The main goal of this paper is to present a new learning algorithm which has been applied to feedforward neural networks. It was used not only during the learning phase of the network, but also to optimise the number of hidden neurons. This learning algorithm is inspired on the classical backpropagation algorithm but it owns some variations due to kind of network used. This algorithm was applie...

Feature selection plays an important role in improving the classification accuracy by handling redundant or irrelevant features present in the dataset. Various soft computing based hybrid approaches like neuro-fuzzy, genetic-fuzzy, rough set-neuro etc. are proposed by researchers to perform feature selection. The existing approaches gives higher complexity and computational cost with low classi...

The paper introduces ontogenic Fuzzy-CID3 algorithm (F-CID3) which combines a neural network algorithm and fuzzy sets into a single hybrid algorithm which generates its own topology. Two new methods, one based on a concept of a neural fuzzy number tree, and a class separation method are introduced in the paper and utilized in the algorithm. The F-CID3 algorithm is an extension of an ontogenic C...

In this paper, a neuro-fuzzy system identification using measured input and output data are carried out. A model-free learning from “examples” methodology is developed to train a neuro-fuzzy model of a smallsize helicopter. The helicopter model is obtained and tuned using training data gathered while a teacher operates the helicopter. Behavior-based model architecture is used, with each behavio...

The synergy of the two paradigms, neural network and fuzzy inference system, has given rise to rapidly emerging filed, neuro-fuzzy systems. Evolving neuro-fuzzy systems are intended to use online learning to extract knowledge from data and perform a high-level adaptation of the network structure. We explore the potential of evolving neuro-fuzzy systems in reinforcement learning (RL) application...

In this paper, an architecture based on the anatomical structure of the emotional network in the brain of mammalians is applied as a prediction model for chaotic time series studies. The architecture is called Brain Emotional Learning-based Recurrent Fuzzy System (BELRFS), which stands for: Brain Emotional Learning-based Recurrent Fuzzy System. It adopts neuro-fuzzy adaptive networks to mimic t...

H. Ghezelayagh and K. Y. Lee are with the Department of Electrical Engineering, The Pennsylvania State University, University Park, PA16802, USA (e-mail: [email protected]). Abstract In an intelligent predictive controller, a neuro-fuzzy identifier predicts the response of the plant in future time interval, and provides a non-model based control approach. This identifier generates fuzzy rules an...

This paper develops a vision-based neuro-fuzzy system to control the weld joint penetration in Gas Tungsten Arc Welding (GTAW) of thin sheets. To this end, a camera equipped with a specially designed composite light-filter is used to observe the weld pool from the topside of the workpiece so that comparatively distinct images of the weld pool are obtained. As the Backside weld Width (BW) reflec...

A modification of the neo-fuzzy neuron is proposed (an extended neo-fuzzy neuron (ENFN)) that is characterized by improved approximating properties. An adaptive learning algorithm is proposed that has both tracking and smoothing properties and solves prediction, filtering and smoothing tasks of non-stationary “noisy” stochastic and chaotic signals. An ENFN distinctive feature is its computation...