Artificial neural network design

Figure 9-16. ArtiFicial neural network architecture with a two-layer design, compri ing Input units, a so-called hidden layer, and an output layer. The squares Inclosing the ones depict the bias which Is an extra weight (see Ref, [10 for further details),...

The results presented here imply that a similar approadi can be used for comparing two different hbraries, for determining the degree of overlap between the compounds in these two Hbraries. Examples of the application of artificial neural networks or GA in drug design are given in [57, 58, 84, 85]. 

Intended Use The intended use of the model sets the sophistication required. Relational models are adequate for control within narrow bands of setpoints. Physical models are reqiiired for fault detection and design. Even when relational models are used, they are frequently developed bv repeated simulations using physical models. Further, artificial neural-network models used in analysis of plant performance including gross error detection are in their infancy. Readers are referred to the work of Himmelblau for these developments. [For example, see Terry and Himmelblau (1993) cited in the reference list.] Process simulators are in wide use and readily available to engineers. Consequently, the emphasis of this section is to develop a pre-liminaiy physical model representing the unit. 

Plumb AP, Rowe RC, York P, Doherty C. The effect of experimental design in the modelling of a tablet coating formulation using artificial neural networks. Eur J Pharm Sci 2002 16 281-8. 

Sheng H, Wang P, Tu J.-S, Yuan L, Pin Q-N. Applications of artificial neural networks to the design of sustained release matrix tablets. Chinese J Pharmaceut 1998 29 352-4. 

By design, ANNs are inherently flexible (can map nonlinear relationships). They produce models well suited for classification of diverse bacteria. Examples of pattern analysis using ANNs for biochemical analysis by PyMS can be traced back to the early 1990s.4fM7 In order to better demonstrate the power of neural network analysis for pathogen ID, a brief background of artificial neural network principles is provided. In particular, backpropagation artificial neural network (backprop ANN) principles are discussed, since that is the most commonly used type of ANN. 

Chapter 17 - Vapor-liquid equilibrium (VLE) data are important for designing and modeling of process equipments. Since it is not always possible to carry out experiments at all possible temperatures and pressures, generally thermodynamic models based on equations on state are used for estimation of VLE. In this paper, an alternate tool, i.e. the artificial neural network technique has been applied for estimation of VLE for the binary systems viz. tert-butanol+2-ethyl-l-hexanol and n-butanol+2-ethyl-l-hexanol. The temperature range in which these models are valid is 353.2-458.2K at atmospheric pressure. The average absolute deviation for the temperature output was in range 2-3.3% and for the activity coefficient was less than 0.009%. The results were then compared with experimental data. 

Sun Y, Peng Y, Chen Y Shukla AJ (2003) Application of artificial neural networks in the design of controlled release drug delivery systems. Adv Drug Deliv Rev 55 1201-1215. 

Schneider, G. and Wrede, R (1998) Artificial neural networks for computer-based molecular design. Prog. Biophys. Mol. Biol. 70, 175-222. 

The methods used for catalyst library design are quite divers. Industrial companies, like Symix, Avantium, hte GmbH, Bayer AG are using their own proprietary methods. In academic research the Genetic Algorithm (GA) is widely applied [11,12]. Recently Artificial Neural Networks (ANNs) and its combination with GA has been reported [13,14]. In these studies ANNs have been used for the establishment of composition-activity relationships. 

A screening design detected significant instrumental and chemical variables to volatilise and measure Sb. They were optimised using response surfaces derived from central composite designs. Findings were confirmed using artificial neural networks... 

The Use of Statistical Design of Experiments and Artificial Neural Networks... 

Another important feature of mathematical modeling techniques is the nature of the response data that they are capable of handling. Some methods are designed to work with data that are measured on a nominal or ordinal scale this means the results are divided into two or more classes that may bear some relation to one another. Male and female, dead and alive, and aromatic and nonaromatic, are all classifications (dichotomous in this case) based on a nominal scale. Toxic, slightly toxic, and non-toxic are classifications based on an ordinal scale since they can be written as toxic > slightly toxic > non-toxic. The rest of this section is divided into three parts methods that deal with classified responses, methods that handle continuous data, and artificial neural networks that can be used for both. 

Huesken, D. et al. 2005. Design of a genome-wide siRNA library using an artificial neural network. Nat. Biotechnol. 23, 995-1001. 

Artificial neural networks (ANNs) are computer programs designed to model the relationships between independent and dependent variables. They are based on the attempt to model the neural networks of the brain [50], Functions are performed collectively and in parallel by the units, rather than there being a clear delineation of subtasks to which various units are assigned. 

Chen, Y., McCall, T. W., Baichwal, A. R., and Meyer, M. C. (1999), The application of an artificial neural network and pharmacokinetic simulations in the design of controlled-release dosage forms,/. Controlled Release, 59, 33 11. 

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