Computational neural network applications

G. Hobson, "Neural Network Applications at PSP," paper presented at NPRA Computer Conference, Seattle, Wash., 1990. 

Anzali, S., Bamickel, G, Krug, M., Sadowski, J., Wagener, M., Gasteiger, J. and Polanski, J. (1996). The Comparison of Geometric and Electronic Properties of Molecular Surfaces by Neural Networks Application to the Analysis of Corticosteroid Binding Globulin Activity of Steroids. J.Comput.Aid.Molec.Des., 10, 521-534. 

Hemmateenejad B, Akhond M, Miri R, Shamsipur M. Genetic algorithm applied to the selection of factors in principal component-artificial neural networks application to QSAR study of calcium channel antagonist activity of 1,4-dihydropyri-dines (nifedipine analogs). J Chem Inf Comput Sci 2003 43 1328-34. 

C. Klawun and C. L. Wilkins,/. Chem. Inf. Comput. Sci., 34,984 (1994). A Novel Algorithm for Local Minimum Escape in Backpropagation Neural Networks Application to the Interpretation of Matrix Isolation Infrared Spectra. 

Kowalski, C.T. Orlowska-Kowalska, T. 2003. Neural networks application for induction motor faults diagnosis. Mathematics and Computers in Simulation 63 435-448. 

Freeman, J. A., Skapura, D. M. Neural Networks Algorithms, Applications and Programming Techniques, Computation and Neural systems Series. Addison Wesley Publishing Company, 1991... 

T A and H Kalayeh 1991. Applications of Neural Networks in Quantitative Structure-Activity ationships of Dihydrofolate Reductase Inhibitors, journal of Medicinal Chemistry 34 2824-2836. ik M and R C Glen 1992. Applications of Rule-induction in the Derivation of Quantitative icture-Activity Relationships. Journal of Computer-Aided Molecular Design 6 349-383. 

Hypercubes and other new computer architectures (e.g., systems based on simulations of neural networks) represent exciting new tools for chemical engineers. A wide variety of applications central to the concerns of chemical engineers (e.g., fluid dynamics and heat flow) have already been converted to run on these architectures. The new computer designs promise to move the field of chemical engineering substantially away from its dependence on simplified models toward computer simulations and calculations that more closely represent the incredible complexity of the real world. 

The applications of NN to solvent extraction, reported in section 16.4.6.2., suffer from an essential limitation in that they do not apply to processes of quantum nature therefore they are not able to describe metal complexes in extraction systems on the microscopic level. In fact, the networks can describe only the pure state of simplest quantum systems, without superposition of states. Neural networks that indirectly take into account quantum effects have already been applied to chemical problems. For example, the combination of quantum mechanical molecular electrostatic potential surfaces with neural networks makes it possible to predict the bonding energy for bioactive molecules with enzyme targets. Computational NN were employed to identify the quantum mechanical features of the... 

Since 1970 the subject of amorphous semiconductors, in particular silicon, has progressed from obscurity to product commercialization such as flat-panel liquid crystal displays, linear sensor arrays for facsimile machines, inexpensive solar panels, electrophotography, etc. Many other applications are at the developmental stage such as nuclear particle detectors, medical imaging, spatial light modulators for optical computing, and switches in neural networks (1,2). 

There have been many books and reviews written on the subject of NN and parallel computing. Only a token one is listed here, for those who need a traditional book reference (Haykin, 1999). It will probably be obsolete before this book is published. Otherwise, a wealth of up-to-date information is always available on the Internet where a neural networks entry produces an avalanche of information. Both lead articles cited for Chapter 10 (Hierlemann et al 1996) and (Jurs et al., 2000) discuss their applications in the context of chemical and biological sensing. 

Court (2), Eberhard (3), and Tyagi et al. (4) have reported some applications of computers and software sensors for fermentation control in experimental research in data acquisition of bioreactors. Neural network models were used to interprete sensor signals in the control of an alcohol fed-batch fermentation (5) and in the detection of the individual components of a gas mixture and to measure the concentration of both gases (6). 

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