in this paper, a recurrent fuzzy-neural network (rfnn) controller with neural network identifier in direct control model is designed to control the speed and exhaust temperature of the gas turbine in a combined cycle power plant. since the turbine operation in combined cycle unit is considered, speed and exhaust temperature of the gas turbine should be simultaneously controlled by fuel command ...

Incremental artificial neural networks grow when they learn and shrink when they forget. Competitive Hebbian learning generates the network structure by addition and removal of cells and links. Thus, no network design phase is necessary. The growing cell structure and the growing neural gas network may replace common feed-forward networks in a lot of classification and interpolation tasks.

the paper deals with data envelopment analysis (dea) and artificial neural network (ann). we believe that solving for the dea efficiency measure, simultaneously with neural network model, provides a promising rich approach to optimal solution. in this paper, a new neural network model is used to estimate the inefficiency of dmus in large datasets.

In this paper, an artificial neural network is proposed for feature extraction of hand written characters. The learning algorithm is developed based on a proposed modified Sammon’s stress for our feedforward neural networks, which can not only minimize intra class pattern distances but also preserve interclass distances in the output feature space. The proposed feature extraction method tries t...

struggling in world's competitive markets, industries are attempting to upgrade their technologies aiming at improving the quality and minimizing the waste and cutting the price. industry tries to develop their technology in order to improve quality via proactive quality control. this paper studies the possible paint quality in order to reduce the defects through neural network techniques in au...

One of the classical topics in neural networks is winnertake-all (WTA), which has been widely used in unsupervised (competitive) learning, cortical processing, and attentional control. With global connectivity WTA networks, however, do not encode spatial relations in the input, and thus cannot support sensory and perceptual processing where spatial relationships are important. We propose a new ...

A routing scheme that uses a neural network has been developed that can aid in establishing point-to-point communication routes through multistage interconnection networks (MINs). The neural network is a network of the type that was examined by Hopfield (Hopfield, 1984 and 1985). In this work, the problem of establishing routes through random MINs (RMINs) in a shared-memory, distributed computi...

In this study we introduce a neural network ensemble composed of several linear perceptrons, to be used as a classifier that can rapidly be trained and effectively deals with nonlinear problems. Although each member of the ensemble can only deal with linear classification problems, through a competitive training mechanism, the ensemble is able to automatically allocate a part of the learning sp...

Detecting network intrusions is becoming crucial in computer networks. In this paper, an Intrusion Detection System based on a competitive learning neural network is presented. Most of the related works use the self-organizing map (SOM) to implement an IDS. However, the competitive neural network has less complexity and it is faster than the SOM, achieving similar results. In order to improve t...

When a stimulus reaches our sensory system, it evokes a perceptual schema. This schema, subsequently, produces a central neiral representation of the stimulus. I want to investigate what properties these neural representations must have to support complex cognitive processes. To do this, I present a description of neural networks that makes it possible to bridge the gap between the neuronal and...