Viral V. Tolat

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In this paper a new method for analyzing Kohonen's self-organizing feature maps is presented. The method makes use of a system of energy functions, one energy function for each processing unit. It is shown that the training process is equivalent to minimizing each energy function subject to constraints. The analysis is used to prove the formation of(More)
An adaptive network with visual inputs has been trained to balance an inverted pendulum. Simulation results show that the network is capable of extracting the necessary state information from time sequences of crude visual images. A single linear adaptive threshold element (ADALINE) was adequate for this task. When tested by simulation, the performance(More)
The ability to recognize sequences is important for applications such as speech processing, vision, and control systems. A self-organizing neural network model that is able to form an ordered map of a sequence is presented. The model is based on extensions to T. Kohonen's self-organizing topology maps (Self-Organization and Associative Memory,(More)
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