Displaying similar documents to “A nonlinear projection neural network for solving interval quadratic programming problems and its stability analysis.”

The Placement of Electronic Circuits Problem: A Neural Network Approach

M. Ettaouil, K. Elmoutaouakil, Y. Ghanou (2010)

Mathematical Modelling of Natural Phenomena

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The goal of this paper is to apply the Continuous Hopfield Networks (CHN) to the Placement of Electronic Circuit Problem (PECP). This assignment problem has been expressed as Quadratic Knapsack Problem (QKP). To solve the PECP via the CHN, we choose an energy function which ensures an appropriate balance between minimization of the cost function and simultaneous satisfaction of the PECP constraints. In addition, the parameters of this ...

Neural networks as a tool for georadar data processing

Piotr Szymczyk, Sylwia Tomecka-Suchoń, Magdalena Szymczyk (2015)

International Journal of Applied Mathematics and Computer Science

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In this article a new neural network based method for automatic classification of ground penetrating radar (GPR) traces is proposed. The presented approach is based on a new representation of GPR signals by polynomials approximation. The coefficients of the polynomial (the feature vector) are neural network inputs for automatic classification of a special kind of geologic structure-a sinkhole. The analysis and results show that the classifier can effectively distinguish sinkholes from...

Local stability conditions for discrete-time cascade locally recurrent neural networks

Krzysztof Patan (2010)

International Journal of Applied Mathematics and Computer Science

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The paper deals with a specific kind of discrete-time recurrent neural network designed with dynamic neuron models. Dynamics are reproduced within each single neuron, hence the network considered is a locally recurrent globally feedforward. A crucial problem with neural networks of the dynamic type is stability as well as stabilization in learning problems. The paper formulates local stability conditions for the analysed class of neural networks using Lyapunov's first method. Moreover,...