ARTIFICAL NEURAL NETWORKS

Abstract

This is a survey of neural network applications within the real-world situation. It provides a taxonomy of artificial neural networks (ANNs) and furnishes the reader with data of current and rising trends in ANN applications analysis and space of focus for researchers. The study assesses ANN contributions, compare performances and critiques strategies. The study found that neural-network models like feedforward and feedback propagation artificial neural networks is performing arts higher in its application to human issues. Therefore, we tend to plan feedforward and feedback propagation ANN models for analysis focus supported information analysis factors like accuracy, process speed, latency, fault tolerance, volume, measurability, convergence, and performance. Moreover, we tend to suggest that instead of applying one technique, future analysis will concentrate on combining ANN models into one network-wide application.