This paper proposes an aproach to designing animal-like behavior of autonomous robots with use of a five-layered hierarchical model of the relation between consciousness and behavior. The hierarchy of behavior include...
This paper presents a new upper bound on overall bit error rate (BER) for a concatenated code which consists of an inner convolutional code and an outer interleaved Reed-Solomon code. The upper bound on BER is derived...
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This paper presents a new upper bound on overall bit error rate (BER) for a concatenated code which consists of an inner convolutional code and an outer interleaved Reed-Solomon code. The upper bound on BER is derived based on a lower bound on the effective minimum distance of the concatenated code. This upper bound can be used for the cases when the interleaver size is small such that the conventional upper bound is not applicable.
This paper is concerned with an application of a digital adaptive control system to a servo mechanism. Mechanical systems in general include nonlinearities and additional uncertainties and which result from inertia an...
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This paper is concerned with an application of a digital adaptive control system to a servo mechanism. Mechanical systems in general include nonlinearities and additional uncertainties and which result from inertia and friction. So it is difficult to ensure robust performance and high accuracy for motion control. The digital adaptive control system presented here is considered to overcome the above stated problem. Experimental results from a feed drive system demonstrate the effectiveness of the proposed control scheme.
This paper presents an application of using a cascade-CMAC (cerebellar model articulation controller) neural network to solve some color calibration problems, which include color differences induced from gamuts mis-ma...
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ISBN:
(纸本)0780341228
This paper presents an application of using a cascade-CMAC (cerebellar model articulation controller) neural network to solve some color calibration problems, which include color differences induced from gamuts mis-match and the nonlinear transformation characteristics between color scanning input devices and color printing output devices. For this purpose, we proposed a scalable learning architecture "cascade-CMAC" to implement an adaptive color calibration system. By analyzing the preliminary learning situation, the scalable architecture can dynamically create a new learning unit to better represent a finer color resolution, so that the learning capacity as well as the color details of the system can be greatly improved. From the experimental results, the proposed cascade-CMAC architecture can improve the rate of convergence and also can adjust the learning architecture effectively. The learning speed can be 2/spl sim/4 times faster than the conventional CMAC. The effectiveness of this neural network has been tested by observing the differences between the calibrated and the un-calibrated output on a number of known samples. By using the Macbeth color-checker which contains 24 color patches as benchmark, the average color differences between the original and the calibrated print-out is improved from 15 /spl Delta/E/sub ab/ to 8 /spl Delta/E/sub ab/ under the 3 /spl Delta/E/sub ab/ convergent criterion for training. The calibration performance is somewhat significant.
Usually, the subspace-based state-space system identification algorithms are focused on discrete-time models, which may cause some numerical problems when the sampling interval is small. This paper proposes an algorit...
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Usually, the subspace-based state-space system identification algorithms are focused on discrete-time models, which may cause some numerical problems when the sampling interval is small. This paper proposes an algorithm of subspace-based state-space system identification for continuous-time systems from sampled input-output data. The ω — operator ω = ( p - α)/( p + α) where p denotes a differential operator and α > 0, is introduced to avoid direct numerical differentiations. And the ω — operator state-space model identified by the 4SID method can be transformed back to the common continuous-time state-space model. The numerical superiority of the ω — operator approach compared to some other methods is verified through simulation study.
Since the layout compaction problem is dual to the minimum cost flow problem, flow algorithms can be applicable to the layout compaction. In this paper, an existing flow algorithm is investigated in terms of the layou...
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Since the layout compaction problem is dual to the minimum cost flow problem, flow algorithms can be applicable to the layout compaction. In this paper, an existing flow algorithm is investigated in terms of the layout compaction, and a fast flow algorithm is devised on the basis of the primal-dual method. Experimental results show that the proposed algorithm is the fastest dedicatedly for the compaction problem.
It is well-known that the angle dependent disturbances in a servo motor caused by nonuniformity of field windings, armature cogging, rotor imbalance, nonuniform load etc., may influence the speed control performance g...
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It is well-known that the angle dependent disturbances in a servo motor caused by nonuniformity of field windings, armature cogging, rotor imbalance, nonuniform load etc., may influence the speed control performance greatly. This often leads to large speed fluctuations which are undesired in practical situations. Therefore speed fluctuation reduction techniques in the presence of angle dependent disturbances are strongly required and have been being challenged by a lot of researchers. In this paper, the authors propose a new approach to this problem via adaptive control with the aid of a radial basis function (RBF) network composed of gaussian basis functions. The angle dependent disturbances which are viewed as a periodic nonlinear function with a period of 2π[rad] in the angle-domain, is approximated by a RBF network in the domain of [0, 2π)[rad]. Then an adaptive linearization control system employing the RBF network which compensates the disturbances is proposed. The RBF network has the advantage that it is linear-in-parameter and hence the parameter adaption is very fast and easy to implement. It is proved through theoretical analysis that the stability of the adaptive control is guaranteed by the Lyapunov stability theory. Finally, simulational and experimental results are included in the paper to show the excellent performance of the proposed method.
This paper discusses a rough set approach for evaluating solutions of scheduling problems. Algorithms for solving scheduling problems are planners and the scheduling problems are modelled as constraint satisfaction pr...
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This paper discusses a rough set approach for evaluating solutions of scheduling problems. Algorithms for solving scheduling problems are planners and the scheduling problems are modelled as constraint satisfaction problems. Conventional approach for the analysis of algorithms often focuses on the time and representational complexities, and assumes an identical cost on all operations. The proposed rough set approach augments conventional approaches for the analysis of algorithms in two ways: 1) it permits the consideration of different costs arising from different operations; and 2) it allows one to define a new utility for a complexity analysis.
The recurrent canonical piecewise linear (RCPL) network is applied to nonlinear blind equalization by generalizing Donoho's minimum entropy deconvolution approach. We first study the approximation ability of the c...
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The recurrent canonical piecewise linear (RCPL) network is applied to nonlinear blind equalization by generalizing Donoho's minimum entropy deconvolution approach. We first study the approximation ability of the canonical piecewise linear (CPL) network and the CPL based distribution learning for blind equalization. We then generalize these conclusions to the RCPL network. We show that nonlinear blind equalization can be achieved by matching the distribution of the channel input with that of the RCPL equalizer output. A new blind equalizer structure is constructed by using RCPL network and decision feedback. We discuss application of various cost functions to RCPL based equalization and present experimental results that demonstrate the successful application of RCPL network to blind equalization.
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