In this paper, the solution used in the context of SEPDS (a Software Development Environment) to the problem of combining interactive behavior specification with functionality description of a distributed interactive ...
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State of the art of information technologies as well as networking in Estonia, Latvia and Lithuania are direct inheritance from dependence on the ex-USSR. Most of the technology is Sovietian, as is the hardware. This ...
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An efficient algorithm for retrieving noise corrupted patterns from an associative memory is presented. In any stable system, an input pattern with b bits differing from the nearest pattern is shown to be asynchronous...
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In this paper,on the basis of alternating logic and complementary logic,a new fault-tolerant design method named as alternate complementary logic is *** properties of this logic are analyzed) and a scheme of the combi...
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In this paper,on the basis of alternating logic and complementary logic,a new fault-tolerant design method named as alternate complementary logic is *** properties of this logic are analyzed) and a scheme of the combinational network realizing an arbitrary alternate complementary logic function is *** the assumption of the stuck-fault model,the network has the capacibilities of on-line self-testing and faul--masking,and its time-delay can be reduced by pipelining.
A neuro-fuzzy technique is presented to improve the standard back propagation learning speed. By adjusting both the learning rate and accelerator parameters based on the system error and change of the error direction,...
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ISBN:
(纸本)0780314212
A neuro-fuzzy technique is presented to improve the standard back propagation learning speed. By adjusting both the learning rate and accelerator parameters based on the system error and change of the error direction, the convergent rate of the proposed technique is found to be superior to that yielded by the conventional approach. Simulation results are given to demonstrate the applicability and efficiency of the proposed method.
The authors present a consistent treatment of the optimal reactive power dispatch problem taking into account the uncertainty associated with load values. Linguistic declarations of loads are translated into possibili...
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The authors present a consistent treatment of the optimal reactive power dispatch problem taking into account the uncertainty associated with load values. Linguistic declarations of loads are translated into possibility distribution functions via fuzzy sets. The problem is decomposed into four subproblems via Dantzig-Wolfe decomposition for reducing the problem dimensions. Voltage constraints within each subproblem are modeled via fuzzy sets to bias the final solution toward the static security region. A numerical example is presented to demonstrate the applicability of the method.< >
These algorithms modify the ordinary LMS algorithm by applying an OS filtering operation to the instantaneous gradient estimate. The OS operation in OSLMS can reduce the bias on filter coefficient estimates (relative ...
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These algorithms modify the ordinary LMS algorithm by applying an OS filtering operation to the instantaneous gradient estimate. The OS operation in OSLMS can reduce the bias on filter coefficient estimates (relative to LMS) when operating in non-Gaussian environments and can also reduce the average squared parameter error when in steady state operation. Some supporting analysis is presented for these effects, and simulation studies are provided. Guidelines are suggested for the selection of the OSLMS algorithms based on the expected noise environment.< >
A multi-module neural network model for high order association have been proposed. It contains plural functional modules each of which is mutually connected to the neural networks with hidden units in order to improve...
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A multi-module neural network model for high order association have been proposed. It contains plural functional modules each of which is mutually connected to the neural networks with hidden units in order to improve the performance of recall. The model comprises two different type of networks; fundamental modular network (FMN) and intermediate network (IN). Each FMN is mutually connected to each other by INs and works dynamically in cooperation with other functional modules. In this paper, it is also shown that this model has great ability of recollection, same as fully, mutually connected neural networks. The higher order association between four 2D character dot patterns, a corresponding three/four-character-word pattern and an image indicated by the word mean are well demonstrated by the model.
It is shown that fractal dimension estimates and Gabor wavelet coefficients are valid features of segmenting high-resolution polarimetric synthetic aperture radar imagery. Results of training a radial basis function n...
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It is shown that fractal dimension estimates and Gabor wavelet coefficients are valid features of segmenting high-resolution polarimetric synthetic aperture radar imagery. Results of training a radial basis function neural network using fractal dimension features, Gabor wavelet coefficients, and a combination of both fractal and Gabor wavelet features are presented. Current research into combining these two techniques both theoretically and empirically is presented. One-foot resolution polarimetric synthetic aperture radar imagery is successfully segmented into culture, tree, field, and shadow regions.< >
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