This paper presents an investigation into classifying myoelectric signals using a new fuzzy clustering neural network architecture for control of multifunction prostheses. Moreover, a comparative study of the classifi...
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Repetitive processes are a distinct class of 2D linear systems with applications in areas ranging from long-wall coal cutting and metal rolling operations through to iterative learning control schemes. The main featur...
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Software cybernetics is a newly proposed area in software engineering. It makes better use of the interplay between control theory/engineering and software engineering. In this paper, we look into the research potenti...
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Recent years has seen much progress in the theory and application of iterative learning control schemes for both linear and (classes of) nonlinear dynamics. In the case of the former, many algorithms based on minimizi...
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Economic, ecology and business systems are often complex systems, and are almost always characterized by imprecision and uncertainty. It is known that in such cases distributed multi-agent intelligent system based on ...
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Economic, ecology and business systems are often complex systems, and are almost always characterized by imprecision and uncertainty. It is known that in such cases distributed multi-agent intelligent system based on soft computing is the most effective approach for systems analysis, decision making, and control in such systems. The key problem in constructing such systems is the problem of granulation, i.e., decomposition of the monolith intelligence of the whole system into autonomous agents' intelligence. The work suggests a method for creation of optimal knowledge bases of coordinating and cooperating intelligent agents. The optimization includes determination of a rational number of autonomous agent and fuzzy rules, optimal scaling factors, shapes and centers of membership functions of fuzzy rules of agents' knowledge bases, and optimal inference engine by using genetic algorithms. computer simulation of the multi-agent distributed system for marketing-mix decision support system and demand forecasting are provided.
This paper presents an investigation into classifying myoelectric signals using a new fuzzy clustering neural network architecture for control of multifunction prostheses. Moreover, a comparative study of the classifi...
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This paper presents an investigation into classifying myoelectric signals using a new fuzzy clustering neural network architecture for control of multifunction prostheses. Moreover, a comparative study of the classification accuracy of myoelectric signals using multi-layer perceptron with back-propagation algorithm, and the new fuzzy clustering neural network (FCNN) is presented. The myoelectric signals considered are used to classify four upper-limb movements, which are elbow flexion, elbow extension, wrist pronation and wrist supination, grasp, and resting. The results suggest that FCNN can generalise better than the multi-layer perceptron without requiring extra computational effort. The proposed neural network algorithm allows the user to learn better and faster.
Most of the existing techniques for document clustering rely on a "bag of words" document representation. Each word in the document is considered as a separate feature, ignoring the word order. We investigat...
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ISBN:
(纸本)9810475241
Most of the existing techniques for document clustering rely on a "bag of words" document representation. Each word in the document is considered as a separate feature, ignoring the word order. We investigate the use of phrases rather than words as document features for the document clustering. We present a phrase grammar extraction technique, and use the extracted phrases as the features in a self-organizing map based document clustering algorithm. We present clustering results using the REUTERS corpus and show an improvement in clustering performance using both entropy and F-measure evaluation measures.
In this paper discrete-time iterative learning control (ILC) systems are analysed from an algebraic point of view. The algebraic analysis shows that an ILC synthesis problem can be considered as a tracking problem of ...
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In this paper discrete-time iterative learning control (ILC) systems are analysed from an algebraic point of view. The algebraic analysis shows that an ILC synthesis problem can be considered as a tracking problem of a multi-channel step-function. Furthermore, the plant to be controlled is a static multivariable plant. Another major contribution of this paper is a general convergence theory of ILC systems in terms of their closed-loop poles. This convergence theory shows that time-variant ILC control laws should be typically used instead of time-invariant control laws in order to guarantee good transient tracking behaviour. Simulations high-light the different theoretical findings in this paper.
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