In this paper, a carbon price forecasting system is proposed to quickly and accurately predict
the carbon price for participants. The data including the carbon trading price, oil price, coal …

The stock market is complex and dynamic in nature, and has been a subject of research for
modelling its random fluctuations. Higher order neural network (HONN) has the ability to …

Algal blooms are one of the most prevalent global problems. Studying the Chlorophyll-a (Chl-
a) predicting model helps to control algal blooms. Predicting the behavior of algae is difficult …

Artificial Neural Networks have emerged as an important tool for classification and have
been widely used to classify a non-linear separable pattern. The most popular artificial …

The ridge polynomial neural network composed of pi-sigma modules is a typical higher-
order feedforward network, which has good non-linear mapping capabilities. Due to the …

Abstract Learning problems for Neural Networks (NNs) has widely been explored from last
two decades. Population based algorithms become more focus by researchers because of …

Over the years, neural networks have shown its strength in various fields of research. There
is a vast improvement in the efficiency and effectiveness of various classification techniques …

Solving nonlinear classification problems by the traditional neural networks with one or more
hidden units is a quite tough task. Various researchers around the globe have made …

Multilayer perceptron (MLP) has been found to be most frequently used model for stock
market forecasting. MLP is characterized with black-box in nature and lack of providing a …

In this paper, a new application of ridge polynomial based neural network models in
multivariate time series forecasting is presented. The existing ridge polynomial based neural …