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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...
This paper presents a programmable analog current-mode circuit used to calculate the distance between two vectors of currents, following two distance measures. The Euclidean (L2) distance is commonly used. However, in many situations, it can be replaced with the Manhattan (L1) one, which is computationally less intensive, whose realization comes with less power dissipation and lower hardware complexity...
The paper presents a new, mixed analog-digital, circuit for analog sorting signals. In comparison to other circuits of this type the proposed solution offers large versatility. The main objective is its application in Neural Gas (NG) learning algorithm used to train unsupervised neural networks (NNs). However, the circuit can also be used in nonlinear processing of analog signals. It is capable of...
The paper presents a simple current-mode conscience mechanism used in hardware implemented Winner Takes All neural networks to eliminate the problem of the, so called, dead neurons. These neurons reduce the number of data classes that can be distinguished by the NN, i.e. they decrease the efficiency of the NN. The circuit has been realized in the TSMC CMOS 0.18 μm process. At data rate of about 10...
The paper presents a new CMOS implementation of the initialization mechanism for Kohonen self-organizing neural networks. A proper selection of initial values of the weights of the neurons exhibits a significant impact on the quality of the learning process. A straightforward realization of the initialization block in software is simple, but in hardware it requires providing the programming signal...
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