ABSTRACT:In this article, a number of posterior probabilistic based equations were introduced to detect the effect of controlling the correlation between variables on the behavior of feed forward neural network weights. In this paper it was proofed that, under certain assumptions, in a feed forward neural network with backprobagation learning algorithm, the correlation between the input variables on one side and the target variable on the other, is directly proportional to the values of the connection weights from the input layer to the output layer through the hidden layer.

No abstract

No abstract

In this paper, we introduce the neighborhood [Formula: see text] of analytic functions [Formula: see text] and [Formula: see text] defined in the open unit disc. Furthermore, we derive some sufficient and necessary conditions to be in [Formula: see text]. In addition, we see some applications of Jack’s lemma.