Neural network models for infrared spectrum interpretation

Munk, Morton E.; Madison, Mark S.; Robb, Ernest W.

doi:10.1007/bf01245535

Cited by 74 publications

(31 citation statements)

References 4 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…The recent use of multilayered feed forward neural networks for the interpretation of spectral data shows good promise: the internal representations used by these networks are non-linear and are built up by a learning process based on examples. After the seminal works by Robb and Munk [2][3], many researchers explored the possibilities of these networks for the interpretation of infrared spectra [4][5][6][7][8]. However, the learning method used is a supervised learning process, and relies upon an existing structural classification.…”

Section: Introductionmentioning

confidence: 99%

Clustering of infrared spectra with Kohonen networks

Cleva

Cachet²,

Cabrol‐Bass³

1999

Analusis

View full text Add to dashboard Cite

Abstract. The design of systems for spectral data interpretation requires clustering of chemical compounds based on their spectral characteristics. Kohonen networks have been shown to be efficient tools to achieve this clustering. These auto-organising systems perform a mapping between a high-dimensional variable space and a two-dimensional one. An application to infrared spectra of organic compounds is presented here. The non-supervised learning algorithm used allows classification of compounds by spectral characteristics without a priori knowledge. An analysis of the distribution of spectra on the resulting maps is used to build models for predicting the presence or absence of specific structural features. The performance of the models in recognising structural features is discussed and compared with the prediction of a multilayered feed forward network (MLFFN). Localisation of compounds wrongly classified by the MLFFN on the Kohonen maps allows to establish a link between the supervised and the unsupervised approaches.

show abstract

Section: Introductionmentioning

confidence: 99%

Clustering of infrared spectra with Kohonen networks

Cleva

Cachet²,

Cabrol‐Bass³

1999

Analusis

View full text Add to dashboard Cite

show abstract

“…The simplest way in which a suitably sized hidden layer may be established, and the method most widely adopted, is to train networks of different sizes and simply select the best in terms of recall performance [22,23]. This has the obvious disadvantage of being potentially very time consuming, especially for large applications requiring long training periods.…”

Section: Class Directed Unsupervised Learning and Related Networkmentioning

confidence: 99%

Enhancements to the dynamic self-organising neural network

Mackenzie¹

1996

Neural Comput & Applic

View full text Add to dashboard Cite

Class Directed Unsupervised Learning (CD UL) [7,8], having an input layer and a fully connected one-dimensional Kohonen layer which is used to conceptually separate data classes. These networks, along with CDUL, have all been developed from the Kohonen Self-Organising Map (SOM) [9,10] and use variations of the associated Kohonen learning rule. In each of these networks, the Kohonen nodes act as exemplars within an adaptive look-up table, and take on values corresponding to the centroids of each distinct data cluster in the feature space. Where CDUL differs is that the input layer is extended to include a class description of each training vector (see Fig. 1), and it is this additional information that provides CDUL with its interesting properties.The reason for the success of CDUL stems from two factors: firstly, the inclusion of class information as an integral section of the input vector, can be used to prevent Kohonen nodes from being trained by vectors of more than one class; and secondly, the size of the Kohonen layer can be computed

show abstract

“…Wythoff [1] used a supervised network to recognise spectral peaks when they occurred against a noisy background. Sybrant [2] detected overlapping peaks in stationary electrode polarography using a pattern recognition method, and much work [3][4][5][6] has been done on the use of neural networks in interpreting IR and absorption spectra.…”

Section: Background and Motivationmentioning

confidence: 99%

A comparison of backpropagation and general regression neural networks in quantative trace metal analysis using anodic stripping voltammetry and cybernetic instrumentation

Manwaring

1994

Neural Comput & Applic

View full text Add to dashboard Cite

Neural network models for infrared spectrum interpretation

Cited by 74 publications

References 4 publications

Clustering of infrared spectra with Kohonen networks

Clustering of infrared spectra with Kohonen networks

Enhancements to the dynamic self-organising neural network

A comparison of backpropagation and general regression neural networks in quantative trace metal analysis using anodic stripping voltammetry and cybernetic instrumentation

Contact Info

Product

Resources

About