Neural networks Hybrid

Production systems, neural networks, hybrid models 319... 

These three models (MLP neural networks, hybrid fuzzy-linear and fuzzy-neural) may be used for process modeling and also for optimization when using the Model Predictive Control approach see [5, 8, 9]. 

Hybrid systems. Depending on the problem to be solved, use can also be made of a combination of techniques leading to a hybrid system. For example, a rule-based system may use neural networks for solving classification subproblems (as is described in [Hopgood, 1993]), or a combination of a rule-based and a CBR system can be used as in the system for URS data interpretation described later in this paper. 

Literature in the area of neural networks has been expanding at an enormous rate with the development of new and efficient algorithms. Neural networks have been shown to have enormous processing capability and the authors have implemented many hybrid approaches based on this technique. The authors have implemented an ANN based approach in several areas of polymer science, and the overall results obtained have been very encouraging. Case studies and the algorithms presented in this chapter were very simple to implement. With the current expansion rate of new approaches in neural networks, the readers may find other paradigms that may provide new opportunities in their area of interest. 

Psichogios, D. C., and Ungar, L. H., A hybrid neural network-first principles approach to process modeling. AIChEJ. 38, 1499 (1992). 

Most AI methods used in science lie within one of three areas evolutionary methods, neural networks and related methods, and knowledge-based systems. Additional methods, such as automated reasoning, hybrid systems, fuzzy logic, and case-based reasoning, are also of scientific interest, but this review will focus on the methods that seem to offer the greatest near-term potential in science. 

J.P. Steyer, D. Roland, J.C. Bouvier, and R. Moletta. Hybrid fuzzy neural network for diagnosis - Application to the anaerobic treatment of wine distillery wastewater in a fluidized bed reactor. Wat. Sci. TechnoL, 36(6-7) 209-217,... 

Greaves, M. A., Hybrid Modelling, Simulation and Optimisation of Batch Distillation Using Neural Network Techniques. Ph.D. Thesis, (University of Bradford, Bradford, UK, 2003). 

Dynamic sets of process-model mismatches data is generated for a wide range of the optimisation variables (z). These data are then used to train the neural network. The trained network predicts the process-model mismatches for any set of values of z at discrete-time intervals. During the solution of the dynamic optimisation problem, the model has to be integrated many times, each time using a different set of z. The estimated process-model mismatch profiles at discrete-time intervals are then added to the simple dynamic model during the optimisation process. To achieve this, the discrete process-model mismatches are converted to continuous function of time using linear interpolation technique so that they can easily be added to the model (to make the hybrid model) within the optimisation routine. One of the important features of the framework is that it allows the use of discrete process data in a continuous model to predict discrete and/or continuous mismatch profiles. 

In Greaves et al. (2001) and Greaves (2003), instead of using a rigorous model (as in the methodology described above), an actual pilot plant batch distillation column is used. The differences in predictions between the actual plant and the simple model (Type III and also in Mujtaba, 1997) are defined as the dynamic process-model mismatches. The mismatches are modelled using neural network techniques as described in earlier sections and are incorporated in the simple model to develop the hybrid model that represents the predictions of the actual column. 

Neural Network based hybrid dynamic modelling and optimisation methods for conventional and unconventional column configurations... 

So and Karplus [51] have developed a hybrid method that combines a GA for descriptor selection with an artificial neural network for model building. They found improved models for the Selwood data set when compared with the GFA and evolutionary programming methods, with the success being attributed to the ability of the neural network to select nonlinear descriptors. 

Ferran, E. A. Pflugfelder, B. (1993). A hybrid method to cluster protein sequences based on statistics and artificial neural networks. ComputAppl Biosci 9,671-80. 

Vivarelli et al. (1995) used a hybrid system that combined a local genetic algorithm (LGA) and neural networks for the protein secondary structure prediction. The LGA, a version of the genetic algorithms (GAs), was particularly suitable for parallel computational architectures. Although the LGA was effective in selecting different... 

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