A novel method is presented capable of constructing rule-bases via self-learning for the use of fuzzy controllers. The controlled process is assumed to be a multivariable system with strong interaction within variable...
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A novel method is presented capable of constructing rule-bases via self-learning for the use of fuzzy controllers. The controlled process is assumed to be a multivariable system with strong interaction within variables and with pure time delays in control. The objective of the proposed system is to build, in the case of two-input two-output systems, two separated and decoupled rule-bases for two control loops with some design requirements. The paper is divided into two parts. In the first part, a system structure comprising four functional modules is proposed. Then, the paper focuses on the issues concerning the learning algorithm. By introducing learning errors, three learning update laws are suggested. Furthermore, the convergence property of the learning algorithms is analysed in the sense of some defined norms. In addition, some comments and remarks about the proposed algorithms are given. The second part of the paper deals mainly with the issues of the methodology for rule-base formation and the application to the problem of multivariable control of blood pressure.
作者：
Y. Y. Yang M. Mahfouf D.A.LinkensDepartment of Automatic Control and Systems Engineering
University of Sheffield Mappin St. Sheffield S1 3JD UK Department of Automatic Control and Systems Engineering University of Sheffield Mappin St. Sheffield S1 3JD UK Department of Automatic Control and Systems Engineering University of Sheffield Mappin St. Sheffield S1 3JD UK
The accuracy of numerical simulations and many other material design calculations, such as the rolling force, rollingtorque, etc., depends on the description of stress-strain relationship of the deformed materials. On...
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The accuracy of numerical simulations and many other material design calculations, such as the rolling force, rollingtorque, etc., depends on the description of stress-strain relationship of the deformed materials. One common methodof describing the stress-strain relationship is using constitutive equations, with the unknown parameters fitted byexperimental data obtained via plane strain compression (PSC). Due to the highly nonlinear behaviour of the constitutive equations and the noise included in the PSC data, determination of the model parameters is difficult. Inthis paper, genetic algorithms were exploited to optimise parameters for the constitutive equations based on thePSC data. The original PSC data were processed to generate the stress-strain data, and data pre-processing wascarried out to remove the noise contained in the original PSC data. Several genetic optimisation schemes have beeninvestigated, with different coding schemes and different genetic operators for selection, crossover and *** was found that the real value coded genetic algorithms converged much faster and were more efficient for theparameter optimisation problem.
It is shown that when examining the controlability of the continuous-flow stirred-tank reactor the dynamics of the cooling coil, the thermometer and other additional loop elements must not be neglected. A first-order ...
It is shown that when examining the controlability of the continuous-flow stirred-tank reactor the dynamics of the cooling coil, the thermometer and other additional loop elements must not be neglected. A first-order approximation of the cooling system is determined and combined with the linearized heat and mass balances of the reactor, this giving the transfer function between the velocity of the cooling water and the temperature of the reactor. The temperature control system is analysed by means of the root locus method. It is shown how a simple root locus may be found which approximates that of the temperature control loop at high natural frequencies.
In the present study, the Volterra series theory is adopted to theoretically investigate the force transmissibility of multiple degrees of freedom （MDOF） structures, in which an isolator with nonlinear anti-symmetri...
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In the present study, the Volterra series theory is adopted to theoretically investigate the force transmissibility of multiple degrees of freedom （MDOF） structures, in which an isolator with nonlinear anti-symmetric viscous damping is assembled. The results reveal that the anti-symmetric nonlinear viscous damping can significantly reduce the force trans- missibility over all resonance regions for MDOF structures with little effect on the transmissibility over non-resonant and isolation regions. The results indicate that the vibration isolators with an anti-symmetric damping characteristic have great potential to solve the dilemma occurring in the design of linear viscously damped vibration isolators where an increase of the damping level reduces the force transmissibility over resonant frequencies but increases the transmissibility over non-resonant frequency regions. This work is an extension of a previous study in which MDOF structures installed on the mount through an isolator with cubic nonlinear damping are considered. The theoretical analysis results are also verified by simulation studies.
Antipsychotics and antidepressants are essential psychotropic medications used for treating various mental health conditions such as depression, schizophrenia, and bipolar disorder. However, when exposed to light, the...
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This paper presents an investigation into the performance evaluation of homogeneous and heterogeneous parallel architectures in the real-time implementation of finite element (FE) simulation algorithm of a flexible ma...
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This paper presents an investigation into the performance evaluation of homogeneous and heterogeneous parallel architectures in the real-time implementation of finite element (FE) simulation algorithm of a flexible manipulator. The algorithm is implemented on a number of homogeneous and heterogeneous architectures incorporating high-performance processors. The partitioning and mapping of the algorithms on both the homogeneous and heterogeneous architectures are investigated and finally a comparative assessment of the performance of the architectures in implementing the algorithm revealing the capabilities of the architectures in relation to the nature of the algorithm is presented in terms of execution time and speedup.
The derivations of orthogonal least-squares algorithms based on the principle of Hsia's method and generalized least-squares are presented. Extensions to the case of non-linear stochastic systems are discussed and...
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The derivations of orthogonal least-squares algorithms based on the principle of Hsia's method and generalized least-squares are presented. Extensions to the case of non-linear stochastic systems are discussed and the performance of the algorithms is illustrated with the identification of both simulated systems and linear models of an electric arc furnace and a gas furnace.
The performance demands in modern signal processing and control applications have motivated a trend towards utilisation of complex algorithms. This, in turn, has resulted in a resurgence in the development of high per...
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The performance demands in modern signal processing and control applications have motivated a trend towards utilisation of complex algorithms. This, in turn, has resulted in a resurgence in the development of high performance systems to make the real-time realisation of such algorithms feasible in practice. This paper presents an investigation into the performance of sequential architectures/uni-processor based systems in the real-time implementation of finite element algorithm of a flexible manipulator. The algorithm is implemented on a number of high-performance processors. A comparative assessment of the performance of the processors in implementing the algorithm, revealing the capabilities of the processors in relation to the nature of the algorithm, is presented in terms of execution time and speedup. The effect of compiler optimisation facilities on the execution time in implementing the algorithm is also demonstrated.
This paper presents the development of a neuro-adaptive active noise control (ANC) system. Radial basis function neural networks with an orthogonal forward regression algorithm are considered in both the modelling and...
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This paper presents the development of a neuro-adaptive active noise control (ANC) system. Radial basis function neural networks with an orthogonal forward regression algorithm are considered in both the modelling and control contexts. A feedforward ANC structure is considered for optimum cancellation of broadband noise in a three-dimensional propagation medium. An on-line adaptation and training mechanism allowing a neural-network architecture to characterise the optimal controller within the ANC system is developed. The neuro-adaptive ANC algorithm thus developed is implemented within a free-field environment,and simulation results verifying its performance are presented and discussed. Copyright (C) 1997 Elsevier Science Ltd.
This paper describes a binary image representation scheme, called Image Block Representation and presents new algorithms for a number of basic binary image processing and analysis operations, which are rapidly impleme...
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This paper describes a binary image representation scheme, called Image Block Representation and presents new algorithms for a number of basic binary image processing and analysis operations, which are rapidly implemented on block-represented binary images. The main purpose of the Image Block Representation is to provide an efficient binary image representation that permits the execution of operations on image areas instead of image points.
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