Artificial neural networks software

Chen, Y. Jiao, T. McCall, T.W. Baichwal, A.R. Meyer, M.C. Comparison of four artificial neural network software programs used to predict the in vitro dissolution of controlled release tablets. Pharm. Dev. Technol. 2002, 7, 373-379. 

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. 

Sometimes fuzzy logic controllers are combined with pattern recognition software such as artificial neural networks (Kosko, Neural Networks and Fuzzy Systems, Prentice Hall, Englewood Cliffs, New Jersey, 1992). 

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 ... 

In the human brain, it is the combined efforts of many neurons acting in concert that creates complex behavior this is mirrored in the structure of an ANN, in which many simple software processing units work cooperatively. It is not just these artificial units that are fundamental to the operation of ANNs so, too, are the connections between them. Consequently, artificial neural networks are often referred to as connectionist models. 

Classifier systems are software tools that can learn to control or interpret complex environments without help from the user. This is the sort of task to which artificial neural networks are often applied, but both the internal structure of a classifier system and the way that it learns are very different from those of a neural network. The "environment" that the classifier system attempts to learn about might be a physical entity, such as a biochemical fer-mentor, or it might be something less palpable, such as a scientific database or a library of scientific papers. 

The artificial neural network (ANN) is a relatively new technique and possibly the preferred one for current and future (Q)SAR development. Basically, ANNs can be regarded as multinonlinear regression methods. Thus, the neural network software simply multiplies the input by a set of weights that in a nonlinear way transforms the... 

Artificial neural networks are versatile tools for a number of applications, including bioinformatics. However, they are not thinking machines nor are they black boxes to blindly feed data into with expectations of miraculous results. Neural networks are typically computer software implementations of algorithms, which fortunately may be represented by highly visual, often simple diagrams. Neural networks represent a powerful set of mathematical tools, usually highly nonlinear in nature, that can be used to perform a number of traditional statistical chores such as classification, pattern recognition and feature extraction. 

Abdul-Wahab, S A. and S. M. Al-Alawi Assessment and prediction of tropospheric ozone concentration levels using artificial neural networks, Environ. Model. Software 17 (2002) 219-228. 

Todeschini R, Consonni V, Mauri A, Pavan M (2004) MobyDigs software for regression and classification models by genetic algorithms in Nature-inspired methods in chemometrics genetic algorithms and artificial neural networks (R. Leardi Ed.), Chapter 5, Elsevier pp 141-167... 

Many instruments aheady have some chemometrics routines built into their software in such a way that their use is totally transparent to the final user (and sometimes the word chemometrics is not even mentioned, to avoid possible aversion). Of course, they are closed routines, and therefore the user cannot modify them. It is quite obvious that it would be much better if chemometric knowledge were much more widespread, in order that the user could better understand what kind of treatment the data have undergone and eventually modify the routines in order to make them more suitable to the user s requirements. As computers become faster and faster, it is nowadays possible to routinely apply some approaches requiring very high computing power. Two of them are Genetic Algorithms (GA) and Artificial Neural Networks (ANN). 

Sousa, S.I.V., Martins, F.G., Alvim-Ferraz, M.C.M and Pereira, M.C., 2007. Multiple linear regression and artificial neural networks based on principal components to predict ozone concentrations. Environmental Modelling Software 22, p.97-103. 

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