New Scopes in Artificial Neural Network

In this paper I describe the use of neural network in various related fields. Artificial neural networks are parallel computational models, comprised of densely interconnected adaptive processing units. These networks are fine-grained parallel implementations of nonlinear static or dynamic systems. A very important feature of these networks is their adaptive nature where "learning by example" replaces "programming" in solving problems. This feature makes such computational models very appealing in application domains where one has little or incomplete understanding of the problem to be solved, but where training data is available. Another key feature is the intrinsic parallel architecture which allows for fast computation of solutions when these networks are implemented on parallel digital computers or, ultimately, when implemented in customized hardware.