Abstract
In this research the performance of the BJT has been improved using the "Feed Forward – Back Propagation Artificial Neural Network" (FFBPANN). The use of this type of networks led to improve the pre specified functions, by widening its bandwidth, improving its sensitivity to the minimum and maximum values of input signals, and reduce the effect of the rise of the temperature on its performance. The improvement done on the type "npn" of the code "2N2222A /ZTX". The execution of this work passed through three stages using various types of computer's programs. The first step have been done using the "Orcad Pspice" program, the second stage; the collected data from the first stage have been introduced as the input data of the "FFBPANN" that represented using "MATLAB R2013b" and the third stage have been done using the (ISE, Project navigator (P.14.2)) in order to apply the results of second stage on the "Field Programmable Gate Array" chip (FPGA).
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