6 We present in this paper a variation of the self-organising map algorithm where the original 7 time-dependent (learning rate and neighbourhood) learning function is replaced by a time8 invariant one. This allows for on-line and continuous learning on both static and dynamic 9 data distributions. One of the property of the newly proposed algorithm is that it does 10 not fit the magnification l...

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...

We present two separate algorithms for unsupervised multimodal processing. Our first proposal, the singlepass Hebbian linked self-organising map network, significantly reduces the training of Hebbian-linked selforganising maps by computing in a single epoch the weights of the links associating the separate modal maps. Our second proposal, based on the counterpropagation network algorithm, imple...

Speech recognition is a diicult problem due to the inability of current systems to cope with connected speech. Neural networks are able to learn some aspects of this task. An unsupervised learning scheme like the self-organising map can be used to both classify and order the speech sounds and provide a front end to higher level processing. A map of phonemes (phonotopic map) is used to trace tra...

induction motor bearing is one of the key parts of the machine and its analysis and interpretation are important for fault detection. in the present work vibration signal has been taken for the classification i.e. bearing is healthy (h) or defective (d). for this purpose, clustering based classification of bearing vibration data has been carried out using principal component analysis (pca) and ...

The freely available law on the Internet could be one of the best application areas of text classification and labelling. This paper explores the high potential of the self-organising map for information reconnaissance by classifying and describing unknown legal text collections. The maps can be seen as topic-oriented libraries that are automatically created without intellectual input. The clus...

Fruit quality and productivity datasets, obtained over two seasons from 24 New Zealand persimmon orchards, were associated with a self organising map trained on the spatial coordinates and geographic region of each orchard. Using this approach, a summary representation of the geographic distribution of the orchards was obtained from the projection plane of the self organising map. By overlaying...

We propose a method for decreasing the computational complexity of self-organising maps. The method uses a partitioning of the neurons into disjoint clusters. Teaching of the neurons occurs on a cluster-basis instead of on a neuron-basis. For teaching an order in a self-organising map, and show that the introduced algorithm behaves as well as the original algorithm.