A machine learning technique develops the best-fit model by adjusting weights based on
learning from data. Similarly, adaptive neuro-fuzzy inference system (ANFIS) is also one of …

Adaptive neuro-fuzzy inference system (ANFIS) has produced promising results in model
approximation. The core of ANFIS computation lies in the training of its parameters …

Abstract Adaptive Neuro-Fuzzy Inference System (ANFIS) has been widely applied in
industry as well as scientific problems. This is due to its ability to approximate every plant …

In this study, we propose an Adaptive and Hybrid Artificial Bee Colony (aABC) algorithm to
train ANFIS. Unlike the standard ABC algorithm, two new parameters are utilized in the …

This paper presents the result of research in developing a novel training model for Adaptive
Neuro-Fuzzy Inference Systems (ANFIS). ANFIS integrates the learning ability of Artificial …

In recent years, many applications based on the Neural Network, Neuro-Fuzzy, and
optimization algorithms have been more common for solving regression and classification …

Since the last decade, classification methods are useful in a wide range of applications.
Classification is a task to segregate the sample into different groups. This capability can be …

The result of training parameters described Adaptive Neuro-Fuzzy Inference System (ANFIS)
performance. The speed and reliability of training effect depend on the training mechanism …

The impact of innovated Neuro-Fuzzy System (NFS) has emerged as a dominant technique
for addressing various difficult research problems in business. ANFIS (Adaptive Neuro …

Abstract Artificial Neural Networks (ANN) performance depends on network topology,
activation function, behaviors of data, suitable synapse's values and learning algorithms …