Current Issue : April - June Volume : 2018 Issue Number : 2 Articles : 5 Articles
We introduce the Aumann fuzzy improper integral to define the convolution product of a fuzzy mapping and a crisp function in\nthis paper. The Laplace convolution formula is proved in this case and used to solve fuzzy integro-differential equations with kernel\nof convolution type.Then, we report and correct an error in the article by Salahshour et al. dealing with the same topic....
This paper proposes an enhanced ant colony optimization with dynamic mutation and ad hoc initialization, ACODM-I, for\nimproving the accuracy of Takagi-Sugeno-Kang- (TSK-) type fuzzy systems design. Instead of the generic initialization usually\nused in most population-based algorithms, ACODM-I proposes an ad hoc application-specific initialization for generating the\ninitial ant solutions to improve the accuracy of fuzzy system design. The generated initial ant solutions are iteratively improved\nby a new approach incorporating the dynamic mutation into the existing continuous ACO (ACOR). The introduced dynamic\nmutation balances the exploration ability and convergence rate by providing more diverse search directions in the early stage of\noptimization process. Application examples of two zero-order TSK-type fuzzy systems for dynamic plant tracking control and\none first-order TSK-type fuzzy system for the prediction of the chaotic time series have been simulated to validate the proposed\nalgorithm. Performance comparisons with ACOR and different advanced algorithms or neural-fuzzy models verify the superiority\nof the proposed algorithm. The effects on the design accuracy and convergence rate yielded by the proposed initialization and\nintroduced dynamic mutation have also been discussed and verified in the simulations....
Cocluster structure analysis is a basic technique for revealing intrinsic structural information from cooccurrence data among\nobjects and items, in which coclusters are composed of mutually familiar pairs of objects and items. In many real applications,\nit is also the case that we have not only cooccurrence information among objects and items but also intrinsic relation among\nitems and other ingredients. For example, in food preference analysis, users� preferences on foods should be found considering not\nonly user-food cooccurrences but also the implicit relation among users and cooking ingredients. In this paper, two FCM-type\nfuzzy coclustering models, that is, FCCM and Fuzzy CoDoK, are extended for revealing intrinsic cocluster structures from threemode\ncooccurrence data, where the aggregation degree of three elements in each cocluster is maximized through iterative updating\nof three types of fuzzy memberships for objects, items, and ingredients. The characteristic features of the proposed methods are\ndemonstrated through a numerical experiment....
The output power of a photovoltaic (PV) module depends on the solar irradiance and\nthe operating temperature; therefore, it is necessary to implement maximum power point tracking\ncontrollers (MPPT) to obtain the maximum power of a PV system regardless of variations in climatic\nconditions. The traditional solution for MPPT controllers is the perturbation and observation (P&O)\nalgorithm, which presents oscillation problems around the operating point; the reason why improving\nthe results obtained with this algorithm has become an important goal to reach for researchers.\nThis paper presents the design and modeling of a fuzzy controller for tracking the maximum power\npoint of a PV System. Matlab/Simulink (MathWorks, Natick, MA, USA) was used for the modeling of\nthe components of a 65 W PV system: PV module, buck converter and fuzzy controller; highlighting\nas main novelty the use of a mathematical model for the PV module, which, unlike diode based\nmodels, only needs to calculate the curve fitting parameter. A P&O controller to compare the results\nobtained with the fuzzy control was designed. The simulation results demonstrated the superiority\nof the fuzzy controller in terms of settling time, power loss and oscillations at the operating point....
The accuracy of energy management system for renewable microgrid, either grid-connected or isolated, is heavily dependent on the\nforecasting precision such as wind, solar, and load. In this paper, an improved fuzzy prediction horizon forecasting method is\ndeveloped to address the issue of intermittence and uncertainty problem related to renewable generation and load forecast. In\nthe first phase, a Takagi-Sugeno type fuzzy system is trained with many evolutionary optimization algorithms and established\ncoverage grade indicator to check the accuracy of interval forecast. Secondly, a wind, solar, and load forecaster is developed for\nrenewable microgrid test bed which is located in Beijing, China. One day and one step ahead results for the proposed forecaster\nare expressed with lowest RMSE and training time. In order to check the efficiency of the proposed method, a comparison is\ncarried out with the existing models. The fuzzy interval-based model for the microgrid test bed will help to formulate the energy\nmanagement problem with more accuracy and robustness....
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