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The self-organizing algorithm of Kohonen is well known for its ability to map an input space. That technique is named as Self-Organizing Map — SOM. A SOM can be trained in a short period of time with a few optimization techniques such as “winning” neurons search scope limit. In this paper we propose alternative options for improving the SOM learning speed. The basic idea of the proposed modification...
Artificial neural networks have occupied significant niche in IT world, but the general concept still has some unresolved issues. This paper is devoted to Bayesian network, which is the probabilistic model organized in acyclic graph. Providing brief introduction in the world of artificial neural networks and Bayesian approach in particular we then move to the proof of the idea that Bayesian networks...
The paper considers the issue of effective formation of a representative sample to train a neural network of a multilayer perceptron (MLP). As it is known, the key problem of MLP training is the factor space division into the test, validation and training sets. To solve this problem, an approach based on the use of clustering and a Lipschitz constant estimate is put forward. Kohonen's self-organizing...
The main objective of this research is to develop a method to construct a neural tuner of PI-controller parameters. Such tuning is necessary to improve energy efficiency of nonlinear industrial plants since the PI-controller is linear, and its parameters certain values are optimal only for a certain plant functioning mode. The tuner consists of a neural network calculating the controller parameters...
A lot of artifiicial neural networks were proposed by scientists over the last time. Each of them can cope with the tasks of limited difficulty level, determined by their properties and capabilities. The aim of this paper is to outline difference of them and to define their positive and negative sites in different tasks of identification and control.
The article discusses spiking neural networks, their uniqueness, their ability to training, architecture, and the possibility of a hardware implementation. Special attention is given to reveal the prospects for the development and application of spiking neural networks for the implementation in robotics and control systems.
Artificial Neural networks (ANN) are used to solve various problems which are difficult to be solved by traditional linear methods or even not solved. In practice, ANN are used in two ways — as software running on conventional computer and as a specialized hardware and software systems. For the control system the signal processing is required to be performed in real time and that can be achieved only...
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