newton's method and the fastest descent in network optimization

The behavior of learnable systems is expressed byfeedback algorithms, which are called learning rules. thereare different types of learning rules for neural networks, andfunctional learning is one of them. in this type of learning,the parameters of the network are adjusted in such a way thatthe performance of the network is optimized. optimizingnetwork performance means minimizing the error that existsbetween the experimental values and the network response.in this article, in order to optimize network performance, wecompare the two methods of fastest descent and newton'smethod