LEARNING MONOTONIC‐CONCAVE INTERVAL CONCEPTS USING THE BACK‐PROPAGATION NEURAL NETWORKS
Shouhong Wang · Computational Intelligence · 1996
Monotonicity and concavity play important roles in human cognition, reasoning, and decision making. This paper shows that neural networks can learn monotonic‐concave interval concepts based on real‐world data, Traditionally, the training of neural networks has been based only on raw data. In cases where the training samples carry statistical fluctuations, the products of the training have often suffered. This paper suggests that global knowledge about monotonicity and concavity of a problem domain can be incorporated in neural network training. This paper proposes a learning scheme for the back‐propagation layered neural networks in learning monotonic‐concave interval concepts and provides an example to show its application.