The emergence of artificial neural networks has made it conducive to integrate fuzzy logic controllers and neural models for the development of adaptive fuzzy control systems. In this paper, the authors proposed an ad...
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The emergence of artificial neural networks has made it conducive to integrate fuzzy logic controllers and neural models for the development of adaptive fuzzy control systems. In this paper, the authors proposed an adaptive fuzzy-neural control scheme by integrating two neural network models with a basic fuzzy logic controller. Using the backpropagation algorithm the first neural network is trained as a plant emulator and the second neural network is used as a compensator for the basic fuzzy controller to improve its performance on-line. The function of the neural network plant emulator is to provide the correct error signal at the output of the neural fuzzy compensator without the need for any mathematical modeling of the plant. The difficulty of fine-tuning the scale factors and formulating the correct control rules in a basic fuzzy controller may be reduced using the proposed scheme. The scheme is applied to the temperature control of a water bath process. The performance of the adaptive fuzzy-neural controller is compared to the basic fuzzy logic controller and a conventional digital-PI controller under identical conditions of varying complexities in the process. The experimental results show that the adaptive fuzzy-neural control scheme is superior in performance than the other two controllers.< >
A hybrid architecture, called EXP1, automatically balances exploration and exploitation to solve mazes with large real numbered search spaces. It employs a genetic algorithm (GA) to search and optimise each movement. ...
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A hybrid architecture, called EXP1, automatically balances exploration and exploitation to solve mazes with large real numbered search spaces. It employs a genetic algorithm (GA) to search and optimise each movement. The GA fitness function is supplied by a radial basis function (RBF) neural network which acts as an adaptive heuristic critic (AHC). Over successive trials it learns the V-function, a continuous mapping between real numbered positions in the maze and the value of being at those positions. EXP1 solved all the mazes with which we tested it and proved to be quite robust to changes in internal parameters.< >
We proposed a decentralized high gain adaptive control which stabilizes an interconnected subsystems and analyzes the stability and fault tolerance of the system. In order to analyze the fault tolerance, it is given a...
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We proposed a decentralized high gain adaptive control which stabilizes an interconnected subsystems and analyzes the stability and fault tolerance of the system. In order to analyze the fault tolerance, it is given a structure of systems which is invariant under some failures of local controllers and an adaptive control which stabilizes a system with such structure is presented. Backstepping is applied to the decentralized high gain adaptive control system in order to relax the restriction about the relative degree of each subsystem.< >
This paper deals with a segmentation Method of texture image with randomness. For the texture segmentation, the statistical characteristics of texture are extracted by a texture measure, and it is necessary to discrim...
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This paper deals with a segmentation Method of texture image with randomness. For the texture segmentation, the statistical characteristics of texture are extracted by a texture measure, and it is necessary to discriminate textures by using the measure. In this paper new discriminant functions based on two-dimensional autoregressive models fitted to the texture image being considered and Kullback information which can measure the distance between two probability distributions are proposed. Furthermore, the texture segmentation method using this function is proposed. By numerical examples the validity of the method is verified.
A multivariable self-tuning controller with a proportional plus integral plus derivative (PID) structure is derived. The algorithm features a combination of the self-tuning property, in which the controller parameters...
A multivariable self-tuning controller with a proportional plus integral plus derivative (PID) structure is derived. The algorithm features a combination of the self-tuning property, in which the controller parameters are tuned automatically on-line, and also the structure of a multivariable PID controller, making it more favourable to be used in industry. A stability proof of the algorithm is also presented here. Comparison of the performance of the algorithm with a fixed, tuned, multivariable PID controller in the simulation examples demonstrates the effectiveness of the proposed algorithm.
In this paper, a multivariable self-tuning controller with a proportional plus integral plus derivative (PID) structure is derived. The algorithm features a combination of the self-tuning properly in which the control...
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In this paper, a multivariable self-tuning controller with a proportional plus integral plus derivative (PID) structure is derived. The algorithm features a combination of the self-tuning properly in which the controller parameters are tuned automatically on-line and also the structure of a multivariable PID controller, making it more favourable to be used in the industries. The algorithm is applied to a microcomputer based multi-input multi-output (MIMO) furnace. Some experiments were conducted to observe the ability of the controller in the temperature control of MIMO furnace under set-point changes and its relative robustness as compared with a fixed tuned multivariable PID (FTMPID) controller. The experimental results prove that the controller is capable of giving a good control result for the process.
Though there are many models and methodology on autonomous decentralized system (ADS), there are few theoretical analyses. ADS is reconsidered from decentralized adaptive control (DAC). Through fault tolerance analysi...
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Using the nonlinear mapping capability of neural networks (NNs) a tuning method of a proportional integral derivative (PID) controller based on a backpropagation (BP) method of multilayered NNs is derived. Simulated a...
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Using the nonlinear mapping capability of neural networks (NNs) a tuning method of a proportional integral derivative (PID) controller based on a backpropagation (BP) method of multilayered NNs is derived. Simulated and experimental results show that the proposed method can identify the appropriate parameters of the PID controller when it is implemented to both linear and nonlinear plants.< >
It is reported that the generalized predictive controller originally proposed by Clarke, et al. is useful for the real plants since it has long-range prediction scheme. This method, however does not guarantee the stab...
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It is reported that the generalized predictive controller originally proposed by Clarke, et al. is useful for the real plants since it has long-range prediction scheme. This method, however does not guarantee the stability of the closed loop system. In this paper, we present a pole-assignment control algorithm based on the original generalized predictive control. A priori information about time-delay is not needed in our method. Furthermore, we show the boundness of input and output signals from stability analysis.
This paper presents a technique to classify images that have been elongated or contracted. It is first shown that the conventional regular moment invariant remains no longer invariant when the image is scaled unequall...
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This paper presents a technique to classify images that have been elongated or contracted. It is first shown that the conventional regular moment invariant remains no longer invariant when the image is scaled unequally in the x-and y-directions. A method is proposed to form moment invariants that do not change under such unequal scaling. Results of computer simulations for images are also included verifying the validity of the method proposed.
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