Fuzzy rule derivation is often difficult and time-consuming, and requires expert knowledge.
This creates a common bottleneck in fuzzy system design. In order to solve this problem …

In this paper, we present a fuzzy neural network (FNN) to realize the rule reasoning of fuzzy
inference systems. The proposed fuzzy neural network can acquire the fuzzy rules by …

Abstract Nowadays, Linguistic Modeling (LM) is considered to be one of the most important
areas of application for Fuzzy Logic. It is accomplished by descriptive Fuzzy Rule-Based …

Artificial neural networks (ANNs) are mathematical models inspired from the biological
nervous system. They have the ability of predicting, learning from experiences and …

Fuzzy-neural-network-based inference systems are well-known universal approximators
which can produce linguistically interpretable results. Unfortunately, their dimensionality can …

Artificial neural network (ANN) inherently cannot explain in a comprehensible form how a
given decision or output is generated, which limits its extensive use. Fuzzy rules are an …

Fuzzy systems are widely used for solving complex and non-linear problems that cannot be
addressed using precise mathematical models. Their performance, however, is critically …

It is relatively easy to create rough fuzzy rules for a target system. However, it is time-
consuming and difficult to fine-tune them for improving their behavior. Meanwhile, in the …

In this paper, a novel fuzzy neural network termed as dynamic parsimonious fuzzy neural
network (DPFNN) is proposed. DPFNN is a four layers network, which features coalescence …

This paper presents a fuzzy neural network system (FNNS) for implementing fuzzy inference
systems. In the FNNS, a fuzzy similarity measure for fuzzy rules is proposed to eliminate …