Artificial neural networks fundamentals

Basheer, LA. and Hajmeer, M. (2000) Artificial neural networks fundamentals, computing, design and application. Journal of Microbiological Methods, 43, 3-31. 

A challenging task in material science as well as in pharmaceutical research is to custom tailor a compound s properties. George S. Hammond stated that the most fundamental and lasting objective of synthesis is not production of new compounds, but production of properties (Norris Award Lecture, 1968). The molecular structure of an organic or inorganic compound determines its properties. Nevertheless, methods for the direct prediction of a compound s properties based on its molecular structure are usually not available (Figure 8-1). Therefore, the establishment of Quantitative Structure-Property Relationships (QSPRs) and Quantitative Structure-Activity Relationships (QSARs) uses an indirect approach in order to tackle this problem. In the first step, numerical descriptors encoding information about the molecular structure are calculated for a set of compounds. Secondly, statistical and artificial neural network models are used to predict the property or activity of interest based on these descriptors or a suitable subset. 

In the human brain, it is the combined efforts of many neurons acting in concert that creates complex behavior this is mirrored in the structure of an ANN, in which many simple software processing units work cooperatively. It is not just these artificial units that are fundamental to the operation of ANNs so, too, are the connections between them. Consequently, artificial neural networks are often referred to as connectionist models. 

Hassoun, M. H. (1995). Fundamentals of Artificial Neural Networks. MIT P, Cambridge. 

Artificial neural networks (ANN) are mathematical algorithms inspired on the structure of biological neural systems and the way information is processed by the brain, with good capabilities to handle complex information with unknown and highly nonlinear functional relationships among the different variables. Haykin [33] describes fundamental and application aspects of these methods. 

Hassoun, M.H. Fundamentals of artificial neural networks. The MIT Press, Cambridge (1995) Haykin, S. Neural networks, a comprehensive foundation. Prentice Hall, Upper Saddle River... 

Hassoun MH (1995) Fundamentals of artificial neural networks. MIT Press Cambridge... 

The chapter presents a brief overview of the current research on V205/Ti02 catalysts for o-xylene oxidation to phthalic anhydride at Clariant. Phthalic anhydride is produced in tubular, salt-cooled reactors with a capacity of about 5 Mio to per annum. There is a rather broad variety of different process conditions realized in industry in terms of feed composition, air flow rate, as well as reactor dimensions which the phthalic anhydride catalyst portfolio has to match. Catalyst active mass compositions have been optimized at Clariant for these differently realized industry processes utilizing artificial neural networks trained on high-throughput data. Fundamental pilot reactor research unravelling new details of the reaction network of the o-xylene oxidation led to an improved kinetic reactor model which allowed further optimizing of the state of the art multi-layer catalyst system for maximum phthalic anhydride yields. 

Improvements might be possible via applying a conventional optimization of catalyst composition in the compositional range of maximal performance. The catalysts may be further improved by taking all experimental performance data into account by fitting them to an artificial neural network which describes the relationships between catalytic performance and composition over the whole multi-dimensional space (for details see Chapter 6). Some fundamental insights in eatalysis may be eventually derived from such results. 

There are many other examples of applications of ANNs in pharmaceutical technology, dted in Sun et al. [ 16]. It has been shown that many artificial intelligence systems, especially neural networks, can be applied to the fundamental investigations of the effects of formulation and process variables on the delivery system. 

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