This paper presents a neural network based model predictive control (MPC) strategy to control a strongly exothermic reaction with complicated nonlinear kinetics given by Chylla-Haase polymerization reactor that requires a very precise temperature control to maintain product uniformity. In the benchmark scenario, the operation of the reactor must be guaranteed under various disturbing influences...

This paper focus on a new approach to automatic speech recognition in noisy environments where the noise has either stationary or non-stationary statistical characteristics. The aim is to perform automatic recognition of speech in the precence of additive car noise. The technique applied is based on a combination of the Hidden Markov Model (HMM) decomposition method [ 11, for speech recognition...

Based on the radial basis functions (RBF) and T-Trefftz solution, this paper presents a new meshless method for numerically solving various partial differential equation systems. First, the analog equation method (AEM) is used to convert the original patial differential equation to an equivalent Poisson’s equation. Then, the radial basis functions (RBF) are employed to approxiamate the inhomoge...

In this paper we study the role of movement strategies during learning in object recognition models. We show that a simple model, the RBF, can outperform a more complex hierarchical model, the HMAX, when rotation and scale invariance are provided by the training phase. Moreover, we assess the exploitation of temporal information by the RBF using optic flow. The results show that the RBF model c...

This paper reports a preliminary investigation of the use of modern data mining tools for mortgage scoring. Using IBM's Intelligent Miner (a data mining toolbox), we built a model of serious delinquency on a sample of data from Mortgage Information Corpo-ration's Loan Performance System, which contains over 20 million loans with a volume of over $1.6 trillion. Currently, two technologies prevai...

This paper presents a comparison between two Artificial Neural Network (ANN) approaches, namely, Multilayer Perceptron (MLP) and Radial Basis Function (RBF) networks, in flood forecasting. The basic difference between the two methods is that the parameters of the former network are nonlinear and those of the latter are linear. The optimum model parameters are therefore guaranteed in the latter,...

A precise predictive model is important for obtaining a clear understanding of the changes in dissolved oxygen content in crab ponds. Highly accurate interval forecasting of dissolved oxygen content is fundamental to reduce risk, and three-dimensional prediction can provide more accurate results and overall guidance. In this study, a hybrid three-dimensional (3D) dissolved oxygen content predic...

This paper studies the synchronization of SA and AV Node Oscillators using PSO optimized RBF-based controllers systems. High levels of control activities may excite unmodeled dynamics of a system. This matter changes the rules of controlling the system and achieving an acceptable control performance. In fact, the objective here is to reach a trade-off between tracking performance and parametric...

This paper proposes a neural net based on-line scheme for modelling discrete-time mul-tivariable nonlinear dynamical systems. Taking the advantage of structural features of RBF (Radial-Basis-Function) neural nets, the method approaches the modelling problem by setting up a coarse RBF model structure in the light of the spatial Fourier transform and spatial sampling theory, then devising appropr...

In this article an attempt is made to study the applicability of a general purpose, supervised feed forward neural network with one hidden layer, namely. Radial Basis Function (RBF) neural network. It uses relatively smaller number of locally tuned units and is adaptive in nature. RBFs are suitable for pattern recognition and classification. Performance of the RBF neural network was also compar...