General Methodologies for Neural Network Programming

This paper presents a general methodology for designing neural networks by using a priori information on the process under study. The approach is especially useful in developing efficient neural networks for the applications where full-scale models are available but too complicated to implement on conventional computer systems. Traditional neural network development techniques are known to have considerable disadvantages, including a tedious (trial-and-error-based) design process and a long training phase for most real world problems. The presented network design paradigm overcomes these well-known shortcomings. The paper also illustrates the method's application to a real-world problem on the estimation of milling forces in an ideal machining process.