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The brain-computer interface (BCI), identify brain patterns to translate thoughts into action. The identification relies on the performance of the classifier. In this paper identification of electroencephalogram (EEG) based BCI for motor imagery (MI) task is done through asynchronous approach. Transferring the brain computer interface (BCI) from laboratory state to real time application desires BCI...
FCM is sensitive to initialization and tends to result in local minimum in iterations. This paper studies the crossover and mutation probability of genetic algorithm and presents a new crossover and mutation probability. The proposed clustering scheme based on genetic algorithm and fuzzy c-means takes full advantage of the global optimization of genetic algorithm and the local search ability of FCM...
This paper proposes an ANFIS indoor positioning system based on improved genetic algorithm (GA). In the offline phase, fuzzy rules are abstracted by means of subtractive clustering algorithm with training data, generating the structure of each ANFIS positioning subsystem in X and Y directions. Then each positioning subsystem is trained with improved-GA. In this training algorithm, BP algorithm acts...
Because have very high ability of overall situation searching and convergence speed, show excellently in keeping solution variety, the niche genetic algorithm(NGA) is widely used to solving various kinds of combination optimization problem, but the traditional niche genetic algorithm(T-GA) have the problem that the discrimination standard of Euclidean distance between two individuals is not development...
In the process of codebook design of vector quantization, traditional LBG algorithm owns the advantage of fast convergence, but it is prone to local optimum and is influenced greatly by initial codebook. Given that the Genetic Algorithm has the capability to produce global optimal results, this paper proposes a new clustering algorithm GA-L based on GA and LBG to improve the quality of codebook. This...
With the wide application of GIS to all kinds of fields, and developing of the technique of data mining and spatial data collection, the technique of data mining in spatial database-spatial data mining is coming out. In order to satisfy the people's demand for the interesting and potentially useful knowledge from the spatial database, this thesis used a wide using spatial clustering algorithm: k-means...
The commercial banks need identify exceptional client in their large number of customers to prevent abnormal customer's risk. In this paper, four types of abnormal data detection method is introduced, present a new method- the k-medoids clustering algorithm combining genetic algorithm to detect the outlier. Finally, apply the algorithm to analysis credit data sets, detect outlier and identify abnormal...
As K-means Clustering Algorithm is sensitive to the choice of the initial cluster centers and it is difficult to determine the cluster number and it is easy to be impacted by isolated points, propose the K-means multiple Clustering Method Based on Pseudo Parallel Genetic Algorithm. In the method, adopt the strategy of Variable-Length Chromosome real-coded. Through the introduction of chromosome retreading...
From the view of granularity, this paper presents a genetic clustering algorithm based on dynamic granularity. In view of a parallel, random search, global optimization and diversity characteristics of genetic algorithm, it is combined with dynamic granularity model. In the process of granularity changing, appropriate granulation can be made by coarsening and refining the granularity, which can ensure...
Clustering is an important research topic in data mining that appears in a wide range of unsupervised classification applications. Partitional clustering algorithms such as the k-means algorithm are the most popular for clustering large datasets. The major problem with the k-means algorithm is that it is sensitive to the selection of the initial partitions and it may converge to local optima. In this...
The key to the implementation of dynamic forensics is how to mine in real-time and effectively criminal invasion information from voluminous data. Towards the disadvantages of Fuzzy C-means clustering (referred to as FCM) forensics analysis that it is very sensitive to initial data and impacted greatly by noise, a dynamic forensics analysis technology based on genetic-fuzzy clustering algorithm is...
The paper discusses the irregular parts packing problem based on an improved immune genetic algorithm, and a NIGA based on crowing mechanism is proposed. For improving the packing efficiency, the clustering idea and algorithm are introduced and the effective characteristics of matching packing-graphics are extracted and analyzed. GA, an improved immune genetic algorithm, and NIGA are applied to practical...
Aircraft-sequencing problem (ASP) is a major issue in air traffic control operations and it is also an NP-hard problem with large-scale and multi-constraint, thus it is hard to find optimal solution efficiently. This paper proposes a hybrid algorithm by means of integrating bee evolutionary genetic algorithm with modified clustering method (named BEGA-CM) for solving ASP. In details, clustering method...
Fuzzy K-prototypes is a very efficient algorithm for processing large scale mixed data set, but the selection of initial clustering center has an important impact on the clustering effect of algorithm. FKP algorithm is improved by using genetic algorithm in this paper. Seeking the initial clustering center for fuzzy K-prototypes algorithm by using genetic algorithm overcomes the shortcoming effectively,...
In traditional Gene Expression Programming (GEP), individuals' survival too much depends on fitness while their relationships are ignored. Borrowing the idea from the minority protection in real life, this study introduces a novel Cluster Delegate algorithm (CDA) and makes the following contributions: (1) propose several new concepts including individual similarity, ??- cluster, and the farthest neighborhood...
By researching all kinds of methods for document clustering, we put forward a new dynamic method based on genetic algorithm (GA). K-means is a greedy algorithm, which is sensitive to the choice of cluster center and very easily results in local optimization. Genetic algorithm is a global convergence algorithm, which can find the best cluster centers easily. Among the traditional document clustering...
An improved genetic K-means clustering algorithm is proposed and is applied to image segmentation. According to the characteristics of the image, the feature vector of the pixel is properly chosen and the weight factors of the feature vector are adjusted, which enhances the segmentation precision. The selection of conventional genetic algorithm and the modification of mutation operations improve the...
In simplex hybrid genetic algorithm, widely using Nelder-Mead simplex method (NMSM) would lead to precocity of genetic algorithm and increase in computation quantity, so a novel simplex hybrid genetic algorithm is proposed in this paper. First, we propose a new efficient simplex crossover operator. Second, using the successful experiences of dividing the vertexes in NMSM into the best vertex, the...
Searching is an important procedure in optimization problems. As is an effective clustering method especially in spatial data mining, the role of searching is essential. While many searching methods focus themselves on particle swarm optimization and genetic algorithms, we propose a new searching algorithm based differential evolution (DE). It proves that DE is a simple optimization algorithm effective...
For the reliable real time video transmission of overlay network, optimal multicast service nodes (MSNs) should be selected to build highly efficient hierarchical overlay multicast tree. In this paper, a MSNs selection algorithm based on immune evolution is proposed. MSNs are encoded by real-coded mechanism, and the K-medoids clustering distance is used to measure the similarity between MSN and other...
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