These days, development of computer device leads to computer user explosion. But conventional computertechnology cannot handle user's diversity, because computer cannot treat each user's subjectivity. In huma...
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This paper deals with the estimation of an unknown process transfer function in the presence of colored measurement noise. A three-step estimation procedure has been previously developed for transfer functions, the de...
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This paper deals with the estimation of an unknown process transfer function in the presence of colored measurement noise. A three-step estimation procedure has been previously developed for transfer functions, the delay steps and the orders of which are known in advance. The procedure is extended to deal with transfer functions with unknown delay steps and orders. The auto-correlation function of the error between the process output and model output is utilized for evaluating the model fitness. The effectiveness of the proposed method is demonstrated by a simulation study using a sample set of data in MATLAB.
Equilibrium structure of ternary blends of A and B homopolymers and symmetric AB block copolymer is investigated using self-consistent field theory by means of computer simulations. We demonstrate that bicontinuous po...
Equilibrium structure of ternary blends of A and B homopolymers and symmetric AB block copolymer is investigated using self-consistent field theory by means of computer simulations. We demonstrate that bicontinuous polymeric microemulsions (PME) can appear as a result of microphase separation without any long-range order.
Many relics from ruins are small fragments and work is required to restore their original shapes in order to research, analyze, or display them. However, such work is very difficult, and there are problems associated ...
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A linear analog network model is proposed to characterize the function of the outer retinal circuit in terms of the standard regularization theory. Inspired by the function and the architecture of the model, a vision ...
A linear analog network model is proposed to characterize the function of the outer retinal circuit in terms of the standard regularization theory. Inspired by the function and the architecture of the model, a vision chip has been designed using analog CMOS Very Large Scale Integrated circuit technology. In the chip, sample/hold amplifier circuits are incorporated to compensate for statistic transistor mismatches. Accordingly, extremely low noise outputs were obtained from the chip. Using the chip and a zero-crossing detector, edges of given images were effectively extracted in indoor illumination.
The Connectionist Analogy Processor (CAP) is a neural network. The paradigm of CAP assumes relational isomorphism for analogical inference. An internal abstraction model is formed by backpropagation training with the ...
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The Connectionist Analogy Processor (CAP) is a neural network. The paradigm of CAP assumes relational isomorphism for analogical inference. An internal abstraction model is formed by backpropagation training with the ...
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The Connectionist Analogy Processor (CAP) is a neural network. The paradigm of CAP assumes relational isomorphism for analogical inference. An internal abstraction model is formed by backpropagation training with the aid of a pruning mechanism. CAP also automatically develops abstraction and de-abstraction mappings to link the general and specific entities. CAP is applied to incremental analogical learning that involves multiple sets of analogy. It is shown that a new set of target data are selectively bound to the right one of internal abstraction models acquired from the previous analogical learning, i.e., the abstraction model acts as the attractor in the weight parameter space.
When using computers for the automatic condition diagnosis of plant machinery, symptom parameters (SP) extracted from some signals are indispensable. Currently, however there is no acceptable method for attracting the...
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When using computers for the automatic condition diagnosis of plant machinery, symptom parameters (SP) extracted from some signals are indispensable. Currently, however there is no acceptable method for attracting the optimum SP. In order to overcome this difficulty and ensure highly accurate condition diagnosis, a new method called the "automatic generation of symptom parameters" is proposed by using genetic algorithms (GA). The authors have applied the method to many diagnoses of plant machinery and, in each case, the optimum SP has been quickly discovered. In this paper, they show the example of gear equipment diagnosis to verify the efficiency of this method.
This paper proposes a robust adaptive nonlinear controller for position tracking problem of a magnetic levitation system, which is governed by an SISO second-order nonlinear differential equation. The controller is de...
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This paper proposes a robust adaptive nonlinear controller for position tracking problem of a magnetic levitation system, which is governed by an SISO second-order nonlinear differential equation. The controller is designed in a backstepping manner, based on the nonlinear system model in the presence of parameter uncertainties. At the first step, a PI controller is designed to remove the offset position error of the levitated object. Then at the second step, a robust adaptive nonlinear controller composed of an adaptive feedback linearization control term and a robust nonlinear damping term is designed, to attenuate the effects of parameter uncertainties. This helps to overcome some well-known practical problems such as high-gain feedback of the robust controller, and poor transient performance of the adaptive controller. Experimental results are included to show the excellent position tracking performance of the designed control system.
This paper addresses the inverse optimal robust control problem for uncertain nonlinear systems. A new version of robust backstepping is proposed in which inverse optimality is achieved through the selection of genera...
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