An input-output clustering approach to the synthesis of ANFIS networks

Abstract

A useful neural network paradigm for the solution of function approximation problems is represented by adaptive neuro-fuzzy inference systems (ANFIS). Data driven procedures for the synthesis of ANFIS networks are typically based on clustering a training set of numerical samples of the unknown function to be approximated. Some serious drawbacks often affect the clustering algorithms adopted in this context, according to the particular data space where they are applied. To overcome such problems, we propose a new ANFIS synthesis procedure where clustering is applied in the joint input-output data space. Using this approach, it is possible to determine the consequent part of Sugeno first-order rules and therefore the hyperplanes characterizing the local structure of the function to be approximated. Successivelyl the fuzzy antecedent part of each rule is determined using a particular fuzzy min-max classifier, which is based on the adaptive resolution mechanism. The generalization capability of the resulting ANFIS architecture is optimized using a constructive procedure for the automatic determination of the optimal number of rules. Simulation tests and comparisons with respect to other neuro-fuzzy techniques are discussed in the paper, in order to assess the efficiency of the proposed approach

ENSO-based tropical cyclone forecasting using CF-ANFIS

“…Here the author has paid attention to the forecast during El Nino or La Nina episodes while we also have to predict for the neutral phase; therefore, many studies on forecasting cyclones season have further analysed the relationships between climate factors and other circulations not related to ENSO. For example, one of the important factors in predicting seasonal cyclones in Northwest Pacific region is a factor regarding the Western North Pacific Subtropical High often reflected in some layers of the atmosphere on regional circulation in the region [15][16][17][18][19][20][21][22][23][24][25] • N, 115-150 • E. Cyclones cannot occur in the subtropical high-pressure area of Northwest Pacific. Subtropical high pressure in the Pacific northwest region, when intensifying and encroaching the west land, will block the cyclone from sweeping north and west, so in these cases, cyclones tend to land more inclined on the west coast.…”

“…ANFIS is one of the most popular types of fuzzy neural network [20,[25][26][27]. The clustering techniques are commonly used to create fuzzy rules of ANFIS.…”

“…Unfortunately, there can be contradictory rules having similar input MFs but different output coefficients, which is unacceptable in ANFIS networks. To overcome these problems, M. Panella [24][25][26] and Dzung [20] presented a clustering method in conjunct space for ANFIS network construction. The clustering in input-output space mentioned in the previous works combines a linear cluster (e.g.…”

“…According to [25], several clusters of the input space could be associated with the same hyperplane. To solve this problem, the well-known Simpson's min-max models for classification (min-max classification) were applied by Panella [24,25] and Dung [20]. The combination of the hyperplane clustering and the max-min classification on the input space supports to effectively determine the ANFIS network for a given number of rules.…”

“…There are several approaches for min-max classification such as Simpson's min-max models, ARC in [25] and CSHL in [20]. Here, the CSHL algorithm is applied for this work.…”