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The aim of this study was to develop an adaptive state estimator with discontinuous parameter adjustment law for the catalytic ozonation system. A nonlinear transformation defined an equivalent system presented in chain-of-integrators form with uncertain structure in the dynamics of the last state. A step-by-step state estimator using a sequence of super-twisting algorithms (STAs) estimated the unmeasured states of the uncertain system. A class of differential neural network (DNN) with discontinuous learning law served to estimate the uncertain section of the catalytic process. The learning method was developed by implementing a strong lower-semi-continuous Lyapunov function. The method used to generate the laws that adjusted the weights, also yields the estimation of the parameters included in the catalytic ozonation system. A set of numerical simulations demonstrated the application of the DNN-based state observer to solve the estimation of the non-measurable information in the catalytic ozonation system. The available output signal was the concentration of the ozone gas at the output of the reactor. This was the only information used by the observer. The state estimator with discontinuous learning laws was also evaluated with experimental information obtained by a catalytic ozonation system using NiO as catalyst and phtalic acid as model contaminant. The effect of aggregating the DNN in the observer structure was compared with the observer using only the sequence of STA. The superior performance of the observer developed in this study was confirmed by evaluating the mean square error of the identification error. [ABSTRACT FROM AUTHOR]

There are many examples in science and engineering which are reduced to a set of partial differential equations (PDEs) through a process of mathematical modelling. Nevertheless there exist many sources of uncertainties around the aforementioned mathematical representation. Moreover, to find exact solutions of those PDEs is not a trivial task especially if the PDE is described in two or more dimensions. It is well known that neural networks can approximate a large set of continuous functions defined on a compact set to an arbitrary accuracy. In this article, a strategy based on the differential neural network (DNN) for the non-parametric identification of a mathematical model described by a class of two-dimensional (2D) PDEs is proposed. The adaptive laws for weights ensure the ‘practical stability’ of the DNN-trajectories to the parabolic 2D-PDE states. To verify the qualitative behaviour of the suggested methodology, here a non-parametric modelling problem for a distributed parameter plant is analysed. [ABSTRACT FROM AUTHOR]

Abstract: Biofiltration is an economical and environmentally friendly process to eliminate air pollutants. Results obtained by different authors showed the enhanced performance of the fungal biofiltering systems. Consequently, there is a necessity to develop methodologies not only to design more efficient reactors but to control the reaction behavior under different conditions: pollutants feeding, air flows, humidity and biomass production. In this study, a continuous neural network observer was designed to predict the toluene vapors elimination capacity (EC) in a fungal biofilter. The observer uses the carbon dioxide (CO2) production and the pressure drop (DP) (on line measurements) as input information. The differential neural network observer proved to be a useful tool to reconstruct the immeasurable on-line variable (EC). The observer was successfully tested under different reaction conditions proving the robustness of estimation process. This software sensor may be helpful to derive adaptive control functions optimizing the biofilter reaction development. [Copyright &y& Elsevier]

• The paper presents four recurrent and differential artificial neural networks (ANN) structures to construct different versions of dynamic automatic pattern classifiers. • Two different annotated and validated databases of diverse physiological signals were used to evaluate the capacities of all the ANNs proposed in this study. • Two validation methods were used to justify the application of dynamic ANNs as pattern classifiers: generalization-regularization and k-fold cross validation. • The recurrent neural network was also implemented in a 32-bits microcontroller embedded device. In the last few years, recurrent and continuous algorithms have became key factors in the solution of diverse pattern recognition problems. The main goal of this study is to introduce four classes of recurrent and continuous artificial neural networks (ANN) that can be implemented for pattern recognition of electrophysiological signals. Such networks are generally known as dynamic neural networks (DNN). The proposed DNN based pattern recognizer uses biosignals raw data as input. This processing method allows capturing the signal time dynamics, which is considered as an intrinsic characteristic of physiology signals. Therefore, recurrent and differential ANN structures were developed to construct different versions of dynamic automatic pattern recognizer. The first one describes the application of Recurrent Neural Networks (RNN) to enforce the biosignal analysis which evolves over time with a fixed sampling period. Three different DNNs with continuous dynamics are introduced. Differential neural network (DifNN) with the capability of learning the evolution of the signal in continuous time, a time-delay neural network (TDNN) for classification is implemented to consider the time-delayed characteristics of the electrophysiological signals and a complex valued neural network (CVNN) which considered the signals to be classified may be pre-processed with a frequency analysis technique. Two different databases of diverse physiological signals are used in this study to validate the application of dynamic neural networks. A first database considers electromiographic (EMG) signals which are tested using the DifNN, TDNN and CVNN. The second database includes gait in Parkinson's disease database signals which are used in the evaluation procedure of RNN. Two validation methods are used to justify the application of dynamic ANNs as pattern recognizer for the EMG activities and the health level classification of patients suffering from Parkinson's: generalization-regularization and the k -fold cross validation. The accuracy estimation and the confusion matrix evaluation confirm the superiority of the proposed approach compared to classical feed-forward ANN pattern recognizer. The particular case of the RNN is also implemented in a 32-bits micro-controller embedded device. [ABSTRACT FROM AUTHOR]