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This paper investigates the design and evaluation of a personalized Recommender System (RS) using implicit social trust from Online Social Networks (OSNs), particularly to solve new users' recommendation problems. The proposed system builds implicit trust based on the interrelation between an active user and his/ her friends in the popular social microblogger Twitter, by considering aspects such as...
Accurate predictions of both process-execution time and process status are crucial for the development of an intelligent enterprise information system (EIS). We have developed new automated learning-based process-execution time-prediction and process status-prediction methods that can be embedded into an EIS. Process-execution time prediction is a regression problem and state-of-the-art (baseline)...
Pricing decision support systems have been developed in order to help retail companies optimise their prices. This paper aims to enhance the essential forecasting and optimisation techniques that underlie these systems. This is first done by applying the method of Dynamic Linear Models in order to provide more accurate forecasts compared with current models. We also present a new pricing optimisation...
This paper investigates the universal fuzzy models problem and universal fuzzy controllers problem for discrete-time general nonlinear systems based on a class of generalized T-S fuzzy models. The generalized T-S fuzzy models, which are shown to be universal function approximators, are also proved to be universal fuzzy models for non-affine nonlinear systems under some sufficient conditions. The results...
Serious ice covering on transmission line will make the line overload. As a result, it may cause many problems to equipments of the power system, such as power towers falling down, lines broken, icing flashover, network disconnecting and so on. With the development of power grid, there are more and more ring networks in the grid. Considering the unique structure of this network, this paper proposes...
This paper proposes a Simplified Structure Evolving Method (SSEM) for Fuzzy Systems, which improves our previous work of Structure Evolving Learning Method for Fuzzy Systems (SELM [1]). SSEM keeps all the advantages of SELM [1] and improve SELM by starting with the simplest fuzzy rule set with only one fuzzy rule (instead of 2n fuzzy rules in SELM) as the starting point. By doing this SSEM is able...
This article presents a clustering-based approach to fuzzy system identification. In order to construct an effective initial fuzzy model, this article tries to present a modular method to identify fuzzy systems based on a hybrid clustering-based technique. Moreover, the determination of the proper number of clusters and the appropriate location of clusters are one of primary considerations on constructing...
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