Random weight network-based fuzzy nonlinear regression for trapezoidal fuzzy number data

Abstract This paper proposes a random weight network (RWN)-based fuzzy nonlinear regression (FNR) model, abbreviated as TraFNR RWN , to solve the FNR problem in which both inputs and outputs are trapezoidal fuzzy numbers. TraFNR RWN is a special single hidden layer feed-forward neural network which does not require any iterative process to train the network weights. The input-layer weights of TraFNR RWN are randomly assigned and its output-layer weights are analytically determined by solving a constrained-optimization problem. In addition, a new strategy is used to construct the fuzzy membership degree function for the predicted fuzzy-out based on the derived output-layer weights of TraFNR RWN . A fuzzification method is developed to fuzzify the crisp numbers of data sets into trapezoidal fuzzy numbers. Twelve fuzzified data sets were used in the experiments to compare the performance of TraFNR RWN with five different FNR models. The experimental results have shown that TraFNR RWN obtained better prediction performance with less training time because it did not require time-consuming weight learning and parameter tuning.

• Publication year

  2017

• Venue

  Applied Soft Computing

• Publication date

  2017-08-01

• Fields of study

  Mathematics, Computer Science

• Identifiers
  DOI 10.1016/j.asoc.2017.08.006
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

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