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In this paper, generalized versions of two ensemble methods for regression based on variants of the original AdaBoost algorithm are proposed. The generalization of these regression methods consists in restricting the unit simplex for the weights of the instances to a smaller set of weighting probabilities. Various imprecise statistical models can be used to obtain a restricted set of weighting probabilities, whose sizes each depend on a single parameter. For particular choices of this parameter, the proposed algorithms reduce to standard AdaBoost-based regression algorithms or to standard regression. The main advantage of the proposed algorithms compared to the basic AdaBoost-based regression methods is that they have less tendency to over-fitting, because the weights of the hard instances are restricted. Several simulations and applications furthermore indicate a better performance of the proposed regression methods in comparison with the corresponding standard regression methods. [ABSTRACT FROM AUTHOR]

Abstract: In this paper, we propose a multiobjective credibilistic model with fuzzy chance constraints of the portfolio selection problem. The key financial criteria used are short-term return, long-term return, risk and liquidity. The model generates portfolios which are optimal to the extent of achieving the highest credibility values for the objective functions. The problem is solved using a hybrid intelligent algorithm that integrates fuzzy simulation with a real-coded genetic algorithm. The approach adopted here has advantage of handling the multiobjective portfolio selection problem where fuzzy parameters are characterized by general functional forms. Numerical examples are provided to demonstrate effectiveness of the solution approach and efficiency of the model. [Copyright &y& Elsevier]

Abstract: The occurrence of imprecision in the real world is inevitable due to some unexpected situations. The imprecision is often involved in any engineering design process. The imprecision and uncertainty are often interpreted as fuzziness. Fuzzy systems have an essential role in the uncertainty modelling, which can formulate the uncertainty in the actual environment. In this paper, a new approach is proposed to solve a system of fuzzy polynomial equations based on the Gröbner basis. In this approach, first, the h-cut of a system of fuzzy polynomial equations is computed, and a parametric form for the fuzzy system with respect to the parameter of h is obtained. Then, a Gröbner basis is computed for the ideal generated by the h-cuts of the system with respect to the lexicographical order using Faugère’s algorithm, i.e., F 4 algorithm. The Gröbner basis of the system has an upper triangular structure. Therefore, the system can be solved using the forward substitution. Hence, all the solutions of the system of fuzzy polynomial equations can easily be obtained. Finally, the proposed approach is compared with the current numerical methods. Some theorems together with some numerical examples and applications are presented to show the efficiency of our method with respect to the other methods. [Copyright &y& Elsevier]

Abstract: Fuzzy clustering is an effective clustering approach which associates a data point with multiple clusters. Standard fuzzy clustering models like fuzzy c-means are based on minimizing the total cluster variation, which is defined as the sum of the distances between the data points and their corresponding cluster centers weighted by the membership degrees. In this paper, we propose a fuzzy minimax clustering model by minimizing the maximum value of the set of weighted cluster variations in such a way that they satisfy a prior distribution. We derive a necessary condition for the extremum point of the fuzzy minimax clustering model, and then design an iterative algorithm for solving the extremum point. Several numerical examples on comparing fuzzy c-means and fuzzy minimax clustering models are given, which demonstrate that the prior distribution improves the quality of the clustering results significantly. [Copyright &y& Elsevier]

Abstract: A method of analysis for a class of Petri nets (PNs) called parallel process net with resources (PPNRs) is presented in this paper. The proposed analysis method is based on reduced reachability graph (RRG) of PPNRs to verify the correspondence between required specification of manufacturing system and its PN representation. In order to reduce the reachability graph (RG), a new technique is proposed which incorporates the transition vectors (TVs) to determine all the enabled transitions at a given state of system and to recognize them as dependent or independent. An algorithm, based on the idea of simultaneous execution of concurrently enabled independent transitions, is developed to reduce the RG and its analysis is also performed. Moreover, relationship between the reduction of RG and parallel structure in the PN model is discovered. The proposed technique replaces the RG by a structure which directly depicts concurrent execution and does not show the irrelevant states by presenting the concurrent behavior of system in the reduced state space. The analysis of PPNRs based on RRG generated by proposed method is also presented and demonstrated by a practical example. [Copyright &y& Elsevier]