Artificial neural network theory

Patterson D W (1996), Artificial Neural Networks Theory and Applications, London, Prentice-Hall. 

Patterson DW (1998) Artificial neural networks theory and applications. Prentice Hall PTR, New... 

Artificial Neural Networks. An Artificial Neural Network (ANN) consists of a network of nodes (processing elements) connected via adjustable weights [Zurada, 1992]. The weights can be adjusted so that a network learns a mapping represented by a set of example input/output pairs. An ANN can in theory reproduce any continuous function 95 —>31 °, where n and m are numbers of input and output nodes. In NDT neural networks are usually used as classifiers... 

Xu J, Chen B, Liang H (2008) Accurate prediction of theta (lower critical solution temperature) in polymer solutions based in 3D descriptors and artificial neural networks. Macromol Theory Simul 17 109-120... 

White, H., Artificial Neural Networks Approximation and Learning Theory. Blackwell Publishers, Cambridge, 1992. 

The breakthrough of novel classes of IPRs (chaotropic additives and ILs) challenged the theoretical description of the dependence of retention on typical optimization parameters impose order on the complex welter of the theory is asked to retention patterns, and artificial neural networks are a versatile tool to describe them. 

Dynamics calculations of reaction rates by semiempirical molecular orbital theory. POLYRATE for chemical reaction rates of polyatomics. POLYMOL for wavefunctions of polymers. HONDO for ab initio calculations. RIAS for configuration interaction wavefunctions of atoms. FCI for full configuration interaction wavefunctions. MOLSIMIL-88 for molecular similarity based on CNDO-like approximation. JETNET for artificial neural network calculations. More than 1350 other programs most written in FORTRAN for physics and physical chemistry. 

Some individuals can produce speech, but it is dysarthric and very hard to understand. Yet the utterance does contain information. Can this limited information be used to figure out what the individual wanted to say, and then voice it by artificial means Research labs are now employing neural network theory to determine which pauses in an utterance are due to content (i.e., between a word or sentence) and those due to unwanted halts in speech production. 

Support vector machine (SVM) is originally a binary supervised classification algorithm, introduced by Vapnik and his co-workers [13, 32], based on statistical learning theory. Instead of traditional empirical risk minimization (ERM), as performed by artificial neural network, SVM algorithm is based on the structural risk minimization (SRM) principle. In its simplest form, linear SVM for a two class problem finds an optimal hyperplane that maximizes the separation between the two classes. The optimal separating hyperplane can be obtained by solving the following quadratic optimization problem ... 

T. (2014) An efficient rule-based screening approach for discovering fest lithium ion conductors using density functional theory and artificial neural networks./. Mater. Chem. A, 2 (3), 720-734. 

Domine and co-workers utilized the family of Adaptive Resonance Theory (ART and ART 2-A) based artificial neural networks for unsupervised and supervised pattern recognition (142,143). The simplest ART network is a vec-... 

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