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- Rafal Dlugosz, Tomasz Talaska, Witold Pedrycz, Ryszard Wojtyna
- IEEE Transactions on Neural Networks
- 2010

This paper presents a complementary metal-oxide-semiconductor (CMOS) implementation of a conscience mechanism used to improve the effectiveness of learning in the winner-takes-all (WTA) artificial neural networks (ANNs) realized at the transistor level. This mechanism makes it possible to eliminate the effect of the so-called ¿dead neurons,¿ which do not… (More)

- R. WOJTYNA, T. TALAŚKA
- 2006

A simple analog circuit is presented which can play a neuron role in static-model-based neural networks implemented in the form of an integrated circuit. Operating in a transresistance mode it is suited to cooperate with transconductance synapses. As a result, its input signal is a current which is a sum of currents coming from the synapses. Summation of… (More)

In this study, we present a hardware implementation of the conscience mechanism in Kohonen self-organizing maps. The proposed realization of the conscience mechanism is important to the functioning of the neural network as it eliminates so-called dead (inactive) neurons. As a result the network learning, the level quantization error can be reduced. The… (More)

- Ryszard Wojtyna
- Proceedings of the 17th International Conference…
- 2010

The paper deals with hardware implemented Kohonen neural networks capable of fast learning on silicon. An analog network for calculating Euclidean distance is presented. The circuit is well suited to be used in competitive learning using a WTA (Winner Takes All) as well as WTM (Winner Takes Most) methods, where the Euclidean distance can be applied as a… (More)

- Marta Kolasa, Ryszard Wojtyna, Rafał Długosz, Wojciech Jóźwicki
- 2009

This paper presents an application of an artificial neural network to determine survival time of patients with a bladder cancer. Different learning methods have been investigated to find a solution, which is most optimal from a computational complexity point of view. In our study, a model of a multilayer perceptron with a training algorithm based on an… (More)

- R. Dlugosz, T. Talaska, J. Dalecki, R. Wojtyna
- 2008 15th International Conference on Mixed…
- 2008

In this paper, we present an experimental current-mode Kohonen neural network (KNN) implemented in a CMOS 0.18 μm process. The network contains four output neurons. Each neuron has three analog weights related to three inputs. The presented KNN has been realized using building blocks proposed earlier by the authors, such as binary tree current-mode… (More)

- Jaroslaw Majewski, Ryszard Wojtyna
- Signal Processing Algorithms, Architectures…
- 2010

In this paper, a problem of discovering numeric laws governing a trained neural network is considered. We propose new multilayer perceptrons implementing fractional rational functions, i.e. functions expressed as ratio of two polynomials of any order with a given number of components in the function numerator and denominator. Our networks can be utilized… (More)

- T. Talaska, R. Wojtyna, R. Dlugosz, K. Iniewski
- Proceedings of the International Conference Mixed…
- 2006

Hardware implementation of the conscience mechanism in Kohonen's neural networks is presented in this work. The conscience mechanism is an important component of the neural network as it eliminates so called dead neurons leading to larger network efficiency and smaller quantization error. The conscience mechanism itself and winner take all (WTA) block have… (More)

- Jaroslaw Majewski, Ryszard Wojtyna
- 2012 Joint Conference New Trends In Audio & Video…
- 2012

The issue of effective learning specific neural networks capable of creating symbolic description of rules governing a set of empirical data is considered. In the field of our interests are atypical perceptrons suitable for implementing partial-rational or polynomial functions that describe the data set. A novel factor of the presented approach is an… (More)

- Ryszard Wojtyna
- Signal Processing Algorithms, Architectures…
- 2009

In this paper, possibilities and restrictions of analog signal processing in the context of its application to neural networks are presented. Factors influencing the processing effectiveness are outlined. Current-mode as well as voltage mode techniques are considered. Both techniques possess advantages and disadvantages but the number of situations where… (More)