Key words
cellular neural networks, oscillating network, optical neurons, stochastic algorithms of the neural network learning, algorithm of iterative search, optoelectronic amplifier, data transmission systems with many carriers.
articles
The author:
Lavrenkov Y. Published in
№ 5(71)
31 october 2017 year
Rubric: Network Technologies
Rubric: Network Technologies
Adaptive control of frequency efficient data transmission system based on the neural network with optically-coupled elements
The transmission of data in multichannel systems with many carriers is implemented in order to increase
the reliability of delivery of data messages. The article is dedicated to the solution of problem
of optimal use of the available frequency band (limitations on power capability of the transmitter and
complexity of the receiver should be taken into account). The construction of the adaptive system,
which can be used for the determination of optimal set of frequencies for data distribution, requires the
usage of cellular neural networks. These networks can be used for the analysis of signal environment
and selection of set of carrier frequencies. Characteristics of cellular networks correspond to principles
of fast tunable transformations, which can be effectively implemented at the hardware level. The reduction
of the number of synaptic connections increases the computational efficiency and makes it
possible to use such networks for processing of high-dimensional data. The article considers the structural
synthesis of the cellular network, which has optical connections between neurons and has a high
efficiency in data processing. The proposed connections can be used as a basis for the construction of
modular intellectual structures, which consist of homogeneous fragments. The neural network learning
is implemented with the help of methods of local learning (with elements of the swarm intelligence).
The presence of 32‑bit microcontroller in every neuron core justifies the use of this strategy (despite
high memory requirements). The proposed modular structure effectively combines the local learning
of neural structures and global learning of the whole network. The evaluation of the obtained set of
frequencies at specific moments of time proved the effectiveness of the use of reconfigurable cellular
neural networks. The structure of such network can be optimized for a specific computational task. The
unitization of the structure allows you to simultaneously construct a large number of networks. Such
approach makes it possible to form a large number of carriers within the predefined frequency band.
The author:
Lavrenkov Y.
Degree:
PhD in Technique, Kaluga Branch of the Bauman Moscow State Technical University
Location:
Kaluga
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