In this paper we present a self-organizing neural network model of early lexical development called DevLex. The network consists of two self-organizing maps (a growing semantic map and a growing phonological map) that are connected via associative links trained by Hebbian learning. The model captures a number of important phenomena that occur in early lexical acquisition by children, as it allo...

A hybrid recurrent neural network is shown to learn small initial mealy machines (that can be thought of as translation machines translating input strings to corresponding output strings, as opposed to recognition automata that classify strings as either grammatical or nongrammatical) from positive training samples. A well-trained neural net 1 is then presented once again with the training set ...

The current work addresses quantum machine learning in the context of Quantum Artificial Neural Networks such that the networks’ processing is divided in two stages: the learning stage, where the network converges to a specific quantum circuit, and the backpropagation stage where the network effectively works as a self-programing quantum computing system that selects the quantum circuits to sol...

This paper gives an overview of some classical Growing Neural Networks (GNN) using soft competitive learning. In soft competitive learning each input signal is characterized by adapting in addition to the winner also some other neurons of the network. The GNN is also called the ANN with incremental learning. The artificial neural networks (ANN) mapping capability depends on the number of layers...

The paper presents the design of municipal creditworthiness parameters. Further, the design of model for municipal creditworthiness classification is presented. The realized data pre-processing makes the suitable economic interpretation of results possible. Municipalities are assigned to clusters by unsupervised methods. The combination of Kohonen’s self-organizing feature maps and K-means algo...

The objective of this study is to investigate the correlation between the internal topological organization in neural network and the learning ability of the neural network. This study is motivated by the interesting neurophysiological examination that shows the significance of topographic map of adult mammals’brains to their learning ability and plasticity. In this study we propose a model of ...

The problem of image segmentation can be formulated as one of vector quantization. Although self-organizing networks with competitive learning are useful for vector quantization, they, in their original single-layer structure, are inadequate for image segmentation. This paper proposes and describes a hierarchical self-organizing neural network for image segmentation. The hierarchical self-organ...

This paper introduces the DANTE project (Detection of Anomalies and Novelties in Time sEries with self-organizing networks), the goal of which is to evaluate several self-organizing networks in the detection of anomalies/novelties in dynamic data patterns. In this paper, we first describe three standard clustering-based approaches which use well-known self-organizing neural architectures, such ...

In this paper, we study instances of complex neural networks, i.e. neural networks with complex topologies. We use Self-Organizing Map neural networks whose neighborhood relationships are defined by a complex network, to classify handwritten digits. We show that topology has a small impact on performance and robustness to neuron failures, at least at long learning times. Performance may however...

In this paper, we study instances of complex neural networks, i.e. neural networks with complex topologies. We use Self-Organizing Map neural networks whose neighbourhood relationships are defined by a complex network, to classify handwritten digits. We show that topology has a small impact on performance and robustness to neuron failures, at least at long learning times. Performance may howeve...