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检索条件"机构=ICOS: Research Unit on Intelligent Control"

共 57 条记录，以下是21-30 订阅

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Optimal control for Discrete Large Scale Nonlinear Systems Using Hierarchical Fuzzy Systems

Optimal Control for Discrete Large Scale Nonlinear Systems U...

引用

International Conference on Machine Learning and Computing

作者： Najla Krichen Masmoudi Chokri Rekik Mohamed Djemel Nabil Derbel Research unit on Intelligent Control design and Optimisation of complex Systems (ICOS) Sfax National Engineering School Sfax Tunisia

This paper presents a method to compute optimal control strategies of discrete large scale nonlinear systems by using hierarchical fuzzy systems. The method is based on the decomposition principle of the global system into interconnected subsystems becoming easier to study. Then, the differential dynamic programming procedure is applied in order to obtain the rule basis. After that, we construct limpid-hierarchical Mamdani fuzzy system in order to compute optimal control laws, for each subsystem. Simulation results of a rotary crane show that the proposed method yields to satisfactory performances. The robustness of the proposed approach is verified.

关键词： Optimal control Large-scale systems Nonlinear systems Fuzzy systems Input variables Fuzzy logic Influenza Dynamic programming Machine learning intelligent control

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An evolutionary neuro-fuzzy approach to breast cancer diagnosis

An evolutionary neuro-fuzzy approach to breast cancer diagno...

引用

IEEE International Conference on Systems, Man and Cybernetics

作者： R. El Hamdi M. Njah M. Chtourou Research Unit on Intelligent Control Design & Optimization of Complex Systems (ICOS) Sfax Engineering School University of Sfax Sfax Tunisia

The important role that mammography is playing in breast cancer detection can be attributed largely to the technical improvements and dedication of radiologists to breast imaging. A lot of work is being done to ensure that these diagnosing steps are becoming smoother, faster and more accurate in classifying whether the abnormalities seen in mammogram images are benign or malignant. In this paper, an evolutionary approach for design of TSK-type fuzzy model (TFM) is proposed to solve the breast cancer diagnosis problem. In the proposed method, both the number of fuzzy rules and adjustable parameters in the TFM are designed concurrently combining the compact genetic algorithm (CGA) and the steady-state genetic algorithm (SSGA). The computational experiments show that the presented approach can obtain better generalization than some existing methods reported recently in the literature using the widely accepted Wisconsin breast cancer diagnosis (WBCD) database.

关键词： Breast Niobium Databases Cancer

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Optimized fuzzy controller for mobile robot navigation in a cluttered environment

Optimized fuzzy controller for mobile robot navigation in a ...

引用

IEEE SSD International Multi-Conference on Systems, Signals and Devices

作者： Najah Yousfi Chokri Rekik Mohamed Jallouli Nabil Derbel Research unit on Intelligent Control design & Optimization of complex Systems (ICOS) Sfax Engineering School Sfax BP W Tunisia

Obstacle avoidance and path planning are the most important problems in mobile robots. In this paper, a fuzzy logic controller has been constructed in order to train an intelligent robot. Gradient method is used to optimize consequences of a Sugeno fuzzy logic controller for the mobile robot navigation, in order to reach a target in a clutterd environment. Not only simulation results are shown in this paper, but also the “real-time” implementation has been realized onto the mini robot Khepera II. Simulation results verify successfully the application of the proposed method to real motion situations.

关键词： Robot sensing systems Mobile communication Argon Optimization Trajectory

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Breast cancer diagnosis using a hybrid evolutionary neural network classifier

Breast cancer diagnosis using a hybrid evolutionary neural n...

引用

Mediterranean Conference on control and Automation (MED)

作者： R. El hamdi M. Njah M. Chtourou Research unit on Intelligent Control design & Optimization of complex Systems (ICOS) Sfax Engineering School University of Sfax Sfax Tunisia

The important role that mammography is playing in breast cancer detection can be attributed largely to the technical improvements and dedication of radiologists to breast imaging. A lot of work is being done to ensure that these diagnosing steps are becoming smoother, faster and more accurate in classifying whether the abnormalities seen in mammogram images are benign or malignant. This paper takes a step in that direction by introducing a hybrid evolutionary neural network classifier (HENC) combining the evolutionary algorithm, which has a powerful global exploration capability, with gradient-based local search method, which can exploit the optimum offspring to develop a diagnostic aid that accurately differentiates malignant from benign pattern. The computational experiments show that the presented HENC approach can obtain better generalization and much lower computational cost than the existing methods reported recently in the literature using the widely accepted Wisconsin breast cancer diagnosis (WBCD) database with some improvements.

关键词： Artificial neural networks Training Classification algorithms Neurons Breast cancer Databases

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Multi objective optimization for piloting a Quality Management System: Study case

Multi objective optimization for piloting a Quality Manageme...

引用

2010 International Conference on System Science and Engineering, ICSSE 2010

作者： Mahmoud, H. Ben Ketata, R. Romdhane, T. Ben Ahmed, S. Ben Dept. of Physics and Instrwnentation Engineering INSAT: National Institute of Applied Science and Technology Tunis Tunisia Dept. of Physics and Instrwnentation Engineering ICOS: Research Unit on Intelligent Control Design and Optimization of Complex System Tunis Tunisia Dept. of Physics and Instrumentation Engineering MOSIC Research Unit on Methods and Tools for Complex Computer Systems Tunis Tunisia

ISBN: (纸本)9781424464746

This paper presents a decision support tool for the effectiveness of a Quality Management System (QMS) in a company. To develop this tool, a new approach PAHP based on the combination of the Pareto Optimality Concept (Poq and the Analytical Hierarchy Process (AHP). This approach is used to model and optimize the multi-objective function of real company which presents a major problem for controlling the quality level of production. © 2010 IEEE.

关键词： Multiobjective optimization

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Adaptive multi-robots synchronization

Adaptive multi-robots synchronization

引用

IEEE International Symposium on Industrial Electronics (ISIE)

作者： Yassine Bouteraa Gérard Poisson Jawhar Ghommam Nabil Derbel Institut PRISME Université d''Oran-Es-senia Bourges France Engineering School of Sfax (ENIS) Research unit on Intelligent Control design and Optimization of complex Systems (ICOS) University of Sfax Sfax Tunisia

We address the problem of designing decentralized laws to control a team of general fully actuated manipulators which synchronize their movements while following the same desired trajectory. To this effect we investigate an adaptive control of an identical multiple manipulators, with unknown inertia parameters, tracking a common trajectory. It is also assumed that all the robots in the synchronization system have the same number of joints and equivalent joint work spaces, i.e. any possible configuration of a given robot in the system can be achieved by any other robot in the system. Adaptive controls are derived for group of manipulators using a consensus algorithm. This consensus algorithm is applied on the group level to estimate the time-varying group trajectory information in a distributed manner. The proposed strategy only requires local neighbor-to-neighbor information exchange between manipulators and does not assume the existence of any explicit leaders in the team. The interaction topology of a network of agents is represented using an indirected graph. The coordination strategy is to let each manipulator track its desired trajectory while synchronizing its motion with the others manipulator's motions so that differential position errors amongst robots converge to zero.

关键词： Robot kinematics Synchronization Trajectory Manipulator dynamics Joints

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controllability analysis of 1-DOF linear juggling system

Controllability analysis of 1-DOF linear juggling system

引用

IEEE SSD International Multi-Conference on Systems, Signals and Devices

作者： Behij Ben Amor Nehla Kraief Haded Faical Mnif Research unit on Intelligent Control design & Optimization of complex Systems (ICOS) Sfax Tunisia Sultan Qaboos University Oman

The paper deals with the controllability study of a subclass of juggling systems, named linear juggler. It comes that despite the simple representation of such systems, their controllability is in general not easy to establish, since they remain highly nonlinear dynamical systems. In this paper we are interested in the controllability properties of one degree of freedom linear juggling system.

关键词： controllability control system analysis Manipulator dynamics Robots Mechanical systems Nonlinear dynamical systems Nonlinear systems Signal analysis intelligent control Signal design

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Robust and stable indirect fuzzy adaptive control for SISO nonlinear systems

Robust and stable indirect fuzzy adaptive control for SISO n...

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IEEE SSD International Multi-Conference on Systems, Signals and Devices

作者： D. Ben Halima M. Chtourou Research Unit on Intelligent Control design and Optimization of complex Systems (ICOS) National School of Engineers of Sfax Sfax Tunisia

An adaptive fuzzy controller is constructed from a collection of fuzzy IF-THEN rules whose parameters are changed or adapted according to adaptive laws for the purpose of controlling a nonlinear plant to track a reference trajectory. The fuzzy adaptive control is composed in two classes: direct and indirect one. The indirect fuzzy adaptive controller will be designed through two steps. The first one consists in the construction of a controller based on linguistic description about the unknown plant from human expert. To guaranty stability, a supervisory controller is added. The second step adjusts, on line, the parameters of the fuzzy controller based on the gradient descend. To ensure the robustness, fuzzy controller parameters are adjusted, on line, by a robust gradient descent method. Simulation results show the effectiveness of proposed algorithms in tracking of the inverse pendulum output.

关键词： Robust control Fuzzy control Fuzzy systems Adaptive control Nonlinear systems Programmable control Adaptive systems Trajectory Humans Stability

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On the performances of neuronal classifiers for pattern recognition

On the performances of neuronal classifiers for pattern reco...

引用

IEEE SSD International Multi-Conference on Systems, Signals and Devices

作者： Hayet Boughrara Mohamed Chtourou Research unit on Intelligent Control design and Optimisation of complex Systems (ICOS) Sfax Engineering School University of Sfax Sfax Tunisia

This work consists on the evaluation of the performances of three neural classifiers. The Multi-Layer Perceptron (MLP), the Self-Organizing Map (SOM), the Learning Vector Quantization (LV Q) are considered by this study. The example that will be considered in the evaluation of the technical classifications's performances is the handwritten character recognition.

关键词： Pattern recognition Character recognition Performance evaluation Multilayer perceptrons Vector quantization Data mining intelligent control Signal design Design optimization Design engineering

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Decomposition and hierarchical control for discrete complex systems by fuzzy logic controllers

Decomposition and hierarchical control for discrete complex ...

引用

IEEE SSD International Multi-Conference on Systems, Signals and Devices

作者： Najla Krichen Masmoudi Chokri Rekik Mohamed Djemel Nabil Derbel Research unit on Intelligent Control design and Optimisation of complex Systems (ICOS) Sfax Engineering School University of Sfax Sfax Tunisia

This paper proposes a method to compute sub-optimal control strategies of discrete time large-scale non-linear systems by fuzzy logic controllers. The method is based on the principle of decomposition of the global system into inter-connected subsystems. We consider that the non-linearities are located in the interconnections terms. Then, a mixed method of coordination procedure between different subsystems is formulated. So, for each subsystem, local optimal feedback gains are expressed as a function of the interconnection vector. Within this approach, first order Tkagi-Sugeno fuzzy logic systems have been constructed in order to identify these gains. Simulation results of a rotary crane show the effectiveness of the method and the robustness of the proposed approach.

关键词： control systems Fuzzy logic Large-scale systems Nonlinear control systems Vectors Cranes Fuzzy systems Space technology Optimal control intelligent control

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