Gestures and neural networks in human-computer interaction Russell Beale and Alistair D N Edwards

Department of Computer Science University of York

Heslingt on York YO1 5DD. U.K.

russell@uk.ac.york.minster

Abstract

Neural networks are recognised as being able to learn to solve classification problems, and their generalization properties make them suitable for interpreting imprecise input values. This paper utilizes these features of neural networks by applying networks to the problem of recog- nising gestural input. The signs made by a user are interpreted and classified by the network, allowing a natural method of communication between the user and the system.

The Effect of ANN Hidden Layer Neurons on Boundary Identification for Constrained Wtimization

ShuoJen Lee

Department of Mechanical Engineering Yuarr-Ze Institute of Techmlogy

Nei-Li, Tao-Yuan Taiwan, R.O.C.

Computer Address:unitl82@twnmoelO

Abstract

The process of constrained optimization involves first finding a feasible region which satisfies all design criteria and then optimizing the design goal while remains in this feasible region. In this paper, the constraint boundaries which are the borders between feasible and infeasible regions were identified using the pattern classification capability of backpropagation nets. In addition, various number of hidden layer neurons were chosen to study their effects on boundary approximation. The results showed that the identified boundaries did evolve from a crude approximation to a shape that is close to true boundaries. The increase in the number of hidden layer neurons did not improve the results of the training significantly. These preliminary results indicated that the ANN pattern classi- fication training is robust against the choice of hidden layer neurons and the procedure of constraint boundary identification can be incorporated into constrained optimizatian problems.

I1 A-892