The Artificial Neural Networks method is applied on visual working efficiency of cockpit. A Self-Organizing Map (SOM) network is demonstrated selecting material with near properties. Then a Back-Propagation (BP) network automatically learns the relationship between input and output. After a set of training, the BP network is able to estimate material characteristics using knowledge and criteria...

A timbre classification system based on auditory processing and Kohonen self organizing neural networks is described. Preliminary results are given on a simple classification experiment involving 12 instruments in both clean and degraded conditions.

Feedforward neural networks have been successfully developed and applied in many areas because of their universal approximation capability. However, there still remains the problem of determining a suitable network structure for the given task. In this paper, we propose a novel self-organizing neural network which automatically adjusts its structure according to the task. Utilizing both the con...

This second part of a Tutorial on neural networks focuses on the Kohonen self-organising feature map and the Hopfield network. First a theoretical description of each type is given. The practical issues concerning applications of the networks are then discussed. For each network, a description is given of the types of problems which can be tackled by the specific neural network, followed by a p...

In this paper we analyze the convergence properties of a class of self-organizing neural networks, introduced and popularized by Kohonen, using the ODE approach. It is shown that Kohonen's learning law converges to the best locally a ne feature map. A new integrally distributed self-organizing learning law which converges to the equiprobable feature map for inputs which have arbitrary random pr...

‘Integrated’ classification refers to the conjunctive or competitive use of two or more (neural) classifiers. A cooperative neural network system comprising two independently trained Kohonen networks and co-operating with the help of a Hebbian network, is described. The effectiveness of such a network is demonstrated by using it to retrieve images and related texts from a multi-media database. ...

The reduction of harmful emissions is affecting increasingly the modern-day production of energy, while higher objectives are set also for the efficiency of combustion processes. Therefore it is necessary to develop such data analysis and modeling methods that can respond to these demands. This paper presents an overview of how the formation of nitrogen oxides (NOx) in a circulating fluidized b...

A three layer perceptron network is used to classify the /i/ sound using isolated words from diierent speakers. A classiication accuracy of 97% has been achieved. A map of phonemes is used to trace trajectories of utterances using the self-organising neural network. A crinkle factor is proposed which allows using the self-organising map to determine the inherent dimensionality of a set of point...

This paper deals with the Kohonen Self-organizing maps for cluster analysis applications. The cluster analysis represents a group of methods whose aim is to classify the objects into clusters. For solving cluster analysis applications many new algorithms using neural networks have been used. This paper describes the use of an advanced method of neural network represented by Kohonen self-organiz...

Maximizing the profit and minimizing the loss notwithstanding the trend of the market is always desirable in any investment strategy. The present research develops an investment strategy, which has been verified effective in the real world, by employing self-organizing map neural network for mutual funds tracking the trends of stock market indices according to macroeconomics indicators and weig...