Some recent trends in manufacturing in particular and business in general, lead to new approaches regarding the organisation and software architecture, mainly adopting distributed solutions. Such organisations imply o...
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Some recent trends in manufacturing in particular and business in general, lead to new approaches regarding the organisation and software architecture, mainly adopting distributed solutions. Such organisations imply organisational and technological evolution through agility, distribution, decentralisation, reactivity and flexibility. New organisational and technological paradigms are needed in order to reply to the modern manufacturing systems challenges. The Multi-Agent paradigm represents one of the most promising approaches to build complex, flexible, and cost-effective scheduling systems because of its distributed and dynamic nature. Modelling the Scheduling of Manufacturing Systems by means of two technologies like Meta-Heuristics and Multi-Agent Systems seems to be an interesting way to see Industrial Systems in the future. A multi-agent based model for support dynamic scheduling in manufacturing environments is proposed.
Molecular substructure mining is currently an intensively studied research area. In this paper we present an implementation of an algorithm for finding frequent substructures in a set of molecules, which may also be u...
ISBN:
(纸本)1595932100
Molecular substructure mining is currently an intensively studied research area. In this paper we present an implementation of an algorithm for finding frequent substructures in a set of molecules, which may also be used to find substructures that discriminate well between a focus and a complement group. In addition to the basic algorithm, we discuss advanced pruning techniques, demonstrating their effectiveness with experiments on two publicly available molecular data sets, and briefly mention some other extensions. Copyright 2005 ACM.
The paper presents a general architecture for a P2P data sharing facility within a multi-agent framework, where peers as autonomous high-level nodal agents cooperate with each other to solve global tasks. A node may h...
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The paper presents a general architecture for a P2P data sharing facility within a multi-agent framework, where peers as autonomous high-level nodal agents cooperate with each other to solve global tasks. A node may have several lower level local agents including local databases and partial global ontologies. In addition there are also minder agents coordinating the activities of the peers that offer the same type of service, thus providing fault-tolerance. The ability of this architecture in data and task sharing has been demonstrated by considering query processing and directory update strategies.
Fuzzy Cognitive Maps (FCMs) can represent and reason causal knowledge with stronger semantics. And the causal knowledge widely exists in knowledge Grid. To provide information services with stronger semantics in Knowl...
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knowledge-based web information extraction methods can achieve very high precision in restricted domains;they are however slow and suffer from performance degradation beyond their specific domain. We thus plan to adap...
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knowledge-based web information extraction methods can achieve very high precision in restricted domains;they are however slow and suffer from performance degradation beyond their specific domain. We thus plan to adapt an existing XML storage and query engine to act as efficient pre-processor for such methods. The critical point of the approach is the amount of information provided as XML environment of the start-up terms/elements. For this purpose, we carried out a statistical analysis of depth distribution in the WebTREC collection.
In this paper, we propose a new supervised compound learning algorithm for training our constructed approximated bivariate non-tensor product adaptive pre-wavelet neural network (APWNN). On the one hand, the linear we...
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In this paper, we propose a new supervised compound learning algorithm for training our constructed approximated bivariate non-tensor product adaptive pre-wavelet neural network (APWNN). On the one hand, the linear weights of APWNN are trained by the self-adaptive learning rate method. On the other hand an extended Kalman filter method is used to update the nonlinear parameters such as dilation parameters and translation parameters. Additionally we demonstrate the efficiency of our proposed method through a concrete example of function approximation.
This paper presents a new model to incorporate decision theory into Graphplan framework, which enables our planner to handle uncertainty and make decision to choose the optimal one among a set of hypothesis valid plan...
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This paper presents a new model to incorporate decision theory into Graphplan framework, which enables our planner to handle uncertainty and make decision to choose the optimal one among a set of hypothesis valid plans. This planer, called UTDP is tested on several experimental domains. And the experimental results show that UTGP is sound and efficient and performs better than the famous probabilistic planner Buridan.
Fuzzy cognitive maps (FCMs) can represent and reason causal knowledge with stronger semantics. And the causal knowledge widely exists in knowledge grid. To provide information services with stronger semantics in Knowl...
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Fuzzy cognitive maps (FCMs) can represent and reason causal knowledge with stronger semantics. And the causal knowledge widely exists in knowledge grid. To provide information services with stronger semantics in knowledge Grid, we need to know the reasoning mechanism and the characteristics of FCMs. In this paper, we have proved that the reasoning process of FCMs is a discrete topological semi- dynamic system. This theory can help us analyze the reasoning process and find the new characteristics of FCMs, which can guide us using FCMs to provide intelligent information services flexibly in knowledge Grid.
The optimal partition algorithm (OPA) is applied to the training of parameters in the radial basis function (RBF) neural network. The appropriate modification for the OPA is performed according to the characteristics ...
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The optimal partition algorithm (OPA) is applied to the training of parameters in the radial basis function (RBF) neural network. The appropriate modification for the OPA is performed according to the characteristics of the RBF neural network. The approach for determining the centers and widths of the clustering is added in the modified OPA and applied to choose the centers and widths of the neural network. A method for adjusting the structure of the neural network dynamically is presented by using the difference of the objective functions of the clustering. Thus it is realized to select the number of the hidden nodes adaptively. Simulation results of the stock price prediction demonstrate the effectiveness of the proposed approach. Comparisons with traditional algorithms show that the proposed OPA method possesses obvious advantages in the precision of forecasting, generalization, and forecasting trends. Simulations also show that the algorithm combining the OPA with the orthogonal least squares (OLS) possesses more superior performance in the rightness of forecasting trends.
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