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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This paper builds on work toward the simplification of fault diagnosis within electric distribution systems using a fuzzy expert system. Based on the symptom description derived from customers, the developed system dr...
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One missing link between CAD and CAM is the lack of a systematic methodology for generating and evaluating alternative ways to manufacture a proposed design. To address this problem, we are developing a systematic app...
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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.
Genetic algorithms are searching strategies available for finding the globally optimal solution. The problem of genetic algorithms is that they are inherently slow. A hybrid of genetic and backpropagation algorithms (...
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Genetic algorithms are searching strategies available for finding the globally optimal solution. The problem of genetic algorithms is that they are inherently slow. A hybrid of genetic and backpropagation algorithms (GA-BP) that should always find the correct global minima without getting stuck at local minima is presented. Various versions of the GA-BP method are presented and experimental results show that GA-BP algorithms are as fast as the backpropagation algorithm and do not get stuck at local minima. The proposed GA-BP algorithms are also not sensitive to the values of momentum and learning rate used in backpropagation and can be made independent of the learning rate and momentum. It is shown that the adaptive GA-BP algorithm can provide the optimal learning rate and better performance than simple backpropagation.< >
These algorithms modify the ordinary LMS algorithm by applying an OS filtering operation to the inst.ntaneous 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 inst.ntaneous 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.< >
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.< >
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.
To access distant information from an interoperable multi object-oriented database (IM-OODB), a user needs to construct retrieval path-methods through one or several OODB schemas. The path-method generator (PMG) syste...
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To access distant information from an interoperable multi object-oriented database (IM-OODB), a user needs to construct retrieval path-methods through one or several OODB schemas. The path-method generator (PMG) system supports the generation of such path-methods for a single OODB, using precomputed access relevance between pairs of classes. Efficient algorithms to compute access relevance in an autonomous OODB exist. The authors use a novel approach for deriving efficient online algorithms for the computation of access relevance in an IM-OODB, which will be used in generating path-methods between classes of different OODBs in an IM-OODB.< >
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