Interpretation Trained Neural Networks Based on Genetic Algorithms

Abstract

In this paper, constructive learning is used to train the neural networks. The results of neural networks are obtained but its result is not in comprehensible form or in a black box form. Our goal is to use an important and desirable model to identify sets of input variable which results in a desired output value. The nature of this model can help to find an optimal set of difficult input variables. Accuracy. Genetic algorithms are used as an interpretation of achieving neural network inversion. On the other hand the inversion of neural network enables to find one or more input patterns which satisfy a specific output. The input patterns obtained from the genetic algorithm can be used for building neural network system explanation facilities.

Tourism Demand Forecasting Model Using Neural Network

“…This study used different operators for selection and crossover operations (Table 2) Table 2. Description of different operators for select and crossover operations in GA [26][27][28][29][30] Operation Operator Description Select Best Selects the best chromosome. Random…”

“…GA improves the performance of ANNs by selecting the optimum hidden nodes of the neural network. This study used different operators for selection and crossover operations (Table 2) [26][27][28][29][30].…”