Please use this identifier to cite or link to this item: http://hdl.handle.net/10525/932

 Title: Intellect Sensing of Neural Network that Trained to Classify Complex Signals Authors: Reznik, A.Galinskaya, A. Keywords: Neural NetworksComplex Signals Issue Date: 2003 Publisher: Institute of Information Theories and Applications FOI ITHEA Abstract: An experimental comparison of information features used by neural network is performed. The sensing method was used. Suboptimal classifier agreeable to the gaussian model of the training data was used as a probe. Neural nets with architectures of perceptron and feedforward net with one hidden layer were used. The experiments were carried out with spatial ultrasonic data, which are used for car’s passenger safety system neural controller learning. In this paper we show that a neural network doesn’t fully make use of gaussian components, which are first two moment coefficients of probability distribution. On the contrary, the network can find more complicated regularities inside data vectors and thus shows better results than suboptimal classifier. The parallel connection of suboptimal classifier improves work of modular neural network whereas its connection to the network input improves the specialization effect during training. URI: http://hdl.handle.net/10525/932 ISSN: 1313-0463 Appears in Collections: Volume 10 Number 2

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