Optimization neural network

Tan, S.. and Mavrovouniotis, M. L., Reducing data dimensionality through optimizing neural networks inputs. AIChE J. 41(6), 1471-1480 (1995). 

Whitley, D. and Hanson, T. (1989) Optimizing neural networks using faster more acurate genetic search. Proceedings of Third International Conference on Genetic Algorithms (eds J.D. Schaffer, C.A. San Mateo, and M. Kaufmann), pp. 391-396. 

GA-based approaches have optimized the production of many different types of models (artificial neural network architectures in particular) and simultaneously selected variables and optimized neural network mod-els. ° ° GAs coupled with well-known and less well-known modeling methods have also been used by scientists in variable selection. The combination of a GA with multiple linear regression was shown to perform well on datasets containing 15, 26, and 35 descriptors. PLS coupled with a GA has also been shown to be useful in variable selection. Spline fitting... 

Gurcan MN, Chan HP, Sahiner B et al (2002) Optimal neural network architecture selection improvement in computerized detection of microcalcifications. Acad Radiol 9 420-429... 

Back propagation neural network (BPNN) BPNN is a multi-layer dynamic system optimization neural network. BPNN workflow achieves the objective function which is shown in Figure 6. In general, BPNN consists of three layers (input layer. 

Beside trial and error methods, other methodologies were tested in order to obtain the best performance of the neural models evolutionary algorithms represent appropriate methods for determining optimal neural network structure. 

Bhardwaj, A., Tiwari, A. Breast cancer diagnosis using genetically optimized neural network model. Expert Syst. Appl. 42(10), 4611-4620 (2015)... 

As explained in Chapter 8, descriptors are used to represent a chemical structure and, thus, to provide a coding which allows electronic processing of chemical data. The example given here shows how a GA is used to Rnd an optimal set of descriptors for the task of classification using a Kohoncii neural network. The chromosomes of the GA are to be used as a means for selecting the descriptors they indicate which descriptors are used and which are rejected ... 

Chemoinformati.cs is involved in the drug discovery process in both the lead finding and lead optimization steps. Artificial neural networks can play a decisive role of various stages in this process cf. Section 10.4.7.1). 

Concomitantly with the increase in hardware capabilities, better software techniques will have to be developed. It will pay us to continue to learn how nature tackles problems. Artificial neural networks are a far cry away from the capabilities of the human brain. There is a lot of room left from the information processing of the human brain in order to develop more powerful artificial neural networks. Nature has developed over millions of years efficient optimization methods for adapting to changes in the environment. The development of evolutionary and genetic algorithms will continue. 

S. Moorthy and P. Iyer, "Neural Network Simulation for Plant Control Optimization," in Proceedings of the Industrial Computing Conference, ISA, Anaheim, Calif., 1991. 

D. A. Sofge and D. A. White, "Neural Network Based Process Optimization and Control," in Proceedings of the 29th Conference on Decision and Control,... 

Transfer function models are linear in nature, but chemical processes are known to exhibit nonhnear behavior. One could use the same type of optimization objective as given in Eq. (8-26) to determine parameters in nonlinear first-principle models, such as Eq. (8-3) presented earlier. Also, nonhnear empirical models, such as neural network models, have recently been proposed for process applications. The key to the use of these nonlinear empirical models is naving high-quality process data, which allows the important nonhnearities to be identified. 

SS So, M Karplus. Evolutionary optimization in quantitative structure-activity relationship An application of genetic neural networks. J Med Chem 39 1521-1530, 1996. 

Controller emulation A simple applieation in eontrol is the use of neural networks to emulate the operation of existing eontrollers. It may be that a nonlinear plant requires several tuned PID eontrollers to operate over the full range of eontrol aetions. Or again, an LQ optimal eontroller has diffieulty in running in real-time. Figure 10.28 shows how the eontrol signal from an existing eontroller may be used to train, and to finally be replaeed by, a neural network eontroller. 

The proposed neural network model with the nonlinear optimization routine is similar to many nonlinear... 

Several nonlinear QSAR methods have been proposed in recent years. Most of these methods are based on either ANN or machine learning techniques. Both back-propagation (BP-ANN) and counterpropagation (CP-ANN) neural networks [33] were used in these studies. Because optimization of many parameters is involved in these techniques, the speed of the analysis is relatively slow. More recently, Hirst reported a simple and fast nonlinear QSAR method in which the activity surface was generated from the activities of training set compounds based on some predefined mathematical functions [34]. 

Figure 28.2 Diagram of a genetic algorithm linked to a neural network for modeling and optimization.

Pigmented film coating formulations have recently been modeled and optimized to enhance opacity and reduce film cracking with neural networks combined with genetic algorithms [46, 47] as well as being studied with neurofuzzy [48]. In the latter study the rules discovered were consistent with known theory. 

Takayama K, Fujikawa M, Nagai T. Artificial neural networks as a novel method to optimize pharmaceutical formulations. Pharm Res 1999 16 1-6. 

Colbourn EA, Rowe RC. Modelling and optimization of a tablet formulation using neural networks and genetic algorithms. Pharm Tech Eur 1996 8(9) 46-55. 

Rocksloh K, Rapp F-R, Abu Abed S, Mueller W, Reher M, Gauglitz G, Schmidt PC. Optimization of crushing strength and disintegration time of a high dose plant extract tablet by neural networks. Drug Dev Ind Pharm 1999 25 1015-25. 

Takahara J, Takayama K, Nagai T. Multi-objective simultaneous optimization technique based on an artificial neural network in sustained release formulations. 

Zupancic Bozic D, Vrecar F, Kozjek F. Optimization of diclofenac sodium dissolution from sustained release formulations using an artificial neural network. Eur... 

Ibric S, Jovanovic M, Djuric A, Parojcic J, Petrovic SD, Solomun L, Stupor B. Artificial neural networks in the modelling and optimization of aspirin extended release tablets with Eudragit LlOO as matrix substance. Pharm Sci Tech 2003 4 62-70. 

Wu T, Pao W, Chen J, Shang R. Formulation optimization technique based on artificial neural network in salbutamol sulfate osmotic pump tablets. Drug Dev Ind Pharm 2000 26 211-15. 

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