Ubiquitous systems use context information to adapt appliance behavior to human needs. Even more convenience is reached if the appliance foresees the user’s desires and acts proactively. This paper proposes neural prediction techniques to anticipate a person’s next movement. We focus on neural predictors (multi-layer perceptron with back-propagation learning) with and without pre-training. The ...

A multilayer perceptron is a feed forward artificial neural network model that maps sets of input data onto a set of appropriate output. It is a modification of the standard linear perceptron in that it uses three or more layers of neurons (nodes) with nonlinear activation functions and is more powerful than the perceptron in that it can distinguish data that is not linearly separable, or separ...

This paper discusses the development of a system for the automated real-time measurement of crystal size distributions during crystallisation. An optimised selection of the Daubechies wavelet coefficients is used as input to a Multi–Layer Perceptron artificial neural network to characterise the crystal scale lengths. This technique gives significant advantages over the sampling-based measuremen...

Solar photovoltaic power (PV) generation has increased constantly in several countries in the last ten years becoming an important component of a sustainable solution of the energy problem. In this paper, a methodology to 24-hour or 48-hour photovoltaic power forecasting based on a Neural Network, trained in a Bayesian framework, is proposed. More specifically, an multi-ahead prediction Multi-L...

We present an Artificial Neural Network (ANN) approach to predict stock market indices, particularly with respect to the forecast of their trend movements up or down. Exploiting different Neural Networks architectures, we provide numerical analysis of concrete financial time series. In particular, after a brief résumé of the existing literature on the subject, we consider the Multi-layer Percep...

Forecasting of wave height is necessary in a large number of ocean coastal activities. Recently, neural networks are used for prediction and approximation of wave heights in sea and ocean due to their great convergence rate. In this paper a cascade correlation neural network is used for prediction of wave heights at given times due to the useful capability of this network for prediction and app...

In this paper, a neural network based predictive controller is designed to govern the dynamics of a heat exchanger pilot plant. Heat exchanger is a highly nonlinear process; therefore, a nonlinear prediction method can be a better match in a predictive control strategy. Advantages of neural networks for the process modeling are studied and a neural network based predictor is designed, trained a...

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