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AN INFORMATION THEORETICAL APPROACH TO NEURAL NETWORKS

Systems Science and Mathematical Sciences 1993年第4期6卷 353-372页

作者： caro lucas Electrical Engineering Department Tehran University P. O. Box 14155/6181 Tehran IRAN

The purpose of this paper is to present a unified theory of several differentneural networks that have been proposed for solving various computation, pattern recog-nition, imaging, optimization, and other problems. The functioning of these networks ischaracterized by Lyapunov energy functions. The relationship between the deterministicand stochastic neural networks is examined. The simulated annealing methods for findingthe global optimum of an objective function as well as their generalization by injectingnoise into deterministic neural networks are discussed. A statistical interpretation of thedynamic evolution of the different neural networks is presented. The problem of trainingdifferent neural networks is investigated in this general framework. It is shown how thisapproach can be used not only for analyzing various neural networks, but also for the choiceof the proper neural network for solving any given problem and the design of a trainingalgorithm for the particular neural network.

关键词： Neural networks stochastic systems information energy entropy

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Detection of Subsurface Cavities in a Power Plant Through Artificial Neural Network from Micro-Gravity Data

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地学前缘 2009年第S1期16卷 59-59页

作者： Alireza Hajian caro lucas Center of Excellence for Control and Intelligent Processing ECE DepartmentTehran University

Subsurface cavities are very susceptible subsurface locations for down-lifting of a dam *** order to detect the low-density zones of a power plant a micro-gravity survey was conducted in a site considered for construction of a power plant site in *** we gain the residual anomalies through bouger anomalies and then we design an Artificial Neural Network(ANN)which is trained by a set of training *** ANN was tested for both synthetic and real *** real data some suitable features are derivate from residual anomalies and applied to

关键词： artificial neural network power plant microgravity cavity

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