Neural network production

Feed section Known (measurable) Neural network Product- dependent Possible... 

Kito, S., Hattori, T. and Murakami, Y. (1994). Estimation of catalytic performance by neural network — Product distribution in oxidative dehydrogenation of ethylbenzene, Appl. Catal., A General, 114, L173-L178. 

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. 

An example of the neural network prediction of NMR chemical shifts for a natural product is illustrated in Figure 10.2-7 together with the calculations from other methods. This molecule was chosen as it had been discovered [47]... 

Figure 10.2-7. Predictions oF h NMR cheinical shifts for a complex natural product by neural networks, a database-centered method (ACD), and an increment-based method (Upstream).

Since 1970 the subject of amoiphous semiconductors, in particular silicon, has progressed from obscurity to product commercialisation such as flat-panel hquid crystal displays, linear sensor arrays for facsimile machines, inexpensive solar panels, electrophotography, etc. Many other appHcations 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). 

J. J. Ferrada, M. D. Gordon, and I. W. Osbome-Lee, "AppHcation of Neural Networks for Fault Diagnosis in Plant Production," paper presented tAIChE National Meetings San Francisco, 1989. 

Non-linear PCA can be obtained in many different ways. Some methods make use of higher order terms of the data (e.g. squares, cross-products), non-linear transformations (e.g. logarithms), metrics that differ from the usual Euclidean one (e.g. city-block distance) or specialized applications of neural networks [50]. The objective of these methods is to increase the amount of variance in the data that is explained by the first two or three components of the analysis. We only provide a brief outline of the various approaches, with the exception of neural networks for which the reader is referred to Chapter 44. 

In recent years there has been much activity to devise methods for multivariate calibration that take non-linearities into account. Artificial neural networks (Chapter 44) are well suited for modelling non-linear behaviour and they have been applied with success in the field of multivariate calibration [47,48]. A drawback of neural net models is that interpretation and visualization of the model is difficult. Several non-linear variants of PCR and PLS regression have been proposed. Conceptually, the simplest approach towards introducing non-linearity in the regression model is to augment the set of predictor variables (jt, X2, ) with their respective squared terms (xf,. ..) and, optionally, their possible cross-product... 

Goodacre, R. Kell, D. B. Rapid and quantitative analysis of bioprocesses using pyrolysis mass spectrometry and neural networks—Application to indole production. Anal. Chim. Acta 1993, 279,17-26. 

Kang, S. G. Lee, D. H. Ward, A. C. Lee, K. J. Rapid and quantitative analysis of clavulanic acid production by the combination of pyrolysis mass spectrometry and artificial neural networks. J. Microbiol. Biotechnol. 1998, 8, 523-530. 

Sisson, P. R. Freeman, R. Law, D. Ward, A. C. Lightfoot, N. F. Rapid detection of verocytotoxin production status in Escherichia coli by artificial neural network analysis of pyrolysis-mass spectra. J. Anal. Appl. Pyrolysis 1995, 32, 179-785. 

The brain s remarkable ability to learn through a process of pattern recognition suggests that, if we wish to develop a software tool to detect patterns in scientific or, indeed, any other kind of data, the structure of the brain could be a productive starting point. This view led to the development of artificial neural networks (ANNs). The several methods that are gathered under the ANN umbrella constitute some of the most widely used applications of Artificial Intelligence in science. Typical areas in which ANNs are of value include ... 

ANNs are the favorite choice as tools to monitor electronic noses,8 where the target response may be less tangible than in other studies (although, of course, it is still necessary to be able to define it). Many applications in which a bank of sensors is controlled by a neural network have been published and as sensors diminish in size and cost, but rise in utility, sensors on a chip with a built-in ANN show considerable promise. Together, QSARs and electronic noses currently represent two of the most productive areas in science for the use of these tools. 

A helpful starting point for further investigation is Learning Classifier Systems From Foundations to Applications.1 The literature in classifier systems is far thinner than that in genetic algorithms, artificial neural networks, and other methods discussed in this book. A productive way to uncover more... 

Huang and Tang49 trained a neural network with data relating to several qualities of polymer yarn and ten process parameters. They then combined this ANN with a genetic algorithm to find parameter values that optimize quality. Because the relationships between processing conditions and polymer properties are poorly understood, this combination of AI techniques is a potentially productive way to proceed. 

Neural Networks using Genetic Algorithm for On-Line Property Estimation of Crude Fractionator Products. 

McGovern et al.26 analyzed the expression of heterologous proteins in E. coli via pyrolysis mass spectrometry and FT-IR. The application was to a2-interferon production. To analyze the data, artificial neural networks (ANN) and PLS were utilized. Because cell pastes contain more mass than the supernatant, these were used for quantitative analyses. Both the MS and IR data were difficult to interpret, but the chemometrics used allowed researchers to gain some knowledge of the process. The authors show graphics indicating the ability to follow production via either technique. 

In order to develop an ANN model for the FCC process, we use here the same data set as in the previous section (Section 2.4). This data set was divided into two sets, one set for training and one set for testing the neural network. The prepared network model is able to predict the yields of the various FCC products and also the CCR number. During training of the neural network, first, only one hidden layer with five neurons was used. This network did not perform well against a pre-specified tolerance of 10-3. 

Optical Storage and Retrieval Memory, Neural Networks, and Fractals, edited by FrancisT. S. Yu and Suganda Jutamulia Devices for Optoelectronics, Wallace B. Leigh Practical Design and Production of OpticalThin Films,... 

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