications. In this paper, we present the novel hybrid view for a financial series and critic adaptation stock price forecasting architecture using direct reinforcement. A new utility function called policies-matching ratio is also proposed. The need for the …

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Adaptive Critic Neural Network-Based Object Grasping Control Using A Three-Finger Gripper, Gustavo Galan, Jagannathan Sarangapani Jan 2004

Adaptive Critic Neural Network-Based Object Grasping Control Using A Three-Finger Gripper, Gustavo Galan, Jagannathan Sarangapani

Electrical and Computer Engineering Faculty Research & Creative Works

Grasping of objects has been a challenging task for robots. The complex grasping task can be defined as object contact control and manipulation subtasks. In this paper, object contact control subtask is defined as the ability to follow a trajectory accurately by the fingers of a gripper. The object manipulation subtask is defined in terms of maintaining a predefined applied force by the fingers on the object. A sophisticated controller is necessary since the process of grasping an object without a priori knowledge of the object's size, texture, softness, gripper, and contact dynamics is rather difficult. Moreover, the object has …

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Adaptive Force-Balancing Control Of Mems Gyroscope With Actuator Limits, Mohammed Hameed, Jagannathan Sarangapani Jan 2004

Adaptive Force-Balancing Control Of Mems Gyroscope With Actuator Limits, Mohammed Hameed, Jagannathan Sarangapani

Electrical and Computer Engineering Faculty Research & Creative Works

This work presents an adaptive force-balancing control (AFBC) scheme with actuator limits for a MEMS Z-axis gyroscope. The purpose of the adaptive force-balancing control is to identify major fabrication imperfections so that they are properly compensated unlike the case of conventional force-balancing controlled gyroscope. The proposed AFBC scheme controls the vibratory modes of the proof mass while ensuring that the control input satisfies the magnitude constraints and the performance of the gyroscope is enhanced in the presence of fabrication uncertainties. Consequently, commonly reported problems of MEMS gyroscope such as quadrature compensation, drive and sense axes frequency tuning are not needed …

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Decentralized Neural Network Control Of A Class Of Large-Scale Systems With Unknown Interconnection, Wenxin Liu, Jagannathan Sarangapani, Donald C. Wunsch, Mariesa Crow Jan 2004

Decentralized Neural Network Control Of A Class Of Large-Scale Systems With Unknown Interconnection, Wenxin Liu, Jagannathan Sarangapani, Donald C. Wunsch, Mariesa Crow

Electrical and Computer Engineering Faculty Research & Creative Works

A novel decentralized neural network (DNN) controller is proposed for a class of large-scale nonlinear systems with unknown interconnections. The objective is to design a DNN for a class of large-scale systems which do not satisfy the matching condition requirement. The NNs are used to approximate the unknown subsystem dynamics and the interconnections. The DNN is designed using the back stepping methodology with only local signals for feedback. All of the signals in the closed loop (system states and weights estimation errors) are guaranteed to be uniformly ultimately bounded and eventually converge to a compact set.

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A Distributed Power Control Mac Protocol For Wireless Ad-Hoc Networks., Maciej Jan Zawodniok, Jagannathan Sarangapani Jan 2004

A Distributed Power Control Mac Protocol For Wireless Ad-Hoc Networks., Maciej Jan Zawodniok, Jagannathan Sarangapani

Electrical and Computer Engineering Faculty Research & Creative Works

A novel distributed power control (DPC)⋅ scheme and a MAC protocol for wireless ad hoc networks in the presence of radio channel uncertainties such as path loss, Shadowing and Rayleigh fading is presented. The DPC quickly estimates the time-varying nature of the channel and uses the information to select a suitable transmitter power value in order to maintain a target Signal-to-Interference ratio (SIR) at the receiver. The standard assumption of a constant interference during a link's power update used in other works is relaxed. The performance of the proposed DPC is demonstrated analytically. The power used for all RTS-CTS-DATA-ACK frames …

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Discrete-Time Neural Network Output Feedback Control Of Nonlinear Systems In Non-Strict Feedback Form, Pingan He, Jagannathan Sarangapani Jan 2004

Discrete-Time Neural Network Output Feedback Control Of Nonlinear Systems In Non-Strict Feedback Form, Pingan He, Jagannathan Sarangapani

Electrical and Computer Engineering Faculty Research & Creative Works

An adaptive neural network (NN)-based output feedback controller is proposed to deliver a desired tracking performance for a class of discrete-time nonlinear systems, which is represented in non-strict feedback form. The NN backstepping approach is utilized to design the adaptive output feedback controller consisting of: 1) a NN observer to estimate the system states with the input-output data, and 2) two NNs to generate the virtual and actual control inputs, respectively. The non-causal problem in the discrete-time backstepping design is avoided by using the universal NN approximator. The persistence excitation (PE) condition is relaxed both in the NN observer and …

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Neural Network Controller For Manipulation Of Micro-Scale Objects, Vijayakumar Janardhan, Pingan He, Jagannathan Sarangapani Jan 2004

Neural Network Controller For Manipulation Of Micro-Scale Objects, Vijayakumar Janardhan, Pingan He, Jagannathan Sarangapani

Electrical and Computer Engineering Faculty Research & Creative Works

A novel reinforcement learning-based neural network (RLNN) controller is presented for the manipulation and handling of micro-scale objects in a microelectromechanical system (MEMS). In MEMS, adhesive, surface tension, friction and van der Waals forces are dominant. Moreover, these forces are typically unknown. The RLNN controller consists of an action NN for compensating the unkoown system dynamics, and a critic NN to tune the weights of the action NN. Using the Lyapunov approach, the uniformly ultimate houndedness (UUB) of the closed-loop tracking error and weight estimates are shown by using a novel weight updates. Simulation results are presented to substantiate the …

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Discerning Attributes Which Stimulate Performance In Quality Improvement Teams, Dwan Lamar Prude Jan 2004

Discerning Attributes Which Stimulate Performance In Quality Improvement Teams, Dwan Lamar Prude

Masters Theses

"Total quality management (TQM) can be summed up as people and the way they work. One key element of the philosophies of TQM is the heavy emphasis on utilizing quality improvement teams (QITs) and quality tools to effectively create high performance organizations. Specifically, this investigation asks the following questions: 1) What are the key attributes that contribute to performance in QITs? 2) What is the relationship between team communication and QIT performance? 3) What is the relationship between the number of quality tools utilized in a team and QIT performance? Participants for this study were 101 students from the University …

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Understanding Market Stakeholder Perspectives: Application In The Biopharmaceutical Industry, Halvard Nystrom, Kalayanee Poon-Asawasombat Jun 2003

Understanding Market Stakeholder Perspectives: Application In The Biopharmaceutical Industry, Halvard Nystrom, Kalayanee Poon-Asawasombat

Engineering Management and Systems Engineering Faculty Research & Creative Works

New products are sometimes perceived as risky. This article contributes a process that new product development teams can use to better understand stakeholders' concerns. the stakeholder analysis process is appropriate for products that can be perceived as risky since it enables developers to address key issues of concern prior to new product introduction, mitigating market acceptance risk. It uses a stakeholder model that facilitates dialogue to understand market risks and guide the assessment process. This article includes a case study describing a successful application of introducing biopharmaceutical products, as well as some of the key findings to demonstrate usefulness. © …

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Nonlinear Time Series Prediction By Weighted Vector Quantization, Amaury Lendasse, D. Francois, V. Wertz, M. Verleysen Jan 2003

Nonlinear Time Series Prediction By Weighted Vector Quantization, Amaury Lendasse, D. Francois, V. Wertz, M. Verleysen

Engineering Management and Systems Engineering Faculty Research & Creative Works

Classical Nonlinear Models for Time Series Prediction Exhibit Improved Capabilities Compared to Linear Ones. Nonlinear Regression Has However Drawbacks, Such as overfilling and Local Minima Problems, User-Adjusted Parameters, Higher Computation Times, Etc. There is Thus a Need for Simple Nonlinear Models with a Restricted Number of Learning Parameters, High Performances and Reasonable Complexity. in This Paper, We Present a Method for Nonlinear Forecasting based on the Quantization of Vectors Concatenating Inputs (Regressors) and Outputs (Predictions). Weighting Techniques Are Applied to Give More Importance to Inputs and Outputs Respectively. the Method is Illustrated on Standard Time Series Prediction Benchmarks. © Springer-Verlag …

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Web Personalization Using Neuro-Fuzzy Clustering Algorithms, Kartik Menon, Cihan H. Dagli Jan 2003

Web Personalization Using Neuro-Fuzzy Clustering Algorithms, Kartik Menon, Cihan H. Dagli

Engineering Management and Systems Engineering Faculty Research & Creative Works

Different users have different needs from the same web page and hence it is necessary to develop a system which understands the needs and demands of the users. Web server logs have abundant information about the nature of users accessing it. In this paper we discussed how to mine these web server logs for a given period of time using unsupervised and competitive learning algorithm like Kohonen''s self organizing maps (SOM) and interpreting those results using Unified distance Matrix (U-matrix). These algorithms help us in efficiently clustering users based on similar web access patterns and each cluster having users with …

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Software Development In The Grid: The Damien Tool-Set, Edgar Gabriel, Rainer Keller, Peggy Lindner, Matthias S. Müller, Michael M. Resch Jan 2003

Software Development In The Grid: The Damien Tool-Set, Edgar Gabriel, Rainer Keller, Peggy Lindner, Matthias S. Müller, Michael M. Resch

Engineering Management and Systems Engineering Faculty Research & Creative Works

The development of applications for Grid-environments is currently lacking the support of tools, which end-users are familiar with from their regular working environment. This paper analyzes the requirements for developing, porting and optimizing scientific applications for Grid-environments. A toolbox designed and implemented in the frame of the DAMIEN project which closes some of the gaps and supports the end-user during the development of the application and its day-to-day usage in Grid-environments is then presented. © Springer-Verlag Berlin Heidelberg 2003.

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Cooperative Cleaning For Distributed Autonomous Robot Systems Using Fuzzy Cognitive Maps, H. Subramanian, Cihan H. Dagli Jan 2003

Cooperative Cleaning For Distributed Autonomous Robot Systems Using Fuzzy Cognitive Maps, H. Subramanian, Cihan H. Dagli

Engineering Management and Systems Engineering Faculty Research & Creative Works

Cooperative Autonomous Cleaning is a simple challenge that can be implemented with the help of Fuzzy Cognitive Maps (FCM) by simulating the actual thinking process of the human. The human mind organizes its thoughts in priorities and this feature could be exploited well if a priori knowledge of the system exists. This technique has been attempted here for a DARS.

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Neuro Emission Controller For Minimizing Cyclic Dispersion In Spark Ignition Engines, Pingan He, Jagannathan Sarangapani Jan 2003

Neuro Emission Controller For Minimizing Cyclic Dispersion In Spark Ignition Engines, Pingan He, Jagannathan Sarangapani

Electrical and Computer Engineering Faculty Research & Creative Works

A novel neural network (NN) controller is developed to control spark ignition (SI) engines at extreme lean conditions. The purpose of neurocontroller is to reduce the cyclic dispersion at lean operation even when the engine dynamics are unknown. The stability analysis of the closed-loop control system is given and the boundedness of all signals is ensured. Results demonstrate that the cyclic dispersion is reduced significantly using the proposed controller. The neuro controller can also be extended to minimize engine emissions with high EGR levels, where similar complex cyclic dynamics are observed. Further, the proposed approach can be applied to control …

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Emergence And Artificial Life, Nil H. Kilicay, Cihan H. Dagli Jan 2003

Emergence And Artificial Life, Nil H. Kilicay, Cihan H. Dagli

Engineering Management and Systems Engineering Faculty Research & Creative Works

This paper focuses on emergent phenomena and the utilization of computer simulations, basically agent-based modeling to understand emergent phenomena. Agent-based simulation models have a promising future in the social sciences, from management to economies, political science, sociology and anthropology. This paper attempts to realize their full scientific potential by reviewing recent applications in engineering management and addresses the set of challenges confronted by this method. Common methodology for constructing an agent-based model is also discussed with the aim of highlighting how artificial life and management can be brought together to develop decision making aid tools.

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Performance & Project Spirit Of Student Design Competition Teams, Bradley Marcus Davis Jan 2003

Performance & Project Spirit Of Student Design Competition Teams, Bradley Marcus Davis

Masters Theses

"Student design competition teams present a valuable research possibility in the field of work group research. They are widely available and present greater learning opportunities than do class project groups. This study intended to create an instrument to measure the level of project spirit and team performance in order to establish a link between these two concepts. In the present study, nine student design competition teams from the University of Missouri - Rolla (UMR) campus (n = 186) were surveyed. The results indicate a strong correlation of the project spirit variables (project culture, project commitment, project citizenship, and project satisfaction) …

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Rate-Based End-To-End Congestion Control Of Multimedia Traffic In Packet Switched Networks, Mingsheng Peng, S. R. Subramanya, Jagannathan Sarangapani Jan 2003

Rate-Based End-To-End Congestion Control Of Multimedia Traffic In Packet Switched Networks, Mingsheng Peng, S. R. Subramanya, Jagannathan Sarangapani

Electrical and Computer Engineering Faculty Research & Creative Works

This paper proposes an explicit rate-based end-to-end congestion control mechanism to alleviate congestion of multimedia traffic in packet switched networks such as the Internet. The congestion is controlled by adjusting the transmission rates of the sources in response to the feedback information from destination such as the buffer occupancy, packet arrival rate and service rate at the outgoing link, so that a desired quality of service (QoS) can be met. The QoS is defined in terms of packet loss ratio, transmission delay, power, and network utilization. Comparison studies demonstrate the effectiveness of the proposed scheme over New-Reno TCP (a variant …

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An Enhanced Least-Squares Approach For Reinforcement Learning, Hailin Li, Cihan H. Dagli Jan 2003

An Enhanced Least-Squares Approach For Reinforcement Learning, Hailin Li, Cihan H. Dagli

Engineering Management and Systems Engineering Faculty Research & Creative Works

This paper presents an enhanced least-squares approach for solving reinforcement learning control problems. Model-free least-squares policy iteration (LSPI) method has been successfully used for this learning domain. Although LSPI is a promising algorithm that uses linear approximator architecture to achieve policy optimization in the spirit of Q-learning, it faces challenging issues in terms of the selection of basis functions and training samples. Inspired by orthogonal least-squares regression (OLSR) method for selecting the centers of RBF neural network, we propose a new hybrid learning method. The suggested approach combines LSPI algorithm with OLSR strategy and uses simulation as a tool to …

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Combining Evolving Neural Network Classifiers Using Bagging, Sunghwan Sohn, Cihan H. Dagli Jan 2003

Combining Evolving Neural Network Classifiers Using Bagging, Sunghwan Sohn, Cihan H. Dagli

Engineering Management and Systems Engineering Faculty Research & Creative Works

The performance of the neural network classifier significantly depends on its architecture and generalization. It is usual to find the proper architecture by trial and error. This is time consuming and may not always find the optimal network. For this reason, we apply genetic algorithms to the automatic generation of neural networks. Many researchers have provided that combining multiple classifiers improves generalization. One of the most effective combining methods is bagging. In bagging, training sets are selected by resampling from the original training set and classifiers trained with these sets are combined by voting. We implement the bagging technique into …

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