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By Pankaj Mehra

The objective of this booklet is to make man made Neural Networks obtainable to scholars, academicians, engineers, and different execs who are looking to find out about the sector , in addition to to researchers, who can use this educational to develop into trained approximately present learn.

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J. Op. Res. Soc. : Power and Interdependence. : Ties that bind: The role of trade in international conflict processes, 1950–1992. : Proper nouns and methodological propriety: pooling dyads in international relations data. Int. Organ. : Designing Social Inquiry: Scientific Inference in Qualitative Research. : Territorial disputes and interstate war, 1945–1987. J. Polit. : Assessing different bayesian neural network models for militarised interstate dispute outcome and variable influence. Social Sci.

Sci. : Improving quantitative studies of international conflict: a conjecture. Am. Polit. Sci. Rev. : Theory and evidence in international conflict: a response to de Marchi, Gelpi and Grynaviski. Am. Polit. Sci. Rev. : Research design and estimator choices for analyzing interstate dyads: when decisions matter. J. Conflict Resolut. : What Is Computational Intelligence? Computational Intelligence Imitating Life. : Do intergovernmental organizations promote peace? World Polit. : Dangerous dyads: conditions affecting the likelihood of interstate war.

In simple terms, back-propagation is applied to identify the network weights given the training data, using an optimization method. 2 Mathematical Framework 29 can be identified using the following iterative method (Werbos et al. 1974; Zhao et al. 6) In Eq. 6, the parameter Á is the learning rate while fg represents a vector. The minimization of the objective function, E, is achieved by calculating the derivative of the errors in Eq. 3 with respect to the network’s weight. 2/ In Eq. aj / and ak D M j D0 wkj yj .

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