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A method of query decomposition based on ontology mapping is proposed. By analyzing the domain models and schemas of data sources, a global ontology and local ontology are established, and the mapping between them are generated as well. During the query process, the query is expressed in the form of a tree, and by taking advantage of mapping rules, the query is decomposed into sub-queries.
The neural network has been applied to the area of power load forecast successfully, but it has such disadvantages of local optimization and slow convergence speed. A new kind of neural networks forecast model based on culture particle swarm optimization was proposed for overcoming those disadvantages. Utilizing the colony aptitude of particle swarm and the ability of conserving the evolving knowledge...
Feed forward neural network based on corner classification (CC neural network, CC) can classify document instantly with cosine similarity as criteria. Usually the number of hidden neuron in a CC network equal to the number of sample documents. That is the prime shortcoming of a CC network because too much hidden neuron in a CC network will decrease its efficiency on classification instantly. TextCC...
To hurdle the major drawback of neural network, this paper developed researches on rule extraction. For problems with continuous-valued and discrete-valued attributes, the paper present an approach to extract understandable rules. Rules extracted are comprehensible not only for discrete value but also for continuous value. Our experiment results on real-word dataset validate our approach and show...
This paper proposes an improved boost learning algorithm, the SceBoost algorithm, and its application in developing fast and robust features for citrus canker detection by machine vision. The algorithm use symmetric cross entropy to eliminate redundancy among selected features using AdaBoost algorithm. Selected features are subjected to recognize citrus canker symptoms on given pictures of citrus...
Short-term load forecasting (STLF) has always been a very important issue in power system planning and operation. Recently, along with power system privatization and deregulation, accurate forecast of electricity load has received increasing attention. However, forecasting electricity load is difficult because of the randomness and uncertainties of load demand. Support vector machine (SVM) is a novel...
In the present paper, a bidirectional associative memory (BAM) neural networks with time delays is studied. Without strict conditions imposed on self regulation functions, some new sufficient conditions for the existence and global asymptotic stability of a unique equilibrium are established. It is believed that these results are significant and useful for the design and applications of BAM-type biological...
Grid-based algorithms for quantitative association rule mining are high efficient, but they are fundamentally low dimension oriented. This paper extends the grid-based concept and proposes a metarule-guided generalized linked list-based algorithm aimed to mine multidimensional quantitative association rules from relational databases. Based on the metarule, the algorithm stores data tuples into the...
This paper proposes a sub-optimal multiuser detector (MUD) algorithm for CDMA system based on the neural network with Time-varying Gain Chaotic Simulated Annealing Neural Network (TGCSANN), and gives a concrete model of the MUD after appropriate transformations and mappings. By refraining from the serious local optimal problem of Hopfield-type neural networks, the TGCSANN makes use of the time-varying...
As powerful theoretical and computational tools, support vector machines (SVMs) have been widely used in pattern classification of many areas. A key issue of applying SVMs to language identification of speech signals is to find a SVM kernel that compares a sequence of feature vectors with others efficiently. In this paper, we introduce a sequence kernel used in language identification, and develop...
Texture analysis has been efficiently utilized in the area of terrain classification. The widely used co-occurrence features have been reported most effective for this application. Since the number of co-occurrence features is very high, a terrain classifier based on co-occurrence features should deal with high dimensionality problem. This paper deals with how to solve high dimensionality problems...
Neural networks are widely used in many applications including astronomical physics, image processing, recognition, robotics, and automated target tracking, etc. Their ability to approximate arbitrary continuous functions is the main reason for this popularity. The authors of this paper show by constructive methods that for any continuous function f on [a, b], the function can be approximated by a...
The numerical integration is an important computing method in science and engineering. An algorithm of neural network based on cosine and sine basis functions is proposed. It uses the output of neural network to approximate to the integrand by training the weights a_k and b_k . The accuracy and efficiency of the algorithm presented are verified by numerical examples.
In order to solve the problem of bad separation result of Anti-Hebbian algorithm, a weighted algorithm of blind signal based on the feed back neural network is discussed, i.e. the influence of source signal is overcame by the weighted process. And furthermore, the similarity algorithm is presented. The main process of the blind signal separation algorithm is to construct neural network by the characteristic...
Improved radial basis function (RBF) neural network is applied on eddy current nondestructive quantitative detecting. Owning to the disadvantages of OLS in network structure optimization, authors put forward using Fisher ratio method to optimize the RBF centers, orthogonal transform and forward selection search method are used to optimize structure. The result shows that the neural structure is simplified...
The traditional blind source separation (BSS) usually estimates M source signals from N observed mixtures and N \geqslant M. When there are less observed mixtures than source signals, i.e., N \le M, BSS becomes a challenging underdetermined problem. So far, most of the techniques for solving the underdetermined BSS problem focus on simultaneous separation of all sparse sources. Motivated by the fact...
Based on current work about high order Boltzmann Machine (BM) and unsupervised BM, an unsupervised learning algorithm based on high order BM is proposed. It is different from supervised BM in that it has no training samples for output units. In the unsupervised BM, the maximization of the mutual information based on Shannon entropy is used as an unsupervised criterion. As we all know, the computation...
In modern global market, one of the most important issues of the supply chain (SC) management is to satisfy changing customer demands, and enterprises should enhance the long-term advantage through the optimal inventory control. In this study, we model a supply chain framework by multi-agent with mixed inventory policies of facilities to consider the impact factors of the total supply chain cost....
The characteristic of worsted fore-spinning working procedure and the BP neural network modeling technology all have been summarily analyzed. Based on roving craft parameters' relevant characteristic, the principal component analysis method is proposed to pretreat the sample data set, which results are the new sample data of the BP neural network. The input layer node numbers reduce; the relevancies...
Prematurely born babies usually suffer from nutrition deficiency. Total parental nutrition (TPN) has been one of the major treatments commonly used by clinicians to improve the nutritional needs of prematurely born babies. This paper described the application of an artificial neuromolecluar (ANM) chip to a database of prematurely born babies who were treated with TPN. The objective was to investigate...
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