Genetic Algorithmised Neuro Fuzzy System for Forecasting the Online Journal Visitors

Abstract

Artificial Neural Network (ANN) is recognized as one of effective forecasting engines for various business fields. This approach fits well with non-linear data. In fact, it is a black box system with random weighting, which is hard to train. One way to improve its performance is by hybridizing ANN with other methods. In this paper, a hybrid approach, Genetic Algorithm-Neural Fuzzy System (GA-NFS) is proposed to predict the number of unique visitors of an online journal website. The neural network weight is precisely determined using GA. Afterwards, the best weight has been used for testing data and processed using Sugeno Fuzzy Inference System (FIS) for time-series forecasting. Based on experiment, GA-NFS have been produced accuracy with 0.989 of root mean square error (RMSE) that is lower than the RMSE of a common NFS (2,004). This may indicate that the GA based weighting is able to improve the NFS performance on forecasting the number of journal unique visitors.

State-of-Health Prediction of Lithium-Ion Batteries Using Exponential Smoothing Transformer With Seasonal and Growth Embedding

“…Battery life estimation is a prime example of a time series forecasting challenge. Various approaches to solving these challenges exist, including Recurrent Neural Networks (RNN), Fuzzy Neural Systems, and Extreme Learning Machines (ELM) [6,13,14]. Among the recent advancements, the Exponential Smoothing Transformer (ETSformer) model has emerged [25].…”