ART 2-A for Optimal Test Series Design in QSAR

Domine, D.; Devillers, James; Wienke, D.; Buydens, L.M.C.

doi:10.1021/ci960376p

Cited by 12 publications

(5 citation statements)

References 41 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The ART-2a method consists in constructing a weight matrix that describes the centroid nature of a predicted class [62,63]. In the literature, there are several chemical studies that employ the ART-based neural networks [64][65][66][67][68][69][70][71][72][73].…”

Section: Other Important Neural Networkmentioning

confidence: 99%

Applications of Artificial Neural Networks in Chemical Problems

Matarollo¹,

Honório²,

Silva³

2013

Artificial Neural Networks - Architectures and Applications

View full text Add to dashboard Cite

Section: Other Important Neural Networkmentioning

confidence: 99%

Applications of Artificial Neural Networks in Chemical Problems

Matarollo¹,

Honório²,

Silva³

2013

Artificial Neural Networks - Architectures and Applications

View full text Add to dashboard Cite

“…After general ART principles were formulated by Grossberg [34], a large family of selforganizing NNs for unsupervised and supervised learning was developed. ART-based NNs are well suited for practical classification applications in different fields [35][36][37][38] including some interesting applications in chemistry [12,13,[39][40][41][42]. Their advantages in comparison to other neural algorithms are the ability of stable fast learning, adaptively growing structure, simple computations and tuning.…”

Section: Fam Classifiermentioning

confidence: 99%

“…A widespread use of NNs in electrochemistry is calibration [10][11][12][13]: identifying and quantifying electroactive species in mixtures when electrochemical responses are complicated by highly overlapping signals or reactions between components [11]. A simple example of such an application is the estimation of the concentration for one or more analytes of interest from a multiple number of measurements.…”

Section: Introductionmentioning

confidence: 99%

EChem++ – An object-oriented problem solving environment for electrochemistry. 3. Classification of voltammetric signals by the Fuzzy ARTMAP neural network with respect to reaction mechanisms

Sapozhnikova

Bogdan

Speiser

2006

Journal of Electroanalytical Chemistry

View full text Add to dashboard Cite

A method using the artificial neural network Fuzzy ARTMAP (FAM) was developed to classify cyclic voltammograms according to underlying reaction mechanisms. Different preprocessing methods for reducing input dimensionality, including Principal Component Analysis (PCA), feature extraction, and Wavelet Transform (WT), were compared. Results obtained for simulated and experimental voltammograms show that FAM can be applied to the classification into E, E qr , EC and E qr C mechanisms successfully. The efficiency of WT for data compression was also confirmed. Experiments demonstrate a significant correspondence between misclassifications and intersections of class distributions for different reaction mechanisms. It was found by analyzing the error distributions of FAM that the most classification errors arise in the overlapping areas of two reaction mechanisms. The relationship of the resulting class distribution to the mechanistic zones of classical zone diagrams is discussed.

show abstract

“…The use of Kohonen neural networks has been described in Refs 35,77,78; a neural network based on the theory of adaptive resonance (ART 2-A, see above) and intended for preliminary classification of the whole set of experimental data has been described. 79 The choice of a correct size of training set is yet another critical factor.…”

Section: Choice Of An Optimum Training Setmentioning

confidence: 99%

Neural networks as a method for elucidating structure–property relationships for organic compounds

Halberstam¹,

Baskin²,

Palyulin³

et al. 2003

Russ. Chem. Rev.

View full text Add to dashboard Cite

The published data devoted to the use of the neural The published data devoted to the use of the neural network approach in the simulation of structure ± property rela-network approach in the simulation of structure ± property relationships for organic compounds are reviewed. The basic princi-tionships for organic compounds are reviewed. The basic principles of the neural network simulation are discussed along with the ples of the neural network simulation are discussed along with the characteristic features of the neural network approach typical of characteristic features of the neural network approach typical of the representation and classification of structural chemical data. the representation and classification of structural chemical data. Brief information on neural network models of spectral character-Brief information on neural network models of spectral characteristics, reactivities, physicochemical properties and biological istics, reactivities, physicochemical properties and biological activities of organic compounds is presented. The bibliography activities of organic compounds is presented. The bibliography includes 159 references includes 159 references. . Neural networks consist, as a rule, of a set of relatively simple computing elements termed neurons; the latter are arranged in several layers and are linked to one another by numerous connections. The layers are usually subdivided into three groups, viz., input, hidden and output (Fig. 1). Each connection between two neurons is defined by a real number termed connection

show abstract

ART 2-A for Optimal Test Series Design in QSAR

Cited by 12 publications

References 41 publications

Applications of Artificial Neural Networks in Chemical Problems

Applications of Artificial Neural Networks in Chemical Problems

EChem++ – An object-oriented problem solving environment for electrochemistry. 3. Classification of voltammetric signals by the Fuzzy ARTMAP neural network with respect to reaction mechanisms

Neural networks as a method for elucidating structure–property relationships for organic compounds

Contact Info

Product

Resources

About