This paper presents a new learning algorithm for the fuzzy adaptive resonance theory. The modification allows us to supervise the fuzzy ART and to simplify ARTMAP network. It consists to find network’s parameters (comparison, training and vigilance) which gave the minimum quadratic distances between the output of the training base and those obtained by the network. A comparative study of these ...

This paper implements a Neuro-Fuzzy (FNN) approach to autonomously navigate a car-like robot in an unknown environment. The applied technique allows the robot to avoid obstacles and locally search for a path leading to the goal after learning and adaptation. It is based on two Fuzzy Artmap neural networks, a Reinforcement trial and error neural network and a Mamdani fuzzy logic controller (FLC)...

A neural network recognition and tracking system is proposed for classification of radar pulses in autonomous Electronic Support Measure systems. Radar type information is considered with position-specific information from active emitters in a scene. Type-specific parameters of the input pulse stream are fed to a neural network classifier trained on samples of data collected in the field. Meanw...

In this paper we are examining the issue of overtraining in Fuuy ARTMAP. Over-training in Fuuy ARTMAP manifests itself in two different ways: (a) it degrades the generalization performance of Fuzzy ARTMAP as training progresses, and (b) it creates unnecessarily large Fuuy ARTMAP neural network architectures. In this work we are demonstrating that overtraining happens in Fuuy ARTMAP and we propo...

In this article a modification of the algorithm of the fuzzy ART network, aiming at returning it supervised is carried out. It consists of the search for the comparison, training and vigilance parameters giving the minimum quadratic distances between the output of the training base and those obtained by the network. The same process is applied for the determination of the parameters of the fuzz...

FAMR (Fuzzy ARTMAP with Relevance factor) is a FAM (Fuzzy ARTMAP) neural network used for classification, probability estimation [3], [2], and function approximation [4]. FAMR uses a relevance factor assigned to each sample pair, proportional to the importance of that pair during the learning phase. Due to its incremental learning capability, FAMR can efficiently process large data sets and is ...

In this paper we introduce, to the best of our knowledge, the first adaptation of the Fuzzy ARTMAP neural network for its execution on a GPU, together with a self-designed neural network based on ART models called SOON. The full VisTex database, containing 167 texture images, is proved to be classified in a very short time using these GPU-based neural networks. The Fuzzy ARTMAP neural network i...

ARTMAP based architectures have several desirable properties that make them very suitable for pattern classification problems. However, they suffer from category proliferation. Distributed coding has been proposed as a solution for memory compression. dARTMAP neural network has been introduced as a modification of Fuzzy ARTMAP that, due to distributed learning, achieves code compression while f...

This paper presents a new method of recognising hand-written postcodes using the Fuzzy ARTMAP neural network, a relatively new architecture of the neural network family. It has the capability to process in parallel as well as the ability to learn and make decisions. The image of the specially formatted envelopes are captured by a frame grabber and processed by an image processing software calle...