Artificial neural networks overview

For a detailed treatment of artificial neural networks, readers are again referred to specific monographs [35, 49-51], for a survey of their applications in chemistry to overview books [52, 53], reviews [54—56], and relevant sections of publications [57-59]. For heterogeneous catalysis, a recent overview has explained the applicability of feedforward networks to the approximation of unknown dependencies and to the extraction of logical rules from experimental data [22]. 

This part of the book consists of one chapter (Chapter 1) to provide an overview of the domain field, genome informatics, with its major research areas and technologies a brief summary of the computational technology, artificial neural networks , and a summary of genome informatics applications. The latter two topics are further expanded into Part II, Neural Network Foundations, and Part III, Genome Informatics Applications. 

Table 8.1 Typing oiS. aureus using MALDI-TOF mass spectra and a hierarchical artificial neural network (ANN) overview of the ANN classification results of the external test data set with spectra from S. aureus. The MALDI-TOF-MS-based classification was established by teaching a hierarchical ANN model with mass spectra ofS. aureus fiom six clonal complexes. With the exception of CC8, the data indicated no reliable differentiation between spectra from the individual clonal complexes of y. aureus. Reproduced from Lasch et al. (2014) with permission...

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. 

In this overview section, both traditional statistical methods and the more recent machine-learning methods are briefly surveyed. Excluded here is only the main tool for data analysis and data mining of catalytic materials, i.e., the application of artificial neural networks, to which two of the remaining chapters will be devoted. 

For further information about basic concepts pertaining to artificial neural networks in general, and to multilayer perceptrons in particular, the reader is referred to specialised monographs such as White (1992), Hagan et al. (1996), Mehrotra et al. (1996) and Haykin (1999). An overview of traditional kinds of ANN applications in chemistry can most easily be obtained from the books by Zupan and Gasteiger (1993, 1999), and from the survey papers by Meissen et al. (1994), Smits et al. (1994) and Henson (1998). 

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