From the viewpoints of both fuzzy system and fuzzy reasoning, a new fuzzy reasoning method which contains the α- triple I restriction method as its particular case is proposed. The previous α-triple I restriction pr...
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From the viewpoints of both fuzzy system and fuzzy reasoning, a new fuzzy reasoning method which contains the α- triple I restriction method as its particular case is proposed. The previous α-triple I restriction principles are improved, and then the optimal restriction solutions of this new method are achieved, especially for seven familiar implications. As its special case, the corresponding results of α-triple I restriction method are obtained and improved. Lastly, it is found by examples that this new method is more reasonable than the α-triple I restriction method.
High dimensional data in several applications seriously spoils classification computation of several types of learning. In order to relieve the difficulties of such a high dimension, this paper proposes the classifica...
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High dimensional data in several applications seriously spoils classification computation of several types of learning. In order to relieve the difficulties of such a high dimension, this paper proposes the classification computation, which refers to a modified neural network: the neural network with weights optimized by particle swarm intelligence. The contemporary is placed on the combination of the non-linear feature extraction and such a classification method. 10-fold cross-validation experiments of each method are performed on five medical data sets. The results indicate not only the improvement of classification based on non-linear feature extraction, but also indicate the reduction of the number of features for classification.
Although several highly accurate blind source separation algorithms have already been proposed in the literature, these algorithms must store and process the whole data set which may be tremendous in some situations. ...
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Although several highly accurate blind source separation algorithms have already been proposed in the literature, these algorithms must store and process the whole data set which may be tremendous in some situations. This makes the blind source separation infeasible and not realisable on VLSI level, due to a large memory requirement and costly computation. This paper concerns the algorithms for solving the problem of tremendous data sets and high computational complexity, so that the algorithms could be run on-line and implementable on VLSI level with acceptable accuracy. Our approach is to partition the observed signals into several parts and to extract the partitioned observations with a simple activation function performing only the "shift-and-add" micro-operation. No division, multiplication and exponential operations are needed. Moreover, obtaining an optimal initial de-mixing weight matrix for speeding up the separating time will be also presented. The proposed algorithm is tested on some benchmarks available online. The experimental results show that our solution provides comparable efficiency with other approaches, but lower space and time complexity.
Self-recovery micro-rollback synthesis (SMS) has currently become an important issue in high level synthesis. The problem of SMS combines the problem of functional unit scheduling and assignment with the problem of ch...
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Self-recovery micro-rollback synthesis (SMS) has currently become an important issue in high level synthesis. The problem of SMS combines the problem of functional unit scheduling and assignment with the problem of ch...
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Self-recovery micro-rollback synthesis (SMS) has currently become an important issue in high level synthesis. The problem of SMS combines the problem of functional unit scheduling and assignment with the problem of checkpoint insertion and microprogram optimization. It has been shown that these problems are NP-complete. The most studied problem is functional unit scheduling and assignment. Several heuristic techniques, including as soon as possible (ASAP), as last as possible (ALAP), integer programming, spring elasticity model, graph based mobility model, and genetic algorithm, are proposed. However, there are few studies on self-recovery micro-rollback synthesis and the technique of solution space searching by genetic algorithm has not been attempted. We study the feasibility of the genetic algorithm for the problem of SMS constrained on: the number of functional units, control steps, number of checkpoints, and the functional unit areas.
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