This report presents solutions of EUNITE Competition 2003 problem “Prediction of product quality in glass manufacturing process”. The first solution is based on Local Transfer Function Approximator (LTF-A) neural network, while the next three solutions utilize simple rules to predict glass quality. Despite advanced data preprocessing, LTF-A did not performed very well on the competition data. T...

An artificial neural network is proposed as a function approximator for empirical modeling of a Lyapunov function for a nonlinear dynamic system that projects stable behavior as potentially observable in its state space. Theoretical framework for the methodology of designing the so-called Lyapunov neural network, which empirically models a Lyapunov function, is described. Algorithms for trainin...

We present a decision tree based approach to function approximation in reinforcement learning. We compare our approach with table lookup and a neural network function approximator on three problems: the well known mountain car and pole balance problems as well as a simulated automobile race car. We find that the decision tree can provide better learning performance than the neural network funct...

This paper describes the importance of the RBF model quality in a model-based predictive control scheme. We show that a good neuronal approximator does not necessarily correctly model the intrinsic behaviour of the identified system. We have used a simulated example to show the harmful effects of a particular type of incorrect behaviour, the non-invertibility of the model relative to the contro...

We present a decision tree based approach to function approximation in reinforcement learning. We compare our approach with table lookup and a neural network function approximator on three problems: the well known mountain car and pole balance problems as well as a simulated automobile race car. We find that the decision tree can provide better learning performance than the neural network funct...

In order to improve the design method of robust controller for ship course-keeping, a nonlinear controller design is presented by combining neural network (NN) approximator with adaptive Backstepping technology. The simulation research is carried out based on the training ship "Yu long" of Dalian Maritime University as an example. The results show that the control algorithm has good adaptabilit...

This paper presents a working combination of input selection strategy and a fast approximator for time series prediction. The input selection is performed using Tabu Search with the Delta Test. The approximation methodology is called Optimally-Pruned k -Nearest Neighbors (OP-KNN), which has been recently developed for fast and accurate regression and classification tasks. In this paper we demon...

When dealing with time series the one step ahead forecasting problem based on experimental data is the problem of estimating the autoregression function of the underlying process When minimizing the expected forecast ing error is the main goal the exible approach has to be used to be able to adjust the complexity of the model to the complexity of the data Multilay ered perceptrons are a popular...

In real world dataset, there are often large amount of discrete data that the concern is the interpolation and/or extrapolation by an approximation tool. Therefore, a training process will be actually used for definition and construction of the approximator parameters. Huge amount of data may lead to high computation time and a time consuming training process. To this concern a fast learnt fuzz...