Application of Neural Network in Prediction of Financial Viability

Abstract

42 approach and to improve learning efficiency.  Genetic Algorithm Genetic Algorithms are general-purpose search and optimization procedures. They are inspired by the biological evolution principle of survival of the fittest. The genetic algorithm (GA) is a search heuristic that mimics the process of natural evolution. This heuristic is routinely used to generate useful solutions to optimization and search problems. Genetic algorithms belong to the larger class of evolutionary algorithms (EA), which generate solutions to optimization problems using techniques inspired by natural evolution, such as inheritance, mutation, selection and crossover.  Fuzzy c-means Clustering The Fuzzy c-means algorithm is a method of clustering which allows one piece of data to belong to two or more clusters. This method was developed by Dunn (1973) and improved by Bezdek (1981). It is frequently used in pattern recognition. Under the Fuzzy c-means approach, each given datum does not belong exclusively to a well defined cluster, but it can be placed in a middle way. This belonging to more than one cluster is represented by probability coefficients. This method is based on minimization of the following objective function  Multivariate Adaptive Regression Splines (MARS)