Journal of Intelligent & Fuzzy Systems - Volume 28, issue 4

Authors: Zhang, Jing | Lei, Hang

Article Type: Research Article

Abstract: Effective acquisition of transition probability matrix is directly related to Internetware reliability computation. The characteristics of Markov chain in Internetware are discussed and analyzed, the construction of Markov chain and the acquisition of transition probability of Internetware are studied, the Internetware model based on Markov chain is constructed. Quantitative calculation method of transition probability based on the smallest quadratic difference is presented by using the occupancy of component executing the transition as the sample statistics to calculate transition probability. The approximation algorithm for computing transition probability matrix based on the modified projection gradient is designed, and it effectively guarantees the …transition law of Markov chain and the characteristics of transition probability matrix. The experiment proves that the presented method and the designed algorithm can effectively compute transition probability matrix with great value in Internetware reliability computation. Show more

Keywords: Internetware, transition probability, matrix, compute method, algorithm

DOI: 10.3233/IFS-141479

Citation: Journal of Intelligent & Fuzzy Systems, vol. 28, no. 4, pp. 1921-1930, 2015

Authors: Savaş, Ekrem

Article Type: Research Article

Abstract: In this paper, we introduce new notions, namely, ideal statistical convergence and ideal lacunary statistical convergence for fuzzy numbers, their relationship and also make some observations about these classes.

Keywords: Ideal, ideal statistical convergence, ideal lacunary statistical convergence, fuzzy number sequence

DOI: 10.3233/IFS-141480

Citation: Journal of Intelligent & Fuzzy Systems, vol. 28, no. 4, pp. 1931-1936, 2015

Authors: Gholizade-Narm, Hossein | Shafiee Chafi, Mohammad Reza

Article Type: Research Article

Abstract: In this paper, a new perspective is presented for prediction of chaotic time series by combining the phase space reconstruction and fuzzy approach. Before applying time series to the predictor system, the reconstruction parameters, including embedding dimension and time delay, are determined in an off-line manner by using nearest neighbor and mutual information methods. Then, the structure of the fuzzy system is specified and the input number of fuzzy system is set to the embedding dimensions. According to the embedding dimension and time delay, the phase space is reconstructed point-by-point at the entry of the fuzzy system. Fuzzy system is …composed of two separated parts: predictor and tracker. The predictor part forecasts the next point for new entry with fine-tuned parameters using the last step, and the tracker part adjusts the parameters for the next step. This adjustment is done iteratively. The proposed method is compared with some references' results. Simplicity and appropriate speed with sufficient accuracy are the advantages of this method. Show more

Keywords: Chaos, time series, fuzzy method, forecasting

DOI: 10.3233/IFS-141481

Citation: Journal of Intelligent & Fuzzy Systems, vol. 28, no. 4, pp. 1937-1946, 2015