In this paper, a methodology to obtain a set of fuzzy rules for classification systems is presented. The system is represented in a layered fuzzy network, in which the links from input to hidden nodes represents the antecedents of the rules, and the consequents are represented by links from hidden to output nodes. Specific genetic algorithms are used in two phases to extract the rules. In the f...

SUMMARY Representing the concept of numerical data by linguistic rules is often desir­ able. In this paper, we present a novel rule-extraction algorithm from the radial basis function (RBF) neural network classifier for representing the hidden concept of numerical data. Gaussian function is used as the basis function of the RBF network. When training the RBF neural network, we allow for large o...

Before symbolic rules are extracted from a trained neural network, the network is usually pruned so as to obtain more concise rules. Typical pruning algorithms require retraining the network which incurs additional cost. This paper presents FERNN, a fast method for extracting rules from trained neural networks without network retraining. Given a fully connected trained feedforward network, FERN...

1. Poverty is relative. 2. Poverty occurs in all races and countries. 3. Economic class is a continuous line, not a clear-cut distinction. 4. Generational poverty and situational poverty are different. 5. This work is based on patterns. All patterns have exceptions. 6. People bring with them the hidden rules of the class in which they were raised. 7. Schools and businesses operate from middle c...