Learning Logic Programming in Radial Basis Function Network via Genetic Algorithm

TL;DR: The result shows that some of the modified versions of firefly algorithm produce superior results with a tradeoff of high computational time, which will help practitioners to decide which modified version to apply based on the computational resource available and the sensitivity of the problem.

TL;DR: This chapter will review modifications done on the standard firefly algorithm based on parameter modification, modified search strategy and change the solution space to make the search easy using different probability distributions.

TL;DR: The proposed supervised logic mining that integrates supervised learning via association analysis to identify the most optimal arrangement with respect to the given logical rule demonstrated superiority and the least competitiveness compared to the existing method.

TL;DR: The result demonstrates the capability of EA in terms of accuracy and effectiveness as the learning algorithm in HNN for RANkSAT with a different number of neurons compared to ES and GA.

TL;DR: The simulation results obtained on 16 datasets of the Farsi optical character recognition problem prove the advantages of the BST algorithm, and the sigmoid activity function is preferred over others, since it results in less sensitivity to learning parameters, faster convergence and lower recognition error.

TL;DR: In this paper, the problem of training a radial basis function (RBF) neural network for distinguishing two disjoint sets in R/sup n/ is considered, where the network parameters can be determined by minimizing an error function that measures the degree of success in the recognition of a given number of training patterns.

TL;DR: In this paper, the computation and approximate computation by neural networks of semantic operators T P determined by logic programs P is studied and new definitions are presented which avoid embedding spaces of interpretations in the real line.