Many researchers are interested in electric wheelchair automation because of the mobility problemsmet by a certain number of disabled and aged people. In this paper, we propose to associate a system's help navigat...
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Many researchers are interested in electric wheelchair automation because of the mobility problemsmet by a certain number of disabled and aged people. In this paper, we propose to associate a system's help navigation to a standard electric *** system is equipped with unit control and sensors to extract some *** elaborate a fuzzy controller which generates speed wheels orders in order to join the target position.
In this paper, a new approach for neural PID tuning is presented based on the use of a neural network and the internal model control (IMC) principle. The neural network is used to adjust on line the PID controller aft...
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In this paper, a new approach for neural PID tuning is presented based on the use of a neural network and the internal model control (IMC) principle. The neural network is used to adjust on line the PID controller after an off line training step. The developed approach is based on the use of a neural supervisor having as inputs the control signal, its correspondent output and the filter time constant and the PID parameters as outputs. The design of the supervisor is based on the procedures of linearization and IMC principle. We briefly outline the content of the tuning course and finish the paper with illustrative example where good performances have been obtained.
This paper presents a new learning algorithm for feedforward neural networks. This algorithm uses the vigilance parameter to generate the hidden layer neurons. This process improves the initial weight problem and the ...
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This paper presents a new learning algorithm for feedforward neural networks. This algorithm uses the vigilance parameter to generate the hidden layer neurons. This process improves the initial weight problem and the adaptive neurons of the hidden layer. The proposed approach is based on combined unsupervised and supervised learning. In this algorithm, the weights between input and hidden layers are firstly adjusted by Kohonen algorithm with fuzzy neighborhood, whereas the weights connecting hidden and output layers are adjusted using gradient descent method. Two simulation examples are provided to demonstrate the efficiency of the approach compared with a number of other methods.
This paper presents the stability analysis of parameter identification. The Takagi Sugeno fuzzy model is employed to represent the discrete time nonlinear dynamical systems. Once the structure of the fuzzy model is fi...
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This paper presents the stability analysis of parameter identification. The Takagi Sugeno fuzzy model is employed to represent the discrete time nonlinear dynamical systems. Once the structure of the fuzzy model is fixed, the parameters can be optimized. The parameter identification is accomplished by applying the gradient method where the iteration rates are specific to each parameter. The stability of this algorithm is discussed by using two approaches which guarantee that the system is stable if the iteration rates satisfy sufficient conditions. The first approach deals with the consequence parameters and the second one deals with the premise parameters.
Iris recognition, a relatively new biometric technology, has great advantages, such as variability, stability and security, thus is the most promising for high security environment. Iris recognition is proposed in thi...
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A three hierarchical sliding mode control is presented for a class of an underactuated system which can overcome the mismatched perturbations. The considered underactuated system is a double inverted pendulum (DIP), c...
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A three hierarchical sliding mode control is presented for a class of an underactuated system which can overcome the mismatched perturbations. The considered underactuated system is a double inverted pendulum (DIP), can be modeled by three subsystems. Such structure allows the construction of several designs of hierarchies for the controller. For all hierarchical designs, the asymptotic stability of every layer sliding mode surface and the sliding mode surface of subsystems are proved theoretically by Barbalat’s lemma. Simulation results show the validity of these methods. [ABSTRACT FROM AUTHOR]
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 sy...
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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.
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 stu...
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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.
This paper presents a method to compute sub-optimal control strategies of discrete time large scale nonlinear systems by neural networks. The method is based on the principle of decomposition of the global system into...
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This paper presents a method to compute sub-optimal control strategies of discrete time large scale nonlinear systems by neural networks. The method is based on the principle of decomposition of the global system into interconnected subsystems for which we consider that non-linearities are located in the interconnection terms. Then, a mixed method is considered to coordinate between different subsystems in order to compute the optimal control. So, for each subsystem, local optimal feedback gains are expressed in terms of the interconnection vector. For this purpose, neural networks have been used in order to identify these gains. Simulation results of a rotary crane show that the proposed method yields to satisfactory performances. The robustness of the proposed approach is analysed.
This work proposes a new methodology to evaluate and improve the QMS (Quality Management System) effectiveness and efficiency to succeed for a factual approach including all monitoring parameters. This idea is justifi...
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This work proposes a new methodology to evaluate and improve the QMS (Quality Management System) effectiveness and efficiency to succeed for a factual approach including all monitoring parameters. This idea is justified by the difficulties incurred by the organizations to establish a continuous improvement's system based on the objectives and the process approach. In addition, the guidelines defined by the FDX 50-174 part published by AFNOR to assess the QMS effectiveness, still guides defining the assessment criteria and progress levels. Indeed, to facilitate the continuous improvement system's integration, we propose a new methodology based on a broad range of monitoring tools (functional and operational indicators, audits, quality control, customer satisfaction) and including the requirements of the process and the system approach.
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