Neural network models for a resource allocation problem

University admissions and business personnel offices use a limited number of resources to process an ever-increasing quality of student and employment applications. Application systems are further constrained to identify and acquire, in a limited time period,those candidates who are most likely to accept an offer of enrollment or employment. Neural networks are a blew methodology; to this particular domain. Various neural network architectures and learning algorithms are analyzed comparatively to determine the applicability of supervised learning neural networks to the domain problem of personnel resource allocation and to identify optimal learning strategies in this domain. This paper Focuses on multilayer perceptron backpropagation, radial basis function, counterpropagation, general regression, fuzzy ARTMAP, and linear vector quantization neural networks. Each neural network predicts the probability of enrollment and nonenrollment for individual student applicants. Backpropagation networks produced the best overall performance. Network performance results are measured by the reduction in the counselor's student case load and corresponding increases in student enrollment. The backpropagation neural networks achieve a 56% reduction in counselor case load.