Neuro-fuzzy systems have attracted the growing interest of researchers in various scientific
and engineering areas due to its effective learning and reasoning capabilities. The neuro …

The enormous consumption of fossil fuels from various human activities leads to a significant
amount of anthropogenic CO 2 emission into the atmosphere, which has already massively …

In this paper, to compute the firing strength values of type-2 fuzzy models, a soft version of
minimum is presented, which endows the fuzzy model with the ability to solve large …

An evolutionary recurrent network which automates the design of recurrent neural/fuzzy
networks using a new evolutionary learning algorithm is proposed in this paper. This new …

The present article is a novel attempt in providing an exhaustive survey of neuro-fuzzy rule
generation algorithms. Rule generation from artificial neural networks is gaining in …

Reliable and precise multi-step-ahead flood forecasts are crucial and beneficial to decision
makers for mitigating flooding risks. For a river basin undergoing fast urban development, its …

In this paper, a TSK-type recurrent fuzzy network (TRFN) structure is proposed. The proposal
calls for the design of TRFN by either neural network or genetic algorithms depending on the …

This paper presents a novel recurrent fuzzy neural network, called an interactively recurrent
self-evolving fuzzy neural network (IRSFNN), for prediction and identification of dynamic …

This paper presents a fuzzy modeling approach for identification of dynamic systems. In
particular, a new fuzzy model, the Dynamic Fuzzy Neural Network (DFNN), consisting of …

In this paper, a locally feedback dynamic fuzzy neural network (LF-DFNN) for modeling of
temporal processes is suggested. The model is composed of dynamic TSK-type fuzzy rules …