Neural network algorithm

Literature in the area of neural networks has been expanding at an enormous rate with the development of new and efficient algorithms. Neural networks have been shown to have enormous processing capability and the authors have implemented many hybrid approaches based on this technique. The authors have implemented an ANN based approach in several areas of polymer science, and the overall results obtained have been very encouraging. Case studies and the algorithms presented in this chapter were very simple to implement. With the current expansion rate of new approaches in neural networks, the readers may find other paradigms that may provide new opportunities in their area of interest. 

Examples Simulated annealing. Tabu search. Ant algorithms. Neural networks etc. 

Computing The second use is for computing models. Formal Graphs are in fact neural netwoiks, which are easily transposed into algorithms. Neural network-based softwares are widely used for solving many complex and real-world problems in engineering, science, economics, and finance. 

Theodoridis, Sergio, and Konstantinos Koutroumbas. Pattern Recognition. 4th ed. Boston Academic Press, 2009. A complex introduction to the field, with information about pattern recognition algorithms, neural networks, logical systems, and statistical analysis. 

Chen, X., Xie, H., Chen, H. and Zhang, F. (2009), Optimization for CFRP pultrusion process based on genetic algorithm-neural network . International Journal of Material Forming, 3 (Suppl 2), S1391-S1399. 

Kovalishyn et al. used the cascade correlation neural net to select variables in QSAR smdies (131). Their results suggest that these pmning methods can be successfully used to optimize the set of variables for the cascade-correlation learning algorithm neural networks. The use of variables selected by the elaborated methods provides an improvement of nemal network prediction ability compared to that calculated using the unpruned sets of variables. 

Biological studies have always constituted a large pool of inspiration for the design of systems. In the last decades, two biological systems have provided a remarkable source of inspiration for the development of new types of algorithms neural networks and evolutionary algorithms. 

Hopgood, F.F., N. Woodcock, N.J. HaUam, and P.D. Picton, Interpreting ultrasonic images using rules, algorithms and neural networks , European Journal of NDT, Vol. 2, No. 4, April 1993, pp. 135-149. 

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

Problems involving routine calculations are solved much faster and more reliably by computers than by humans. Nevertheless, there are tasks in which humans perform better, such as those in which the procedure is not strictly determined and problems which are not strictly algorithmic. One of these tasks is the recognition of patterns such as feces. For several decades people have been trying to develop methods which enable computers to achieve better results in these fields. One approach, artificial neural networks, which model the functionality of the brain, is explained in this section. 

Other methods consist of algorithms based on multivariate classification techniques or neural networks they are constructed for automatic recognition of structural properties from spectral data, or for simulation of spectra from structural properties [83]. Multivariate data analysis for spectrum interpretation is based on the characterization of spectra by a set of spectral features. A spectrum can be considered as a point in a multidimensional space with the coordinates defined by spectral features. Exploratory data analysis and cluster analysis are used to investigate the multidimensional space and to evaluate rules to distinguish structure classes. 

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. 

A common use of statistics in structural biology is as a tool for deriving predictive distributions of strucmral parameters based on sequence. The simplest of these are predictions of secondary structure and side-chain surface accessibility. Various algorithms that can learn from data and then make predictions have been used to predict secondary structure and surface accessibility, including ordinary statistics [79], infonnation theory [80], neural networks [81-86], and Bayesian methods [87-89]. A disadvantage of some neural network methods is that the parameters of the network sometimes have no physical meaning and are difficult to interpret. 

New developments which have still to be checked for their usability in data evaluation of depth profiles are artificial neural networks [2.16, 2.21-2.25], fuzzy clustering [2.26, 2.27] and genetic algorithms [2.28]. 

The sigmoid aetivation funetion is popular for neural network applieations sinee it is differentiable and monotonie, both of whieh are a requirement for the baek-propagation algorithm. The equation for a sigmoid funetion is... 

Learning in the context of a neural network is the process of adjusting the weights and biases in such a manner that for given inputs, the correct responses, or outputs are achieved. Learning algorithms include ... 

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