In this paper, a type of compensation-based recurrent fuzzy neural network (CRFNN) for identifying dynamic systems is proposed. The proposed CRFNN uses a compensation-based fuzzy reasoning method, and has feedback connections added in the rule layer of the CRFNN. The compensation-based fuzzy reasoning method can make the fuzzy logic system more adaptive and effective, and the additional feedbac...

Zusammenfassung Dieser Beitrag beschreibt die Anwendung zeitdiskreter rekurrenter Fuzzy-Systeme (DRFS) zur Fehlerdetektion und Fehlerisolation. Zum einen wird gezeigt, wie auf Basis datengestützt entworfener DRFS geeignete Residuen gebildet werden können. Zum anderen wird ein Ansatz zur Mehrfehlerisolation mit DRFS präsentiert. Dieser Ansatz basiert auf der Erweiterung klassischer statischer Fu...

This paper presents a comparative study of six soft computing models namely multilayer perceptron networks, Elman recurrent neural network, radial basis function network, Hopfield model, fuzzy inference system and hybrid fuzzy neural network for the hourly electricity demand forecast of Czech Republic. The soft computing models were trained and tested using the actual hourly load data obtained ...

In this paper, we propose a novel recurrent interval type-2 fuzzy neural network with asymmetric membership functions (RT2FNN-A). The RT2FNN-A uses the interval asymmetric type-2 fuzzy sets and it implements the fuzzy logic system (FLS) in a five-layer neural network structure. The RT2FNN-A is modified from the type-2 fuzzy neural network to provide memory elements for capturing the system’s dy...

This paper proposes the development of a fuzzy predictive control. Genetic algorithms (GA’s) are used to automatically tune the controller. A recurrent neural network is used to identify the process, and then provides predictions about the process behavior, based on control actions applied to the system. These predictions are used by the fuzzy controller, in order to accomplish a better control...