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The paper presents a novel circuit for the calculation of Manhattan distance between two vectors of signals, suitable for various machine learning algorithms realized at the transistor level. In Self-Organizing Artificial Neural Networks, for example, one of the basic operations is the calculation of a distance between input learning patterns and vectors of neuron weights. In pattern recognition two...
The paper presents a novel transistor level implementation of a triangular neighborhood function (TNF) suitable for self-organizing maps (SOMs) realized as Application-Specific Integrated Circuit (ASIC). Our previous investigations have shown that using the TNF instead of more complex Gaussian neighborhood function (GNF) is sufficient to achieve good learning properties. It can be said that the TNF...
The paper presents a new initialization mechanism based on a Convex Combination Method (CCM) for Kohonen self-organizing Neural Networks (NNs) realized in the CMOS technology. A proper selection of initial values of the neuron weights exhibits a strong impact on the quality of the overall learning process. Unfortunately, in case of real input data, e.g. biomedical data, proper initialization is not...
Initialization of neuron weights is one of key problems in artificial neural networks (ANNs). This problem is particularly important in ANNs implemented as Application Specific Integrated Circuits (ASICs), where the number of the weights becomes large. When ANNs are implemented in software, the weights can be easily programmed. In contrast, in parallel systems of this type realized as ASICs it is...
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