Artificial Neural Networks (ANNs) are being used increasingly to predict and forecast water resources variables. In this paper, the steps that should be followed in the development of such models are outlined. These include the choice of performance criteria, the division and pre-processing of the available data, the determination of appropriate model inputs and network architecture, optimisation of the connection weights (training) and model validation. The options available to modellers at each of these steps are discussed and the issues that should be considered are highlighted. A review of 43 papers dealing with the use of neural network models for the prediction and forecasting of water resources variables is undertaken in terms of the modelling process adopted. In all but two of the papers reviewed, feedforward networks are used. The vast majority of these networks are trained using the backpropagation algorithm. Issues in relation to the optimal division of the available data, data pre-processing and the choice of appropriate model inputs are seldom considered. In addition, the process of choosing appropriate stopping criteria and optimising network geometry and internal network parameters is generally described poorly or carried out inadequately. All of the above factors can result in non-optimal model performance and an inability to draw meaningful comparisons between different models. Future research efforts should be directed towards the development of guidelines which assist with the development of ANN models and the choice of when ANNs should be used in preference to alternative approaches, the assessment of methods for extracting the knowledge that is contained in the connection weights of trained ANNs and the incorporation of uncertainty into ANN models.

Neural networks for the prediction and forecasting of water resources variables: a review of modelling issues and applications

Data-driven Soft Sensors in the process industry

The field of chaotic synchronization has grown considerably since its advent in 1990. Several subdisciplines and “cottage industries” have emerged that have taken on bona fide lives of their own. Our purpose in this paper is to collect results from these various areas in a review article format with a tutorial emphasis. Fundamentals of chaotic synchronization are reviewed first with emphases on the geometry of synchronization and stability criteria. Several widely used coupling configurations are examined and, when available, experimental demonstrations of their success (generally with chaotic circuit systems) are described. Particular focus is given to the recent notion of synchronous substitution—a method to synchronize chaotic systems using a larger class of scalar chaotic coupling signals than previously thought possible. Connections between this technique and well-known control theory results are also outlined. Extensions of the technique are presented that allow so-called hyperchaotic systems (syste...

/pdf/fundamentals-of-synchronization-in-chaotic-systems-concepts-2z1qufd6jz.pdf

Fundamentals of synchronization in chaotic systems, concepts, and applications.

In the literature on dynamical systems analysis and the control of systems with complex behavior, the topic of synchronization of the response of systems has received considerable attention. This concept is revisited in the light of the classical notion of observers from (non)linear control theory,.

/pdf/an-observer-looks-at-synchronization-18sw4m9a4c.pdf

An observer looks at synchronization

Adsorption isotherm models: Classification, physical meaning, application and solving method

Negativity of the conditional Lyapunov exponents is a necessary and in many cases a sufficient condition for the occurrence of synchronization between a chaotic drive and the response subsystem. Treating one of the variables of coupled logistic maps as the drive, our numerical simulations show that the negativity of the conditional Lyapunov exponents does not always guarantee synchronization and, additionally, the domain of initial conditions for the drive variables needs to be specified in which case the synchronization occurs. Additionally, an example showing the pitfalls of the numerical study of synchronization when the conditional Lyapunov exponent is positive is presented.

Driving systems with chaotic signals.

Soft-sensor development for fed-batch bioreactors using support vector regression

Estimation of gross calorific value of coals using artificial neural networks

Hybrid process modeling and optimization strategies integrating neural networks/support vector regression and genetic algorithms: study of benzene isopropylation on Hbeta catalyst

Sanjeev S. Tambe

Papers

Driving systems with chaotic signals.

Soft-sensor development for fed-batch bioreactors using support vector regression

Estimation of gross calorific value of coals using artificial neural networks

Hybrid process modeling and optimization strategies integrating neural networks/support vector regression and genetic algorithms: study of benzene isopropylation on Hbeta catalyst

Modeling and monitoring of batch processes using principal component analysis (PCA) assisted generalized regression neural networks (GRNN)