Neural Networks for Measurement and Instrumentation in Virtual Environments
Emil M. Petriu · 2003
Abstract Neural Networks (NNs), which are able to learn nonlinear behaviors from a limited set of measurement data, can provide efficient modeling solutions for many virtual reality applications. Due to their continuous memory behavior, NNs are able to provide instantaneously an estimation of the output value for input values that were not part of the initial training set. Hardware NNs consisting of a collection of simple neuron circuits provide the massive computational parallelism allowing for higher speed realtime models. A virtual prototyping environment for Electronic Design Automation (EDA) and a NN model for the 3D electromagnetic field are discussed in a representative case study.