909 research outputs found
Comparison of different strategies of utilizing fuzzy clustering in structure identification
Fuzzy systems approximate highly nonlinear systems by means of fuzzy "if-then"
rules. In the literature, various algorithms are proposed for mining. These algorithms commonly utilize fuzzy clustering in structure identification. Basically, there are three different approaches in which one can utilize fuzzy clustering; the �first one is based on input space clustering, the second one considers clustering realized in the output space, while the third one is concerned with clustering realized in the combined input-output space. In this study, we analyze these three approaches. We discuss each of the algorithms in great detail and o¤er a thorough comparative analysis. Finally, we compare the performances of these algorithms in a medical diagnosis classi�cation problem, namely Aachen Aphasia Test. The experiment and the results provide a valuable insight about the merits and the shortcomings of these three clustering approaches
Structure Identification of Dynamical Takagi-Sugeno Fuzzy Models by Using LPV Techniques
In this paper the problem of order selection for nonlinear dynamical Takagi-Sugeno (TS) fuzzy models is investigated. The problem is solved by formulating the TS model in its Linear Parameter Varying (LPV) form and applying a recently proposed Regularized Least Squares SupportVector Machine (R-LSSVM) technique for LPV models. In contrast to parametric identification approaches, this non-parametric method enables the selection of the model order without specifying the scheduling dependencies of the model coefficients. Once the correct model order is found, a parametric TS model can be re-estimated by standard methods. Different re-estimation approaches are proposed. The approaches are illustrated in a numerical example
Business Intelligence from Web Usage Mining
The rapid e-commerce growth has made both business community and customers
face a new situation. Due to intense competition on one hand and the customer's
option to choose from several alternatives business community has realized the
necessity of intelligent marketing strategies and relationship management. Web
usage mining attempts to discover useful knowledge from the secondary data
obtained from the interactions of the users with the Web. Web usage mining has
become very critical for effective Web site management, creating adaptive Web
sites, business and support services, personalization, network traffic flow
analysis and so on. In this paper, we present the important concepts of Web
usage mining and its various practical applications. We further present a novel
approach 'intelligent-miner' (i-Miner) to optimize the concurrent architecture
of a fuzzy clustering algorithm (to discover web data clusters) and a fuzzy
inference system to analyze the Web site visitor trends. A hybrid evolutionary
fuzzy clustering algorithm is proposed in this paper to optimally segregate
similar user interests. The clustered data is then used to analyze the trends
using a Takagi-Sugeno fuzzy inference system learned using a combination of
evolutionary algorithm and neural network learning. Proposed approach is
compared with self-organizing maps (to discover patterns) and several function
approximation techniques like neural networks, linear genetic programming and
Takagi-Sugeno fuzzy inference system (to analyze the clusters). The results are
graphically illustrated and the practical significance is discussed in detail.
Empirical results clearly show that the proposed Web usage-mining framework is
efficient
Rule Optimization of Fuzzy Inference System Sugeno using Evolution Strategy for Electricity Consumption Forecasting
The need for accurate load forecasts will increase in the future because of the dramatic changes occurring in the electricity consumption. Sugeno fuzzy inference system (FIS) can be used for short-term load forecasting. However, challenges in the electrical load forecasting are the data used the data trend. Therefore, it is difficult to develop appropriate fuzzy rules for Sugeno FIS. This paper proposes Evolution Strategy method to determine appropriate rules for Sugeno FIS that have minimum forecasting error. Root Mean Square Error (RMSE) is used to evaluate the goodness of the forecasting result. The numerical experiments show the effectiveness of the proposed optimized Sugeno FIS for several test-case problems. The optimized Sugeno FIS produce lower RMSE comparable to those achieved by other well-known method in the literature
Granular Fuzzy Regression Domain Adaptation in Takagi-Sugeno Fuzzy Models
© 1993-2012 IEEE. In classical data-driven machine learning methods, massive amounts of labeled data are required to build a high-performance prediction model. However, the amount of labeled data in many real-world applications is insufficient, so establishing a prediction model is impossible. Transfer learning has recently emerged as a solution to this problem. It exploits the knowledge accumulated in auxiliary domains to help construct prediction models in a target domain with inadequate training data. Most existing transfer learning methods solve classification tasks; only a few are devoted to regression problems. In addition, the current methods ignore the inherent phenomenon of information granularity in transfer learning. In this study, granular computing techniques are applied to transfer learning. Three granular fuzzy regression domain adaptation methods to determine the estimated values for a regression target are proposed to address three challenging cases in domain adaptation. The proposed granular fuzzy regression domain adaptation methods change the input and/or output space of the source domain's model using space transformation, so that the fuzzy rules are more compatible with the target data. Experiments on synthetic and real-world datasets validate the effectiveness of the proposed methods
Development of Neurofuzzy Architectures for Electricity Price Forecasting
In 20th century, many countries have liberalized their electricity market. This power markets liberalization has directed generation companies as well as wholesale buyers to undertake a greater intense risk exposure compared to the old centralized framework. In this framework, electricity price prediction has become crucial for any market player in their decision‐making process as well as strategic planning. In this study, a prototype asymmetric‐based neuro‐fuzzy network (AGFINN) architecture has been implemented for short‐term electricity prices forecasting for ISO New England market. AGFINN framework has been designed through two different defuzzification schemes. Fuzzy clustering has been explored as an initial step for defining the fuzzy rules while an asymmetric Gaussian membership function has been utilized in the fuzzification part of the model. Results related to the minimum and maximum electricity prices for ISO New England, emphasize the superiority of the proposed model over well‐established learning‐based models
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