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In this paper, it is shown how a Leaky Integrate and Fire (LIF) neuron can be applied to solve non-linear pattern recognition problems. Given a set of input patterns belonging to K classes, each input pattern is transformed into an input signal, then the LIF neuron is stimulated during T ms and finally the firing rate is computed. After adjusting the synaptic weights of the neuron model, we expect...
An Autonomous Bee Colony Optimization (A-BCO) algorithm for solving multi-objective numerical problems is proposed. In contrast with previous Bee Colony algorithms, A-BCO utilizes a diversity-based performance metric to dynamically assess the archive set. This assessment is employed to adapt the bee colony structures and flying patterns. This self-adaptation feature is introduced to optimize the balance...
A fault identification with fuzzy C-Mean clustering algorithm based on improved ant colony algorithm (ACA) is presented to avoid local optimization in iterative process of fuzzy C-Mean (FCM) clustering algorithm and the difficulty in fault classification. In the algorithm, the problem of fault identification is translated to a constrained optimized clustering problem. Using heuristic search of colony...
Dissolved gas analysis in transformer oil (DGA) is an important method for power transformer insulating diagnosis. Aiming at the problem that fuzzy C-means (FCM) clustering algorithm is likely to fall into local minimum point when being used for dissolved gas analysis, dynamic tunneling algorithm was introduced for its high global optimization performance. Then a FCM clustering algorithm was presented...
The iterative optimization algorithm is a traditional classification method of the pattern recognition. In the iterative optimization algorithm, the primary center of classes is selected by random method. This choice method causes the iterative time increase greatly in the optimization at anaphase. It also has some serious defects which are the selected samples blindly, the presented a local extremum...
We consider the task of Self-taught Learning (STL) from unlabeled data. In contrast to semi-supervised learning, which requires unlabeled data to have the same set of class labels as labeled data, STL can transfer knowledge from different types of unlabeled data. STL uses a three-step strategy: (1) learning high-level representations from unlabeled data only, (2) re-constructing the labeled data via...
In this study, a novel iterative optimization clustering algorithm is proposed by using a manifold distance based dissimilarity metric which can measure the geodesic distance along the manifold and a criterion function which can express the clustering target, that is the samples in the same cluster being somehow more similar than samples in different one. The steps of the algorithm are discussed in...
In this paper, an adaptive and data-dependent single kernel optimization (SKO) algorithm is developed to improve the performance of radar target feature extraction and recognition by optimizing the kernel function of iterative kernel principal component analysis (KPCA). Based on SKO-KPCA and support vector machine (SVM), a radar target high resolution range profile (HRRP) feature extraction and recognition...
A method of optimization and simplification to network feature using Artificial Fish-swarm Algorithm in intrusion detection is proposed in this paper for solving problems of more features and slower computing speed. This method established mathematic model aimed at achieving higher detection rate and lower false positive rate, and obtaining optimal feature attributes through iterative method by using...
Clustering analysis is an important area of data mining. A kind of new clustering algorithm with ant colony optimization based on cluster center initialization is proposed in this paper. The new algorithm gives initialized cluster centers by different methods, then solves clustering problems by iterated method. Three methods of cluster center initialization are used in clustering algorithm with ant...
Currently many non-tractable considered problems have been solved satisfactorily through methods of approximate optimization called metaheuristic. These methods use non-deterministic approaches that find good solutions which, however, do not guarantee the determination of the global optimum. The success of a metaheuristic is conditioned by capacity to adequately alternate between exploration and exploitation...
In this paper, two fuzzy classification functions of fuzzy c-means for data with tolerance are proposed. First, two clustering algorithms for data with tolerance are introduced. One is based on the standard method and the other is on the entropy-based one. Second, the fuzzy classification function for fuzzy c-means without tolerance is discussed as the solution of a certain optimization problem. Third,...
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