Nowadays, there is increasing demand for an integrated system usable to real-time
environments under limited computational resources and minimum operator supervision. In …

This study investigates the effect of inputs used on event-based runoff forecasting by ANFIS.
Fifteen ANFIS models were compared, differentiated by the choice of rainfall and/or …

Input selection for data-driven rainfall-runoff models is an important task as these models
find the relationship between rainfall and runoff by direct mapping of inputs to output. In this …

Based on the principles of technical analysis, this paper proposes an artificial intelligence
model, which employs the Adaptive Network Fuzzy Inference System (ANFIS) supplemented …

Current state-of-the-art online neuro fuzzy models (NFMs) such as DENFIS (Dynamic
Evolving Neural-Fuzzy Inference System) have been used for runoff forecasting. Online …

The requirement to manage complicated nonlinear systems with high uncertainty is one of
the main drivers of progress in the identification and control discipline. Since most of the …

A study using local learning Neuro-Fuzzy System (NFS) was undertaken for a rainfall–runoff
modeling application. The local learning model was first tested on three different …

For many practical weakly nonlinear systems we have their approximated linear model. Its
parameters are known or can be determined by one of typical identification procedures. The …

Genetic complementary learning (GCL) is a biological brain‐inspired learning system based
on human pattern recognition, and genes selection process. It is a confluence of the …

There are generally two approaches to the design of a neural fuzzy system:(1) design by
human experts, and (2) design through a self-organization of the numerical training data …