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


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

9 Reads
  • Source
    • "-[7], the data prepared for the Santa Fe Time Series Prediction Competition 1 (Data Set A-F) [1][3]-[5][8][10], and the Mackey-Glass series [9][14]. "
    [Show abstract] [Hide abstract]
    ABSTRACT: Numerous studies on time series prediction have been undertaken by a variety of researchers. Most of them typically used unidirectional computation flow, that is, present signals are applied to the model as an input and predicted future signals are derived from the model as an output. On the contrary, bidirectional computation style has been proposed recently and applied to prediction tasks. A bidirectional neural network model consists of two mutually connected subnetworks and performs direct and inverse transformations bidirectionally. To apply this model to time series prediction tasks, one subnetwork is trained a conventional future prediction task and the other is trained an additional task for past prediction. Since the coupling effects between the future and past prediction subsystems promote the model's signal processing ability, bidirectionalization of the computing architecture makes it possible to improve its performance. Furthermore, in order to investigate the acquired signal transformation, two kinds of chaotic time series—the Mackey–Glass time series and “Data Set A”—are adopted in this paper. As a result of computer simulations, it has been found experimentally that the direct and inverse transformations developed independently and their information integration give the bidirectional model an advantage over the unidirectional one. © 2003 Wiley Periodicals, Inc. Electr Eng Jpn, 145(3): 50–60, 2003; Published online in Wiley InterScience ( DOI 10.1002/eej.10232
    Preview · Article · Nov 2003 · Electrical Engineering in Japan
  • Source
    • "Therefore, fuzzy models with the partition types described above do not always produce best results. A fuzzy system incorporating interactions between the input variables into the inference process was proposed in [11]. This paper exposes the details of this system and provides a comprehensive description of its theoretical and practical aspects. "
    [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.
    Full-text · Article · Feb 2003 · IEEE Transactions on Fuzzy Systems
  • Source
    • "Although the use of multidimensional membership functions [1], [2], [7] alleviates this drawback, it introduces the problem of exponential parameter increase and significantly reduces the model inter- pretability. An alternative scenario approximates by composition of binary relations representing linear dependencies [8]. "
    [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 strongest dependencies from numerical data. An application example illustrates the usefulness of the proposed approach.
    Full-text · Article · Feb 2002
Show more