Conference Paper

Fuzzy Neural Network with Relational Fuzzy Rules.

Dept. of Electr. & Comput. Eng., Louisville Univ., KY
DOI: 10.1109/IJCNN.2000.861426 Conference: Neural Networks, 2000. IJCNN 2000, Proceedings of the IEEE-INNS-ENNS International Joint Conference on, Volume: 5
Source: DBLP

ABSTRACT The paper presents a fuzzy neural network whose structure accounts
for relations between input variables of the system under consideration.
This modification results in a simple fuzzy model with improved
approximation accuracy. An example of nonlinear time series prediction
using the proposed fuzzy neural network is also included

  • [Show abstract] [Hide abstract]
    ABSTRACT: Enhancing the robustness and interpretability of a multilayer perceptron (MLP) with a sigmoid activation function is a challenging topic. As a particular MLP, additive TS-type MLP (ATSMLP) can be interpreted based on single-stage fuzzy IF-THEN rules, but its robustness will be degraded with the increase in the number of intermediate layers. This paper presents a new MLP model called cascaded ATSMLP (CATSMLP), where the ATSMLPs are organized in a cascaded way. The proposed CATSMLP is a universal approximator and is also proven to be functionally equivalent to a fuzzy inference system based on syllogistic fuzzy reasoning. Therefore, the CATSMLP may be interpreted based on syllogistic fuzzy reasoning in a theoretical sense. Meanwhile, due to the fact that syllogistic fuzzy reasoning has distinctive advantage over single-stage IF-THEN fuzzy reasoning in robustness, this paper proves in an indirect way that the CATSMLP is more robust than the ATSMLP in an upper-bound sense. Several experiments were conducted to confirm such a claim.
    IEEE Transactions on Systems Man and Cybernetics Part B (Cybernetics) 01/2007; 36(6):1319-31. · 3.24 Impact Factor
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: This paper presents a new approach to fuzzy rule-based modeling of nonlinear systems from numerical data. The novelty of the approach lies in the way of input partitioning and in the syntax of the rules. This paper introduces interpretable relational antecedents that incorporate local linear interactions between the input variables into the inference process. This modification improves the approximation quality and allows for limiting the number of rules. Additionally, the resulting linguistic description better captures the system characteristics by exposing the interactions between the input variables.
    IEEE Transactions on Fuzzy Systems 01/2003; 11(1):121-134. · 5.48 Impact Factor
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: An approach to data-driven linguistic modeling is presented. The methodology is based on a fuzzy system with relational input partition that allows for transparent modeling of linear dependencies between the inputs. An identification algorithm for this type of fuzzy system is proposed. It automatically finds the strongest dependencies from numerical data. An application example illustrates the usefulness of the proposed approach.
    Fuzzy Systems, 2001. The 10th IEEE International Conference on; 01/2002