Computational intelligence is the computational simulation of the bio-intelligence, which includes artificial neural networks, fuzzy systems and evolutionary computations. This article summarizes the state of the art ...
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Computational intelligence is the computational simulation of the bio-intelligence, which includes artificial neural networks, fuzzy systems and evolutionary computations. This article summarizes the state of the art in the field of simulated modeling of vibration systems using methods of computational intelligence, based on some relevant subjects and the authors' own research work. First, contributions to the applications of computational intelligence to the identification of nonlinear characteristics of packaging are reviewed. Subsequently, applications of the newly developed training algorithms for feedforward neural networks to the identification of restoring forces in multi-degree-of-freedom nonlinear systems are discussed. Finally, the neural-network-based method of model reduction for the dynamic simulation of microelectromechanical systems (MEMS) using generalized Hebbian algorithm (GHA) and robust GHA is outlined. The prospects of the simulated modeling of vibration systems using techniques of computational intelligence are also indicated.
The main idea of SVM, i.e. Support Vector Machine, is mapping nonlinear separable data into higher dimension linear space where the data can be separated by hyper plane. Based on Jordan Curve Theorem, a general classi...
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The main idea of SVM, i.e. Support Vector Machine, is mapping nonlinear separable data into higher dimension linear space where the data can be separated by hyper plane. Based on Jordan Curve Theorem, a general classification method HSC, Classification based on Hyper Surface, is put forward in this paper. The separating hyper surface is directly made to classify large database. The data are classified according to whether the intersecting number is odd or even. It is a novel approach which has no need of either mapping from lower dimension space to higher dimension space or considering kernel function. It can directly solve the nonlinear classification problem. The experiments show that the new method can efficiently and accurately classify large data.
With the increasing of information on Internet,web mining has been the focus of data mining. In this paper,we put forwards a semi-supervised learning strategy consisted of two *** stage labels the documents that inclu...
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With the increasing of information on Internet,web mining has been the focus of data mining. In this paper,we put forwards a semi-supervised learning strategy consisted of two *** stage labels the documents that include latent class variables by using Bayes latent semantic model;at the second stage,based on the results from first stage,we label the documents excluding latent class variables with the naive Bayes *** results show that this algorithm has a good precision and recall rate.
This paper focus on swarm intelligence based clustering algorithm.A clustering algorithm based on swarm intelligence is systematically *** derived from a basic model interpreting ant colony organization of *** importa...
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This paper focus on swarm intelligence based clustering algorithm.A clustering algorithm based on swarm intelligence is systematically *** derived from a basic model interpreting ant colony organization of *** important concepts,such as swarm similarity,swarm similarity coefficient and probability conversion function are also proposed.A simplified probability conversion function is given for simplifying adaptation of parameters, meanwhile the importance of swarm similarity coefficient for the algorithm is analyzed. Experimental results show the good performance of the clustering algorithm.
This paper improves on the classical formula of calculating the term weight in Vector Space ***,an approach of multi-hierarchy text classification based on Vector Space Model is *** this approach,all classes are organ...
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This paper improves on the classical formula of calculating the term weight in Vector Space ***,an approach of multi-hierarchy text classification based on Vector Space Model is *** this approach,all classes are organized as a tree according to some given hierarchical relations,and all the training documents in a class are combined into a *** order to construct the class models,it is just only to compare among the class-documents attached to the same node of the same *** it is going to classify the documents,one matching process is hierarchically performed from the root node to the leaf nodes until a corresponding subclass is *** experiment and real systems indicate that the approach is of high classification Precision and Recall.
Human being have capabilities to learn after ***,it is seem that the machine can be let its"brain"or mental development system learn new tasks without a need for *** this paper,we will discuss Automated Ment...
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Human being have capabilities to learn after ***,it is seem that the machine can be let its"brain"or mental development system learn new tasks without a need for *** this paper,we will discuss Automated Mental Development(AMD) in the Multi Agent Environment(MAGE),include framework,and model and the future *** goal is that the agent can autonomously learn like human and various knowledge can be memoried in relevant form autonomously.
This paper focuses on swarm intelligence based clustering algorithm. A clustering algorithm based on swarm intelligence is systematically proposed. It derived from a basic model interpreting ant colony organization of...
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This paper focuses on swarm intelligence based clustering algorithm. A clustering algorithm based on swarm intelligence is systematically proposed. It derived from a basic model interpreting ant colony organization of cemeteries. Some important concepts, such as swarm similarity, swarm similarity coefficient and probability conversion function are also proposed. A simplified probability conversion function is given for simplifying adaptation of parameters, meanwhile the importance of swarm similarity coefficient for the algorithm is analyzed. Experimental results show the good performance of the clustering algorithm.
With the increase of information on Internet, web mining has been the focus of data mining. In this paper, we put forward a semi-supervised learning strategy consisting of two stages. First stage labels the documents ...
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With the increase of information on Internet, web mining has been the focus of data mining. In this paper, we put forward a semi-supervised learning strategy consisting of two stages. First stage labels the documents that include latent class variables by using Bayes latent semantic model; at the second stage, based on the results from first stage, we label the documents excluding latent class variables with the naive Bayes models. Experimental results show that this algorithm has a good precision and recall rate.
Improves on the classical formula of calculating the term weight in the vector space model. Furthermore, an approach to multi-hierarchy text classification based on the vector space model is proposed. In this approach...
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Improves on the classical formula of calculating the term weight in the vector space model. Furthermore, an approach to multi-hierarchy text classification based on the vector space model is proposed. In this approach, all classes are organized as a tree according to some given hierarchical relations, and all the training documents in a class are combined into a class-document. In order to construct the class models, only the class-documents attached to the same node of the same layer are compared. When classifying the documents, one matching process is hierarchically performed from the root node to the leaf nodes until a corresponding subclass is found. The experiment and real systems indicate that the approach is of high classification precision and recall.
Human being have capabilities to learn after birth. In analogy, it seems that the machine can be let its "brain" or mental development system to learn new tasks without the need for programming. In this pape...
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Human being have capabilities to learn after birth. In analogy, it seems that the machine can be let its "brain" or mental development system to learn new tasks without the need for programming. In this paper, we discuss an automated mental development in the multi agent environment (MAGE), including the framework, model and future work. The goal is that the agent can autonomously learn like a human and various knowledge can be memorised in a relevant form autonomously.
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