Analysis of Artificial Neural Network: Architecture, Types, and Forecasting Applications

Abstract

The artificial neural network reduces humanity and society’s burden to solve complex problems highly efficiently. Artificial neural networks resemble brain activities based on the acquired training samples used for various applications such as classification, regression, prediction, smart grid, natural language processing, image processing, medical diagnosis, and so on. This paper illustrates the different artificial neural network architectures, types, merits, demerits, and applications. Therefore, this paper provides valuable information to students and researchers to enrich their knowledge about an artificial neural network and research it. This paper also proposed a multilayer-perceptron-neural-network-based solar irradiance forecasting model, an improved backpropagation neural network-based rainfall forecasting model, and an Elman neural network-based temperature forecasting model. The performances of the proposed neural network-based forecasting models are analyzed with various hidden neurons and validated using the acquired real-time meteorological data. The proposed neural network forecasting models achieve rigorous results with reduced errors for the considered applications and aid sustainability.